Big-Data’s Diminishing Returns

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I continue to be a skeptic when it comes to the Big Data-version in which marketers get to scrape all data they can get their hands on to improve their ability to target prospects and Customers with so-called ‘personalized’ offers.
I’m a skeptic about this since I believe it is an approach that ultimately suffers from the law of diminishing returns, and at an increasingly high speed.
For one I believe this because it has been the case with most one-to-one (predictive analytical) marketing programs that I’ve seen running (or ran myself). In the beginning it looks as if conversions can only go up, but after a while you will be defending ever decreasing conversions to a point it just doesn’t make sense anymore. This is true because you will literally have exploited most opportunities in your Customer base over time.
But mostly this is true because these days your Customers and prospects are increasingly well equipped to find you when they have a need to. They can safely ignore your targeted broadcast, for they don’t need to remember. Not that I believe they actually did so in the past.
Or, to put it in another way: we’ve been long convinced it was our specific offer for a specific prospect/customer that persuaded them to buy more, where in fact it was the Customer who was ALREADY interested in your product or service, who purchased. Whereas you may have been convinced it was BECAUSE OF your doing that people bought, in fact it was the customer who purchased DESPITE your effort.
Control groups, statistical analysis and all, chances are that any net-conversion you thought you realized were just a result of mere chance. You can’t really tell because most evaluations are based upon too few data-points anyway. This problem is not solved by big-data (getting more different kinds of data) for it is a problem of too few of the same data-points over a long period of time.
I like the analogy by Nassim Taleb (see the video embedded in this article; please ignore that it’s a Dutch article, Taleb speaks English) that explains how we humans do not predict when it’s safe to cross a road by adding more different data-points, like e.g. the color of the eyes of by-passing car-drivers, to our decision-making process, but by filtering the data and only assess what’s relevant to get across safely.
Big Data programs that aim to do the same as old-fashioned predictive analytics based marketing programs, will suffer from the same old-fashioned results: conversion rates will drop, efforts to improve (collect more and more data) will increase and as a result cost-per-order will rise beyond any reason. Automation may improve productivity for some time, but all it will actually do is cloud the need to really and fundamentally innovate around value. Your effort to the bottom of things will cost you more time and money than you can really afford. Time and money you cannot spend on increasing co-created value in-use.
Thus, my statement of today is that the Big-Data route towards increasing conversion rates, by predicting contextual relevance for delivering a personalized offer, only knows a slope of diminishing returns.
The alternative route, towards increasing value co-created by improving your value proposition and the experience of finding, understanding, customizing, purchasing and using it to generate desired outcomes, though, is only limited by our ability to understand what drives value co-creation, our imagination and capability to transfer those insights into these very value propositions.
This is a pie that can cannot only get smaller, it’s one that has the potential of getting bigger and bigger, which is a much more fun outlook than the inevitable downward slope of diminishing returns.
This will put you on the route where you will try to understand how you can bridge the gap for Customers between what they are trying to get done or want to achieve and them enjoying those outcomes. Instead of putting yourself in between your Customer and their desired outcomes.
This will put you on the route where you try to generate (relevant) data with Customers (not just harvest it from them) and put it in use for their benefit, not (just) you.
This is where you do not eavesdrop but listen carefully and share back what you know. It may even put you in the spot where you are willing to pay your Customer for their contribution to your capability to better help them (and get paid in return). Or better, where your Customers want to share with you their (use) data, for they trust it will benefit them.
This is when you understand that you do not add value in each step of the value chain but jointly co-create value over it’s emergence.
This will ultimately put you in a spot where Customers value your help, not ignore your (re-)targeted message.
Image via Flickr