Platt Perspective on Business and Technology

Connecting into the crowd as a source of insight and market advantage – 3: determining monetizable value

Posted in macroeconomics, strategy and planning by Timothy Platt on May 24, 2012

This is my third installment in a series on connecting into the crowd as a source of insight and market advantage, but with an important difference – doing this in ways that explicitly allow and support measurement of costs and of value returned in the interactive online context (see Macroeconomics and Business, postings 77 and 81 for Parts 1 and 2.)

In Part 1 I briefly outlined something as to how web site performance metrics have evolved from initial viewer count-only metrics such as eyeball count and sticky eyeball count, to begin to measure monetizable value. I also noted how these approaches were developed in a central publishing only, Web 1.0 framework.

I began a discussion adding in interactivity and social media, online reviews and other Web 2.0 oriented factors and channels in Part 2 of this series and I pick up on that here, noting that simply counting points of interactive feedback is essentially just the interactive and social media-oriented counterpart to early Web 1.0 metrics such as those eyeball and sticky eyeball counts. And I add that simply counting the review scores can be misleading too, as far as understanding monetizable impact and value are concerned. In order to connect these measures to an understanding of monetizable value you have to add an understanding of the value and relevance that these reviews and the reviewers who post them, have on the people who read them.

I began a discussion of that when I noted that review sites and businesses that add in publically shared review feedback capabilities increasingly offer readers the option to rate those reviews for helpfulness. That is a good first step, but most of these features only seem to support positive or at worst neutral reaction options, and lack options for stating that a review is misleading or inaccurate or that a review poster comes across as being a troll (see Trolls and Other Antisocial, Disruptive and Divisive Social Networkers – Part 1 and its Part 2 continuation.) I add that most review evaluation features do not include the option for identifying text feedback as spam either.

The option to both positively and negatively rate reviews and I add reviewers is very important here, as most site visitors who read reviews tend to focus on the extremes and certainly for heavily reviewed items. If a site visitor is checking out a hotel online for a possible vacation stop, for example, and they find hundreds of reviews available to read on some possible selection they will probably focus on the most recent if possible, and not on the reviews that were posted a year and more ago. And they will turn first to the low-score reviews and possibly to the highest score reviews – and be more likely to leave the middle score reviews unexamined. I noted in Part 2 that outliers can carry disproportionate influence and this is a case where that definitely applies – and it is a compelling reason why a site that supports crowd sourced reviews should also crowd source a full range of feedback and review evaluation options too.

Now the issue is in moving these data points out of the anecdotal and finding ways to analyze them and patterns of them statistically, and in ways that can be coordinated causally with customer transaction rates and their valuations. A first step there is in tracking click-throughs and where a site visitor clicks to next when moving on from a review, and where they came from when going to read a review.

• When a visitor is reading a review on a business’ site in most cases they have clicked to that review from within that same site. So at least in principle it should be possible to trace where they have been, leading up to that review and where they go to after reading it – to other reviews, back to look at that same item again or to other and perhaps similar items, to a start point for entering into an online purchasing transaction, or out of the site entirely or whatever.
• When a visitor comes to a site from a third party review site, their click history up to then will be largely invisible to the business site and its performance tracking resources, but the URL for last webpage they visited before going to that business’ website will be visible. So the business should be able to identify when a visitor has come to their site from a specific review site such as Yelp. And that URL will in most cases include both the review site itself and also which specific review they had last read on it before clicking through to the business website.
• Systematic click history reviews for most online businesses would reveal patterns in which site visitors look at specific product or service information on a business web site, click to a review site and then come back to the business web site to look at that product or service, or at others.

Statistically, a business can analyze how last-visited review sites and specific review views correlate with subsequent online sales performance, and the more closely individual click-through patterns can be followed, the finer grained the valuation model that can be developed from this. And this insight can be used to fine tune both inventory and marketing.

• Products and services that do not do well in the crowd sourced conversation can be downplayed, remaindered or removed and those that get positive buzz and that rate more favorably can be highlighted in marketing campaigns and increased in inventory.
• Products that are unfavorably reviewed can be improved and updated to better meet the needs and preferences of the marketplace and of those in it who shape public opinion through their reviews. And this can be marketed with a message that this business really listens and responds.
• All of this can be correlated with ongoing business performance and sales completed, and by stock-keeping unit (SKU) type.

This, I add, can only be considered a first step in developing and pursuing a monetizable valuation approach to web and online performance metrics in an interactive online context. I focused on reviews and websites per se in this posting. I am going to add in the wider conversation and the increasing diversity of social media options for sharing crowd sourced evaluations and buzz, positive and negative in my next series installment. Meanwhile, 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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