Platt Perspective on Business and Technology

Connecting into the crowd as a source of insight and market advantage – 1: starting a new series

Posted in macroeconomics, strategy and planning by Timothy Platt on May 3, 2012

I have been writing on and off for several years now about crowd sourcing (see for example, my short introductory series Crowd Sourcing, Open Innovation and Open Organization as found in Social Networking and Business as postings 44-46.) I have also written about web analytics and web metrics a number of times, and putting them in the perspective of the online store as a working example I would cite my posting Online Store, Online Market Space – part 5. These areas of activity and its modeling connect where analytic metrics, for example, would be used to identify and track overall crowd sourced information flow, and for setting outlier criteria for such things as individual levels of contact and information sharing activity.

Just considering web analytics for a moment, I have written about the fantasy of eyeball counts and sticky eyeball counts – measures of how many people go to a web site at all, and of those who stay and perhaps even click into it to view a second page there before leaving. And as I have noted many times, one of the driving forces behind the first big dot-com bubble burst was a reliance on those two measures in determining monetizable value and business potential for startups and early stage businesses that had not even begun to reach break-even for expenditures going out and revenue coming in. So web analytics metrics have faced conceptual and functional/operational challenges for quite a while now. And I would argue that they still need further refining.

To connect a third line of thought into the puzzle I address here, I recently finished up a series on innovation and its effective development in which I made the following statement:

• New tools are needed for tapping into the crowd for insight, and even as potential sources of market insight open up and become available and even as this insight becomes more crucially important for business success. (See Keeping Innovation Fresh – 16: adding the crowd to this puzzle.)

The metrics that I called for in that statement:

• Need to be replicable for values derived, given a same-set of input data and parameters.
• They need to be as context and industry agnostic as possible, generally applicable for any analysis of the crowd and its impact.
• They need to connect analysis and analytic modeling of the crowd and its flow of information shared to the more effective achievement of business needs, and as such have to mesh with metrics of monetizable value.
• And they need to be framed in ways that feed into and support actionable decision making processes.

And in an interactive, highly participatory online context such measures would quickly become the most important sources of web analytic insight that a business or other site owner could have, and certainly in the context of our increasingly interactive online cultures and environment.

My goal in this posting is to at least begin an outline as to what a next-generation set of crowd-oriented web and online analytic metrics would look like. And I begin by noting a fundamental point of distinction between these proposed metrics and the web analytic and other metrics that have traditionally been used.

• Crowd-based metrics look outward to the context of the web site and not just inwardly to measure a web site’s performance numbers as if it were sited in a social vacuum. More traditional analytic measures of web performance would be needed and taken too, but their findings would be interpreted and used in a larger context and in ways that acknowledge where the activity behind them came from.

Here, I am not simply referring to development of points of comparison in which inwardly-facing analytics from one organization are benchmarked against comparable inwardly facing numbers as obtained from “similar” organizations – direct competitors and others in the same or similar industries, or from businesses comparable by scale or by other rationally explicable general organizational standards. I am explicitly calling for comparisons to the marketplace and to the crowd as it includes individuals and groups that would at least potentially be customers or who would significantly influence them.

Turning back to the now long-discredited metrics of eyeball and sticky eyeball counts, intuitively some genuine and genuinely valuable information is hidden in the numbers as to how many people visit a web site. But taken as-is, these metrics do not offer any information as to how many people go on to start let alone complete business transactions. So they cannot be used as measures of monetizable value. Tracking activity throughout the site and with a specific focus on areas of that site that offer customers transaction opportunities addresses many of the gaps that those earlier first-generation metrics leave you with. But most standard performance tracking metrics are still oriented toward understanding business web sites according to a Web 1.0 paradigm with centrally published content and at most simple forms-based viewer initiated opportunities to connect back. They do not in and of themselves even acknowledge the socially interactive crowd the business exists in, let alone measure its impact.

This brings me to a basic starter question for this series. What types of online interactivity and crowd-sourced behavior are not being taken into account here by standard web analytic metrics, that would directly and monetizably impact on the business and the performance of its actual online presence? And I am going to turn to that in my nest series installment, looking at the range of Web 2.0 enabled connection points that are increasingly being offered by businesses as they seek out new sources of competitive advantage.

And I finish this posting by proposing a basic observation:

• If more traditionally framed web analytic metrics are insufficient for fully, effectively modeling performance in a more fully interactive context, and if more effective analytics have to look at the crowd and the business’ and web site’s context – they have to do this by looking at how the entire online presence with all of its central publishing and interactive parts fit together, and on how the outside public comes in by using them.

You can find this and related postings at Business Strategy and Operations – 2 (and also see Business Strategy and Operations.) You can also find this at Macroeconomics and Business.

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