Platt Perspective on Business and Technology

Building a startup for what you want it to become 31: moving past the initial startup phase 17

Posted in startups by Timothy Platt on March 30, 2018

This is my 31st 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 1-30.)

I focused in Part 28 and again in Part 29 of this series, on the increasingly pivotal role that data, and that big data in particular are coming to play in enabling business success in this emerging 21st century. Then I turned in Part 30 to at least begin addressing a second, follow-up point that arises from that:

• The issues of 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.

I have already, as noted in Part 30, been discussing a number of crucial aspects to this complex set of issues, including the question of how fair, consistent, replicable valuation would be determined for business intelligence per se. And I ended that series installment by stating that I would continue from there by “at least briefly delving into the issues of how information would be selected and organized for use, and for sale as marketable commodities.”

I begin this with the data itself, and with customer-based and derived data as a source of working examples. Some and in fact much of this data would be developed by a business itself, on the basis of their own transactional activities with the individual customers who they do business with and learn about in the process. But depending on the nature and scale of those transactions entered into (e.g. purchase of a new car or other big ticket item or when customers are applying for a store credit card, where creditworthiness would be an important consideration), third party sourced personally identifiable information might also be needed, acquired and held too. In my parenthetically added examples there, this would mean data included in specific customers’ credit reports as purchased from credit reporting agencies such as Experian or Equifax. And citing outside sources such as credit agencies here, only scratches the surface of what might be available and what is increasingly being tapped into by businesses for consumer and marketplace data and insight. Just consider the potential inherent in scrapping data from social media there, and certainly for developing demographics level insight – but also when fleshing out an understanding of specific individual customers when drilling in for those details would be desired. And consider how automation and the application of artificial intelligence agents can enable the widespread development and use of that type and level of fine grained aggregated data too.

Setting aside the issues of confidentiality and the legally mandated requirement of safeguarding specific individual’s personally identifiable information in those increasingly detailed customer files, let’s at least briefly consider how and where this information would gain its value, looking here at least for now, just on the positive value potential side of the benefits and returns in this, as opposed to the risks and costs side of the ledger.

• Value comes here, from aggregating the right combinations of raw (here customer) data in ways that could be used to answer meaningful questions that would help the businesses asking them, to be more competitively effective.

Let me take that out of the abstract with a specific example. A single anonymized customer record, derived from the accumulated raw data concerning them as a specific individual is not going to offer any significant value to any given acquiring business, and certainly if it lacks sufficient context to identify precisely what types of demographics that individual would belong to, and in ways that would offer marketing or other value. Lone and disconnected data of this type would not even be useful in and of itself, and without recourse to correlated outside data, in distinguishing between more mainstream customers and real outliers who might not effectively fit into any simple demographic for their actual consumer behavior.

• Data only gains value when it can be organized into meaningful contexts, and when it arrives in meaningfully connectable bundles.

And with that offered, I step back and reconsider everything I just said about customer data from a second and very different perspective – that happens to be particularly crucially important in the context of product or service innovation, and particularly where that innovative change goes significantly beyond the simple cosmetic.

I begin here with a starting point that I have cited and pursued numerous times in the course of developing this blog up to here: the innovation acceptance curve as it maps out and helps to explain how New enters into a marketplace and how it comes to gain progressively wider acceptance there.

A really significantly new and novel innovation that hits the market, is going to primarily if not exclusively gain its initial foothold there, from the purchasing and usage decisions of a relatively small fraction of that overall market, that would qualify as its pioneer and early adaptors. These people are, by definition outliers when viewed from the perspective of the larger markets that they at least nominally belong to. And my just completed notes on data contexts, was organized and presented with more middle adaptors at least tacitly presumed, and even late adaptors for that matter. My comments offered before this switch in conceptual direction, were at the very least fully compatible with the goal of meeting marketing and sales needs that would connect them to as large and active a purchasing community as possible. But businesses that primarily seek to develop and offer cutting edge New, would be best served by gathering in and acquiring outlier data, and more specifically the right outlier data that would collectively define what for them, would be their best fit specialized niche market.

Focusing here, on this special case-in-point category of innovation-driven data requiring businesses, and the desired perhaps numerically small niche markets of otherwise-outliers that would fit into them, who they would most need to understand and connect to:

• The data that they would accumulate in-house, as supplemented by data acquired from outside sources, would hold an overall aggregate value for them that was at least roughly defined as the overall gross returns on investment from sales made on the basis of this data, net any and all costs accrued from gathering and acquiring it, and organizing it into operationally useful form (a general consideration that would apply to any business),
• As framed in a niche market-oriented business context by consideration of the overall scale and the overall possible sales reach and profit potential that might be obtained from effectively connecting to its particular best possible consumer base, from using this data. (Note that this bullet point at least tacitly presumes that data available, if properly developed and used can help a business identify its best possible target market consumer base to start with. At least initially, but also when moving forward in the face of marketplace and competitive context change, that might not reasonably be presumed, and certainly as anything like an automatic given.)

As noted in earlier postings to this blog, online businesses with their at least potentially global reach, face opportunity for reaching essentially anyone worldwide who would fit into even the most specialized niche market, only provided that the members of that demographic go online and that they would be willing to buy there too. So I offer this narrative as holding increasing importance in our all but ubiquitously online connected 21st century world.

I am going to continue this narrative in a next series installment with an at least brief discussion of businesses that aggregate and organize information for businesses, as their marketable products and in accordance with their business models, as that set of issues enters into this series’ narrative. In this, I will more fully explore the issues and questions of where business intelligence comes from, and how it would be error corrected and kept up to date, as well as free from what should be avoidable risk from holding and using it. And that will mean addressing the sometimes mirage of data anonymization, where the more comprehensive the range and scale of such data is collected and the more effectively it is organized for practical use, the more likely it becomes that it can be linked to individual sources that it ultimately came from.

Looking further ahead here, I will look at all of that from an in-house versus cloud storage, organization and analysis perspective, as initially promised in Part 28, and will proceed from there to consider these issues 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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