Platt Perspective on Business and Technology

Building a startup for what you want it to become 28: moving past the initial startup phase 14

Posted in startups by Timothy Platt on November 30, 2017

This is my 28th 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 1-27.)

I have been writing this blog as a whole, with an emerging 21st century orientation in mind, and with a goal of developing and presenting business approaches and understandings that would offer value for an organization as it faces a flow of oncoming change and disruptive change that is already taking shape around us all and that will only continue and for all. I focused in this regard in Part 27, on the emerging need for, and commoditization of big data capabilities for planning and managing a business, and both in its immediate here and now and as it builds for its future. And I wrote there of how this capability is becoming a necessity for smaller businesses, and just as much as it is for big data’s more traditional big corporate adherents.

Big data, and certainly as it has emerged as an essentially ubiquitous innovation that is capable of offering insight to any desired fineness of granularity, is just one of a growing range of disruptive changes that are redefining the world around us and for all of us, and not just in a business and economic context. So in a real sense that particular disruptive change and source of it just happens to be the one that I am focusing on here, and it is one where overall impact has already proven to be profound. The question that I would raise in this context and here in this series, is one of how a given business can better tap into and benefit from the positives of this change, while more effectively managing and limiting any downsides that would accompany them.

And with that noted, I offer a point of acknowledgement. Simply stating that big data is becoming a necessity and for more and more businesses, as begun in Part 27 and repeated here, offers very little value beyond my stating two obvious points:

• Big data with its use in business planning and execution is more than just a passing fad – it is rapidly becoming a long-term essential, and what we have seen of it up to now is just an early and even still-embryonic stage in development for what is to come.
• And it represents a capability that is evolving and very rapidly to be more and more cost-effective and for more and more types of businesses and for more and more types of use within them. I particularly stress expanding range of use here.

That leads me to the fundamental question:

• How can a business more effectively tap into and benefit from all of this?
• Any valid answer to that has to start within the business itself. Simply looking for a “best” source of hardware and in-house software, and cloud-based resources for this that would reside outside of the organization,
• For collecting and organizing a business’ own data and applying in-house and outside-based resources to it,
• Cannot suffice.

In fact that type of a tools first approach is almost certain to lead you to arrive at a poor-for-you implementation and no matter how diligently and thoroughly you research your tools options. Begin by looking within your business itself and at your needs and opportunities there. Then look for the resources that would best help you specifically meet them, and for both the more replicably generic areas of your business model and plan and their execution, and for the details that set your business apart as unique too.

Begin that by asking questions, and more specifically and importantly by thinking through the key questions that you need to ask and find answers to. And the most important and salient of those questions will in all likelihood have at least a few essential qualities in common:

• Being able to more effectively answer them would better help you achieve and maintain competitive strength and business profitability in your enterprise. Answering them in a direct and specific manner would help you to more readily and cost-effectively fulfill your business plan and business model.
• What questions, as an at least minimally business success-defining set, would help you the most there?
• Now what types and volumes of data would you need to have in order to answer them, and with a degree of reliable accuracy and over a sufficient timeframe to meet your planning needs?
• And how would you best organize all of this data and categorize it by data type and other relevant criteria?

Let me take that out of the abstract with what by now would probably seem to be one of my favorite business analysis contexts: inventory selection and management in a retail business that for its business sector, would be required to have and maintain a large and complex range of product offerings (e.g. a supermarket, or even just a minimart.) And there, I add as a relevant aside that “large and complex” is relative, and a small business with correspondingly smaller cash flow and reserves, might quite legitimately find a size and complexity of overall inventory to be large, that an overall larger business might see as smaller and more constrained.

Inventory selection and management as a complex of business processes, is essentially entirely data driven:

• Begin with your immediate here and now. What does your business have in stock and either on the shelf or otherwise available in your shopping space, or as held in reserve in storage, waiting to be brought up to the storefront now?
• What is your overall inventory turnover rate, and what are the more particular turnover rates of what you offer within that whole and ideally on an individual SKU by SKU basis for real granularity and detail of understanding in your analysis?
• What is your shrinkage rate for that from expiration dates being reached before you can sell, or from breakage or theft – from any cause that would lead to you’re facing loss to your business from having purchased items to sell at a profit, that you cannot ultimately sell?
• Add in items that you might have to discount to sell here too, where loss might not be complete but where your goal there might now be more break-even than anything else. And specifically consider loss leaders here too: items that you might offer at discounted prices to bring people into your business, who you would expect to make profitable full price purchases too, but that you sell for marketing purposes at very reduced profit margins if any.
• And of course, what are the markups and profit margins that you can expect, or in the case of those loss leaders, the scope of the lack of direct profitability that you can expect from these items as your sell them, and both with and without consideration of value depleting, sale preventing factors such as shrinkage?
• And what are you assuming when you make the predictions that I have been indicating in these bullet points and how would you justify them? (I am not going to delve into details such as three scenario modeling for that here, with “best case”, “worst case” and normative expectation calculations as I do so in other places in this blog. I simply note that such analyses do take into account measures of the unknown and what-if scenarios.)

A business of the type under consideration here that would at least attempt this type of inventory review and analysis would also have to take overhead and payroll and insurance coverage and utility bills and more into account there too (e.g. marketing expenses.) So inventory per se might only represent part of this story and even for a store with a minimal physical space footprint such as an online store that seeks to employ just in time inventory management approaches to limit their cash investment tied up in their inventory as actually held at any given time. But even then, everything in such a business is still data-driven. Without essential data the owners and managers of such an enterprise cannot fully know where their business is now, or realistically begin to anticipate where they can take it as they seek to more effectively plan ahead.

• Much of this data would of necessity come from within the business, but it is increasingly important to bring in outside sourced data too, and both on how other competing businesses are doing in your business sector, and for your inventory item sources where changes there can impact upon your business too. And outside sourced market data would also be necessary.
• My point there is that as much data as a business develops internally and from their own systems and processes, they are likely going to need at least as much from outside of themselves too. And this all has to be coordinately organized and analyzed if effective and timely use is to be made of it.

I am going to continue exploring these issues in a next series installment where I will expand on those last two bullet points. Then after completing this phase of this narrative, and after offering some data mining and big data references to help flesh it out, I will turn this discussion around to consider both in-house generated, and outside-sourced business intelligence as marketable commodities, and for how they would be used in business planning and for how this type of resource might be selectively commoditized and sold. And I will do so in large part in terms of the cloud-based approach to this type of data storage and analysis that I have at least begun addressing in this series already. Then after completing that line of discussion, at least for purposes of this series, I will reconsider these issues but from a more business-development timeline perspective, bringing in the issues and challenges of cost-effectively developing a business for all of this and how and when, so as to bring in necessary change while controlling possible risk. That will, among other things, mean reconsidering outside funding and organic, strictly in-house sourced funding where capital development expenses would be faced.

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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Planning for and building the right business model 101 – 33: goals and benchmarks and effective development and communication of them 13

Posted in startups, strategy and planning by Timothy Platt on November 22, 2017

This is my 33rd 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 continuation, postings 499 and loosely following for Parts 1-32.) I also include this series in my Startups and Early Stage Businesses directory and its Page 2 continuation.

I offered three somewhat stereotypic if commonly occurring exit strategies in Part 32, that a startup can consider and pursue as it reaches a point in its development where it has begun to be consistently profitable: and when it is now entering its first real growth phase:

1. A new venture that has at least preliminarily proven itself as viable and as a source of profitability can go public and with all of the organizational change and all of the transparency and reporting requirements that this entails as they begin offering stock shares.
2. A new venture can transition from pursuing an organic growth and development model (as in exit strategy 1, above) but to one in which they seek out and acquire larger individually sourced outside capital investment resources, and particularly from venture capitalists as briefly touched upon in Part 28, Part 29 and Part 30 of this series.
3. A new venture, and certainly one that is built around a growth-oriented business model, might build its first bricks and mortar site, in effect as a prototype effort that it would refine with a goal of replication through, for example a franchise system.

And I then focused in that installment on a set of general issues and business analysis and development approaches that would be applicable to essentially any such business transition decision making process. And at the end of that posting, I stated that I would continue its narrative here, by delving:

• “More into the specifics … where I will consider exit strategy 1 from my above list of three in detail: the fundamental change scenario of a business going public with all that that entails.”

And I added that after that, I will more specifically consider each of the other two exit strategy scenarios under consideration here, as also noted in that anticipatory note. I will do all of that in this series, delving in more detail into these three specific scenarios. But I have decided, on further reflection to expand upon the general principles foundation for my Part 32 discussion before considering the specifics. And I begin doing so by acknowledging a point of detail that I have been reminded of by at least a few readers: some very successful startups and online ones in particular, have successfully pursued exit strategies 1, 2, and/or 3 as touched upon above, before showing anything like established consistent profits of the type I indicate as necessary there. And a select few have done so, and have ultimately succeeded as business ventures too, even when they had not fully developed their still just-potential source of defining value into an explicitly monetizable and marketable product or service first, too. But those are in a fundamental sense the exceptions that prove the rule, that prompted me to set up my three scenarios the way I did. Failure to successfully build a foundation for taking one of these three exit strategies, or any comparable-for-stage alternatives to them, are why so many businesses that were started with innovation in mind, disappear when they are still very young and still forming. I find myself thinking of my startup consulting experience with new young online companies, leading up to the original dot com bubble burst as I write this. I know how the dynamics of what I write of here play out, from personal hands-on experience from having served as an outside consultant then, and to businesses that succeeded and to way too many that did not and often in spite of my best efforts to bring more due diligence based prudence into their decision making and spending.

I have written about this in earlier postings and series, so I will only repeat here in this regard that no, it does not make any sense for a new dot com online business to blow essentially all of their cash reserves in a single Hail Mary pass attempt by purchasing on-air advertising time during the ad breaks in a Super Bowl game. Returning this discussion from that line of thought to this posting and its context, I repeat that I chose three exit strategies for discussion here that are grounded in their planning and execution in solid due diligence and risk management planning and preparation, and in building in the resource base needed to support flexibility in the face of the unexpected – which is sure to arise somewhere, no matter what path forward is actually selected and pursued.

I said at the end of Part 32 that I would more specifically turn to consider the first of the three exit strategies that I repeated listing at the top of this installment. And I will do that. But this has turned out to be more of a general principles, background and foundation building posting. So before turning to exit strategy 1, I would continue my more general background discussion for that, by outlining some specific principles that would go into selecting and specifically pursuing any given, at least initially more generically framed exit strategy. And my goal here is to offer points that would apply to any such exit strategy scenario contemplated or pursued.

• If the first general issues half of this posting is about building a foundation in what has already been done at a business, for it to succeed, my goal in what follows here is in building a foundation that is explicitly designed to support the business for what is to come, and particularly where that means entering into the new and unknown.

And with that point in place, I explicitly turn to consider exit strategies as transitions, and as such as representing moves into the unknown. And I do so by posing a basic checklist of issues and questions that should enter into any due diligence exercise there, and at least as a starting point for more focused analysis to come.

• Know where your business is now,
• And for its strengths and the positive potential that is being developed toward, in accordance with its basic business model and its strategic goals in place,
• And for its weaknesses and its resource and operational systems gaps that would affect its being able to realize that potential.
• Inventory these positives and negatives and prioritize them: all of the realistically potential negatives included, and both in terms of need to address them and in terms of capability to do so, if and as they arise. What, among other things would be needed in order to check off and successfully remediate the high priority items on the negatives list, and what would be the costs faced from attempting to do this from assets and resources currently held, and/or ones that would reasonably be expected to become available if the business were to proceed as-is, along its current growth and develop path and without entering into a more fundamental transitional change of any type?
• If it is found that at least a critical threshold of need for addressing already realized gaps or impending ones, cannot be addressed as-is and with the business simply following a “business as usual” path forward, then it is going to be necessary to at least consider more disruptively changing that path forward, and here through pursuing an exit strategy of some sort as a business transition.
• And this brings me at least generically to the issues of what a possible change of that sort might offer and both in its potential positives and in its potential negatives. And that includes understanding any restrictions and limitations that buying into them would bring. And how does this mesh with the business itself and for how it is developing?

I am going to expand upon the narrative that I began in this brief set of business analysis-oriented bullet points, in my next series installment and with particular attention paid to the last of those points. And I will do so in terms of the three specific exit strategies under consideration here, as repeated at the top of this posting, beginning with the first of them as promised above.

Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 4, and also at Page 1, Page 2 and Page 3 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.

Building a startup for what you want it to become 27: moving past the initial startup phase 13

Posted in startups by Timothy Platt on October 19, 2017

This is my 27th 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 1-26.)

I wrote in largely abstract terms in Part 26 about data and data analysis in a business, as a foundation for developing and pursuing a consistent, effective strategic and operational approach, only briefly citing and selectively developing a quick sketch of a retail store case study for clarification there. And more specifically for that, I cited a still young and still small retail store with a complex inventory, as for example would be found in a grocery or hardware store with its potentially complex sales and inventory-oriented data collection and analyses.

I then stated at the end of that installment that I would at least attempt to bring its discussion into clearer and more actionable focus here, by addressing (by way of working example) how a business might add greater rigor and structure into its systems, to facilitate its orderly development and growth as it moves forward.

• Everything, or at least seemingly everything in a business might start out looking ad hoc and new and novel at first, and certainly for a business such as a startup, and certainly if its founders are new to actually building and running a business venture of their own.
• When and how should organized system and structure first begin to be developed and added to this mix, with its at least up-front additional costs and complexities and certainly for a business owner who has never had to manage inventory, to further pursue this working example, in a structured data-driven manner?
• When and how should that more rigorous, data driven approach to running the business take over and become the basic rule for how things are done there?

I tend write about organized and systematically structured systems, and about the ad hoc approach as an often problematical alternative. My goal for this installment to this series is to at least begin addressing how that circumstance arises. And I begin doing so by directly challenging a statement that I offered in Part 26, in a new and small business context, with the limited financial, personnel and other resources available to it:

• “… And costs and timing demands would constrain them to at most, doing only simple and even cursory data analyses and business performance modeling … and with that largely based on aggregate analysis of product categories and not of individual item types. That definitely holds for smaller and even for medium sized businesses. Massive retail business systems, tend to be the ones that truly dive into the data, and with all of the effort and expenses involved as mistakes or lost opportunities take on scales of impact, financially, that they cannot leave to chance.”

There is still, as of this writing, a significant element of truth to that presumption and certainly for startups and other more cash-strapped businesses. But the advent of big data and of secure outsourceable data storage and analysis in the cloud, as a disruptive source of business practice change, is altering the balance of when complex data collection and analysis becomes cost-effective and even an essential driver of business success. I write this series, at a transition point in the history of how businesses can and do effectively operate. And in a few years every business will have to be effectively data driven and big data driven if it is to competitively succeed, and certainly in the face of an increasingly globalized bricks and mortar plus online context.

With that stated, I offer my case study example for this posting, as promised above: a still small and young retail business that fits the complex inventory criterion of the grocery or hardware stores already cited here, but that as a still early stage business, seeks to develop along a post-transition approach for its data collection, analysis and use, and in planning and carrying out its here-and-now and its next-step development. And to make this more interesting, I will focus on a boutique business that would offer a fairly wide and diverse range of home goods and related items, many of which would appeal to people looking for gifts and well as for making purchases for themselves and their own families. That means this store, which I will refer to as HomeWorks Fashion, has to maintain a much more fluid inventory than a grocery or hardware store would, with their much more standardized basic, core inventories of long-established products.

• A retail business such as HomeWorks Fashion carries a larger percentage of its goods for sale as seasonal and otherwise cyclically sellable stock, than a grocery or hardware store would, and both for their selection of distinct stock keeping units (SKUs) (or range and diversity of distinct types of item carried and sold) and for overall volume of items carried and sold (where numbers of each of those distinct item types are considered too, where that represents the proportion of overall shelf space devoted to seasonal and related items.)
• And at the same time, a store such as HomeWorks Fashion, would be expected to carry a much larger percentage of fad and other short-term readily marketed and sold products than any grocery or hardware store would.
• All of this makes detailed analysis of sales performance on an item-type by item-type basis both more complex and more necessary, than would be the case where a perhaps very large percentage of all SKUs sold, are year-around, steady sellers (e.g. such as eggs, milk and bread in a supermarket.)

This is where capability for outsourcing access to the more expensive to buy and maintain infrastructure for detailed data analysis, through use of cloud based resources, begins to really make sense and both for data collection and storage, and for data analysis using cloud based software as a service resources for carrying out the necessary statistical and related tasks. Effectively organized third party systems of this type, I add, also offer a great deal of help in both determining what types of statistical and related tests should best be performed to address what types of business analysis and planning questions, and how much data and of what types would be needed for those tests to be able to yield actionable solutions. A number of large online businesses, such as Google have in fact actively developed this type of service and supporting resource base for it, as a to-them, marketable product in a business-to-business marketplace. And they definitely target medium sized and small businesses as potential clients for these services.

I am concurrently writing another series in this blog: Career Planning, in which I explore and discuss disruptive change in the workplace as it is reshaping jobs and employability (see Guide to Effective Job Search and Career Development – 3, postings 459 and following for its installments.) The emerging disruptive changes that I write of there, all impact upon what it means to work and to be employable. But they also, and just as significantly impact upon the businesses that those people currently work in or at least potentially might work in too, and generally in ways as implemented in them, that in aggregate benefit those businesses in significant some way. That, in many cases, is why these changes are brought in and intentionally so, and certainly when a business as a real choice there. Big data could very reasonably be added to the list of examples cited and discussed there too, and as a rapidly and impactfully emerging disruptive change to what businesses can do and cost-effectively, that would even dramatically change how they do business if they are to remain competitive in the face of other businesses in their industries that might embrace this change more effectively.

I am going to continue this example in a next series installment where I will consider in-house generated, and outside-sourced business intelligence as marketable commodities, and for how they would be used in business planning and for how this type of resource might be selectively commoditized and sold. And I will do so in large part in terms of the cloud-based approach to this type of data storage and analysis that I have at least begun addressing here. Then after completing that line of discussion, at least for purposes of this series, I will reconsider these issues but from a more business-development timeline perspective, bringing in the issues and challenges of cost-effectively developing a business and how and when, so as to bring in necessary change while controlling possible risk. That will, among other things, mean reconsidering outside funding and organic, strictly in-house sourced funding where capital development expenses would be faced.

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 – 32: goals and benchmarks and effective development and communication of them 12

Posted in startups, strategy and planning by Timothy Platt on October 13, 2017

This is my 32nd 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 continuation, postings 499 and loosely following for Parts 1-31.) I also include this series in my Startups and Early Stage Businesses directory and its Page 2 continuation.

I began discussing three specific exit strategies in Part 31, in the context of discussing exit strategies per se and what that term actually means as a stage of development, fundamental change-based transition point:

1. A new venture that has at least preliminarily proven itself as viable and as a source of profitability can go public and with all of the organizational change and all of the transparency and reporting requirements that this entails as they begin offering stock shares.
2. A new venture can transition from pursuing an organic growth and development model (as in exit strategy 1, above) but to one in which they seek out and acquire larger outside capital investment resources, and particularly from venture capitalists as briefly touched upon in Part 28, Part 29 and Part 30 of this series.
3. A new venture, and certainly one that is built around a growth-oriented business model, might build its first bricks and mortar site, in effect as a prototype effort that it would refine with a goal of replication through, for example a franchise system.

And at the end of that series installment, I stated that I would continue its flow of discussion here, examining these same three transitional changes in greater detail and in terms of goals and benchmarks, and communications issues as they play out in businesses going through them.

• If I were to summarize the basic set of topics that I will at least begin to address here, in a single brief phrase, it would be to note that my goal here is to at least briefly outline the core generic elements of the How, of strategically mapping out and evaluating basic business transition options moving forward, on the basis of specific, carefully gathered, organized and evaluated and communicated empirical evidence.

This overall flow of business process and review steps has, or at least should have its roots in the normal and normative day-to-day practices followed by business owners and their leadership teams as they manage their businesses in the face of more routine change and uncertainly. Efforts to develop and follow effective review and evaluation processes in the face of impending disruptive change, which true business transitions always involve, cannot work for people who have not already built an effective foundation for that from well considered evidence based business management practices, as carried out in the face of simpler, routine change and variety as arises every normal business day. And I will add that the data and insight gained from this more normal and every day review and analysis practice, serves as essential baseline data and both for identifying need for more significant change and early on, and for more effectively planning and preparing for it.

In the context of this series and this portion of it, that means knowing when and how one or another, or one or more of the above three listed exit strategies might be starting to make sense, and how and why. And this posting is all about looking at and measuring and tracking the right performance metrics and looking for and documenting exceptions and exception handling, as need for that arises, and as a part of that same business analysis process.

The goals and benchmarks of this, need to be realistic and that means they need to be clear and precise and framed in terms of the business performance measures actually followed. Subjective can be vitally important here, and certainly when that means coming to an awareness that not all of the right types of data and insight are being considered here. But ultimately, this analysis and the raw data that enters into it need to be objective and specific; subjective impressions and estimates cannot offer any real value and certainly when it comes to the supposedly raw business data that is going to be used for this type of business analysis.

What should you look for here, as measured objective data and as sources of it? Look to the basic business model and what the business does that collectively would, or at least should make it profitable and effective enough in its marketplace to reach that goal and stay there. And at least start addressing all of that, in the financial terms of cash flow and availability, and costs and returns on investment and how systems fit together in those terms. And as this is a business transitions type of analysis under consideration here, look both short-term and longer-term, and project outward according to two distinct models:

• What happens if the business simply continues on with a business as usual approach for the area of the business under immediate consideration here?
• What options might be available for disruptively breaking away from that old pattern and in a new way?
• And what would variously happen if one of these transitional changes were entered into, pro and con, short-term and long?

Ask this of each of the options considered in the second bullet pointed question here, in terms of the basic metrics used for your ongoing business analyses, as augmented where and as gaps in what they can tell you become apparent. And remember: any gaps and uncertainties in how you would answer these questions, represents risk faced from pursuing whatever approach: whatever next step development model that is under direct consideration at the moment. Here, risk represents cost and potential loss faced, and at least ideally for capability to measure and determine, as a product of the sum of direct and indirect costs faced if an adverse event were to occur, as multiplied by the chance that it would take place (as measured as a proportion – e.g. a 1% chance of occurrence represented by the fraction .01 and so on.)

This addressed what is considered, and certainly as a first step analysis where it would become clearer that problems and challenges might be arising. Now consider which stakeholders are included in these conversations, and really involved in them: not just in the room but silently so.

• Effective inclusion here in these conversations is essential for finding better approaches for addressing the gaps and challenges identified here, where a best path forward might mean pursuing some particular type or combination of basic exit strategy options as noted above, by way of those three possible examples.
• It means more effectively and fully characterizing and understanding the gaps and problems faced that would go into that type of determination.
• It means more effectively arriving at workable approaches for carrying out any necessary changes that are agreed to.
• And this is essential for gaining buy-in so the necessary changes and all the work that enters into them, are actually done and in a coordinated manner by all necessarily involved stakeholders.

I have been writing here in general and relatively abstract terms. I am going to delve more into the specifics in my next series installment where I will consider exit strategy 1 from my above list of three in detail: the fundamental change scenario of a business going public with all that that entails. After that, I will more specifically consider each of the other two exit strategy scenarios under consideration here.

Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 4, and also at Page 1, Page 2 and Page 3 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.

Building a startup for what you want it to become 26: moving past the initial startup phase 12

Posted in startups by Timothy Platt on September 9, 2017

This is my 26th 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 1-25.)

I focused in Part 25 on the raw data and the processed knowledge that a business accumulates that it uses, in one direction to help shape its strategic and operational plans, and that it uses in the other direction when executing them and evaluating the results achieved. And as part of that, I posed a brief set of questions, the likes of which would go into any quality control effort for managing and more effectively using those data stores, which I repeat here for purposes of continuity of narrative:

1. Is this data reliable, and if so for what? I parse that question, offered here as a general point of principle, into a set of more focused related questions.
2. Where did it come from? And how reliable is its source from prior experience?
3. Is it complete and unedited or has it been pre-filtered or re-represented in some way, by a stakeholder who might be bringing their own biases or agendas with them when offering it? Answers to this question would in most cases be more presumptive than conclusive but evidence of possible filtering or bias should raise red flags and should always be considered as a possibility. As an example of how pre-filtering can be carried out without any intent of adding bias into a data set but still end up adding that in, consider how data can be “cleaned up” before use by deleting from consideration, unexpected and seemingly out of pattern outliers and other “anomalies”, while removing second copies of duplicated records and the like and doing similar data cleansing. That happens and it should raise red flags.
4. Is this data consistent with other data gathered and with expectations in place, or is it divergent from or contrarian to that? Note, new and different and unexpected should not rule out new data findings. But they should prompt closer and fuller examination and particularly if their inclusion would significantly shape conclusions drawn and actions taken.
5. And of course, what would this data suggest, and certainly when considered in the larger context of what is already known?
6. And what are the consequences of that, and both if this data is correct and reliable and if it is not?

I then added at the end of Part 25 that I would “discuss this set of issues in more detail in a next series installment where I will focus on specific types of raw data as business intelligence, and in the more specific context of an at least briefly sketched out working business example.”

My goal for this posting is to set up a conundrum, or at least a realistic-seeming systems example that highlights within it a combination of competing needs and requirements. And I begin that with a set of seemingly simple questions, that I pose in the context of a retail business that maintains a complex inventory of products that it offers for sale. A store of this type is data-driven, with their overall business performance depending on the aggregate sales performance of what they offer, as cumulatively determined on a product by product basis.

• What sells at what rate and at what volume, at any given point in time? This might or might not have a seasonal or other cyclical element to it, as just one reason why this type of question has to be recurringly reconsidered.
• How is this trending, for the various stock keeping unit (SKU) product types offered?
• What is their turnover rate for the business?
• And what is their profit margin when they do sell? That is actually a more complex question than it might at first seem, where a product that simply sits on a self or in back room storage as inventory waiting its turn on a sales floor shelf, accumulates additional cost to the business by taking up room that faster selling items might fill, and more profitably so. But even that more expansive evaluation leaves out the possibility of loss leader product offerings that might intentionally be sold at or even below cost in order to bring in customers, with them offered as marketing tools for driving larger sales.
• What products might be calling for greater shelf space or greater specific model diversity offered, or both? And what would best be reduced for the shelf space that it commands, or even discounted for clearance sale and discontinued, and either for now because of seasonal or other shifts, or permanently?

These are just sample orienting questions, even if they were selected here for their specific relevance to most retail businesses. And all of them require both specific data, and in quantity, and specific analysis to answer them. More generally:

1. What questions would you need to ask and find answers to, in order to more effectively optimize your business, and both to make it more agile and effective in the face of changing market demands, and to make it more profitable and consistently so?
2. Now what data would you need to answer those questions, and both by type and by quantity, and with what data quality control in place?

There are several ways to parse and categorize data but one that is particularly relevant here is:

• Data that is subject to direct statistical analysis, which can mean numerical, binary (e.g. yes or no) or similar
• And data that cannot be so coded and used, such as free form text responses.

For simplicity, let’s assume that all of the data to be gathered and used fits into the first of those categories, making straightforward statistical analysis possible. The more questions you seek to address through such statistical analyses, the more complex they become for types and combinations of data required to address them. And the greater the certainty in any conclusions reached when doing these analyses, the more data you would need in order to carry out these statistical analyses too. And this leads me to my third and fourth questions:

3. How much data do you actually need, in order to answer your statistical questions and do the statistical modeling that you would require?
4. And precisely what data analysis-based questions do you really have to ask in order to meet your business planning and performance review needs?

I will set question 4 aside for the moment and focus on number 3 of this list. Data becomes expensive and certainly in volume:

• To systematically gather it in and store it in usable forms in usable database records
• And with effective data management systems in place to clear out duplicated or defective records, and old and no longer reliable ones (e.g. for “current” customer identification and tracking) – and without adding in bias.
• And data analysis that is based on this, becomes expensive too and particularly when outside expertise is required for carrying out complex statistical tests, on appropriately scaled data sets.
• And the more complex the tests to be performed, the larger the data samples are required to be, and the larger still, the overall pool of raw data that those test samples would be at least semi-randomly drawn from.

Most retail stores would in fact look for outliers (e.g. items that all but fly off the shelf in sales, or that alternatively only gather dust there.) And costs and timing demands would constrain them to at most, doing only simple and even cursory data analyses and business performance modeling for all that lies between those extremes and with that largely based on aggregate analysis of product categories and not of individual item types. That definitely holds for smaller and even for medium sized businesses. Massive retail business systems, tend to be the ones that truly dive into the data, and with all of the effort and expenses involved as mistakes or lost opportunities take on scales of impact, financially, that they cannot leave to chance.

This posting and this series are about startups and businesses that are still small, even if growing, so that second possibility would only be a still-distant one for them. So I finish this posting with some open questions:

• What data is available and in what quantities and with what quality and reliability?
• What questions really have to be answered, and with what level of assurance in that, from this data? (Question 4 from above, expanded)
• And closely related to that, why are answers to those questions important, and specifically so? More specifically, what specific strategic and operational questions would they help address? This bullet point is all about keeping all of this focused and relevant and in a very practical value and returns on investment-oriented sense. And what would be the consequences of simply proceeding on without rigorously addressing them?

I rephrase that last question for a startup context, by asking:

• What really consequential decisions do you face as a business founder, and which of them are data driven and in ways that would be amenable to more rigorous analysis?

If this posting sounds too abstracted from real world businesses, for it to make meaningful sense when planning and executing their strategies and operations, and certainly in young new ones, I will at least attempt to bring its rationale into clearer focus in my next series installment. My goal there is to at least begin a discussion of when and how to add greater rigor and order into a new business, to facilitate its orderly development and growth as it moves forward. Everything, or at least seemingly everything might start out looking ad hoc and new and novel at first. When and how should order be developed and added to this mix? When and how should it take over and become the basic rule for how things are done there? I tend to write about organized and systematically structured systems, and about the ad hoc approach as an often problematical alternative. My goal in the next installment of this series is to at least begin addressing how that circumstance arises.

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 – 31: goals and benchmarks and effective development and communication of them 11

Posted in startups, strategy and planning by Timothy Platt on September 1, 2017

This is my 31st 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 continuation, postings 499 and loosely following for Parts 1-30.) I also include this series in my Startups and Early Stage Businesses directory and its Page 2 continuation.

I have been actively and very specifically addressing the issue of value creation in a new business, and what that word means to various demographics and constituencies that can become involved there, in the most recent several postings to this series. And I have focused there on business founders and the startup building teams that they bring together, and different categories of possible outside investors. Then at the end of Part 30, I said that I would turn here to consider exit strategies. And I begin doing so here, by clarifying more precisely what I mean by that often unfortunately misnamed business development process:

• Exit strategies represent fundamental change and true transition points where same and linear evolutionary change in a developing business, gives way to fundamentally new and different.
• As such they represent new beginnings for businesses as much as they do ending points, where a once perhaps essential approach to running a business gives way to a distinctly different and new one, and one that probably would not have worked for it earlier even as it would offer a good and even best path forward now.
• An effective exit strategy leads a business into what would at least ideally be its best possible next step forward, and both strategically and operationally.

Why are these transitions generally referred to as “exit” strategies, and not “transition” strategies or something else that would be less bound by implicit assumptions? The first strategically defined transition points to rise to general attention as disruptive changes in this manner, were primarily ones in which a business’ owners sold their new venture and walked away from it, perhaps after some agreed to transition-in-leadership period, or after staying on as a consultant for some period of time. But most of the transitions that I would include here do not involve any walking away of that sort, even if they can and often do include a change (e.g. a widening) of the executive team and an increased diversity of perspective and action coming out of that.

I have at least occasionally touched on this aspect to business development in earlier postings and series here in this blog. But to round out these introductory notes to this topic for purposes of this series, I at least briefly list a few other possibilities here, that are all most likely to start to become viable as options for most businesses, as they starting becoming consistently profitable – if they are to do so at all:

1. A new venture that has at least preliminarily proven itself as viable and as a source of profitability can go public and with all of the organizational change and all of the transparency and reporting requirements that this entails as they begin offering stock shares.
2. A new venture can transition from pursuing an organic growth and development model to one in which they seek out and acquire larger outside capital investment resources, and particularly from venture capitalists as briefly touched upon in Part 28, Part 29 and Part 30 of this series.
3. A new venture, and certainly one that is built around a growth-oriented business model, might build its first bricks and mortar site, in effect as a prototype effort that it would refine with a goal of replication through, for example a franchise system.

Yes there are exceptions to my assertion that these exit strategy options all become more viable only after a business has achieved consistent profitability. Just looking to the performance track records of businesses pursuing the first of the above three options, consider Google and businesses that like it, have gone public with an initial public offering (IPO) before they have actually became consistently profitable, where this has worked out and for essentially all concerned. But businesses like that are still exceptions and other much-hyped ventures that have attempted to follow Google’s lead there have sometimes all but vaporized into failure – after bringing in outside shareholder investor money. So I still argue the case that these three scenarios all become more viable and certainly on an investor risk management level, only after a business in question has transitioned into becoming genuinely profitable and reliably so.

That contextual detail noted, these and other transitions from early development into maturing business, can be viable as next step options and even best choices for moving forward when they are well thought out, timed and executed upon. And of course these basic exit strategies can, in many cases be combined too, with for example an exit strategy goal 3, as just listed here, also seeking to raise working capital by going public, essentially simultaneously pursuing a goal 1 approach too. As a growth-oriented business, such a venture would probably be more inclined to orient its system of publically traded shares so as to return as much of the profits generated back into the business in order to expand it, creating future value for itself and for its investors rather than short-term returns on investment that could be drawn out of the business.

And of course, simply selling off a new venture that has begun to prove itself could be added here as a fourth possibility, but for purposes of this narrative, I focus on the above numbered three, just as I have only addressed a few possible stakeholder categories here (e.g. no marketplace consumer, or supply chain or related participants considered here, at least at this point in the series.) And with this note added as to how I am bringing this discussion into focus, and with these background notes offered, I turn back to further consider the issues cited in the title of this series installment: “goals and benchmarks and effective development and communication of them.”

I am going to continue this discussion in a next series installment, where I will more fully examine goals and benchmarks, and communications issues as they play out in businesses going through the above three stated exit strategies. Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 4, and also at Page 1, Page 2 and Page 3 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.

Building a startup for what you want it to become 25: moving past the initial startup phase 11

Posted in startups by Timothy Platt on July 31, 2017

This is my 25th 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 1-24.)

I focused in Part 24 on the terms “aggressive” and “conservative” as they are used in a business planning and execution context over time, and in the course of that discussion, used those terms in what at first glance might seem to be two different and even somewhat contradictory ways. My goal for this posting is to focus on making business analysis more effectively data-driven and I will primarily address that complex of issues here. But I wanted to begin this posting at least, by further considering what those two terms mean, and how they are used.

I stated in Part 24 that:

• Conservative in this does not necessarily mean building for reduced overall risk, and aggressive does not necessarily mean building from a more risk accepting and risk tolerant perspective, and it does not necessarily mean accepting more of it – even if that can be the case when making specific comparisons between specific businesses.
• The real distinction here can in fact simply be one of where the risk that is allowed for, is considered acceptable in the business’ overall operational systems.

And then I used these terms in a manner that might seem more consistent with an assumption that conservative does in fact mean less risk tolerant and aggressive means more risk tolerant, per se. When you consider overall risk and with both short and long term risk possibilities included, this alternative is not in general always valid. But when you focus on shorter term and even on mid-range risk and their management, conservative approaches do tend to be more risk aversive there, and aggressive tends to be more risk tolerant there. This understanding is consistent with my own experience and my own observations so it underlies how I use these terms as a practical manner. Most organizations and of all types tend to weigh shorter term risk potential more heavily than they do longer-term, and certainly given the perception of uncertainty and of plan-undermining change that longer carries. Overall, however and when all timeframe considerations are accounted for, conservative and aggressive can come to look more and more alike. And long-term strategy has to allow for that as well as explicitly considering the range of timeframes faced.

With that noted, I turn to consider data driven business analysis, and I do so by offering a basic empirically grounded assertion:

• Not all data is created equal – and the challenge of creating effectively useful knowledge out of raw data begins with effectively evaluating, organizing and prioritizing it.

And business analysis, of course, is a process of making useful actionable knowledge out of carefully selected and organized raw data. So this and the strategic and operational planning that come out of it have to be based on a finely tuned understanding of what data is being used, and of its value for this.

I begin addressing this by posing a basic starter set of questions that would apply to essentially any data or data sets that might come up for consideration:

• Is this data reliable, and if so for what? I parse that question, offered here as a general point of principle, into a set of more focused related questions.
• Where did it come from? And how reliable is its source from prior experience?
• Is it complete and unedited or has it been pre-filtered or re-represented in some way, by a stakeholder who might be bringing their own biases or agendas with them when offering it? Answers to this question would in most cases be more presumptive than conclusive but evidence of possible filtering or bias should raise red flags and should always be considered as a possibility. As an example of how pre-filtering can be carried out without any intent of adding bias into a data set but still end up adding that in, consider how data can be “cleaned up” before use by deleting from consideration, unexpected and seemingly out of pattern outliers and other “anomalies”, while removing second copies of duplicated records and the like. That happens and it should raise red flags.
• Is this data consistent with other data gathered and with expectations in place, or is it divergent from or contrarian to that? Note, new and different and unexpected should not rule out new data findings. But they should prompt closer and fuller examination and particularly if their inclusion would significantly shape conclusions drawn and actions taken.
• And of course, what would this data suggest and certainly when considered in the larger context of what is already known?
• And what are the consequences of that, and both if this data is correct and reliable and if it is not?

This type and depth of input analysis is almost certain to be carried out if a set of possible data under consideration is deemed a priori to that, to be actionably important and consequential. But this type of analysis is much less likely to take place and certainly with any thoroughness, at least a priori to using it in planning and execution, if it does not in some way stand out as potentially game changing. And in an increasingly emerging big data context that all businesses face, that means less and less of the data flow coming in faces even cursory review and quality control and can essentially become taken for granted.

I am writing here of a need to automate incoming data quality control, and as an increasingly vital risk management issue. And yes, big data is not just a possibility, or even a necessity for just big and established businesses. Small and new businesses can find themselves immersed in it too, and of fundamental necessity and as part of any realistic execution of their business plan.

I am going to discuss this set of issues in more detail in a next series installment where I will focus on specific types of raw data as business intelligence, and in the more specific context of an at least briefly sketched out working business example. 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 – 30: goals and benchmarks and effective development and communication of them 10

Posted in startups, strategy and planning by Timothy Platt on July 21, 2017

This is my 30th 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 continuation, postings 499 and loosely following for Parts 1-29.) I also include this series in my Startups and Early Stage Businesses directory and its Page 2 continuation.

I began a discussion of outside-sourced business funding, and the consequences of having outside business equity holders in Part 28, with consideration of venture capital and angel investors. I then turned to consider crowd sourced outside investors in Part 29, as a rapidly emerging business development funding option. My goal for this posting is to continue and complete, at least for purposes of this series, my discussion of crowd sourced funding, and to at least begin a more thorough discussion of the more in-house oriented issues of exit strategies as a business plan consideration.

I wrote about the finances of crowd sourcing and of how many people, each making an individually small and even tiny investment, can collectively lend a business a very large amount of money. And I wrote of the largely “no strings attached” nature of these investments where individual investors cannot make significant equity holder claims on a business they crowd source invest in: meaning their not individually having much if any of a say in the running of those businesses.

The one and only real exception to that second point, at least that I can think of off-hand, would arise if a significant group of crowd sourced investors in some particular business, all came to see its behavior and its use of their loaned funds as being so egregious as to prompt them to enter into a class action law suit against that business. This circumstance would probably arise, if it did, as a consequence of negative social media driven viral marketing against the business, with an initial smaller number of irate investors pulling in more and more other potential plaintiffs to such a legal action until they reached what amounted to a critical mass of collective discontent. And at some point in this process, this group would have to hire the services of a hungry law firm to represent them. But the most they could reasonably ask for in claims against this business would be a full refund of monies actually loaned out, and a sizable percentage of that would go to their class action suit lawyer and towards paying off a variety of filing and other legal fees related to their case. Crowd sourced financing loans start out small on a per-lender basis. These irate investors would get back even less than that starting amount potentially due to them, and perhaps a third or more less than they has initially paid out and even if the business paid out every penny received this way to end the suit. So no one would actually get much of anything back and winning here would be more of a moral victory than a financial one; this is probably one of the reasons why I have never heard of such a legal action actually taking place. And this single exception scenario simply reinforces a point that I just made above, of crowd sourced investors “not having much if any of a say on the running of those businesses” that they invest in through the crowd.

The one other aspect of crowd sourced investment that I at least made note of in Part 29 was the marketing value that this can create for a funded venture. Think of the above paragraphs as addressing an at least potential negative viral and crowd sourced marketing and its consequence. Here, I focus more on the positive side to this. And I am going to more fully explore what that means here, and by way of comparison with the at least potential positive marketing value of being able to claim to have received venture capital funding support, as a new and still largely unknown business venture.

Venture capitalists, essentially by definition make significant cash investments in the businesses that they select to work with. And they do this on the basis of in-depth reviews and analyses of the businesses that they consider investing in. So when a venture capitalist or venture capital group invests in a business, they add to that venture’s reputation, the fact that they have been objectively professionally reviewed and found to be a good bet for success. And this is always arguably a significant vote of approval as the investing business offering it, does so by “putting their money where their mouth is” for it. This has marketing and reputation building value for a business that is invested in and particularly when the venture capitalists involved have a good reputation for their own professionalism and for their investment savvy and success.

Most venture capitalists are industry specialists and focus on businesses of types that they have expert familiarity in, when making their investment decisions. They know what to look for and what to look out for there, and both before making their specific investment decisions and as they seek to actively promote the success of their investment choices. And they have the expert familiarity to make meaningful positive contributions to the success of the ventures that they invest in that go beyond the offer of funding support alone, where for example it is common for venture capitalists to join the boards of directors of the businesses they invest in, or offer explicit business development advice to their owners and executives. They reduce their own risks and increase their possible and likely payouts and profits there, in all of this. But the marketing value that their funding and other participation offers to a venture that they invest in, is not going to primarily take the form of supporting their client businesses’ particular marketable offerings: their product or service specialization or what they particularly bring to market. It is going to be in support of those businesses themselves and their strategies and operations, and their capabilities as businesses per se.

The marketing and reputation building value that a venture can accrue from garnering crowd sourced funding support is going to oriented more towards the perceived value of their mission and vision goals and in what they actually produce that would at least attempt to fulfill those generally stated goals.

This distinction is telling, and it also connects in very strongly with where these funds come from:

• Business-oriented professionals and their venture funding businesses, for venture capitalists,
• And end users and consumers and people from the marketplace, in the case of crowd sourced funding.

In both cases these investors and groups of them evaluate and arrive at an understanding of value of a possible investment opportunity. And both categorical types of investors offer marketing support as they do that too, from the perspective of the demographics that they represent. It is just that they tend to use words like “value” there very differently, with venture capitalists focusing on cash flow and monetary return on investment, and crowd source investors focusing more on non-monetary returns, and societal and other “big picture” criteria for success.

And this brings me to the questions and issues of exit strategies as my next to-address topic here. I have been addressing the value and perceived value of a business and its potential, in the last few postings to this series, and in this installment to it too. But I have done so in terms of what could be a fundamentally immutable business development pattern, and with an initial and fundamentally unchangeable goal for the businesses under consideration. Exit strategies represent fundamental change and true transition points where same and linear evolutionary change in a developing business, give way to fundamentally new and different.

The term exit strategy is used in a variety of ways; I use it and certainly in a new and young business context, to represent the goals and strategy transitions that a business and its founders and owners can come to face as their new venture first reaches a point in its development where it is now bringing in at least some profits and at an at least largely consistent period-to-period pace (e.g. month-to-month, quarter-to-quarter, or whatever period the enterprise is strategically planned out for and on a routine basis.) With this brief orienting note in place, I am going to more fully address exit strategies in the context of this series, in my next installment to it. Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 4, and also at Page 1, Page 2 and Page 3 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.

Building a startup for what you want it to become 24: moving past the initial startup phase 10

Posted in startups by Timothy Platt on June 15, 2017

This is my 24th 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 1-23.)

I began explicitly discussing three basic business model approaches in Part 22 and Part 23, that I continue to address here in this posting too:

• A conservative business model,
• A normative business model, and
• An aggressive business model.

And I begin this continuation by repeating a crucially important point of explanation made in Part 23, that is often overlooked as people make unexamined assumptions as to what words like “conservative” and “aggressive” mean, and certainly in a business context:

• Conservative in this does not necessarily mean building for reduced overall risk, and aggressive does not necessarily mean building from a more risk accepting and risk tolerant perspective, and it does not necessarily mean accepting more of it – even if that can be the case when making specific comparisons between specific businesses.
• The real distinction here can in fact simply be one of where the risk that is allowed for, is considered acceptable in the business’ overall operational systems.
• (Following up on my Part 23 new manufacturer example), would risk be best accepted in the form of reduced liquidity with its consequences, in order to better safeguard here-and-now production line continuity, or would it be best accepted in the form of allowing for greater risk there in order to safeguard liquidity levels – that would now be more readily available to meet other business needs? This is where business planning has to be comprehensively inclusive and where it has to take into account all anticipatable factors.

I began addressing a key defining and organizing term in Part 23 that I pick up upon here as I continue this narrative: “essential.” If I were to distinguish between the three basic business models under discussion here in terms of one word, it would be in noting how their owners and managers variously find meaning in that one. Business owners and managers, and from senior executives on down, seek to optimize what they are doing and what they are building for, in terms of their understanding as to what is essential and what is most essential and certainly in their immediate here-and-now. But they can systematically differ in where and how they use this type of word and both in that immediate here-and-now and as they plan forward and for their longer-term too, and certainly when making comparisons between same-type and same-level managers or executives as they work in businesses that follow different business models, according to the tripartite business model distinctions made here.

I have already been considering this from a risk management perspective, even as I have left out that terminology in the last two postings. I explicitly apply this label with its baggage of associated assumptions and presumptions here, where I will more generally consider the issues of stability and opportunity, as pressures towards them can come into alignment and as they can come into at least apparent conflict too. In anticipation of that discussion to come, I will continue to focus on first steps that businesses take when entering their first early growth phase and for the three basic business models under consideration here. And as part of that, I note here that where stability and opportunity and the benefits side to what would enter into a more traditional SWOT analysis, offer a roadmap to where a business seeks to go, risk and threats and potential weaknesses inform how and when and according to what timetable this might be carried out along.

I begin all of this with consideration of timeframes and the question of how far forward, strategic and operational planning would be carried out. The farther out you look and seek to predict and plan, the greater the uncertainty you have to accept and for reasons that arise both internally to your business and from its and your own outside contexts.

• Short term and even essentially here-and-now analysis and planning: tactical analysis and planning can offer real clarity of vision, but do so at the cost of not helping you prepare for new and emerging contexts or contingencies as they arise over time, and often even where simple longer-term change can accurately be predicted.
• Long-term planning has to be able to accept and even actively accommodate alternatively arising contexts, and the possibility that none of the predictive models considered might actually become the reality actually faced. Disruptive novelty and change can arise at any time and in a seeming instant; but the odds are greater that this will have to be faced, the farther forward you plan and predict into and the longer the timeframe you have to allow for.

And this brings me to the key words of this posting’s discussion: “stability” and “opportunity,” or at least planned for stability and predicted opportunity – where this means predicted paths forward in business development that would be expected to maximize value achieved, with ongoing stability while accomplishing that.

• New and still forming and developing businesses carry a significant level of risk in all of their decisions and actions that they take, and certainly insofar as those decisions would impact upon their working budgets and the levels of reserve funds that they might have.
• Ultimately, “essential”, “stability”, “opportunity” and I add “risk” and “cost” are all terms that are financially grounded, and that are most firmly grounded in liquidity terms.

That, I add is a very fiscally conservative assertion; when a business has lean financial reserves, a measure of such conservatism can be essential as a due diligence and risk management position. But let’s consider non-liquid assets, and assets that can only become explicitly financial assets of any sort, over time and as a business actually develops and begins to succeed. And the most important such assets in general and essentially categorically for any startup with potential, is the set of ideas and concepts, vision and understanding that could be developed into a unique value proposition that would make this new venture stand out.

I return here to the absolute essentials here: if you want to build a startup and make a successful go of it, find a path forward in what you could offer to a marketplace that would be uniquely yours, and develop your business into that. Don’t strive to be the 17th best business in town in the business sector that you would build into and the 17th best for offering an already readily available product or service; plan for and strive to be the best, and even the first and the best possible for what you can specifically offer, and with a point of distinction in what you do and offer that the consumers of a marketplace would see as offering special new value to them. And this brings me back to the main thrust of argument of this posting, and to stability and to the uncertainties of longer timeframes:

• On the one hand you need to be prudent and even conservative in managing your resources,
• But on the other hand and at the same time, if your do not take risk and invest towards fulfilling the potential of your initial dream: building to realize your new and novel and value creating vision of what you could accomplish, then conservative management of the resources that you have in place will not help you and certainly long-term.

Success here means finding and reaching an effective balance between taking a conservative and a risk-taking approach. And with this, I return to the notion raised in Part 23 of the normative business model, as noted at the top of this posting, as representing finding an effective balance point between conservative and aggressive, and with a goal of taking the best of both in finding what is hopefully a best combination for you.

I am going to continue this discussion in a next series installment where I will focus on making business analysis more data-driven. And I will delve into the questions and issues of where this source of raw material for insight would come from in a new business setting – and how it would be used there. 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 – 29: goals and benchmarks and effective development and communication of them 9

Posted in startups, strategy and planning by Timothy Platt on June 5, 2017

This is my 29th 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 continuation, postings 499 and loosely following for Parts 1-28.) I also include this series in my Startups and Early Stage Businesses directory and its Page 2 continuation.

I began Part 28 with a briefly stated, essentially checklist formatted outline of how a strategically considered and planned startup would be launched, and in a manner that could lead to stable growth and development from there:

1. Start with as clear as possible a statement of what the new business’ founder and their founding team seek to develop as a business. Couch this in general goals-oriented, mission and vision terms for what this venture would offer to its markets, and for what would competitively set it apart there.
2. Then reality check that characterization of intended overall goals, against an equally clearly stated inventory listing of the resources and assets that that founder and their team can bring to this effort, and starting with what these people personally bring to the table as far as skills and experience are concerned that would support and enable this venture, as well as any financial or other resources that they can commit and devote to it. And to complete this list, include any negatives as well (e.g. anything like non-compete agreements with previous employers, as that would impact upon one or more members of this group actually being able to perform in this new venture as intended and desired.)
3. And now bring this all into more specifically actionable terms with at least the initial planning, outline details of their business plan thinking, that would help them leverage their Point 2 resources in developing a venture towards achieving their Point 1 goals. The goal in this step is to build a foundation for iteratively, step by step fleshing out the business plan that is to be followed here, with a consistent, orderly, mission and vision statement-oriented focus,
4. And to identify where possible, any places where Plan B refinement or initial-idea replacement updates might more predictively be called for, to make this new venture as secure in its succeeding as possible; plan for flexibility and resiliency here. That means being ready to step back and make changes and corrections as needed, and with a goal of making the business plan followed, a dynamic adaptable game plan for moving forward.
5. And this (ongoing) effort enters into completing a full business plan that all involved stakeholders can comfortably sign off upon. And it also enters into early development stage planning and strategic reviews as both the expected and as the unexpected arise and have to be dealt with and resolved too.

Then after concluding, at least for now, a discussion of in-house considerations as to how this would play out, I turned to consider outside forces and factors that can significantly shape a new business and both for what it seeks to do and become, and for how it would build towards that goal. I focused there on the roles that venture capital and angel investors can play in this as they take on what can become significant equity shares in a new venture. And as part of that, I at least briefly addressed a potential source of conflict that can arise between in-house business executives and owners, and outside investors and particularly as their timing and priorities expectations and requirements might differ.

I stated at the end of Part 20 that I would a discuss wider range of in-house and insider, and outside forces and factors that can help shape how the goals of the above numbered list of business development steps would be understood and carried out. In anticipation of that, I added that I would more explicitly discuss crowd sourcing as an outside investor option and from both the crowd sourced investor side and from the side of the business invested in, as a first such example. And to repeat an orienting note that I appended to this, I observed that:

• The factors that hold importance here are those that directly and specifically influence and shape that business’s capacity to create value. That is where these factors impact upon and influence and even shape the business at its most fundamental level, and outward from there.

Angel investors, and venture capital investors in particular: more traditional sources of outside investment besides supportive funding from immediate family and friends, involves significant levels of funding that comes from a small number of sources and certainly for any given venture that is invested in.

• A new business venture that has an arguably exciting mission and vision to offer might capture the interest and funding of a small group of angel investors but even there, that number is going to be limited and very much so, in all but the most exceptional cases.
• A venture capitalist, or more likely a venture capital company that is considering investing in a later stage startup or early growth business is in most cases going to want to be their exclusive source of outside investment funds. Their due diligence analyses would argue against their putting themselves in a position where they might face conflicting claims from other investors, to ownership rights to a share of the overall value of the businesses that they invest in, that would match and cover the investments that they have made there. So here, a limited number of these investors generally means just one and even with second round venture capital investment explicitly considered where the business that is being invested in has already proven itself.

Crowd sourcing reverses all of this. Investment funding from this type of source means small and even minuscule loans of invested funds from what can be hundreds, thousands, tens of thousands or more individual investors.

• Individually, none of them do or can claim hold to any significant share of the equity that a new business they invest in might hold.
• None of them individually would have much if any say in how the business is run and either operationally or strategically.
• Like angel investors, crowd source investors tend to be drawn to what they see as positive and affirming missions and visions as offered by the business investment opportunities that they pursue.
• And at least collectively, like venture capital investors, a community of involved crowd sourced investors can bring together very large pools of investment funds. Individually they might only modestly invest but when thousands and more of them all invest small, the overall result can become very large.

This process has all become a lot more streamlined in recent years, with the advent of crowdfunding platforms such as Kickstarter, RocketHub, GoFundMe and I add quite a few dozen more – and with that just considering funding platforms that have shown significant levels of actual activity and with real investment ventures and real investors. These platforms provide a number of services and both to the crowdfunding community and to ventures that seek such support. One of them, that holds value from the fund provider perspective can be found in how they vet and validate legitimate new ventures that meet their standards as legitimate places to invest money. And one that holds value from the perspective of businesses and organizations that would seek out this funding, is marketing and increased visibility, and through channels that crowd sourced investors would look to when selecting ventures to invest in. These organizations serve as middlemen in the crowd sourced funding arena, but ultimately the people who invest in the businesses they highlight, invest with those new ventures and as small investment level individuals. So the basic points just noted about crowdfunding still apply.

From a new business perspective, crowd funding provides a great deal more than just readily available liquidity, as important as that can be. It creates a marketing presence and market buzz, with all of the additional viral marketing reach that connecting positively with an online and social media-connected crowd can bring. And it does this with minimal loss of control over business decisions as they are prioritized and made.

• Decision making pressures from the funding crowd that has been reached out to, is primarily a matter of meeting a due diligence requirement of actually striving to reach the mission and vision statement goals that their crowd funding campaigns were built around, where an apparent good faith effort would meet the requirements and expectations of most of these investors.
• If a desired goal that was being funded towards were easy, it is unlikely that many would see crowd sourced funding in support of it as being necessary; it is the difficult-appearing challenges that get this type of funding, where real overall success can never simply be taken for granted.

I am going to continue and conclude my discussion of crowd sourced funding in the next installment to this series, at least for the issues that would be addressed here. And then I will turn to consider a more in-house set of issues and factors that can rise to real importance here: exit strategies and what the owners of a new venture seek to build it into, and from when it really begins to bring in consistent profits and moving forward from then. And as a part of that, I will explicitly discuss the issues of growth companies and businesses that really pursue that vision, and profit center companies and businesses that really pursue that vision.

Meanwhile, you can find this and related postings and series at Business Strategy and Operations – 4, and also at Page 1, Page 2 and Page 3 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.

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