I was able to share my big predictions for 2014 on Coffee Break with Game Changers today, sponsored by SAP. Here are my notes regarding convergence forces of big data, predictive analytics, mobile computing and location-based services which is already yielding issues of "intrusion to privacy" and established a Code of Conduct on the topic in October of last year. More can be found on my SCN Blog or WordPress Blog (The View from C-Level, as well as my now-famous holiday Irish Cream recipe).
One of the big news stories in strategy, innovation and tech circles is the growth and convergence of four key trends from the past two years. These trends – social networking, mobile computing, cloud applications and big data – are not new. In fact our firm covered these extensively in 2012 and continue to advise clients on how to leverage these trends strategically, both individually and collectively. What is occurring now as we move into 2014 is the cumulative effect of these trends into force directions of their own. These so-called convergence forces – or what Gartner Group calls nexus of forces (NOF) – have a tendency to amplify and extend innovation in new and more powerful directions, much like strong winds, lunar position, and seismic disturbances can affect the behavior of ocean tides. To put it another way, you might be able to plan to fish based on high tide but planning to fish during a tsunami is, well, a bit more complicated.
One of the areas where we are seeing this play out – which I cover in both my WordPress and SCN blogs - is in the business to consumer pricing strategies of in-store retail. Location based services – either by way of opt-in applications or mobile browsing cookies – allow known customers to log-in to store applications and view special VIP promotions, to quickly locate where items may be found in the store, and to recommend products which based on sentiment analysis and buying pattern might be of interest to the customer. Location based predictive analytics can also help retailers determine the best location in a particular store to position items based on customer traffic (using big data to monitor your path via GPS as you actually browse the store or predict where you will go based on history and profile) as well as to dynamically create promotions based on your position and buying status.
Creating special offers and promotions based on an existing relationship is not new in the business to business world. Suppliers and customers alike receive special treatment and extended services and bundle pricing based on volume of sales, excellent quality, and other relationship management KPIs. In fact in the world of wealth management and retail banking, customers may find that with particularly large financial institutions the first question they are asked after pleasantries may be “what is your current relationship with us?” While this is hardly endearing to the uninformed, it does grant status to those who may, have for example, several accounts, a loan, a trust and other financial products all aggregated under the same customer portfolio with a particular financial institution.
Where things may run amok in the future is when customers (a) receive deferential pricing based on relationship without permission and (b) when a relationship is implied based on socio-demographic profiling or when facial recognition technologies are employed. Let me give two very possible scenarios. I have an account at a sports and recreation retailer and I walk into the store. As a member I have given them permission to my specific profile information (where I live, what I purchase via history, my demographics) in exchange for an annual dividend at no fee. The retailer has the ability while I am in-store to make me aware of specific items I might want and key promotions going on at that store on that day. What the retailer can do is also annotate the base price while I walk through the store. Meaning that what price I may see before logging in and what price I see after I log-in may be different. Imagine digital price tags changing and updating dynamically as I walk through the store. Now in this scenario I am going to assume that the incentive I have to purchase items is a benefit versus a cost so I assume that the store is truly giving me a deal even if they don’t.
Another scenario gets more futuristic but again the convergence forces suggest all plausibility. I walk into a store that I don’t actively have a relationship with nor have I given permission to share my profile and demographic information. However due to advances in facial recognition technology (such as what is available in Facebook and other applications commercially), the store can tap into vast image databases and make a best estimate at who I am based on my movements in the store and camera images obtained while I move throughout the store. This correlation of implied relationship and implied demographics can, under the proper scenario, suggest promotions and product recommendations not aligned to my actual relationship nor my actual demographic and in extreme cases improperly tweak dynamic pricing levels.
Convergence forces already have the attention of retail strategists, ethics experts, media tech publications and even sparked political debate. Earlier this year, U.S. Senator Charles Schumer (D-NY) suggested that analytics companies engaging in such practices without customer knowledge would be “intrusive and unsettling.” prompting the Senator to issue a statement with eight of the key location analytics companies in this space to a new code of conduct which would discourage such practices. Other industry segments have also begun to weigh in on the legitimate and ethical use of predictive analytics including higher education, which can use the technology as a early warning system for right-tracking student performance through degree programs.