Platt Perspective on Business and Technology

Building a startup for what you want it to become 44: moving past the initial startup phase 30

Posted in startups by Timothy Platt on July 20, 2020

This is my 44th 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 1-43.)

I wrote Part 43 of this series, in effect, as a lead-in to my posing an at-least potential conundrum that the founders and owners of a newly forming business can face, which I repeat here as I begin this posting:

• If you need to market and sell specific products or services, or some combination thereof at at-least some minimum threshold volume of completed transactions
• Before you can generate any significantly scaled positive market sourced responses that could come from that, and with even just a possibility of viral marketing value from that,
• But you need to develop that shared communicating reach in order to generate the sales levels that would make that possible,
• Where and how do you start?
• And what are at least some of the possible best practices approaches that you could deploy and build upon to keep a start there going and growing?

Those questions can apply as much to the context of new businesses that seek to sell more standard products or services, as it does for businesses that seek to offer the new and innovative. And that certainly holds true when a new business that would offer what is basically a more standard product or product line, would have to market and sell that in the face of an opposing name brand loyalty headwind, and when the basic commonality of their core products would make it harder to set them apart from their competition as sources of distinguishing value. Better, more customer appealing price points can help but marketing is still going to be essential there and even when such a manufacturer can show a cost-to-customer advantage.

That contextual point offered, I made note of what would for this blog, be more of a stock response to those questions in Part 43, when citing “social networking strategies and social networker taxonomies in that context” and when going on to mention “gatekeepers and opinion shapers.” And I will couch my response to this challenge as offered here, at least in part in terms of those types of information and opinion shaping and sharing resources. But my goal here is to develop this line of discussion in at least a somewhat new direction.

The obvious, and I add here-repeated part of that is that effective marketing of this type is going to call for actively, positively engaging people into conversations who others connect with and look to, and for both knowledge and insight. This obviously includes interactive social networkers; if you only reach out to and gain supportive messaging from people who no one pays any attention to and who very few even know of, you cannot expect to gain any marketing reach or momentum: any marketing traction from that. So connecting and engaging with an explicit awareness of who you would engage with in that way, and from a social networking taxonomy perspective should be a puzzle piece as you assemble your overall marketing campaigns and their underlying strategy here. And in that regard I cite one of my basic reference work resources on who these people are, at least when considered from the perspective of the basic business and related online social networking approaches that they pursue: Social Network Taxonomy and Social Networking Strategy.

Yes, the more actively and the more effectively engaged of the active networkers cited there are important and certainly where interactive is important in developing anything like viral marketing. That is based in very large part on two-way exchanges and two-way interactive communications and on networks of trust that can be built from that. There, leavening these at least potential conversations by actively bringing in people who are well connected and listened to in the first place, can only help. But this can only serve as a possible starting point enabler. So I turn from it here, to more widely consider the second here-repeated resource identifier text as repeated above: “gatekeepers and opinion shapers.” And I do so by in effect challenging a point that I just made:

Garrison Keillor may have come from a town where every child was above average – and even just there: not just when compared to the averages found in larger outside communities. But his town’s exceptionalism notwithstanding, you cannot find that type of pattern in the real world.
• In this context, I note that genuine viral marketing is by its very nature a mass engagement phenomenon. Only a limited few can ever achieve that 1% most connected status. In fact 99% are guaranteed to miss that mark. And 90% and even 95% of all networkers will miss it very significantly and certainly when social networking follows a Pareto principle distribution and one that is very stringent, with only a fraction of 1% of them genuinely qualifying as super-networkers.
• So viral marketing and the positive message spread that drives it has to be carried out for the most part, by people who are individually much more limited in their active networking reach. And that means multitudes of those networkers who I just all but discarded above “who very few even know of.”

How do you engage large numbers of these more locally acting networkers and locally effective opinion shapers?

The more super-networkers of my above cited social networking taxonomy reference can help to start a wider ranging conversation, but if a sufficient number of the less connected who they share a message on this with, do not bother to pass it on and as an accepted and agreed to positive, their effort will die down and disappear and quickly as everyone moves on to other issues and matters.

How do you engage large numbers of these more locally acting networkers and locally effective opinion shapers? I would offer an at least initial response to that question, as framed by the above points by challenging Marshall McLuhan and stating “… no, sometimes the message is the message, and how it is framed and presented can be more important than where it is presented. Sometimes the medium is not the message. And in fact the medium is only the message when that medium is so new and so publically riveting as such that it becomes a separate message in its own right.”

So I turn here to the core issue of this posting and it is one of shaping whatever message that you and your business would deploy in an attempt to break out of the conundrum challenge that I repeated at the start of this posting.

• Your message should be clear and unambiguous.
• It should be easy to remember, and to be remembered as being your business’ message.
• It should at least ideally, be at least somewhat entertaining, thought provoking or both.
• And it should be short enough so that others will repeat it and without message content mutating changes.

Ideally, it would also be enough of a marketing teaser to bring people who share it and who receive it from others, to go directly to your website and other online presence venues, because of it.

I have not been discussing any of this here in terms of strictly central broadcast, one directional messaging. And I only cite that here and now as a possible, more opportunistically achieved marketing augmentation possibility. One-way opinion sharing that fits a central broadcasting model will not disappear, but my focus here is on the growing and already tremendous reach and strength of the larger community as a message creating and sharing megaphone.

And this brings me to the question of what such a memorable and sharable message would include – and the coordinate issues of what it should not include as well. This brings me to the question and the challenges of developing and sharing a positive message, and the question of what “positive” even means there. I will turn to and address that in a next installment to this series, noting in anticipation of what is to come there that how you say what you say, can be more important – or at least more impactful, than what you say in this. 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 – 49: goals and benchmarks and effective development and communication of them 29

Posted in startups, strategy and planning by Timothy Platt on July 5, 2020

This is my 49th 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 1-48.) I also include this series in my Startups and Early Stage Businesses directory and its Page 2 continuation.

I have been successively discussing a set of three basic business development issues in recent installments to this series, as they would be understood and addressed by businesses that seek to follow each of a set of three basic development models. The business types that I include there include an IPO scenario and a venture capital supported one, and an at least initially seemingly unrelated one: a franchise system-initiating scenario that would be built out from an intentionally prototype-developed initial storefront. And the three issues are:

1. 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? (Nota bene: I repeat here that my focus in this specific line of discussion is on initial development and early expansion and scaling up of a new business venture, so I am not at least for here and now, going to include market audiences in my considerations as they arise over time. They enter into, and are in fact the focus of attention for other discussions in this blog, but my focus here is on building the business per se and not so much on what it would bring to market or to whom.)
2. What basic messages would the founders and owners of one of these ventures, 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?

I have already offered at least an initial discussion of the above Point 1 in earlier installments leading up to this. My goal for this posting is to complete an at-least initial analysis of the above repeated Point 2 and its issues too, doing so as a continuation of Part 48 where I began this line of discussion. Then after that, I will turn to Point 3. Addressing that, I add here, will mean revisiting Points 1 and 2 again. And with that noted in anticipation of what is to come here, I return to Point 2 and its decision making challenges, noting that I focused essentially entirely on funding sources and on who influences them in Part 48. I turn here to add in consideration of the wider social media context and both as that would involve outside audiences (customers and potential customers included) and as that would involve the business itself.

Ideally at least, any given consumer-facing business would find a way to connect with and cultivate a stable and reliable market share, and one that with time would come to include repeat business customers. But that is a goal, and one that can in most cases only be realized as that business really takes off and begins to develop a track record and a name for itself in its larger communities. So my focus here is on how that would begin, starting from a position of being new and unknown for essentially all possible here-future customers.

I have written on a number of occasions in this blog of online marketing campaigns and viral marketing. How and where do you start for that? I will focus in more detail on the where of that when discussing the above Point 3, simply noting here that your focus should be on reaching out to connect and communicate where the people who you seek to reach are looking, and where they would be most favorably be inclined to favorably act from – favorably from your perspective.

Let’s consider those funding sources from Part 48 again as a starting point for this posting’s discussion. I wrote there of business oriented social networking and both as a means of researching and identifying valuable contacts, and as a means of actually making contact with them. How can you use online social networking resources to increase your chances that these people who you need to connect with, will want to connect with you too, beyond your offering a (hopefully) well written and up to date online professional profile and you’re reaching out to them through what amount to cold call networking invitations?

Are they listed in their LinkedIn or other professional profiles as being members of topically specific professional networking groups, that it would make sense for you to join too? This means you’re having both relevant interest and experience in the topics and issues of importance there, so that you could make a meaningful contribution to at least some of their discussion threads. My focus here is not on which online groups or professional organizations you would join for this purpose. It is on entering into or even initiating lines of discussion that you can use as a basis for further, more direct conversation, where you join in at all. And it is about you’re establishing yourself as being more than just an online profile, that would all but certainly be incomplete and that could not, for its of-necessity scattershot brevity, actually say that much about you or about what you offer as a professional.

I wrote that in terms of joining online conversations and the groups that support them, in order to connect. And that is a legitimate option; membership in such forums need not be lifelong. Join and participate, then leave and move on as your needs and circumstances dictate, where that can mean your interests and priorities have changed, or a group that you have been involved with has and in ways that lead to it no longer matching your interests, or both.

• Think long term there; think in terms of ongoing participation in relevant online and other professional groups that actually connect with your long-term interests and needs, where you can establish an identity and a positive reputation for what you have to offer.
• And use that overall connectedness to cultivate more specific networking opportunities that would meet your ongoing, more here-and-now needs.

Note that I have written this from the perspective of consultancy and of developing a networking base that can generate productive, profitable work assignments. Most businesses do not follow that path and neither do their founders. But every business founder has to think like a consultant in this if they are to develop a place for themselves in their potential supply chain environment, and with at least major potential business-to-business clients if they seek to engage in that arena.

This addresses reaching out to and connecting with possible funding sources where they might be sought out, early on, in order to accelerate business development. This addresses the challenge of establishing a new business’s founders credentials and credibility for what they would do, professionally. And this would accompany starting efforts in market-facing outreach too, and with consumer-facing messaging too.

Continuing with an “act like a consultant” approach as I have already pursued here, I have already at least begun to more fully address the above Point 3 and its Where issues here in that context. And I will continue that line of discussion in a next series installment where I will address issues of selecting effective forums to communicate and connect through. In anticipation of that, I simply note here, the differences between attempting to enter into significant professionally oriented conversations through a site such as LinkedIn, as compared to trying that through a Facebook page. Where is all about Who and What and For What Purpose, here. And I will delve into at least some of that complex of issues in my next installment here.

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 21

Posted in social networking and business, startups, strategy and planning by Timothy Platt on June 23, 2020

This is my 21st 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 1-20.)

I have been discussing data and big data in particular, and its potential benefits and perils in this series. And in the course of that, I have repeatedly touched upon crucially important points of categorical distinction: the conceptual, and I have to add the operationally functional distinctions that can be drawn between raw data and processed knowledge.

To be more precise here, I have repeatedly stated that there are important points of distinction there, but I have not all that fully discussed or analyzed what those distinctions are or what they would mean to a business that seeks to make effective use of business intelligence. My goal for this posting is to at least begin to shed some light on these issues, and both for what they are and for how and why they are important, and certainly in the context of this series and its areas of concern.

To bring that into focus, at least for where I would begin this narrative, I ended Part 20 by offering this anticipatory note as to what I would address here:

• “And this brings me to what can be the refined gold in big data: metadata and the pools of processed and at least situationally validated (and hopefully context benchmarked) knowledge that has already been developed from that raw data at hand. I am going to turn to that area of discussion in my next series installment.”

Let’s begin this by considering what metadata is. In simplest terms, metadata is simply data about data. So for example, a business might develop a database of raw data coming from their sales transactions and their details, and from their individual customers, and their individual suppliers and others. And one of their key goals would be to use this perhaps with-time vast pool of information, to help them make better strategic and operational decisions. But as an unprocessed, unconsidered mass, this accumulation of informational stuff, is probably not going to actually offer them much if any real value for that. Value comes from processing and analyzing it, and from mining it for meaningful patterns that can be used in making specific business decisions. And a significant part of that value creation begins with the development of specific metadata that can be used to organize and functionally characterize that raw data.

• What is metadata? It can be descriptive and serve a role of identifying data fields and the variables they include in them as to basic type. Consider, for example, individual customer sourced data fields that collectively – and specifically, locate where they live or work or where they make their online purchases from. Or consider descriptive data that would categorically identify vendors or suppliers that a business purchases services or goods from, according to whether they bill as 30 days receivable, or 60 days receivable.
• Metadata can also be structural, and as such can serve to functionally define what amount to compound objects. A complete suite of data fields that would characterize a specific online customer, or a specific supplier would fall into this category. So would a functionally significant specific subset of such a data assembly that might hold specific significance in planning or business operations.
• Metadata can be administrative, or risk-management systems based. And as a working example there, it can be used to manage who would have what levels of access to and control over what data types. Consider those two structural metadata examples. Who needs and should have read-only permissions for seeing those types of data? Who needs and should have read/write permissions and be able to both see and change the data that is held there?
• And metadata can be statistical, and this is where a wide range of statistical analytical findings are added into an overall database, as processed knowledge that would hold some combination of descriptive and predictive value.

So metadata is processed knowledge that is developed from what is initially just raw and largely disconnected data. It can be very low level, simply pointing for example to meaningful connections between two or some other low number, few separate data fields, or it can be higher level, organizing and conceptually connecting large numbers of otherwise seemingly unrelated data types: database data fields, in ways that are insightfully informative. And as one other point of general understanding here, metadata as a whole for a big data holding enterprise, offers just as much insight into that business as it does for that data.

Let’s start addressing that last point by more fully considering statistical metadata, or process metadata as it is also called. And I begin that with the absolute basics:

• Businesses, or at least those that are in any way systematically organized and run, develop around business plans, as carried out through specific strategic and operational planning and execution. And they use the data that they bring in, and that they develop from it (as raw data and as metadata) to answer specific descriptive and predictive, business performance modeling questions.
• Those questions and their data-driven answers, hold value for them insofar as they mesh with that business’ overall goals and priorities: short-term and longer-term, and insofar as they would help shape more effective decision making for that.
• An inventory of the precise search queries and the precise statistical and other analytical tests that would be done on all of this data: raw and metadata, would obviously reveal a great deal about that organization and its planning and its priorities.
• But simply knowing precisely what types of metadata are being processed and developed and used, can be very revealing too, and certainly where it is developed for business-specific purposes, and in ways that would fit into and support it in offering unique sources of value to a marketplace. (I draw an important point of distinction there, setting aside more generic metadata types such as standard customer contact information identifier, structural metadata fields.)
• And importantly to this last point, while it is obvious that much if not most of the process metadata that a business holds, would best be considered highly sensitive, at least some of the data developed of all other basic categorical types would be too – and particularly where control over wider ranges of such data might become access compromised. Consider as a case in point example there, structural data specifications, where analysis of unusual combination, composite metadata is used to address valuable but less considered and less understood business development opportunities and how best to develop them.
• And this brings me to a final defining point in characterizing what metadata is. Metadata creates synergistic value for a business or other data accumulator and user, where the act of effective processing and knowledge creation from raw data, creates new value at the metadata level itself, and at the raw data level too.

I added at the end of Part 20 that I would continue on from this line of discussion to:

• Expand upon and at least seek to address possible “needle in a haystack and combinatorial explosion” problems as can arise in big data. And I will do so at least in part in terms of the issues raised in this posting, where I will at least briefly discuss how effectively developed metadata can help organize an overall data pool to help limit that.
• Then after doing so, I will turn back to the issues of intermediation and disintermediation, and of balancing lean and agile, with information security risk managed. I have addressed that complex of issues a number of times in this blog already, and from a variety of perspectives. This time I will do so as they specifically arise in the context that I have been discussing in this series.

And in further anticipation of what is to come here, when Cambridge Analytica gathered in and used its vast stores of data to help throw elections, among other things, they were able to bring as many as five thousand individual data points to bear, concerning virtually every individual who they had captured information about, in doing that. But their real power there was not in the raw data per se that they accumulated, but in the metadata that they developed in order to organize and make sense of that, in descriptively and predictively meaningful, actionable ways.

So I have been adding a new level of explanatory discourse to an already ongoing discussion of big data and its use (and misuse), here in this posting, that I will continue in the next to come.

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 43: moving past the initial startup phase 29

Posted in startups by Timothy Platt on May 18, 2020

This is my 43rd 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 1-42.)

As I explicitly noted in Part 42 of this, it is one of the defining hallmarks of the 21st century business that it is information driven. And the synergies that become possible from bringing data together to address business needs, and the seemingly ever-expanding possibilities for gathering in, organizing and using both it and the processed knowledge that can be developed from it, drive the growth of big data, and create pressure to tap into it and make use of it for any business that seeks to remain competitive.

I offered a set of basic due diligence and risk management questions in Part 42, as organized from a consumer perspective, that at least point to some of the key potential sources of loss or gain, and of risk averted or faced that arise in this context. Then I briefly discussed those same issues as they would arise from a business perspective. And then I argued a case for thinking about those two perspectives as being inseparably interconnected, posing as a point of conclusion that a failure to understand that can only create increased risk for any consumer-facing business. Ultimately, any business-sourced risk to their customers or to consumers in general, is also risk to the businesses involved in that too. And risk to those businesses can all too easily translate into increased risk for their customers too and certainly where business intelligence, and consumer-sourced information is involved in that.

I have offered this discussion up to here from a more generic perspective that would apply to essentially any business, and certainly any business-to-consumer oriented enterprise. But my goal here is to at least begin to tie that developing narrative back to a specifically startup and early stage business context of this series, where new ventures in this 21st century of necessity have to be social media immersed and big data driven, while supportively presenting themselves as allies of their customers in meeting their fuller sets of needs.

I begin doing so by selectively reconsidering startups and early stage businesses per se, and certainly for details that would consequentially connect with the issues of this developing narrative:

• I have repeatedly discussed how and why businesses in general need to find and reach an effective balance between being lean and agile and careful in prudently maintaining and using their resources, while still maintaining a level of resources and capabilities that would sustain them in the face of the unexpected and the disruptively novel too. That applies as fully to businesses that are just starting out as it does to their more established counterparts. But the issues and challenges that this raises can be both easier to state and more difficult to actually address in those new enterprises as …
• They start out with little if anything in the way of reserves: liquidity that would not generally be expended in meeting immediate here-and-now needs,
• They start out without a proven track record at least as a business per se, with any historically based strength or value that they can show at that stage of their development coming from the careers and histories of individuals participating in and invested in them.
• And they face their markets and their potential customer bases as still unknown quantities to them, and with products, services or both that have no specific histories for them.

I continue this discussion by elaborating on that last point, and particularly as all businesses need to be able to function and succeed in an increasingly social media-driven world where online reviews and evaluations can make or break them.

New business in general start out without established customer bases, and they need to be able to grow and develop them and certainly if they are to move out of the long tail pack of also-rans that are more than just conceptually contained by the Pareto principle. And certainly online, that principle is much more constraining for the businesses that do not come to the top for the levels of sales that they can generate, than would be encapsulated into its other more commonly used name: the 80:20 rule. Online, some 90% or even more of all business transactions completed, go to 10% or fewer of all businesses competing for that activity there. And for some types of online businesses that can mean that lion’s share of all business activity and all profitability going to much fewer than that already-reduced 10% too.

• Businesses are information driven, and online businesses face and need to be able to meet the needs of markets that extend out beyond the scope of any traditional bricks and mortar business’ more usual geographic reach. They need to understand and they need to be able to market to and sell to newer, more complex markets than ever before.

It used to be assumable and even as an essential given, that a business professional who sought to build a new venture of their own, who stayed within their own industry or their own business sector in doing so, would likely start out with the expertise and the market-facing understanding that they would need in order to succeed. They would know enough, and certainly in general about the precise market and market demographics that they need to reach out to, for them to be able to do so from their own prior experience as they begin to build a loyal customer base of their own. There is still an element of truth in that; it is much riskier trying to build a new business if you are a novice for that particular type of enterprise, than it is if you start out with real depth of experience there. But online certainly, if you start a new business, you start out competing with every single other business already out there that is seeking out the same market and the same customer demographics that you do. Geographic isolation and any potential for developing a new business that would cater to a more captive market as a result, no longer applies, or at least it does not and cannot do so in the way, or to the degree that it did in a pre-internet world.

Businesses are information driven, and they have to be if they are to meet the challenges and the opportunities implicit in that. And that basic and even defining assertion applies to all of the points that I have just been raising here. And this brings me to the issues and the questions that I raised in Part 42, and now here from this newer business perspective. And I at least begin a discussion of the issues that that raises, by posing some basic due diligence questions that I will offer here as if conversing with a specific business client (who in this context, I will address as “you”):

• What precisely does your business seek to do that would be directly market-facing?

It is important to look beyond the simple and obvious of what you specifically seek to bring to market and sell there, in answering this question. Yes this is, at least to a significant degree, a product and service offering question. But it is a business process question too, that of necessity has to include how you would do that too.

• How do you expect to reach out to and connect with customers and potential customers?
• And how are you going to be prepared for dealing with potential customers who proactively reach out to you and your business first?
• Perhaps more importantly, what do you seek to do as far as developing and enacting an overall system that will engage with all such customers and potential customers – and in ways that would specifically build upon the features of your business model that would make your new venture stand out from its competition as offering an at least somewhat unique value proposition to them?

Note that I did not focus on unique features of your products or services there. I focused on the business processes that you would use in creating them and in bringing them to market, and with any necessary follow-up customer support included there as well. And that, crucially, would include your marketing and other outreach efforts: aspects of all of this that would draw the most active general-market and specific-customer attention and response.

It is impossible to break out of the lower impact pack of those 80% or even 90% of also-ran businesses, if you cannot engage with potential customers effectively enough to even share with them that your products would offer them special value in some way that would be meaningful to them. So business processes and from marketing and sales, through customer support have to help make you stand out first. Remember those online customer reviews there in this context. And this means connecting with more specific gatekeepers and opinion shapers as well as with larger groups of potential customers.

And this is the context in which I turn to the issues and challenges of jumpstarting all of this:

• If you need to market and sell specific products or services, or some combination thereof at at-least some minimum threshold volume of completed transactions
• Before you can generate any significantly scaled positive market sourced responses that could come from that, and even just a possibility of viral marketing value from that,
• But you need to develop that shared communicating reach in order to generate the sales levels that would make that possible,
• Where and how do you start?
• And what are at least some of the possible best practices approaches that you could deploy and build upon to keep a start there going and growing?

In anticipation of that line of discussion to come, I note here that this calls for precise market demographics information and insight, and both to those target demographics as a whole and for their details too. I will cite issues of social networking strategies and social networker taxonomies in that context and build my discussion of this complex of issues from there. And I will, in the course of that more fully discuss what “gatekeepers and opinion shapers” means in an openly interconnected interactive online and social media context.

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 – 48: goals and benchmarks and effective development and communication of them 28

Posted in startups, strategy and planning by Timothy Platt on May 3, 2020

This is my 48th 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 1-47.) I also include this series in my Startups and Early Stage Businesses directory and its Page 2 continuation.

I have been discussing three early stage business development scenarios in this series since Part 33: an IPO scenario and a venture capital supported one, and an at least initially seemingly unrelated one: a franchise system-initiating scenario that would be built out from an intentionally prototype-developed initial storefront. I add here in this context, that intention to so expand there does not have to built into the initial planning that would go into building such a franchise-based business in the first place. That next step business model detail can be added in later. I only assume here that it would be when the business in question is still in its early stage of development so the lines of discussion that I pursue here would apply.

As part of that discussion, and after initially outlining something of those three development scenarios per se, I began to reconsider them for how their owners and managers would variously address the first of a set of three basic questions that ultimately any new business founder will have to deal with:

1. 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? (Nota bene: I repeat here that my focus in this specific line of discussion is on initial development and early expansion and scaling up of a new business venture, so I am not at least for here and now, going to include market audiences in my considerations. They enter into, and are in fact the focus of attention for other discussions in this blog, but my focus here is on building the business and not so much on what it would bring to market or to whom.)

That parenthetical caveat noted, I offered an initial response to Question 1 in Part 47, and will continue on from there in this posting, by at least beginning a corresponding discussion of the next two questions and their issues, as would arise in the context of my three business model scenarios:

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?

And I begin addressing the above repeated Question 2 from the perspective of what are almost certainly the most basic, fundamental principles of marketing, which I would summarize as two closely connected bullet points:

• Know your audience (with that recapitulating and expanding on my response to Question 1 as I would address it. Effective marketing messages are always targeted and certainly in this type of context, so a here-expanded response to Question 1 is essential for addressing this question too.)
• And know what you are trying to bring the various constituencies of that audience to do with those targeted messages (which would be very targeting business – very marketing business-specific for their details, even if there are basic elements to any such message that would all but certainly apply here too.)

For purposes of this phase of this narrative, the basic, bottom line answer to the second of those bullet point challenges is essentially the same for all three of the scenarios that I have been pursuing here, and certainly when it is considered in general terms. And it would apply as such with equal force to any of a wide range of other possible scenarios that I could have chosen to pursue here too: funding and support, and securing them by establishing real buy-in from potential investors and related funding sources. That said, there are always going to be business model and business-specific differences too. So I will begin addressing Question 2 here, from an audience perspective and then address the second, for-what set of issues next and on the basis of that. And I will start all of this with discussion of the more generally applicable side to the types of calls to action that this marketing would entail, and that it would be built from and with a goal of expanding that out to at least consider more individualized business specifics later.

Let’s start with the would-be venture capital funded business scenario. What is your initial audience, as considered here in the context of this discussion (and as at least initially addressed in a more strictly Question 1 context?) Any answer to that obviously includes venture capitalists who you are actively seeking support from. But perhaps just as importantly, that also includes any identifiable subject matter experts who they might turn to for insight as they make their decisions, and with that starting with an initial decision to even just consider your particular business.

• What does this mean? Research on your part too, and the preparations that effective research can enable, are everything and certainly early on when you are trying to find and actively engage with the right potential backers. And business oriented social media can be everything there. Larger venture capital businesses always have their own in-house researchers and analysts, but most venture capitalists seek out at least some outside specialist expertise too.

I have actively mined knowledge and insight regarding businesses that I have worked with: startups and early stage businesses definitely included, from LinkedIn and related tools – and particularly where I have been able to learn the names of at least one member of a founding team when initially considering them as clients. I have always proceeded from there and looked for others in that budding venture too. And going beyond that, I have made it a practice to carefully review who they all show as also being online connected with. LinkedIn as a case in point example here of a relevant research tool, allows that type of contacts information sharing, and certainly when viewing the profiles of direct connections through that site. I cannot overly stress the value of social media as a research tool here, and for both developing a working understanding of the audience that you want to reach out to and connect with, and to give you the insight that you would need to succeed there.

Where do these people work now, and certainly if they hold several positions at a time? For a senior partner at a venture capital firm certainly, that might very well include their also serving on the boards of directors of one or more other businesses at the same time too, as a working example here. Where do they have identifiable connections, and to professionals who they are likely to trust and respect, who they might turn to for advice and insight? And do you see any who you might want to reach out to as well, and for advice insight that you could use too? Do you see any patterns there? Do any specific identifiable go-to individuals stand out there?

The quick and dirty, and I have to add simplistic answer to Question 2, from the perspective of a business that seeks out venture capital support is just that: venture capitalists. A more enriching answer would also take into account their business social networking contexts and the issues of who influences their decisions as they carry out their investment decision due diligence. Have any of those influencers offered publicly visible content that would help you to understand what they might be sharing with their venture capitalist contacts? Are any of them approachable as direct networking possibilities who you could connect with too?

And yes, such a more enriched answer would also include a wider range of people who you could turn to for direct advice too. Remember those businesses that the venture capitalists are board members of in that. Are they serving in that role there because their venture capital firm is backing those businesses, and participating in them in that way for that reason as part of their own due diligence? Look for businesses that the venture capitalists who you would seek support from, have already offered support to. Connect with them where possible for insight too, and both for how and why they entered into business relationships with the specific venture capitalists they have worked with and for how that has worked out for them.

Now let’s turn to the would-be IPO venture. (I will continue for now with a focus on knowing your audience and will then turn to consider what you would seek to bring them to do after this initial step consideration of all three of the scenarios under discussion here, as noted above.)

The obvious, and also quick and dirty and simplistic answer to the first of those two bullet points, has to be prospective investors (or rather specific demographics that such investors might be more likely to fit into.) And in keeping with my discussion of this business development scenario as already offered up to here in earlier installments, I add business and stock market analysts and particularly ones who have large followings and who hold a correspondingly large level of influence.

If a well placed market analyst pans a would-be IPO venture, that can easily become its kiss of death and certainly if word of that negative review spreads virally. A very positive review most probably helps that venture, but at least in my opinion, really negative there probably has a larger overall impact and effect than a seemingly equal and opposite positive review would. Chalk that up to human nature, if you will.

My point here is that:

• Marketing works best if you have specific and accurately identified demographic groups in mind that you would market too (obvious), and
• If you have particular market analysts in mind too, who you can market to as well and with that list based in large part on precisely what you would offer to market, and precisely what they focus on as fitting into their areas of expertise and interest.
• Note: this does not just mean obvious industry-specific or related foci of attention. Know who the bigger for name recognition analysts are, who might conceivably show interest in the type of business that you are developing. And know what they have reviewed in the past that might address other ventures that are, or were anything like yours, and how they have analyzed and reviewed them. And beyond that, take a look at all of their negative evaluations and particularly where they have been very negative and impactful from that – and regardless of consideration of immediate apparent similarity to your venture. What do those reviews and evaluations show and what do they reveal as to what those analysts favor or disapprove of? What do those reviewers like and what do they dislike and even actively hate to see in a business venture, at least as can be determined from their specific reviews and recommendations as publicly shared?
• You might or might not agree with what these analysts have to say but they are usually fairly clear on these points. (And you can use these expressed opinions and the reasoning offered in their support there, as sources of insight into your own business planning and in the What and How of your business development efforts, and for their own merits as well as you can mine and use them for marketing your venture more effectively too.)

I fully expect to add to my above offered initial responses to this Who side to how I would address the above Question 2, in the context of this scenario. But to round out that phase of this narrative, for at least this initial take on this set of issues, I now turn to the third business development scenario of this set: my would-be franchise empire building venture. And I begin that by offering yet another initial “quick and dirty and simplistic answer” to how I would propose addressing the above audience identification bullet point and its issues. And that first cut answer has to be:

• Prospective franchisees who would make the investment and take the risk of buying into this dream. And when a would-be franchise system is still just starting out as such, it really is a dream that its founders are attempting to turn into a reality. And it is a dream for that twice.

The first dream stage arises when and as those founders set out to build their first, would-be prototype storefront or business outlet. This is a stage that essentially every startup has to face and successfully traverse if it is to succeed and certainly long-term. The second dream stage begins as this single storefront reaches out to bring in entrepreneurs who might share in their vision, and buy into this business as franchise license holders of what would be their first round new outlets. They have to be convinced to buy in early, absent anything like a track record of success for this business. So they obviously belong in any meaningful answer to that Who question.

Who of necessity has to be included in a targeted marketing audience there for this scenario? I will answer this question twice, and my first such response is based on specifically targeted marketing:

• If you are a founder of this type of venture, who do you already know, and who can you specifically reach out to through your networking contacts to directly come to business social network with, who might be a good candidate for becoming a franchisee and who you might be able to interest in that?
• This essentially automatically means you’re marketing your venture to people who can help you to expand out your business networking reach and in meaningful directions – towards people who would in fact be more likely to be a good fit for what you seek to do, and who might be interested in at least considering working with you too.
• It would be easy to expand on that bullet point but I will limit what I offer here on that to simply noting the importance of meaningfully connecting with anyone who you business social network with and particularly when and as you seek help from them. Throw a wide net and network effectively and in a business-like manner with everyone who you connect with in doing so. Everyone who you seek to bring into that effort has to be treated as important members of your overall target audience here.

My second such response to this challenge is based on demographically targeted marketing:

• What types of people, fitting into what types of marketable demographics, can you most profitably market to who might be willing and able to take on the challenge of being a franchisee to your business?

There are a few points of detail there that are highly predictable. You should be focusing on people who are basically entrepreneurial in nature and who are most likely at least somewhat dissatisfied with working for others, and certainly where they have little if any say in what they do or how, and who seek to be more their own bosses. And certainly in a newly forming franchise system context, you should most probably be looking for early adaptors who are comfortable with the New. But at the same time, people who seek out franchise opportunities, rather than seeking to build their own new businesses from scratch, also tend to like the assurance of building and working with a more secure and established system too and according to a more tried and true business model. So your ideal early stage franchisees are most likely to take a nuanced and complex perspective when considering their options and deciding on what they would do next, and certainly as that would meet their needs and concerns.

How do you identify those demographics and how do you reach out to them? Note that I just raised a Question 3 challenge there; all three of the above basic questions and their issues are inextricably interconnected, and that is an important point of detail to keep in mind throughout this ongoing discussion.

I am going to continue this line of discussion in the next installment to this series, adding further thoughts to my here-offered discussion of:

• Know your audience.

Then I will explicitly turn to address the issues of

• Knowing what you are trying to bring them to do.

Then after offering some final (for now) thoughts relevant to Question 2 as a whole and in more general terms, I will turn to and address the above-repeated Question 3 and in a similar manner. And yes, I will also address these issues from a more individualized business perspective too, and from the perspective of the points of difference that help make individual new ventures succeed as they seek to carve out their own identities and their own market niches. 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 20

Posted in social networking and business, startups, strategy and planning by Timothy Platt on April 18, 2020

This is my 20th 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 1-19.)

I briefly discussed two challenges in Part 18 of this series, that directly question what is too often the less than fully considered use of big data, and certainly as it might be assumed to be a descriptive and predictive “magic bullet”, and for all marketing and other business and organizational needs. And I have said since then that I would return to further consider one of those challenges and both for how it can offer very real value and for how it can be misused and become misleading as a result: correlation analysis. My goal for this posting is to flesh out and expand upon my Part 18 beginning to that discussion, at least for the types of issues that would enter into that topic that arise to importance in a series of this type.

To briefly reiterate a set of core details already offered on this, that I will build from here:

• Correlation analysis, when carefully planned out and carried through upon, is a very powerful descriptive and predictive statistical analytical tool.
• But this does not and cannot work when presumably significant and even strong correlations are identified, strictly on the basis of individually low correlation factor to factor data, with for example very large numbers of individually insignificant factors all applied together coordinately in order to describe or predict the behavior in some context, of (for example) some specific demographic.
• Random connectedness with correlations of near zero, do not stop being random for their descriptive or predictive value simply because you shovel lots of them into your statistical software package as input and click to run the program.

The key to that cautionary note is causation and that among other things means you’re focusing on factors that have individually significant levels of correlation statistically, to whatever you seek to prove or disprove as a hypothesis, for you to be able to safely assume that they are not random in the context that you are testing. I continued from there in Part 19 to categorically parse out something of what causation is, for the differing ways that it can arise in cause and effect systems. And I will make use of the categorical distinctions that I made note of there, here. But I begin this posting’s main line of discussion by making note of another, related point of detail that I offered there too:

• “Predictive (n.b. and descriptive) models essentially of necessity become more and more complex and nuanced and the cause and effect relationships that would statistically define them become more and more complex, as the questions that they would seek to realistically address become more complex. Single binary occur-or-not and cause-or-not relationships between a single A and B here, can in general only predictively address some single, often just yes or no question; complex question resolutions arise from multiple factors and their considerations.”

The more complex and nuanced the descriptive and predictive questions that you have, as you seek to plan out what your business will do, and how and with what resources and priorities, and with what specific target audiences in mind, the more data you will need, and the larger the sample sizes that you will need, the more types of raw data you will need, and the more complex the cause and effect, event to event decision and outcomes trees that you will need to be able to model and make sense of. And the more complex all of this becomes, the more of those Part 19 causation categories will enter into any correlational statistical analyses that you would consider carrying out too. And this brings me to a crucially important issue:

• You cannot necessarily know up-front and a priori to any given statistical testing, which of the data fields that you have at your disposal are going to be the most useful there. You might very well not even know what types of data you need to start gathering in, that would prove of importance to you there.

To take that out of the abstract, consider the impact of social media on marketing and sales and in this specific type of context. If an online meme starts to widely circulate on a site such as Facebook, to the effect that some major brand of orange juice might have small pieces of plastic in it (and let’s assume that is false, but that a lot of people begin to doubt that product and its manufacturer simply because they do not want to take changes with their food) then any predictive market demand analyses that might have brought supermarkets to stock that brand of orange juice – because it is a popular brand and a popular product for families with children, will become moot and even counterproductively harmful.

This type of challenge, that could not be anticipated or predictively prepared for and certainly without wider ranging and more real-time data (here including social media sourced data), is just one of the sources of consideration that lead to big data becoming bigger and bigger and for more and more business types. Add that to the fact of more and more businesses carrying out more and more complex analytical questions that call for data-based statistical testing, and how that drives a demand for more and more comprehensive data; this all makes big and BIGGER inevitable. And I would separately add risk management into this brief and still quite incomplete list, and even where that was a crucial factor in my just offered orange juice example. Risk issues and risk management requirements arise in so many ways and contexts of relevance here.

With that noted, let’s further consider causation and its role here, and particularly when considering complex data-driven, statistically based descriptive and predictive modeling:

• A strong and even seemingly absolute causal factor when considered in the context of one branch of a complex tree of possible succeeding events, might become much less causally connected to the hypothesized outcome being tested, when and if a different branch of that tree is actually realized.
• Situational causation can be expected to become more and more important and definitive for its modeling value as overall modeling complexity requirements expand.
• And the more complex these systems become, the more room is created for possible wildcard and outlier events with their resultant causality consequences too.

I mentioned at the end of Part 19, “needle in a haystack problems and the possibilities of facing combinatorial explosions when confronted by what can become an essentially open ended array of data variables (n.b. needed, and quite possibly already present too.)” Those issues definitely arise in the types of contexts that I have been writing of here.

And this brings me to what can be the refined gold in big data: metadata and the pools of processed and at least situationally validated (and hopefully context benchmarked) knowledge that has already been developed from that raw data at hand. I am going to turn to that area of discussion in my next series installment. And I will also in that context, expand upon and at least seek to clarify my above offered “needle in a haystack and combinatorial explosion” point of observation. Then after doing so, I will turn back to the issues of intermediation and disintermediation, and of balancing lean and agile, with information security risk managed. I have addressed that complex of issues a number of times in this blog already and from a variety of perspectives. This time I will do so as they specifically arise in the context that I have been discussing here and in other recent postings to this series.

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 42: moving past the initial startup phase 28

Posted in startups by Timothy Platt on March 13, 2020

This is my 42nd 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 1-41.)

It is one of the defining hallmarks of the 21st century business that it is information driven. This is a point that I have repeated and stressed for its all-but-universality and for its seemingly ever-increasing significance throughout this blog. And it is a point that underlies all that I would offer here in this particular series as offered here too. If, as I have stated, a business’ cash flow and finances represent its life’s blood, the customer sourced and other business intelligence that it gathers in, uses and depends upon is the equivalent of the air that it breaths. And suffocation can lead to at least as rapid and assured a death as bleeding out.

I have been focusing on two increasingly important facets to the 21st century business context that hold fundamental significance for enterprises, from the initial conception of a proposed new startup venture to come, on through to the practices of solidly established businesses in mature industries:

• The value creating synergies that can be obtained from placing data into the explicit contexts of other data, where in effect 2+2 really can equal 5 (or more), and how that drives the development of bigger and bigger data sets – and in ways that can compel businesses to acquire from third party sources, pools of raw data and of processed knowledge that they could never develop on their own, if they are to remain competitive.
• And businesses such as Facebook, that are built around business models that commoditize information of this sort, and that set themselves up as what can amount to essential gateways to customers and to entire market demographics through their control over these vast information resources.

I focused in Part 40 and Part 41 on these issues, addressing them from a more strictly risk-management perspective there (as a continuation of related discussion going back at least as far here as Part 38.) And I continue that line of discussion here too, where I will address a topics point that I raised at the end of Part 41 in anticipation of this series installment:

• Uncertainty, and 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 as repeated here:
• If we are living in an increasingly transparent world, where personal confidentiality is more tightly constrained and limited than ever before 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 types of breach of those protections can and should we allow and from whom and for what reasons and under what circumstances?

I intentionally posed those questions from a consumer perspective, and note that it is crucially important, and both for individuals and societally that we collectively find workable answers to them, as understood from that viewpoint. But for here and now I will address them from the business perspective and with regards to how this flood of data can be used and from the perspective of how any use (or even just the potential for that) can create risks as well as benefits for a user.

Legally drafted and framed disclaimers as offered by online businesses as a requirement for entering into data-sharing transactions with them, can only offer so much protection for a business. And this even holds when a business holds out a requirement to click to agree to the terms of such an agreement as a requirement to even view a business website. Court decisions have, after all, ruled against businesses for their information practices in the face of these types of protective measures by ruling that simply clicking yes to a lengthy legal jargon-filled online document cannot fully abrogate consumer rights in that regard.

The issues that I have been addressing here are complex and mutable. And the boundaries between acceptable and unacceptable risk, and between realized and realizable profit and benefit, and a loss for that potential can shift, and rapidly and in ways that might not be readily predictable too. So what is a business to do about all of this? How can and should it seek out a right balance between acting prudently as risk management and similar due diligence would require, while also acting boldly enough to remain competitive too?

I repeat the key wording to my above-repeated to-address topics points here, as I begin to address that question: “… 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 I do so in order to stress a key defining detail that enters into the determination of operations and operational policy and practice per se: that they center around a basic, core consistency and a basic sense of standardization.

Novelty and the need to acknowledge and act upon exceptions happen. But at its core, a business has to have basic, consistent, reliably widely known policies and practices in place that its employees can follow and that its customers and other outside stakeholders can know and rely upon too. (Nota bene: effectively developed and informative business web sites always have in them, readily available web pages that present both that organization’s mission and vision statements, and their basic customer-protective information policies in place, so as to address the “outside stakeholders can know” side to that.)

And with that noted, I return to those to-address questions and how I framed them from a consumer perspective and not from a business perspective. And I begin addressing them here by acknowledging that from a risk management perspective, that point of distinction is not entirely valid or even meaningful.

Yes, I phrased them from a specifically-consumer perspective. But the only way that a business can safely navigate a way through the challenges that those questions raise is if they thoroughly understand and respect that consumer perspective and both in their publically-facing words and in their deeds.

• Expressed intent to act in the best interests of any and all customers and potential customers and always, can help at least minimize realized sources of possible contention or friction there, and certainly if that business in question actively and openly seeks to turn those expressed intentions into actions with meaningfully customer supportive policies and practices.
• And to put this bluntly, actively pursued intent to do right there, is most likely going to prove to be the most effective defense that a business could offer in the event that a consumer data breech or other problem actually occur – as has to be expected as an inevitability with sufficient time, and regardless of the amount and quality of effort made to prevent that from happening.

I am going to tie this developing narrative back to a specifically startup and early stage business context in the next installment to this series, where new ventures in this 21st century of necessity have to be social media immersed and big data driven, while still supportively presenting themselves as allies of their customers in meeting their fuller sets of needs.

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 – 47: goals and benchmarks and effective development and communication of them 27

Posted in startups, strategy and planning by Timothy Platt on February 27, 2020

This is my 47th 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 1-46.) I also include this series in my Startups and Early Stage Businesses directory and its Page 2 continuation.

I have been discussing three early stage business development scenarios in this series since Part 33: an IPO scenario and a venture capital supported one, and an at least initially seemingly unrelated one: a franchise system-initiating scenario that would be built out from an intentionally prototype-developed initial storefront. And as part of that, and after initially outlining and discussing those three development scenarios per se, I began to reconsider them for how their owners and managers would variously address the first of a set of basic questions that ultimately any business founder will have to deal with:

1. 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?

As part of that, I have addressed businesses that would follow these scenarios according to how they would understand and face a set of interrelated issues, stemming from that:

A. A need to 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, as the strategic and operational considerations of that bullet point would dictate.

And my goal here is to continue and complete that line of discussion and certainly for a third scenario, future franchise system context. And after completing that line of discussion, I will continue on to more directly address the second and third of the list of basic questions that I have been holding up as important here, since Part 33:

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?

But before turning to more directly consider them, I continue my discussion from Part 46 by noting how my goal there was to highlight how all three of those scenarios are grounded in financial terms – franchise system businesses and business models definitely included. And all three are risk and benefits management-based too and certainly for how the basic strategic decisions going into them are made, and how.

I focused in Part 46 on a fundamental point of consistency there, and turn here to address an area of fundamental divergence, and it is one that centers on risk and benefits and their management. Both would-be IPO opportunity startups and would-be venture capital investment opportunity startups, of necessity have to be willing to actively embrace the new and different, and with all of the added risk that this might bring with it. If their founders cannot accept that, and if they do not pursue a new and different that would make them stand out as special and even as uniquely so, how can they attract the types and levels of investor support that they seek out, in order to build out and as quickly and efficiently as possible – in accordance with their basic intended outside-funded business models? But at the same time and in counterbalance to that, they also have to be able to present themselves as representing a safe enough investment opportunity to meet the due diligence requirements of their intended investors too.

• For a would-be venture capital investment target, this means the bookkeeping oriented financially grounded due diligence evaluations that professionals experienced in their industry and type of company would deploy, when deciding which ventures to actively invest in and support.
• For a future prospective IPO this means meeting the due diligence requirements of potential stock buying investors. But at least as importantly this means their meeting the standards of business and stock market analysts who so many of those potential investors turn to for insight and advice as they make their investment decisions, and particularly where they cannot find any significant amount of directly relevant track record to judge future performance in terms of. (Nota bene: track record insight that would be relevant in a new business venture context, can in principle be gained from a review of the prior histories of the key people involved in building it. But you have to know who they are and you need access to records that are often only available for-fee, and that require some expertise to make effective use of.)

But even allowing, in that IPO example, for would-be investors who simply buy some shares on a whim in hopes that a bandwagon effect will make the price per share go up, both of those business development strategies are risk accepting more than they are risk aversive. Franchise systems by their very nature are built to be much more risk aversive, and as briefly noted in Part 46, for all parties involved.

And these two basic strategies are reflected in how these businesses would variously address the specific issues raised in Questions A through C as repeated above. They affect the questions of what would be offered for sale, and how that would be done, and both at a level of business operations and as a matter of how marketing and branding would be carried out. Businesses that seek to present themselves as representing the successful cutting edge of new and different, function accordingly and for all of these measures, and franchise systems that seek to build upon and build from a tried and true successful formula and with a goal of perpetuating that at a larger scale, function accordingly too.

And with that I turn to the second basic question that I have held out as a to-address point here since Part 33, as just repeated above: the question of marketing messages and how they would be framed. I will at least briefly discuss that complex of issues and then turn to and address the where and how of this as raised in the above Question 3. And I will at least begin to address those issues in the next installment to this series.

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 19

Posted in social networking and business, startups, strategy and planning by Timothy Platt on February 12, 2020

This is my 19th 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 1-18.)

More specifically, I have been discussing business intelligence, and marketing-oriented business intelligence in particular since Part 17 – as it would be used in, and as it might be derived and developed from marketing campaigns. And my primary point of focus there has been on disruptive marketing and on gorilla and viral marketing in particular, and even when approaching this area of business activity in more general overall terms.

Business is data driven and increasingly so, and as an absolute prerequisite to any meaningful market-facing campaigns that might be entered into, and certainly where a goal of a consumer-facing business is to become and remain competitive there. And that basic fact has to be assumed to be true for essentially any business or industry now. Marketing and sales have in fact been forerunners for that, and for most businesses too, and certainly in any business-to-consumer contexts.

I began this discussion thread in those and similar more-general terms. Then I shifted directions in what was arguably developing into a standard, basic narrative concerning the obvious, to offer what at first glance might seem to be a fundamental, first principles-based challenge to big data as a solution to those marketing and sales challenges. I led with some all-but-universally held assumptions in place and then turned to challenge them. And I said that I would continue developing and elaborating on that next-step line of reasoning here.

• Put simply, BIG per se in big data does not necessarily offer any real added value in and of itself, and certainly when it would be applied to the specific challenges of making meaningful and usable predictive analyses that can be built from, in converting demographics-targeting marketing into completed sales.

But big data obviously can and does work, and certainly if it is developed and utilized effectively. I cite by way of example, two working examples that would support this contention:

• Facebook and its effective use of big data, and
Cambridge Analytica with its use of it with some 5000 data points collected or at least sought out, on essentially everyone targeted for data mining.

Facebook has built its entire business model and all of its revenue generating efforts around the accumulation, processing, marketing and sale of its data. And this has included both the sale of direct access to member-sourced data and in seemingly open ended quantities, and the sale of demographics targeting insight as developed from their big data stores that would permit other, client businesses to position their ads on precisely those Facebook member pages where, according to their marketing input, that would most likely include the people who they are trying to reach.

Cambridge Analytica, as one of those Facebook client businesses, gathered in vast quantities of sensitive, personally identifying data and more, from close to 100 million Facebook users (with the “official” count there of some 87 million almost certainly an underestimation in fact), for use by others to skew and suborn public elections and referenda, and in many countries globally.

Big data did work for them, and for Facebook at least it still does and very profitably so, and regardless of the societal costs paid for their success there. But how did, and for one of them how does this work, given my mathematically grounded reasoning of Part 18?

An obvious at least initial answer to that can be found in the scale of data accumulated and used, and in the number of individual member sources that it comes from. Even if two thirds of all apparent Facebook members are actual people and all of the rest are robo-artifacts and artificial intelligence constructs, that would still leave something over 700 million member accounts and just for their at least relatively active membership – making it possible to meet sample requirements for incredibly complex and nuanced statistically based analyses.

But I would argue that the key to their success with big data, and for both Facebook and Cambridge Analytica has been in how they (and others like them) use accessible data in order to construct finely nuanced market demographics models per se, which they then monetize and profit from. They do make use of individually sourced data and directly so, but they also feed endless flows of data as obtained at that level of database development, into data processing systems that spit out precise descriptions of demographic groups – and yes, with lists of people as individuals who most likely belong to them.

Then it is their customers who would ask their statistical analytically framed questions concerning the select pools of identified members of those demographics that they have purchased access to, as they develop and carry out their own marketing campaigns, and whether they are sales-oriented, or voter influencing-oriented, or oriented towards some none of the above.

This line of discussion addresses one of the two major sources of challenge that I wrote of in Part 18 – the scale of complexity of statistical analytical modeling that can be carried out depending on sample size limitations. It still leaves the correlations challenge that I wrote of there. I will turn to and address that too. And with that to-address note explicitly offered, I begin the main line of discussion that I would turn to next with this posting, by acknowledging what should be considered an obvious reality. Big data can and does work, as stated above. But as Part 18 argues as an unavoidable side to that … not when you just try to “throw stuff at a wall and see what sticks.” And with that, I turn to and repeat the anticipatory note that I ended Part 18 with, in anticipation of this:

• “My goal for the next installment to this series (n.b. this one) 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.”

I have already delved into and discussed several of the issues that arise in that bullet-pointed text (e.g. the first and last of them.) And I continue on from here to more fully consider the more centrally positioned of them and the question of data specificity and relevance, where that is context and need-driven.

And I begin addressing that by more fully considering the glue that connects obtained raw data into meaningful predictive models: causality. Think of what is to follow here as a first step discussion of how to assemble operationally and strategically valuable predictive models out of arguably non-trivially (non-randomly) correlatable data, where I offer functionally descriptive notes at a categorical level, for thinking about and understanding the basic pieces of that big data puzzle. And I begin with the most automatically assumed puzzle piece types and move on from there:

Direct causation arises when an event or circumstance A directly leads to some B happening, and with that B reliably occurring with a statistically higher incidence than would arise through chance alone when A has happened, or is still taking place. A, I add, will have at least started before B can, given the timing requirements of cause and effect relationships.
Indirect causation arises when some A causes B, but not C and certainly not through anything identifiable as a direct cause and effect relationship. But B does directly cause C, or at least influence its happening and at a higher incidence rate than chance alone would predict.
• These first two terms both have within them the possibility that a potentially causally related next step or outcome might not actually happen. Causality links can be weaker and only more modestly increase the likelihood of a subsequent event or circumstance actually taking place. They only have to increases its odds to the point where it is more likely to happen and with statistically explicable likelihood, than could be accounted for by chance alone – given that the identified causal condition cited has happened. But stronger cause and effect relationships can make B, as abstractly noted in my first two causation bullet points, essentially 100% certain to take place too. Think of this as representing weak causation and strong causation respectively. And this brings me to the last of four terms that I cited in passing before offering these bullet points:
Absolute causation is in fact strong causation where that appears from available data to represent the 100% likelihood of the above bullet point.
Situational causation represents a level of likelihood of some specific next step occurrence taking place, that is statistically significantly higher than just random chance would lead to, and at the same time being statistically significantly less than absolute certainty, 100%.
• And crucially importantly here, situational causation is an emergent property as cause and effect chains arise and elaborate, step by step and with all of the probabilistically definable occurrence or non-occurrence branching that it implies.

Predictive models essentially of necessity become more and more complex and nuanced and the cause and effect relationships that would statistically define them become more and more complex, as the questions that they would seek to realistically address become more complex. Single binary occur-or-not and cause-or-not relationships between a single A and B here, can in general only predictively address some single, often just yes or no question; complex question resolutions arise from multiple factors and their considerations.

That noted, and to round out this preliminary discussion:

• I wrote the above in terms of positive occurrences only. But to cite direct causation here as a source of case in point examples, A happening can also make some B statistically more likely not to happen, than chance alone would predict. And that likelihood can be situational or it can approach and effectively reach 100% too. Think of this point of distinction as a matter of best phrasing what B is, where it would be presented as a positive or a negative (however they are defined) in terms that are easiest to unequivocally formulate and analyze, given the nature and details of the data available and the business question at hand.
• And random and seemingly random do enter into this too; random and low probability but still causally connected occurrences – both of which can trigger subsequent directly analyzable cause and effect consequences, represent wild card events and outlier events here. In anticipation of discussion to come, these possibilities might blend into the background under a wide range of circumstances, but they can become important in specific high consequence-if-realized ones too.

I am going to turn back to my Part 18 discussion of correlations and correlational analyses in the next posting to this series, to complete that phase of this overall series narrative. I wrote there of correlations and causation as fundamental to developing actionable, proactively predictive findings out of data and out of big data in particular – where that can devolve into a collection of anything and everything that can be measured and recorded when big is being developed simply for the benefit of BIG. The key to this is in how causation is analytically defined and in how a set of concepts that revolve around it is used. Application of that is where value arises from data and from big data as an extreme.

I will at least briefly delve into this set of issues in my next series installment here. And in the process I will at least briefly consider and discuss needle in a haystack problems and the possibilities of facing combinatorial explosions when confronted by what can become an essentially open ended array of data variables. And this means my discussing metadata and processed knowledge that is derived from all of that raw data too. And looking further ahead here, that will bring me back to the issues and questions of intermediation and of disintermediation again, and both as they arise entirely within a business and as they arise through its dealings with its surrounding contexts.

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 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 certainly as they market and sell online. 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 an online selling business needs to be able to show that it adheres to now-involved, foreign-enacted and enforced laws as they address those issues too, 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 me 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.

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