Platt Perspective on Business and Technology

Opening up the online business model for new and emerging opportunity 2: online target demographics oriented marketing

Posted in startups, strategy and planning by Timothy Platt on May 7, 2013

This is my second installment to a series on new and emerging online business models and on developing best practices for finding and creating new and novel online business opportunity. I began this series from a largely Web 2.0 oriented perspective (see Part 1: outlining some of the basic issues and challenges) but admit here that my goal for this series is more ambitious than simply that of suggesting some here and now-obvious best practices for developing a business in a current-generation Web 2.0 context, with or without explicit development of matching current Web 3.0 and semantic web capabilities.

I explicitly raised several open issues in Part 1 of this series, and my goal for this installment is to at least start addressing them – beginning with a complex of issues that I raised towards the top of that installment, and only briefly touched upon in what followed: marketing to a targeted demographic as a business’ local community. And I want to begin that by noting a basic fact that should be obvious, but that still can hold significant marketing and sales surprises when considered in practical detail:

• A targeted marketing demographic can be and usually is defined in terms of a set of shared member traits that can be correlated with varying degrees of certainty to likelihood of buying specific types of products and services, at specific price points and when marketed and offered in certain ways.
• That, at least represents the goal, connecting an understanding of who would be marketed to, to their behavior when marketed to, and in ways that can lead to insight as to how best to develop and complete sales.

But essentially any real group of people share traits in common that are not considered or included in the marketing data collected, and even in a big data context – here if data is collected but not effectively integrated into these models, it effectively does not exist from their perspective. And at least as importantly and particularly in an online context, any such group is also going to differ, and to be partitionable according to factors and traits that are not under consideration too, and for which data is not being collected or where it is but once again where these differences are not being allowed for in the marketing model.

As a quick and perhaps more technical digression, consider the impact of determining correlations between measures of predictable marketing impact and group identifying traits, when the overall group under consideration is more diverse that is realized. And a possible correlated trait might show as having an insufficiently low correlation coefficient value as a predictive factor to be meaningful across the entire group, and be discounted from the marketing model as a result – where it is in fact very significant for select and numerically important subsets within that larger group. (I cite and offer a link to a more general and open-ended reference for correlation coefficients there as the basic principle I cite here applies across essentially all statistical modeling approaches that might be in use where these values would be determined and used.)

And with all of that in mind, I explicitly turn to consider the similarities and the equally significant differences and diversities that can be found and even within what should be well-defined marketing demographics when they are globally distributed and cut across diverse traditional cultural and linguistic boundaries.

To take this at least somewhat out of the abstract, consider the following admittedly simplified scenario. You own an online business that produces, markets, and direct-to-consumer sells a line of environmentally friendly products – Green technology products in the West. And you come from a country and a culture where this is explicitly referred to as being green so you build your branding and image around that, and by that name and with that color. This may or may not matter as we people as a species seem fairly comfortable with inconsistencies and nuances, and even with cognitive dissonance, where we simultaneously hold two or even more seemingly contradictory views or opinions at once. But as a matter of possibilities, different peoples can see the color green as carrying very different culturally based connotations and implications. Green is a very nationalistic color in Ireland and it is a color associated with religious virtues in Islam and in predominantly Islamic nations, where it is often incorporated into national flags and other symbols. But depending on where you are, green is also associated with envy and jealousy, and in some countries (e.g. Belize) even with death, so the range of associations that would go through peoples’ minds with regard to this seemingly simple trait can be very diverse. (The Wikipedia entry for green sheds more light on this diversity so I cite it here as a background reference.)

My point in this example is that you might be targeting a demographic with a goal of doing significant levels of business with its members, who should as an overall group be positively predisposed towards your green technology products – but how you market this to them and how you use that color per se in reaching out can have varying impact and meaning depending on the cultural context and expectations of the people you reach, from within that globally dispersed marketing demographic. My guess is that by now, you are likely to be thinking that I am pushing “green” to far here. I can only reply to that objection with two points:

• It is the little details that we do not see as significant that can trip us up here, and particularly where they bring up distinctions and differences of bias and preconceived opinion that others might see as more important than we do.
• And we all make our purchasing decisions on the basis of unconscious decision making processes as well as from our conscious and more analytical thinking – and we at times only seem to use our more analytical and conscious reasoning to justify decisions already made less consciously.

And for green as a working example, I also note that this color has been used as a distinguishing identifier in sectarian and other conflict so even a color choice can have real impact.

• Market globally, but with a local face and awareness – and in this context, local means more accurately and adroitly defining all of those similar but still different and distinctively unique demographics, and really knowing where they can and should be combined as a single audience and where they have to be respected for their differences and diversity.

When I wrote my series: Big Data (see Ubiquitous Computing and Communications – everywhere all the time, postings 177 and loosely following) I wrote of the Holy Grail goal of effectively marketing to the demographic of one. Here I write of the equally compelling goal of marketing to the more finely envisioned and more effectively targeted group demographic too – and in a global setting with its bewildering ranges of potential similarities and difference and in both analytical opinion and unconscious assumption and correlation, that becomes more complex than it ever could in a more physically and geographically local market setting.

I am going to continue this discussion in my next series installment, there turning to consider those open questions that I raised towards the end of Part 1:

• When is pursuit of a true blue ocean strategy and business model realistic and how might a new business’ chances be improved for succeeding there in an online context?
• And what should be its perhaps more conservative Plan B, and when should breakout success and blue ocean strategy development that would lead to it be the Plan B?

Meanwhile, you can find this and other related postings at Startups and Early Stage Businesses. You can also find related material at Business Strategy and Operations and at its continuation page: Business Strategy and Operations – 2.

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