Platt Perspective on Business and Technology

Building a startup for what you want it to become 41: moving past the initial startup phase 27

Posted in startups by Timothy Platt on January 7, 2020

This is my 41st installment to a series on building a business that can become an effective and even a leading participant in its industry and its business sector, and for its targeted marketplaces (see Startups and Early Stage Businesses and its Page 2 continuation, postings 186 and loosely following for Parts 1-40.)

A significant proportion of this series has revolved around the issues of business intelligence: of raw and processed information that is of organizational performance-enabling competitive value, and as it can serve as a basic and even fundamental business shaper and driver. And as a part of that overall narrative I have been focusing on the issues of third party business intelligence providers as sources of such information as a commoditizable product, and on the issues and challenges of startups and early stage businesses, and of small businesses in general as they seek to become and remain competitive, by working with larger and more powerful enterprises that are business intelligence developers and conduits of it, as a key part of their own overall business models.

I began discussing Facebook and its emerging role in small business and marketplace ecosystems, as an essential platform that businesses of all types increasingly connect with their own customers through (see Part 38 and following.) And to cite a point of detail relevant to this, that essentially any reader would see as increasingly familiar if they ever find need to contact such a business electronically (e.g. online, or alternatively by phone): an increasing proportion of them use Facebook to connect with their customers and with the larger marketplace as a whole. And many of them have come to use their Facebook presence as their only customer supportive channel of communications. And this can include outsourcing sales and customer fulfillment services to the Facebook platform, and certainly for customers who need assistance with a purchase already made, or in placing an order in the first place. And this increasingly includes customer feedback opportunities as well, and certainly if a customer seeks to offer a review or rating that would go directly to the business in question and not to an outside agency business such as Yelp.com.

I have addressed this topic area from a risk management and a cautionary-note perspective – not to argue against businesses setting up a Facebook presence and using it, but arguing a need for their owners and managers to understand what that operational and strategic decision actually involves, and for its full anticipatable range of pros and cons. And I continue pursuing that approach here, by repeating a question that I raised at the end of Part 40 but that I held off on addressing until here:

• What of legitimate smaller third party businesses that, for example, find that they have to market through a Facebook if they are to remain competitive and keep their doors open? (Nota bene, I posed that question in the context of just having briefly and selectively considered Cambridge Analytica and their Facebook data-based efforts, hence my “legitimate smaller businesses” phrasing.)

I begin addressing that question for its in-practice complexities, by parsing risk as addressed here into two broad categories:

• The judiciary system as that would be brought into play when alleged violations of personal privacy and related laws are raised and formally prosecuted, and
• The court of public opinion, and particularly as that plays out in a social media context and where messages: pro and con and on anything, can represent the legitimate views of real individuals, or be faked and in ways that are difficult at best to identify as such.

And I begin addressing this with a further consideration of regulatory law and judiciary enforcement of it as a source of risk and opportunity-shaping factors: I topic area that I have turned to on a variety of occasions in the past as I have written to this blog. My goal here is to build upon that already offered foundational discussion, and with that in mind I offer these references as relevant background material for what is to follow:

• Considering a Cost-Benefits Analysis of Economic Regulatory Rules (as can be found at Outsourcing and Globalization, as postings 23 and following),
• Making Regulation Work, (as can be found at Macroeconomics and Business, as postings 118 and following), and
• Regulatory Oversight, Prudent Business Practices and Risk Allocation (as can be found at Macroeconomics and Business, as postings 125 and following).

The first of those reference work series is listed in a directory with a globally spanning focus of attention, and for a reason. As soon as a business goes online and in ways that would be visible to an open marketplace and a wider community, and for whatever reason and with a goal of carrying out any customer-facing functions or services there, it ceases to be entirely local and even just within the confines of its own nation, and it becomes global. Crucially importantly here, this means that if a “local” business seeks to sell online, it has to expect that at least some non-zero percentage of its sales will be to customers living and working in other countries – with their privacy laws, and their laws in place for safeguarding sensitive personally identifiable information. And this means that business selling needs to be able to show that it adheres to now-involved, foreign-enacted and enforced laws as they address those issues, just as they have to address those types of law as hold in their own home country.

And laws change, and court rulings and case law precedent can create new interpretations of existing laws that can change how they would have to be met if a business is still to remain compliant for them. This much, I have already discussed and in some detail in the above-cited series and elsewhere in this blog too. But I would add one more detail to that brief summary here that is particularly relevant to this series and certainly as I have been developing it up to here:

• Big data and the progressively more revealing representational models that can be assembled from it, connecting the dots across vast numbers of data fields and types of them in descriptively and predictively capturing all of us, mean that simply blocking or even deleting protected types of sensitive data cannot prevent it from emerging from the assembly of supposedly safe data – as noted here in Part 40.
• And the growing inevitability of that happening and essentially regardless of any realistic effort to prevent it, means that even the possibility of safeguarding personal confidentiality – or privacy is evaporating.
• So welcome to what has increasingly become our transparent goldfish bowl world. And this brings me to several questions that I will simply raise (at least) here as thought pieces:
• If we are living in an increasingly transparent world, where personal confidentiality is more tightly constrained and limited than ever before, if nothing else and where privacy as we have traditionally known it is too … then what is to replace that?
• What should and can we safeguard, and how and from whom and under what circumstances? And what type of breach of those protections can and should we allow and from whom and for what reasons and under what circumstances?
• What I am writing of here is a rapidly emerging need to fundamentally rethink a series of issues and understandings that have for the most part remained axiomatically set, and from the days of our grandparents’ youth and from before then too: fundamental assumptions that we take so for granted that we do not tend to explicitly consider them, even as they are already beginning to fail us through technology-driven obsolescence.
• And this, of course, all raises still more uncertainty for any businesses that gather, process, store, or purchase access to and use from this flood of raw data and processed knowledge.

And that point of detail brings be directly to the issues of the court of public opinion. And I will begin addressing that source of challenge in all of this by noting that while the legal side to confidentially can change, with once-allowed and even required business practices becoming outmoded and in need of change, public opinion is change. And business practices and their outcomes do not need to violate the law, anywhere, for them to violate public expectations, and particularly where that is driven by online social media and related channels – and where so much of that has been gamed by online trolls and other public opinion manipulators.

This is a posting about uncertainty, and about living and working with it as a constant and unavoidable contextual presence. I am going to build from its narrative in a next series installment where I will at least begin to discuss strategic and operational approaches for better managing risk and opportunity in this type of rapidly evolving and emerging new context. And as part of that narrative, I expect to at least begin to offer some thoughts regarding my above listed but here-unaddressed questions.

Meanwhile, you can find this and related material at my Startups and Early Stage Businesses directory and at its Page 2 continuation.

Planning for and building the right business model 101 – 46: goals and benchmarks and effective development and communication of them 26

Posted in startups, strategy and planning by Timothy Platt on December 26, 2019

This is my 46th posting to a series that addresses the issues of planning for and developing the right business model, and both for initial small business needs and for scalability and capacity to evolve from there (see Business Strategy and Operations – 3 and its Page 4 and Page 5 continuations, postings 499 and loosely following for Parts 1-45.) I also include this series in my Startups and Early Stage Businesses directory and its Page 2 continuation.

I began discussing three specific possible early stage growth scenarios in more general terms, that a new business’ founders might pursue for their venture, starting in Part 33 with an IPO scenario, and with that followed by a corresponding discussion of a venture capital supported scenario and a franchise systems one. Then after outlining and discussing those scenarios as overall business model approaches, I began analyzing and discussing them for how their founders and executive officers would variously address the specific challenges raised by a question that could be generically asked of essentially any business founders or owners, for their own new businesses:

• What constituencies and potential constituencies would ventures following each of the above-cited business development approaches need to effectively reach out to and connect with?

And I began addressing this basic and even generic question in an IPO context and then in a venture capital supported context, with three specific business performance issues in mind. In keeping with that explanatory pattern, I will at least begin addressing this posting’s scenario with an awareness of them too, which I repeat here as:

A. Fine tuning their products and/or services offered,
B. Their business operations and how they are prioritized and carried out, and certainly in the context of that Point A and its issues, and
C. Their branding and how it would be both centrally defined and locally expressed through all of this.

It is important to note here that early funding strategies, as considered in the first two business development scenarios, can only serve as a starting point for what would and should follow. So I discuss operational and strategic issues in the context of this series, at least in part in order to put them into a more functionally consequential context.

That said, my intention here is to continue on from where I left off my IPO and venture capital scenarios, to similarly discuss the third basic scenario that I am holding up for more detailed analysis here. And I begin doing so by repeating the initial bullet pointed description that I offered for it when first introducing the three:

3. And a new venture, and certainly one that is built around a growth-oriented business model, might build its first bricks and mortar site, in effect as a prototype effort that it would refine with a goal of replication through, for example a franchise system. (See Part 36 through Part 39.)

Let’s start considering that developmental scenario with the basic question that I offered here towards the start of this posting, with its focus on who would be involved in all of this type of business building effort. And I begin that by noting a perhaps obvious, but also perhaps somewhat misleading point of difference that might be drawn between the first two business development scenarios that I have been discussing here, and this third one.

• The first two: an IPO funded new business jumpstart model and a venture capital jumpstart one, are obviously oriented around and even fundamentally defined by outside-sourced capital funding infusion and by explicit efforts to obtain it. Both, if successful in that, would bring in large amounts of liquidity that could be used to accelerate the development and growth of a new venture, increasing both its chances of success and its chances of becoming a competitive leader in its business sector and for its markets, if nothing else.
• But the third, franchise system example does not fit that pattern at all, at least if it is not simultaneously following one of the first two scenarios too (which I will assume not to be the case here.)

The second of those two points is where “misleading” at the very least, enters this narrative. And I would explain that point of consideration by raising the issues of trade-offs. All three of the basic scenarios under discussion here are driven by them; understanding these three scenarios and understanding how they actually do and do not relate to each other, depends entirely on knowing and understanding their respective trade-offs and how they align, and how they differ from each other.

Let’s start addressing that by reconsidering the first two scenarios, which for purposes of this line of discussion can be seen as variations on a same commonly held theme. The founders of a new business seek to develop and build and own their own legally incorporated, separate, essentially wholly-owned new business venture. But they see fundamental need to bring in large amounts of outside capital if they are to succeed, and at least as quickly and fully as they think possible and as they are willing to attempt. But outside funding of this type always has strings attached; there are always trade-offs in which those founding owners have to give on some issues in order to gain on others. I have discussed some of those issues in detail in other series in this blog so I will only note a few of the more pressingly important of them here, as they would specifically apply to this series and its narrative.

Entering into, and even just making a preliminarily filing for entering into an initial public offering with its release and sale of publically traded stock shares, forces a business to adhere to very specific due diligence and risk management-based procedural guidelines, and ones that only begin with requirements of specific mandated forms of financial transparency and public reporting. Much of this is even legally mandated and certainly for legal jurisdictions that include within them, stock exchanges of any scale or significance.

“Begin” is important there, as this enforced transparency determines how a wide range of business decisions would be made, and particularly where market analysts that the public respects, read those reports and study those businesses and their already ongoing pre-IPO track records, and offer reviews, and as they make buy, hold or sell recommendations that can affect overall market valuation and realizable value. So entering into an IPO might bring in a flow and even a veritable flood of new liquid wealth. But that always comes with constraints imposed too, and particularly if a business starts out with a large initial cash infusion and a large initial market valuation, placing it squarely in the line of sight of both the general public and of well known market analysts and business analyst reviewers, who might or might not challenge it on the basis of their understanding of its fundamentals.

Venture capitalists obviously provide up-front capital investment funds too. And they do so under terms that are at the very least, largely of their choosing too. They invest in the business ventures that they select for that, with a goal of realizing a profit and a significantly scaled one from their investment in them. So they demand specific types of influence and even control as to how those ventures are set up and run, in order to improve their odds of success at that. And as already discussed elsewhere in this blog that can mean their requiring seats on an invested business’ board of directors and that can even mean their selecting key executive officers there (e.g. a Chief Financial Officer who meets their due diligence requirements.)

If the venture capital investors involved there are knowledgeable about the types of endeavors they invest in and about how to help make them succeed, their demands and fulfilling them on the part of a selected venture’s founding owners, increases their chances of success too. But those business founders still have to be willing and able to pay back the initial funds invested in them along with any and all required venture capital profits too.

• While I am oversimplifying a more complex dynamic here, think of this as a business model agreement in which venture capitalists agree to take on some of the risks faced by the founders of the businesses that they would invest in, in exchange for a profitable fee. And those (generally) new or early stage business founders agree to set their business development timing if nothing else, so as to be meet those payback requirements from their revenue streams, in exchange for an improved chance of their actually developing such revenue streams in the first place.

Think of both of these scenarios as risk accepting, aggressive business development approaches. Now what are the trade-offs that a franchise system corporation and a would-be franchisee to them, accept and take on? I would argue that they are also based on a financials, and on a risk reduction versus a control and self-determination decision. It is just that in this case, the participants involved tend to be more risk aversive and conservative in what they do and in how they do it and on both sides of the business relationships entered into.

A franchisee agrees to build and run a locally, personally owned outlet according to a presumably well proven model and for what is to be offered for sale, and for how it would be offered, and for everything from the basic storefront design and branding it would be sold through to price point determination and what those offered products and services would actually be sold for. And in exchange for this risk-controlled business opportunity with its more limited range of unknowns, and with its already built-in established customer base that knows that basic business and its products, they relinquish a very significant amount of the decision making authority that they would have if they simply set out to build a more stand-alone business on their own. And they take on ongoing obligations to pay out a contractually set percentage of their gross receipts (generally) to the parent company too.

And that parent company agrees to these terms too, and particularly where they can write the contract agreed to in ways that would limit any risk to them, and certainly any risk that might arise from outside of normal business operations (e.g. a franchise delivery vehicle getting into an auto accident with injuries), while insuring an all but certain positive cash flow back to them from this.

With this in place, let’s consider the basic question that I would address here, and the above-repeated business performance issues that I have also been addressing in this type of context:

• What constituencies and potential constituencies would ventures following (in this case a franchise system) business development approach need to effectively reach out to and connect with?

I have already offered at least a significant start to answering this question in the preceding text of this posting. First of all, a would-be franchisee and a good fit franchise system-organized business that they would secure a license from, have to find each other. That matches the situation faced by entrepreneurs who would seek out venture capital funds and support, and good-fit venture capitalist investors at that, as noted in Part 45 when I focused on that scenario.

From a franchise perspective, this means would be franchisees finding franchise system parent businesses that operate in an industry they would like to work in (e.g. convenience store, versus fast food, versus quick service auto maintenance such as oil changes or car washes, and so on.) And a parent company that they might potentially sign a contract with, is going to want to find people who would be good fits for working with them and who look from their backgrounds and their experience to be good candidates for successfully running a local business and according to their patterns and rules.

On the market-facing side to this, a well established franchise chain and its parent company might have name recognition and at least a measure of positive market-based opinion, even when just moving into a new-to it-area. But many franchise systems face at least a measure of push-back too, with that often based on concern over the impact they can have on local businesses and their owners, and on the communities that those local businesses have traditionally served.

And in that, it is important to note that as a general principle:

• Marketing in these systems come from corporate but sales come from and are local.

But local franchisees are still going to have to reach out so as to become welcome members of their communities, for what they and their storefronts do.

And pertinently to this discussion, and returning to consider the finances of all of this, a franchise system business has to, in most cases, make an up-front investment towards setting up a new franchisee owned outlet, even as they require what can be a very significant up-front investment from a new franchise holder too, as they purchase their franchise license to join that system. If the first two scenarios that I have delved into here are finances driven, this one is too. And that point of detail brings me back to the three business performance issues that I have been discussing these scenarios in terms of:

A. Fine tuning their products and/or services offered,
B. Their business operations and how they are prioritized and carried out, and certainly in the context of that Point A and its issues, and
C. Their branding and how it would be both centrally defined and locally expressed through all of this.

I am going to continue this discussion in a next installment to this series. Then I will turn to and address the second and third generic questions that I have posed for this portion of this series, as they would be understood and responded to by businesses in general that fit each of the three basic development scenarios under consideration here:

2. What basic messages would they have to convincingly and even compellingly share with those audiences, to create value for themselves from their marketing efforts?
3. And where and how would they best accomplish this?

(In anticipation of discussion to come, I will raise and discuss what I would argue are some overly simplistic assumptions raised in this posting, when discussing those questions in a franchise system context.)

Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 5, and also at Page 1, Page 2, Page 3 and Page 4 of that directory. And you can find this and related material at my Startups and Early Stage Businesses directory too and at its Page 2 continuation.

Leveraging social media in gorilla and viral marketing as great business equalizers: a reconsideration of business disintermediation and from multiple perspectives 18

Posted in social networking and business, startups, strategy and planning by Timothy Platt on December 11, 2019

This is my 18th posting to a series on disintermediation, focusing on how this enables marketing options such as gorilla and viral marketing, but also considering how it shapes and influences businesses as a whole. My focus here may be marketing oriented, but marketing per se only makes sense when considered in the larger context of the business carrying it out and the marketplace it is directed towards (see Social Networking and Business 2 and its Page 3 continuation, postings 278 and loosely following for Parts 1-17.)

The primary topic that I raised and discussed in Part 17 of this, was market and consumer-sourced and related data and the ever-expanding need for it in a competitive marketplace, and particularly as businesses do business in an interactive online context, and one that includes both gorilla and viral marketing: marketing campaigns that are novel in form and that create uncertainties from that, and that are both at least significantly shaped by market-sourced participants – people from outside of the marketing business itself and who cannot be expected to hew to that business’ established market-facing message. And I concluded that posting by posing at least the first of a set of challenges that this raises, that I stated I would begin to address here. And I begin doing so by repeating them:

• I have been writing here (nota bene, in Part 17) of the need for more and more data, with more and more variable types of it to fill their database fields. And I add here a corresponding need for all of this data to be more and more accurate and more and more real-time up to date too.
• And augmenting the number of such variables (and the data accuracy for what populates their database fields) does in principle mean an increased and improved capability to analytically study a consumer and potential consumer base in finer and finer detail, parsing it into progressively more refined demographics and sub-demographics and in ways that would lead to more effective business decisions and of all types.
• But the more data types that would be called upon and used in any given such analysis or set of them: the more variables that would have to be coordinately analyzed in making use of this data, the larger the numbers of consumers that data would have to come from, in order to achieve sufficient data set sizes so as to make the requisite statistical tests that would be used, even just mathematically valid.

I would suggest approaching the issues and challenges raised there, and particularly in the last of those three bullet points, by stepping back and asking precisely what this data would be used for, at least in general terms as would apply to essentially any businesses in essentially any business sectors or industries. And I begin by stating a point that I would at least hope would be obvious:

• The only data that would offer real value there, is data that correlates by type with the likelihood of desired transactional outcomes that those customers might enter into. Ultimately, the only data that really counts here is data that can be used to predict completed sales and that can be used in marketing and sales efforts, so as to improve the odds of those completed transactions happening.
• And within that set of constraints, the only data-to-outcomes correlations that really matter are ones that arguably represent explicit cause and effect relations, and ideally at least, ones that can offer predictive value.
• When considered in these terms, marketing is all about creating and delivering messages in the right way to the right people, as they are at least categorically identified from the demographics they are presumed to belong to, that will increase the odds of those favorable outcomes predictions being realized in their subsequent behavior.

And with that I frame this data use, and the data selection and filtering that would enter into making that possible, as a multiple-step process. And for purposes of this discussion, I will collapse that down to a stereotypic two-step representation. And I begin that with seemingly simple correlations analyses.

• Significant observable correlation, linking the occurrence of two conditions, events, outcomes, or circumstances does not in and of itself show, let alone prove causal connection between them; correlation does not imply causation.
• But a carefully arrived at determination of a lack of apparent statistical correlation (with a correlation coefficient value that is at least close to zero in value), can be construed as offering presumptive proof that they are not causally connected and that any co-occurrence that does appear between them is likely a result of more random chance than anything else.

With that noted, I cite what is probably the single commonest, and most telling mistake that people make when carrying out correlation analyses, and certainly when they seek to combine factors that individually do not offer high enough correlation coefficient values in and of themselves to reliably predict some test factor under consideration, but that might offer such value together – when they (potentially) predictively co-occur. To take that out of the abstract, consider as a possible set of factors that might together, highly correlate with a sales transaction being completed. And for this example, consider that an online storefront visitor is a repeat customer who has made at least other types of purchases from a business in question in the past. And add to that, that they have a store credit card from that business. Note that these factors: these circumstances would of necessity be correlated to each other as it is unlikely that anyone would get a store-branded Visa, MasterCard or other credit card through a business if they were not already a customer there who has made purchases from them.

Even with the correlational overlap that that purchasing history to credit card account occurrence would involve, that would have to be accounted for when arriving at a true overall correlation with the likelihood of a next purchase going through, these two factors might offer predictive value for what would come out of a next visit to that business’ online storefront website. But let’s assume, as often proves the case, that no overtly obvious single factors come to mind or to analytical models as previously developed, that would highly correlate with whatever test factor a business would like to be able to make occurrence predictions about. So a statistician there, or rather someone using a statistical analysis software package there, starts to “throw stuff at a wall and see what sticks.”

They look through their data fields and start running lots of single factor to single factor correlations to see what if anything seems to connect with the test factor they want to be able to correlate to. And they find a whole bunch of them that individually show correlation coefficients that are on the order of 0.02 to 0.05 (2% to 5%) in value. And when they combine them, they collectively seem to predict a 0.87 (87%) correlation to their targeted test factor or condition. So they can really effectively, highly reliably predict a set of conditions and circumstances where that factor: call it X is going to occur, and as desired by the business, simply by running the numbers for that perhaps large number of carefully selected input variables! No!

• Low value correlation is more suggestive of random noise and random chance in a system, than anything else. And dumping a lot of random co-occurrences into a box together and adding glue, does not change that and either individually for them or collectively across the set of them.

And with that, I challenge a basic assumption that I built into the three repeated (and at least somewhat expanded) bullet points that I began this posting with, carrying them over from Part 17. Or to be more precise here, I have just challenged several such assumptions here starting with:

1. An implicit assumption that simply tossing more data and more types of data into the statistical analytical mill that you would use, will automatically and of necessity yield more and more precise and more and more operationally and strategically useful insight,
2. And an assumption that simply acquiring and accumulating more and more data and for the sake of that more and more, will of necessity make a business-held or business-accessible big data repository more and more valuable to it.

I will begin addressing those two points here, with a cautionary note that applies to both, for anyone who might assume that I am going to take a more automatically limiting Occam’s Razor approach here (or even a still-more limiting Occam’s Procrustean Bed approach.) New and novel, and the disruptively new in particular, challenges both of those numbered points for how they would be addressed. And to tie this back to this series as a whole, that is precisely where marketing approaches such as gorilla and viral marketing enter this posting’s narrative. And my goal for the next installment to this series is to at least begin to address all of that. Note: this will of necessity call for my more fully discussing causality too, which I will categorically parse out as being direct or indirect, and absolute or situational.

And with that offered and as a first here, I conclude this series installment with an anticipatory note as to what will follow it, doing so with the precise same wording that I offered at the end of the last installment to this series and for the exact same purpose again – and even as I complete a posting that has in fact been addressing those issues already.

“My goal for the next installment to this series is to begin with an orienting discussion of these points, and how they arise as valid sources of concern. And then I will discuss data evaluation at the trade-off levels of knowing what of a set of possible information held, holds the most value and would offer the most actionable insight in a given situation: in the course of developing, running and evaluating the outcomes of specific marketing campaigns. And I will also discuss how this opens doors for third party data providers to enter this narrative and very profitably for themselves.”

Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 5, and also at Page 1, Page 2, Page 3 and Page 4 of that directory. You can find this and related postings at Social Networking and Business 3, and also see that directory’s Page 1 and Page 2. And I also include this posting and other startup-related continuations to it, in Startups and Early Stage Businesses – 2.

Building a startup for what you want it to become 40: moving past the initial startup phase 26

Posted in startups by Timothy Platt on November 2, 2019

This is my 40th installment to a series on building a business that can become an effective and even a leading participant in its industry and its business sector, and for its targeted marketplaces (see Startups and Early Stage Businesses and its Page 2 continuation, postings 186 and loosely following for Parts 1-39.)

I have been discussing business intelligence and third party sourced customer and related marketplace data, and its positive use and its potential pitfalls in this series. And as a special case in point example of how both of those consequential sides to that arise in importance, and for any business that might make use of its web site, I have been discussing Facebook and its business practices, and its business model since Part 38.

I concluded my Part 39 continuation of that discussion by stating that I would continue this overall narrative here, by considering:

• Online marketing and sales as whole, as they are information and information-access shaped and driven, and
• Opt-in versus opt-out, and how an at least de facto opt-out can and does find its way as a default into so many contexts that hold overriding risk management and quality control management significance.

And I added that I would discuss these issues from both a consumer and a small business perspective. And I will do that, and with my recent discussion of Facebook in mind, and both as a source of actionable business intelligence, and as a platform for making use of it. My basic approach here will be more general and perhaps even generic in nature, but I will of necessity refer back to that source of working examples and cautionary notes too.

And with that noted, I begin addressing the first of those two to-address bullet points, by bringing it into a tighter focus and with a goal of considering marketing, and sales that are driven by it, in terms of consumer and marketplace-sourced data: sensitive and confidential included. And I begin doing so by directly challenging and in fact discarding a basic assumption that is all but automatically assumed, but that has become suspect and even fundamentally wrong.

• Ultimately, any piece of consumer-sourced or consumer-facing and describing data can become sensitive, directly leading to specific data insight that is legally considered personally identifying and confidential, if enough correlated data is collected together with it and if that is all expertly organized and processed in order to discern meaningful and actionably useful patterns.
• So the real distinction here, and certainly in an age of big data, is not one that would be drawn between sensitive and in need of special access limitations and protection, and safe and readily shared. It is between sensitive and even risk-creating dangerous to collect, hold or use without care, and potentially but likely sensitive and risk creating, where increased scale and complexity of overall data collected can and will shift the seemingly safe into gray areas of uncertainty for this if nothing else.

Marketing, and sales that are driven by that are both, very fundamentally information driven. That is a fundamental truth that has been known for as long as marketing has existed as an organized, identifiable area of business practice and expertise. And it is as old as sales and salesmanship too. The advent of consumer protection laws governing and controlling access to and use of personally identifiable information in all of that, added whole new dimensions of risk management consideration to that overall area of business activity, adding that to the already-present issues of truth in advertising for what marketing messages and sales pitches would and could even just be legal. This side to big data expands that risk management side to marketing and sales, that much more widely. And this is a risk-potential expansion that is more significant than the earlier, personally identifiable information version 1.0 challenge of sensitive versus safe information per se. That is because that earlier, pre-big data version 1.0 form of risk involved fixed risk sources for the specific data involved, while big data’s version 2.0 form of this does not.

Let’s consider two specific pieces of information as working examples here, to take that assertion out of the abstract, both of which are quite real: an individual person’s social security number and the fact that an individual who is tracked in a big data repository happens to have a particular profession or occupation. In this specific case in point example, let’s assume them to be a board certified physician, and a licensed obstetrician to be more precise.

• If you have that person’s social security number, you can obviously go a long way from that in stealing their identity and certainly if you have their full name.
• But let’s consider the following. You know that the individual of my here-specific example lives, and most probably works in or near a small town in a sparsely populated rural part of the country. You do not know their name or anything else except for the fact that they are a physician with that specialty and that they live (and probably work) in or near that same rural town, which you have the name of. And it turns out there is only one such physician who lives and works there who has that type of medical practice. And with that, you can readily identify them by name and any presumed confidentiality concerning them, quickly begins to unravel.

I intentionally picked a cartoonishly simple second example there, where knowing two pieces of information that do not in and of themselves indicate individual identity, do so when brought together. In the real world, making use of what is just potentially identity revealing “non-sensitive” information of this type to actually break any presumed confidentiality concerning them, is more of a “connect the dots” exercise, and one of establishing probabilities of likelihood of precise identity. In that, breaching individual identity and confidentiality is a statistical exercise, where the analyst carrying it out gets to set the minimum level of statistical significance that would be required to presume a likely positive identity determination as real and valid.

• If this type of analysis is conducted on a statistically significant sample size of relevant data files on a correspondingly large number of specific individuals, and it is carried out with a statistical significance p-value of 0.05 (5%), then it can be expected that approximately 95% of apparently identifiable patterns, actually do correctly match relevant data and in ways that reveal more data that was not already explicitly visible to the analyst – and even specifically sensitive personally identifiable information as protected by consumer protection laws.

What I am doing here is to at least briefly outline how the vast troves of seemingly innocuous data that sites such as Facebook collect, organize, data mine and commoditize, can be more broadly used and even in ways that are explicitly harmful to the people who that data relates to. This, to be more precise, briefly outlines by way of simplified example, one way in which such data can be used.

Cambridge Analytica and its use of seemingly “safe” data – data not covered under any current consumer protection laws, shows that not only are supposedly protected data types vulnerable to unintended discovery and use,
• But also that the range of such individually sourced data that is currently legally protected as a matter of principle, is hopelessly inadequate. (And see this piece on the Facebook–Cambridge Analytica Data Scandal.)

And this brings me to the second above-offered, to-address topics point:

• Opt-in versus opt-out and how opt-out can and does find its way as a default into so many contexts that hold overriding risk management and quality control management significance.

And I begin addressing that by more fully considering a point of detail that I raised in Part 39, when writing about user account management options, and how the most important access and permissions, and related decisions that could be made through them can be buried so deeply in them as to effectively not even be there.

This is in fact a legally important issue, and both for site users, and for the businesses that offer those web sites. And it is important as a source of potential risk in both of those directions too; a business cannot simply assume that just because a user clicks to agree to a terms of service and liability limiting agreement, in order to use a web site or other online service, that that fact automatically and fully indemnifies them for all details they have had their legal counsel include there. Clarity is important, and any attempt to draft such an agreement that might be subject to differences of interpretation – by another attorney, can lead to that user agreement being thrown out in a court of law. And this is precisely where the issues of opt-in and opt-out enters this narrative.

• Opt-in if improperly implemented, can become moot and devoid of any legally defensible value and for either an original data source business or for any business that knowingly makes third party use of that data. And it would be an after the fact court decision that would determine this if, for example, it was found that a given opt-in was too narrowly stated, leaving too much as opt-out if even just that, and by default.
• And an opt-out that is deemed to be too deeply buried and out of sight of a reasonable person as that term is legally understood, risks losing even that status as a meaningful consumer-facing option, if it is brought to court too.

So right now, as of this writing, Facebook and I add Google and other major online data-collecting and monetizing businesses are under fire, and both in the court of public opinion and in current political debate and in state and federal legislatures, and certainly in the United States. Proposed legislation to control these mega-corporations on this, and an increasing likelihood of law suits and court action to break them up as violating anti-monopoly laws, are reliably recurring as in-the-news, current events stories. The primary foci of those news stores are on those businesses themselves, and on the myriad of individuals whose personal data is caught up in their data repositories, as profitable commodities. What of legitimate smaller third party businesses that, for example, find that they have to market through a Facebook if they are to remain competitive and keep their doors open? I am going to continue this overall discussion in a next series installment where I will at least begin to focus on that set of issues.

Meanwhile, you can find this and related material at my Startups and Early Stage Businesses directory and at its Page 2 continuation.

Planning for and building the right business model 101 – 45: goals and benchmarks and effective development and communication of them 25

Posted in startups, strategy and planning by Timothy Platt on October 18, 2019

This is my 45th posting to a series that addresses the issues of planning for and developing the right business model, and both for initial small business needs and for scalability and capacity to evolve from there (see Business Strategy and Operations – 3 and its Page 4 and Page 5 continuations, postings 499 and loosely following for Parts 1-44.) I also include this series in my Startups and Early Stage Businesses directory and its Page 2 continuation.

I have been discussing three specific possible early stage growth scenarios that a new business’ founders might pursue for their venture, in recent installments to this series, which I repeat here for smoother continuity of narrative as I continue addressing them:

1. A new venture that has at least preliminarily proven itself as viable and as a source of profitability can go public through an initial public offering (IPO) and the sale of stock shares. (See Part 33 and Part 34.)
2. A new venture can transition from pursuing what at least begins as if following an organic growth and development model (as would most likely at least initially be followed in exit strategy 1, above too) but with a goal of switching to one in which they seek out and acquire larger individually sourced outside capital investment resources, and particularly from venture capitalists. (See Part 35.)
3. And a new venture, and certainly one that is built around a growth-oriented business model, might build its first bricks and mortar site, in effect as a prototype effort that it would refine with a goal of replication through, for example a franchise system. (See Part 36 through Part 39.)

And after offering more generally considered, basic-outline analyses of these three business development models, I began a process of discussing them for how businesses following them, might more normatively be expected to address a series of issues that would variously, but reliably arise for essentially any business and regardless of its precise business model. I have focused here on how these issues would arise and play out for businesses pursuing one or another of the above-repeated early development scenarios. And as part of that process, I began discussing those business development approaches and their predictably expected consequences as they would shape how these types of businesses would address three specific questions (see Part 44):

• What constituencies and potential constituencies would ventures following each of the above-repeated business development approaches need to effectively reach out to and connect with?
• What basic messages would they have to convincingly and even compellingly share with those audiences, to create value for themselves from their marketing efforts?
• And where and how would they best accomplish this?

More specifically, I have responded to the first of those questions in Part 44, there focusing entirely on the first of the three scenarios: the IPO-oriented business development scenario that I have been discussing here. And my goal moving forward from here is to offer a response to that same question as it would arise in a second scenario: a venture capital seeking business development attempt. And after that, I will turn to and consider the same question again, but from the perspective of a prospective or early-stage franchise system business. And underlying all of this, are three fundamental business performance issues that I keep referring back to:

A. Fine tuning their products and/or services offered,
B. Their business operations and how they are prioritized and carried out, and certainly in the context of that Point A and its issues, and
C. Their branding and how it would be both centrally defined and locally expressed through all of this.

And with all of that stated, I turn to the venture capital funds-seeking scenario and the basic Who question that I would explicitly address here.

I divided my response to that question so as to separately discuss two generally stated stakeholder categories in an IPO-facing context, when considering the external stakeholders and gatekeepers who the founders and owners of such a business, would have to work with:

• Potentially business and business model-supportive stakeholders, and
• Outwardly-connected and response-connecting marketplace participants.

And I will follow that same pattern here in the context of this business development scenario too, once again starting with stakeholders and potential stakeholders who would be approached, directly or indirectly for supporting a new business, and through provision of capital funding investments.

Who would belong there, at least as reasonable audiences that the founders and owners of a business might market their venture to, in hopes of gaining such support?

• Individual venture capitalists and venture capital firms are obvious target audiences here, but simply noting that does not add much if anything to this line of discussion. The real question is one of precisely which such investors to approach and how. And that means the owners of a business doing their homework, and their reaching out to potential investors who have a track record for having already invested in their particular industry, or who have a focused specialization that prominently involves supporting businesses that would make significant use of the same core technologies that their new business does and will use, and as central to what they would do as competitive ventures. Venture capitalists tend to specialize in where they invest their funds, depending on what they themselves have particular hands-on and managerial expertise in. So from a Who perspective here, this means finding and reaching out to potential investors who would see what a new venture is doing and seeking to do, as comfortably familiar ground – and even when they also want to see sources of novelty in that, that would make a potential investment business stand out for its competitive strength potential.
• This all obviously involves addressing the issues of my above-repeated Points A, B and C. Marketing to the right investors here, for example, of necessity means marketing what this business can and will do and how, and marketing for what it will produce or offer as services, and to whom. That is all about branding and marketing, and both as that would be directed towards potential customers and clients, and as it would be directed towards potential investors and backers too.
• And this brings me to the issues and questions of how to identify and reach the right people, besides simply doing Google or similar online searches. LinkedIn and similar business and professional-oriented online social networking sites can be very important there, and both for finding who has turned to what venture capital funding sources for what, and with what success, and for reaching out to these investors more directly too. And there are also businesses that explicitly function as matchmakers, bringing venture capital, and I add angel investors, together with entrepreneurs seeking such funding support. (A Google search for “entrepreneur forum” can offer value here, as one of many possible relevant search queries.)

And with that offered, I turn to consider the marketplace and potential customers and their demographics.

• Obviously, consumers and at least potential repeat customers are important here. Predictively specifying possible and likely market reach and market penetration of a proposed new business and its offerings, is one of the core foundational elements of any effective business plan. And that document with its reasoned analysis of possible target markets and consumer demographics, is crucially important when seeking outside business development funding, and particularly where larger investments are sought out from professionals in that arena, such as venture capitalists.
• Critically reviewing and analyzing business plans and efforts to fact check and reality check them, are among the most important due diligence and risk management exercises that these investors carry out as they make their basic investment decisions and as they decide what they would require that an investment business’ owners do, in order to qualify for such supportive help.
• And the analysis offered there in this business plan, of what a new venture would offer, and to whom, and with what likelihood of success for its target markets is crucial for all of that.

I am going to continue this discussion in a next series installment where I will turn to and consider my third business development scenario: the would-be franchise system business. And I will follow the same organizing pattern there, that I have followed in Part 44 and again here in Part 45: addressing the issues raised in the first of the above three questions:

• What constituencies and potential constituencies would ventures following each of the above-repeated business development approaches need to effectively reach out to and connect with?

Then I will continue on from there to analyze the three business model approaches that I have been discussing here, in terms raised by the remaining two of those questions. Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 5, and also at Page 1, Page 2, Page 3 and Page 4 of that directory. And you can find this and related material at my Startups and Early Stage Businesses directory too and at its Page 2 continuation.

Leveraging social media in gorilla and viral marketing as great business equalizers: a reconsideration of business disintermediation and from multiple perspectives 17

Posted in social networking and business, startups, strategy and planning by Timothy Platt on October 3, 2019

This is my 17th posting to a series on disintermediation, focusing on how this enables marketing options such as gorilla and viral marketing, but also considering how it shapes and influences businesses as a whole. My focus here may be marketing oriented, but marketing per se only makes sense when considered in the larger context of the business carrying it out and the marketplace it is directed towards (see Social Networking and Business 2 and its Page 3 continuation, postings 278 and loosely following for Parts 1-16.)

I have been discussing gorilla and viral marketing in more general terms in this series, laying a foundation for more detailed analysis as to their reach and effectiveness, and for even knowing how well they are performing for your business –if that is, they are at all. So for example, I explicitly if briefly discussed the issues of outside-shaping control in any genuinely viral marketing campaign, where “viral” in that context ultimately means “coming from members of the consumer community and marketplace” and where publically visible messaging contributions offered from there, might or might not be legitimately grounded and as either false-flag negative, trolling efforts or as equally false-flag positive messages. And even more genuine viral sourced messaging might or might not have real impact potential depending on a variety of factors too, many of the more important of which might be predictively understood.

Then I ended Part 16 by offering the following four point tool set of basic issues for consideration when thinking about, developing, reviewing and refining a gorilla marketing campaign per se:

• If you want gorilla marketing to work effectively for your business, as a generally developed creative ongoing effort, you need to know the market that you would reach out to and connect with, from your business’ side of the conversations that you seek to develop there.
• And you need to know that market and the people who comprise its defining demographics, as its actively involved participants at the very least, help co-create this marketing reach with you from their feedback and reviews. And I stress that collaborative “with” here as their individual and collective voices are crucially important to all of this.
• And you need to know this, your market as well as you would know your own Marketing and Communications staff, and the guidelines that they work under in a more traditional, business-centric orienting marketing campaign.
• And the urgency of these points of observation doubles, at the very least in a genuine viral marketing context, as does the degree of challenge in helping to make this type of marketing campaign work, and reliably and effectively so.

I offered this checklist of value determining, question-raising issues: this analytical tool set if you will, in Part 16, in the context of having just reconsidered one of the early tools that was used in attempts at determining the effectiveness of online marketing and sales, and commercial web design and development per se for that matter and certainly as they would support online marketing and sales success: eyeball counts. And I began addressing that earlier analytical approach by stating that no one knew how to develop actionable value out of the data that they were accumulating from this. No one knew how or when simply viewing online content translated into action and ultimately into successful sales transactions – even as online marketers and web developers touted the overall eyeball count numbers that their clients achieved through their web site and related development efforts.

• No one really knew and certainly at first, when or how to best determine when page views and eyeball counts actually meant anything.
• That meant they did not know how to develop an online presence and design it in detail so as to improve the numbers of consumers who would do more than just look, increasing their conversion rates: the rate at which those page and content viewers actually entered into a sales transaction from this experience, and completed it.

More is known now, of course, as to what those numbers mean and most of that insight comes from developing more nuanced understanding as to what a site visitor and viewer is actually doing, with that including an understanding of metrics such as:

• Where they clicked from to reach a page that is being page view-count tracked,
• What links if any on that page they click to when leaving,
• And where they in fact leave to.

There is a lot more to this, of course, but the basic idea offered there should be fairly clear. Eyeball counts, in and of themselves offer very little real marketing analysis value; it is the context that those views arise in that tells everything. And I offered that perspective there, and briefly recapitulate it here because a very similar set of underlying principles applies in the context of the later generation marketing, and marketing analysis demanding approaches that I have been discussing here: viral and gorilla marketing and their more effective use.

• Context and contextual understanding and the accumulation of data that can support that type and level of understanding is everything here, and exactly as proved necessary in an earlier, simpler eyeball count measured, central broadcasting model online marketing world.

The primary difference here, in fact is that when interactive supplants central broadcasting, and two-way and multi-direction communications and information sharing supplants a simpler one-way information flow model, the level and diversity of detail needed in that contextual data increases by orders of magnitude if any effectiveness at all is to be achieved. And the forces of competition for market share that have continued on and continued growing, and from way before the advent of internet and from the earliest marketplaces, simply make the scale of this data required now, essentially open-ended – and certainly as that imperative might be argued for by market analysts and by the data providing businesses that service their needs. And with this noted, I turn to consider the role of and the limitations of big data in this still rapidly evolving business and marketplace context.

• Eyeball counts and the demand for progressively more complex and comprehensive contextual data that would make it possible to derive meaningful, actionable insight from its numbers, have come to include and even fundamentally require the accumulation of progressively more and more complex data sets, that only began with the three basic metrics that I just listed above here.
• Big data as a business enabler began there, and certainly as online became critically important to business success and for more and more businesses and business types.
• Modern online marketing with its newer gorilla and viral marketing manifestations: forms that can explicitly take advantage of the interactive internet, have made big data a business survival essential, and certainly where a business seeks to do better than simply get by.

I am going to continue this discussion in a next series installment where I will consider a basic conundrum that this dynamic has built into it:

• I have been writing here of the need for more and more data, with more and more variable types to fill their database fields. And I add here a corresponding need for all of this data to be more and more accurate and more and more real-time up to date too.
• And augmenting the number of such variables (and the data accuracy for what populates their database fields) does in principle mean an increased and improved capability to analytically study a consumer and potential consumer base in finer and finer detail, parsing it into progressively more refined demographics and sub-demographics and in ways that would lead to more effective business decisions and of all types.
• But the more data types that would be called upon and used in any given such analysis or set of them: the more variables that would have to be coordinately analyzed in making use of this data, the larger the numbers of consumers that data would have to come from, in order to achieve sufficient data set sizes so as to make the requisite statistical tests that would be used, even just mathematically valid.

My goal for the next installment to this series is to begin with an orienting discussion of these points, and how they arise as valid sources of concern. And then I will discuss data evaluation at the trade-off levels of knowing what of a set of possible information held, holds the most value and would offer the most actionable insight in a given situation: in the course of developing, running and evaluating the outcomes of specific marketing campaigns. And I will also discuss how this opens doors for third party data providers to enter this narrative and very profitably for themselves.

And as already noted at the end of Part 16, I will also at least briefly outline how and why I would cite big data’s use here as holding potential for creating both business systems-positive and societally-negative impact, depending on how it is done and on how it is regulated.

Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 5, and also at Page 1, Page 2, Page 3 and Page 4 of that directory. You can find this and related postings at Social Networking and Business 3, and also see that directory’s Page 1 and Page 2. And I also include this posting and other startup-related continuations to it, in Startups and Early Stage Businesses – 2.

Building a startup for what you want it to become 39: moving past the initial startup phase 25

Posted in startups by Timothy Platt on August 25, 2019

This is my 39th installment to a series on building a business that can become an effective and even a leading participant in its industry and its business sector, and for its targeted marketplaces (see Startups and Early Stage Businesses and its Page 2 continuation, postings 186 and loosely following for Parts 1-38.)

This is a series about startups, and this is a posting in that narrative that addresses business intelligence and its sourcing and development and use, as identified in Part 38 as the equivalent to the air we breathe for its immediate and ongoing importance for even just basic survival. And this is also, in many respects a risk-management focused series too, where good and effective information practices can lead to increased competitive strength and business vitality, and poor practices and their consequences there, can lead to disaster.

I began a discussion of Facebook in Part 38, and both as a source of actionable business intelligence, and as a platform for businesses to make use of that information through: smaller enterprises and startups definitely included. And I said at the end of that installment that I would continue its line of discussion here, by more fully discussing Facebook and how and why it has become so problematical and both for its individual members and the communities (and marketplaces) that they collectively comprise, and for other businesses that would buy access to their vast data accumulations and seek to make use of it for their own purposes. And I begin to address that by identifying Facebook’s information collection, management and commoditization policy and practices for what they are: a tightly interconnected whole that forms the supportive core and the actionable framework of their business model as a whole. And for purposes of discussion of that here, I will refer to all of this collectively as the Facebook gestalt.

What is the Facebook gestalt? I begin by noting the original meaning of gestalt, as applied here in thinking about and understanding the Facebook business model. A gestalt is a larger organized structure that might be natural in origin and nature, or that might be contrived and developed, that should be and even can only fully be viewed and understood as a whole and not as an analytically dissected collection of its perceived parts. Such indivisibility can arise for a number of reasons, with:

• Synergies that would be lost upon part by part description and analysis, and
• An accumulation of emergent properties that cannot be captured at a part by part level,

offering two possible categorically stated sources there. And I would argue that sheer complexity and particularly sheer obfuscating complexity can achieve that same result too. I would argue that all three of these possible reasons for our having to at least consider a gestalt understanding of what Facebook does here, apply in force. So what are they doing and how, in all of this at that company?

• Facebook requires that anyone: individual person or client/member business, who wants to access any content as organized and showing on any of their social media pages, must join their site.
• And when they join, they have to agree to the Facebook terms and conditions in place, with that including an agreed to acknowledgment of Facebook’s right to unilaterally change its personal privacy and information access policy and practices at any time.
• And as a core policy and practice approach, and to maintain “transparency” and “ease of immediate and unhindered use,” on the part of its members, and of all types, a majority of all possible access and visibility decisions that those members could in principle make for managing who can see what of their information, and under what circumstances, are set up as opt-out if they can be managed at all by the members involved. So members can in principle dig through the layered screens of their individual membership’s account management pages. But unless they do so, and except where opt-in is explicitly required by law, they would have to do this to limit or otherwise control their own data and the data that is posted by others to their page walls, as it shows there.
• And any and all information and of all types that is not explicitly opted out and for essentially all third party visibility, can and does enter into the marketable data pools that Facebook effectively commoditizes and markets and sells to its business partner/clients as they seek to more effectively place ads on member pages, in their targeted marketing campaigns.
• Facebook has more recently promised to redevelop their site design and their member page layouts, so as to at least limit the flood of unwanted clutter that has come to inundate any intended content as provided by those Facebook members themselves, or by their actual friends who are also on the site.
• This may cut down on the flood of spam YouTube sourced video clips and other distractions that come from other individual human (and robo) “friends.” But what would this filtering do for third party businesses that seek to market and sell through their targeted Facebook ad placements? It would increase the value of these ad placements by making them more visible to site visitors where they have been getting lost in the spam and related flood too, and degraded in value from that. And it would increase the pressure on those marketing businesses to buy their marketing and advertisement placements more carefully and on the basis of a more refined understanding of how to reach their targeted share of the overall Facebook community. And this means increased pressure to pay more for Facebook’s member-related and member-sourced data.

All of the puzzle pieces that I have just made note of here, fit together, and in ways that cannot meaningfully be parsed apart, and certainly since Facebook as a whole, reached and surpassed a threshold scale in which it became an essential social media channel and for millions: hundreds of millions of people. (There, Facebook claims to have some two billion active members: actively posting member accounts. So if I pick a number that is probably at least of the right order of magnitude, and assume that somewhere around two thirds of those members are robotic in nature: artificial specialized intelligence agents that have been set up and that are run for trolling and related ulterior purposes, that would still leave a very impressively large number of actual human members – at up to 700 million and maybe more.) Either way, two billion or 700 million: that represents an overall target market that few if any marketing businesses could afford to simply walk away from and ignore. And its reach and its commonality of use make it essentially impossible for vast numbers of individual people to simply walk away from Facebook and ignore it too, and certainly when their actual friends and family are on the site too, and posting there and looking for new posts there too.

This, up to here, has outlined something of the conceptual and business process tangle that Facebook offers. They do not charge members to post to their site or to read other members’ pages there with their showing content: at least with direct monetary fees. But they do in effect commoditize those members and harvest their information, and the information attached to their pages by others and with that interconnections metadata included. And they do so the way cattle are harvested for their cuts of meat, to put this somewhat bluntly.

So what should a business do, that sees what might perhaps be an overriding need to be on Facebook and to market their offerings there? What should such a business do when Facebook as a corporate entity makes it so compellingly attractive to them to market and advertise through the site and for doing so much more there too, such as making online reservations (at restaurants and more) and for posting and reading community-sourced reviews and more?

I am going to offer one modest proposal in the direction of addressing that challenge here. Business members of Facebook need to have and use an option whereby they can specify and filter, where their marketing and sales content shows on the site that is limited very explicitly to potential viewers who have opted in to see them. This, in principle, could mean those businesses having an option to send a link to a customer or other who has reached out to them, that would take them to a page where they could specifically opt-in then and there if they so choose. There are ways to do this, and even with that meaning those Facebook members being taken to their own accounts management pages to opt-in or not, though that is only one possible way to manage this type of access agreement transaction. My point is that those member businesses need, and should demand some form of obligatory opt-in for how they do and do not show on the site and to whom. And it should be one that they and their customer base and its members can actively control, and without anyone involved there falling through the cracks in that is currently a way too opt-out only environment.

Facebook would claim that they are already opt-in for member data access. But the more complexly comprehensive and the more complexly interconnected their information management systems become, as can be expected with increased systems scale, the more likely it becomes that any intended opt-in will become compromised as alternative database search query access routes lead to the same result of what amounts to direct member data sharing.

I am going to step back from the specific example of Facebook in a next series installment, to consider online marketing and sales as whole, as they are information and access shaped and driven. And in anticipation of that, I add here, that I will have more to say about opt-in versus opt-out and how opt-out can and does find its way as a default into so many contexts that hold overriding risk management and quality control management significance. And I will discuss this from both a consumer and a small business perspective.

Meanwhile, you can find this and related material at my Startups and Early Stage Businesses directory and at its Page 2 continuation.

Planning for and building the right business model 101 – 44: goals and benchmarks and effective development and communication of them 24

Posted in startups, strategy and planning by Timothy Platt on August 10, 2019

This is my 44th posting to a series that addresses the issues of planning for and developing the right business model, and both for initial small business needs and for scalability and capacity to evolve from there (see Business Strategy and Operations – 3 and its Page 4 and Page 5 continuations, postings 499 and loosely following for Parts 1-43.) I also include this series in my Startups and Early Stage Businesses directory and its Page 2 continuation.

I have been discussing three specific possible early stage growth scenarios that a new business’ founders might pursue for their venture, in recent installments to this series, which I repeat here for smoother continuity of narrative as I continue addressing them:

1. A new venture that has at least preliminarily proven itself as viable and as a source of profitability can go public through an initial public offering (IPO) and the sale of stock shares. (See Part 33 and Part 34.)
2. A new venture can transition from pursuing what at least begins as if following an organic growth and development model (as would most likely at least initially be followed in exit strategy 1, above too) but with a goal of switching to one in which they seek out and acquire larger individually sourced outside capital investment resources, and particularly from venture capitalists. (See Part 35.)
3. And a new venture, and certainly one that is built around a growth-oriented business model, might build its first bricks and mortar site, in effect as a prototype effort that it would refine with a goal of replication through, for example a franchise system. (See Part 36 through Part 39.)

I initially focused on those business development models themselves, with a goal of fleshing them out with the types and levels of detail needed for purposes of this series and this portion of it. Then I continued discussing and analyzing them as working examples of how a business’ founders and managers would more likely address a specific set of business performance requirements that they would face when working to make their specific enterprises succeed:

A. Fine tuning their products and/or services offered,
B. Their business operations and how they are prioritized and carried out, and certainly in the context of that Point A and its issues, and
C. Their branding and how it would be both centrally defined and locally expressed through all of this.

I have already at least touched upon all three of these functional processes and the goals they would be directed towards achieving, since Part 39 and leading up to this posting, focusing on Points A and B in those five series installments. My goal here is to continue this line of discussion by focusing in at least some detail on the above Point C, turning back to reconsider Points A and B in its context as needed. And in that:

• Think of Points A and B as addressing the issues of what a business does and how, and Point C as addressing how that business presents the fruits of that combined effort to the world.

And with that noted and my above-repeated business development models in mind, I begin to more formally address Point C and its issues by posing a set of three basic if essential questions:

• What constituencies and potential constituencies would ventures following each of the above-repeated business development approaches need to effectively reach out to and connect with?
• What basic messages would they have to convincingly and even compellingly share with those audiences, to create value for themselves from their marketing efforts?
• And where and how would they best accomplish this?

Interaction and two-way communications, of necessity, enter into all of these questions and into how they would be understood operationally, and addressed. And I begin considering the first of these questions with that in mind, by noting that the constituencies and potential constituencies that it cites, fit into at least two fundamentally important, distinct groups: at least potentially business and business model-supportive stakeholders, and outwardly-connected and response-connecting marketplace participants.

The first of those groups are effectively defined by the business development strategy that is being attempted if not already actively pursued, and certainly for purposes of this discussion with its topical focus. And to take that out of the abstract, let’s begin by considering my above-offered:

Development Scenario 1 (the would-be IPO business): Any business that would go IPO has to be prepared to secure the positive support and recommendation of a set of potential high level investors and investment influencers, as key early participants. And I begin discussing that by noting those influencers: stock market analysts and particularly those with real followings. They more generally do not any put any of their own personal financial skin in this game as they make their investment recommendations; they do not generally run out and buy shares in the companies that they positively recommend, as most respectable stock market analysts would see their own investment in a business that they have just recommended others to buy, as representing a conflict of interest challenge that they would rather do without. But they do offer more immediate business valuation and longer-term value-prospects analyses that others would and in fact do pay close attention to. And their analyses and reviews can go a long way in establishing the price per share that an IPO would at least initially go for, with the aggregate total valuation as determined from that for all shares offered, serving as a benchmark valuation of the business as a whole, at least for that day.

For an explicitly investor example, I would cite larger institutional investors and aggregated publically traded funds managers, and particularly those who seek out new ventures to add to their funds’ portfolios. Early adaptors can be crucially important there, and word of mouth, viral marketing is important there too where large numbers of individually smaller investors can play a determinative role in how an IPO roll-out proceeds and for what comes of it as an initial public evaluation of a new business and its overall value.

I have only touched upon a few of the business development-oriented stakeholder categories here, that an IPO-facing new venture is going to have to successfully reach out to if its IPO is to really succeed for it. But this should suffice at least for here in this phase of this overall line of discussion. Bottom line, all of the stakeholders types that I have even just briefly made note of here, as well as others that I could have delved into, are going to be looking for secure places that they can invest portions of their available liquid capital in, and with at least reasonable prospects for gaining positive returns on investment from their buying into this new business. And this brings me directly to the market-facing side of my Who-oriented first due diligence question as offered above, and the importance of coordinately marketing to that group of stakeholders too: purchasing consumers and end users.

That last statement could very reasonably be seen as my opening a door to a more general discussion of marketing per se and at all levels. But for purposes of this narrative, I would focus on how early marketplace outreach from a new venture, and early market-sourced response back to it, can and does fundamentally shape how potential business development-oriented stakeholders as just discussed, respond to a new business investment opportunity, or even if they do so at all.

Investors and investment influencers look for new businesses that have a real potential for success in their markets. Let me stress that point by noting that I am not necessarily saying that they look for already-achieved marketplace success and certainly at the level of revenue flows already achieved and profits reaped. Google, famously all but took over the search engine side to the online experience and for huge widespread demographics of internet users, before they really determined how best to monetize this reach and influence as a realized flow of profits and overall fiscal strength. But their early success at becoming the leading search engine, and globally, brought them tremendous cash infusions and positive marketing spin from day one of their first day of trading as a new IPO.

• Market impact and marketplace success drive investor decision making.
• And certainly in an IPO context, where vast amounts of liquidity as capital investment funds are at stake, visible success in securing the backing of investors can only serve but to enhance and strengthen marketplace position too, and for what that can say about rapid growth potential if nothing else.

I am going to continue this discussion in a next series installment where I will turn to consider my second, venture capital-backed business development scenario, from the perspective of this same Who question. Then after completing that, and continuing on to address the third above-repeated scenario as well in light of that same question, I will address the remaining two questions that this Who question came with, in a similar scenario-by-scenario manner.

Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 5, and also at Page 1, Page 2, Page 3 and Page 4 of that directory. And you can find this and related material at my Startups and Early Stage Businesses directory too and at its Page 2 continuation.

Leveraging social media in gorilla and viral marketing as great business equalizers: a reconsideration of business disintermediation and from multiple perspectives 16

Posted in social networking and business, startups, strategy and planning by Timothy Platt on July 26, 2019

This is my 16th posting to a series on disintermediation, focusing on how this enables marketing options such as gorilla and viral marketing, but also considering how it shapes and influences businesses as a whole. My focus here may be marketing oriented, but marketing per se only makes sense when considered in the larger context of the business carrying it out and the marketplace it is directed towards (see Social Networking and Business 2, postings 278 and loosely following for Parts 1-15.)

I have been working my way through a to-address topics list since Part 11, that would apply to the analysis and planning efforts of a still resource-lean startup, as an important at least categorical business type here. And I have been addressing the first of those points since then, leading up to an initial discussion of a Marketing and Communications oriented, working example that in practice has become important for a great many ventures and certainly as they seek to connect with their markets online and through social media: gorilla marketing, and the sometimes closely related phenomenon of viral marketing. And I repeat that list here, for purposes of smoother continuity of narrative as I continue and complete my discussion of the following Point 2, as begun there:

1. What types of change are being considered in building this new business, and with what priorities? In this context the issues of baseline, and of what would be changed from become crucially important, and even for startups where that means building new with an awareness of past experience elsewhere.
2. Focusing on the business planning and development side to that, and more specifically on high priority, first business development and operations steps that would be arrived at and agreed to for carrying out, and setting aside more optional potential goals and benchmarks that would simply be nice to be able to carry through upon too,
3. Where exactly do those must-do tasks fit into the business and how can they best be planned out for cost-effective implementation (in the here and now) and for scalability (thinking forward)? Functionally that set of goals and their realization, of necessity ranges out beyond the boundaries of a Marketing or a Marketing and Communications context, applying across the business organization as a whole. But given the basic thrust of this specific series, I will begin to more fully discuss communications per se, and Marketing, or Marketing and Communications in this bullet point’s context. And I will comparatively discuss communications as a process, and as a functional area in a business there.

The core of my gorilla and viral marketing example and its analysis for how these approaches work, or fail to work in practice, as begun in Part 15, was on the uncertainties that arise when measuring performance, or of even knowing how best to try to develop and gather data for the right performance metrics there.

I made note of the novelty of these marketing approaches and how that can contribute to uncertainties here, but I also and primarily raised the issues of how their real activity, and certainly for genuine viral marketing, takes place and takes shape outside of the business that is at least presumptively at its center with its marketable, consumer-facing products or services. And in the course of that, I both raised and challenged a “standard business process or business systems example,” based on gathering in and reviewing and analyzing sales and related data for marketplace insight, in order to set an initial starting point benchmark to measure the success of such a campaign from. And to complete the cycle, this insight would serve as feedback for shaping and refining next step products and how they would be marketed and sold too, and with any refinements added to customer service or support that this insight would suggest as needed, included there too.

I added at the end of Part 15 that I would continue that series-relevant example here, by at least offering some thoughts on how to make the measures and metrics used for this type of marketing analysis more rigorous and more definitively useful as a result. And I begin discussing that by explicitly acknowledging an admittedly largely reputed early online marketing data collection and analysis approach that I of necessity at least touched upon in that posting, but that I intentionally refrained from naming there for the baggage that that label carries with it: marketing for a maximum number of eyeballs reached and with that sought after as a performance goal in its own right.

Ultimately, marketing is all about message – and how many people receive it and how they respond to it, and it is about shaping that message to both maximize reach: that number of eyeballs here, and its effectiveness as a call to action. So the number of eyeballs reached here is vitally important. The problem with the earlier eyeball capture metric of online marketing’s birth and neonatal phases, was in how those numbers became essentially everything, and precisely because no one knew how to translate them into a more complete, realistically actionable understanding of the market and its participants, or of how to more effectively respond to that type of number.

I reprise this already ancient online marketing history, to highlight a challenge that the newer and still emerging opportunities of gorilla and viral marketing of today carry with them, that in fact has roots that go back to the early successes and failures of online marketing in the age of maximizing eyeballs reached, as a goal in and of itself.

Do some businesses already carry out successful gorilla and viral marketing campaigns now? Yes, definitely, and the same could be said for early online marketing, when way too many businesses were being misled by eyeball counts, but some thrived anyway. Success happens. But lost opportunities and failures do to, and businesses that seek to create effective gorilla marketing campaigns and that seek to create effective online contexts that would draw in and involve positive market participation, might benefit from the learning curve lessons from the “eyeball age” of their profession, too.

• What can you accurately measure? And by extension if nothing else, what types of at least potential metrics would more likely give you squishy, equivocal numbers that you would find difficult to pin down for their actual meaningful values?
• How much measurement precision do you in fact need, and from what types of data and for what types of analytical use?
• And what actionable insight can you directly gain or consistently and reliably develop out of the data gathered there, as per the first of these three bullet points, that would acceptably meet the accuracy limitation requirements of the second of them?

Eyeballs were easy to count, and simply by tracking the number of times in a period that a given page or other content unit was clicked to online by a site visitor. But few really knew how to effectively go beyond simply gathering data according to that first step metric, to develop actual market insight that a business could use, going forward.

• Should you track how long a site visitor stays on a page?
• Should you track where they arrive at that page from, or where they click to when leaving that page?
• Obviously, a click from, into a sales process on that site and initiating a transaction process there, conveys a different message and holds different marketing outcomes value, than clicking from a marketing-oriented page on a business’ web site to return to an outside search engine.
• There are, very clearly, a great many coordinate types of data that can be gathered in and collectively analyzed, in developing real and even profound insight from online marketing, and certainly from the more standard and established forms of it that we have all come to know. And most businesses now know how to develop real value from eyeball counts – when gathered in combination with suites of other data types that collectively can tell a very meaningful story with that.
• But the novelty of gorilla marketing with its more free-wheeling structures, and its dependence on outside and largely market-driven and market-shaped participation, creates new gaps in what needs to be measured and in what even some of our by-now standard metrics mean. And viral marketing with its fundamental grounding in the uncertainties and vagaries of the marketplace, simply confounds that challenge, and particularly when trolls, on the negative messaging side, and their equally false-flag positive message counterparts, enter this narrative.

And even if you discount what are essentially fake negative and positive messages and their impact on a viral marketing campaign, including where “honest broker” marketplace participants can and do pass along less reliable messages in good faith, you still face the metrics uncertainties that I raised in Part 15, and other challenges too.

I said in a Point 2 context that I would address the issues of better metrics. And up to here, I have primarily just focused on clarifying an understanding that this is in fact something that is still needed, and that we still face real knowledge and understanding gaps from the limitations of some of the marketing data that we gather, at least through more standard Marketing and Communications means. This brings me to the issues and challenges of big data, and to a point of assumption, or presumption if you will that I see as holding a key to doing better here. And that at-least piece to this puzzle that I would raise here, has both business effectiveness and overall societally challenging aspects, for how it is grounded in the open-ended data collection that drives it.

I begin this phase of this developing narrative by offering what might be considered more of a point of conclusion that I will work my way towards reaching in what is to follow, as organized into four bullet points:

• If you want gorilla marketing to work effectively for your business, as a generally developed creative ongoing effort, you need to know the market that you would reach out to and connect with, from your business’ side of the conversations that you seek to develop there.
• And you need to know that same market as its actively involved participants at the very least, help co-create this marketing reach with you from their feedback and reviews,
• And you need to know this, your market as well as you would know your own Marketing and Communications staff and the guidelines that they work under in a more traditional, business-centric orienting marketing campaign.
• And the urgency of these points of observation doubles, at the very least in a genuine viral marketing context, as does the degree of challenge in helping to make this type of marketing campaign work, and reliably and effectively so.

I am going to continue this narrative in the next installment to this series, where I will at least begin to offer an analysis of how big data enters into this line of discussion and why. I will at least briefly explain how and why I see this as an essential piece of this problem’s solution. And I will also at least briefly outline how and why I would cite its use here as holding potential for creating both business systems-positive and societally-negative impact, depending on how it is done and on how it is regulated.

Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 5, and also at Page 1, Page 2, Page 3 and Page 4 of that directory. You can find this and related postings at Social Networking and Business 3, and also see that directory’s Page 1 and Page 2. And I also include this posting and other startup-related continuations to it, in Startups and Early Stage Businesses – 2.

Building a startup for what you want it to become 38: moving past the initial startup phase 24

Posted in startups by Timothy Platt on June 17, 2019

This is my 38th installment to a series on building a business that can become an effective and even a leading participant in its industry and its business sector, and for its targeted marketplaces (see Startups and Early Stage Businesses and its Page 2 continuation, postings 186 and loosely following for Parts 1-37.)

I often refer to a business’ financials and its cash availability and flow as representing an equivalent to its life’s blood – vitally essential to its life and with any real interruptions in it quickly leading to business failure and even outright business death. And loss there, as traditionally documented in red ink, simply illustrates the general validity of this understanding as it has more generally been held by others too. Information and as both raw business and marketplace data and as processed actionable knowledge, can also be considered vitally essential to any business and on an equally ongoing and pressingly impactful basis. So if a business’ cash flow and related financials can be seen as being analogous to its blood supply, think of its information flow and availability, and access to accurate timely information at that, as being comparable to the air that that business would breathe if it were a living organism in a biological sense.

I have been addressing a set of risk management and related due diligence issues in recent installments to this series, that all directly involve the challenges of information development, use, storage and sharing, where business intelligence per se has become an increasingly valuable and sought-after marketable commodity in its own right. And the points of observation and conclusion that I have been raising and addressing here can only become more valid and more consequentially important:

• And both for their ranges of applicability within specific organizations
• And across all industries and business types that they would be included in,
• And for how their information management practices impact on the markets and the people who enter into them that those businesses ultimately all do business with.

I focused in the immediately preceding installment to this narrative progression: Part 37, on an increasingly pressing challenge that all information requiring businesses will come to face if they have not done so already.

• Legal requirements and restrictions as well as business ethics concerns, demand that personally identifiable and other sensitive information regarding customers and employees among others, that might cause harm to them if made openly publically available, must be protected.
• And I stress the importance of the ethical side to this imperative here, as a failure to safeguard sensitive, potentially risk-creating personal information can create marketing and image challenges for an inattentive business that can cost it financially and much longer-term than any regulatory agency-demanded monetary fine could. If a business comes to be seen in its target markets as being unreliable there and unsafe to do business with for its failures to safeguard information such as credit card numbers, potential customers who know of that failing will at the very least think twice before doing business with them and giving them those credit card numbers. And sales transactions with them are likely to slow down or stop as a consequence.
• Information, as noted above can be thought of as the air that a business breathes. And a failure to safeguard it, and a public awareness of that failure can become a noose around that business’ neck. But at the same time, businesses that seek to remain competitive find themselves in races to acquire and more effectively make use of seemingly ever-increasing volumes of this air: this all important information and both for more immediate transactional purposes as for example when developing effective customer relations and at point of sale events, and for their overall business planning and its execution.
• And that led me to a direct consideration of the challenge that I discussed in at least broad outline in Part 37 where the bigger and the more effectively, actionably organized and processed, big data becomes, the more of a mirage any attempt to anonymize individually sourced raw data that is included in it becomes.

I repeat and stress the above, both to allow for smoother continuity of narrative in this series and to make this posting more meaningful as a stand-alone narrative. And I also repeat, and I add expand on what I have already written of in this blog on this matter, because the challenge that I am raising and at least briefly addressing here is one of the most important ones that businesses are increasingly going to face and head-on, as this 21st century advances.

• Businesses increasingly face a conundrum here, from the conflicting needs they face to simultaneously wring as much possible descriptive and predictive value from the information that they hold as they possibly can,
• While at the same time limiting what can be inferred from it in understanding the people this data comes from, so as to explicitly protect their personal privacy and certainly as effective use of this information requires its sharing among wider ranges of potential stakeholders.

I offered a necessary part of any realistic resolution of this conundrum, when I noted the importance of actually checking to see how the progressively more inclusive big data systems in place in a business, might actually compromise any data anonymization and related risk minimization efforts that are also in place, through intentional effort made to “break” that anonymization as a risk management exercise. And that is certainly important for any business that initially develops those information resources, and certainly if any of their in-house developed and maintained data might go out of its doors, and either as marketable, profit generating commodities or as transactional data shared with supply chain or other partner businesses when carrying out specific sales transaction and related activities. But it also applies to acquiring businesses and certainly where they might intentionally or inadvertently further share this, and where their big data accumulation, aggregation and processing might further degrade any still-effective data source anonymization that was still in place from its various individual, more original sources.

I said at the end of that posting that I would turn here to more fully consider the three basic participant classes that enter into all of this, in light of the issues that I raise here:

• Data sourcing and providing businesses (which might or might not actually be data aggregating, developing and selling businesses as determined by their business models),
• Data acquiring and using businesses, and
• The original sources of all of this data with that ultimately coming to a large degree from individual consumers and customers.

I in fact begin this next step analysis here with businesses that explicitly gather in, aggregate, develop and sell individually sourced data as at least a key part of their business models, as explicitly cited in my anticipatory note as offered at the end of Part 37. And I do so at least in part with a goal of explaining why the white hat hacker approach to testing and validating any data anonymization system in place in a business, as touched upon in Part 37, cannot succeed, and certainly if it is employed as a stand-alone solution to this problem and not simply as one brick in a larger and more inclusive edifice. And I begin this with what has become the publically visible poster child if you will, for how not to behave as a business as far as personally sourced information is concerned: Facebook.

I begin that line of discussion by at least attempting to expose and perhaps explode what has become something of an overly simplistic and even toxic myth. When you look to the laws in place regarding personally identifiable, sensitive, risk-carrying information, and when you follow the ongoing public discussions as more commonly address this challenge, you essentially always see the same small set of data types showing up. And I of course, refer to social security numbers and related government-systems sourced personal identifiers, credit card numbers and the generally three digit security codes that also appear in conjunction with them, full names and addresses and phone numbers, etc, and precise healthcare and health status information to add in at least one general information category to this list. These data types, and categories are important and they do in fact represent genuine sources of risk and exposure vulnerability and both for identity theft and for direct monetary value theft and for other immediately impactful risk-creating reasons. But it is a mistake to focus essentially entirely on this smaller set of possible high value targets, for use and possible misuse. Ultimately the real risk can come from the cumulative amassing of vast and even seemingly open-ended amounts of individually sourced information that is in and of itself not sensitive and compromising but that holds a potential for collectively causing harm.

I only touch on one aspect of that possible and progressively more likely exposure problem in my here-continuing discussion of data anonymization through selective redaction, as begun in Part 37. And I only point beyond that to one small part of how such big data can be used for harmful purpose when I go beyond credit card number exposure and the like as more commonly considered, to make note of what Cambridge Analytica did in its efforts to subvert elections in the United States and elsewhere, beginning in 2013 (and also see Facebook and Cambridge Analytica: What You Need to Know as Fallout Widens.)

Facebook and its executive leadership have been called out on this, and more specifically for how their business practices in organizing, commoditizing and selling access to their members’ data made a Cambridge Analytica scandal both possible and even inevitable. But that is still only the now-visible tip of a much larger iceberg.

I am going to continue this discussion in a next series installment where I will, among other things discuss how Facebook incentivizes large, and even vast numbers of small businesses to use their platform for any online connectivity with their customers that they might enter into, in effect forcing those business’ customers to join Facebook if they need to online connect with these small business members. I will also discuss how Facebook sells information, and the impact of this on businesses that buy rights to it, and that buy advertising space on Facebook member pages that they target as members of specific market audiences based on this data. This will, among other things mean my specifically addressing the opportunities and challenges that startups and other newer businesses face as they make their due diligence, participate or not decisions here. And I will, of course, discuss the impact of all of this on individual Facebook members as they share more and more and more of their information through the site and with all of that going into Facebook’s marketable and sellable databases.

Facebook is currently, as of this writing, rolling out a new site design that is supposedly more privacy oriented and protective and that would be freed from at least a significant amount of the paying business sourced and other “friend”-deluge that floods most individual Facebook user’s pages now, drowning out any shared content that they might wish to see from people who they actually know. I will discuss their new website design roll-out too, where bottom line, data and member data in particular is still going to be Facebook’s most valuable marketable product and the most important source of revenue that they have too, and with its accumulation and sale still held as a central feature of their still ongoing business model from before.

Meanwhile, you can find this and related material at my Startups and Early Stage Businesses directory and at its Page 2 continuation.

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