How the element of surprise affects customer analytics

Director, IBM Analytics Strategy & Initiatives, IBM

What do the ancient city of Troy circa 1184 BC, the Hessian mercenaries garrisoned at Trenton, New Jersey in 1776 and the allied forces in Austerlitz in 1805 have in common? They were all overcome by the element of surprise. As demonstrated by these famous historic incidents, the element of surprise can be either very rewarding or terribly catastrophic.

The same is true for ultra-personal marketing outreach to clients. The element of surprise in the form of a custom ad or email can elicit delight, or maybe just plain creepiness. And being comfortable with digital tracking and personalization isn't even a matter of generational preferences: a study of 1,016 consumers conducted by RichRelevance shows that millennials are aligned with their older counterparts when it comes to in-store personalization, even if they are more comfortable with advanced capabilities such as dynamic pricing or face recognition.

Cool or creepy?

Responding too quickly to a critical and contextual piece of information (such as posting ads on the right side of the screen that are linked to what the user is currently typing) could be perceived as intrusive and can lead to trust issues. However, sharing relevant updates around product orders, for example, is much appreciated by the consumer. Alerting customers that their order will be shipped the next day at no charge instead of the five business days they paid for is a pretty nice surprise. This is the difference between “How could they know that about me?” and “They really care about my business!” This line is very personal and highly contextual (see Figure 1).

Figure 1. The fine line and the four stages of time responses (based on Gartner research)

Be relevant and transparent

Striving to remain on the right side of the surprise line requires engaging a positive loop from relevance to trust through transparency. Being relevant demands a set of pertinent and clean data along with solid analytics capabilities and a keen sense of the business at hand. 

Relevancy leads to trust, pressing users to recognize the efforts to understand their cares and desires in context. Trust leads to better relationships that likely generate more positive data, which leads to more relevancy. Securing trust also means reinforcing this positive loop with consistent transparency—letting users know how recommendations were made and from what type of data (see Figure 2 below).  

That transparency is also critical to open the dialog with customers where businesses can learn more about how their customers tolerate personalization. This knowledge is then used to enhance the granularity of preference controls, all through predictive analytics techniques.

Figure 2: Transparency leads to trusting relationships


Know your customer

Customer analytics is at the core of the positive side of the element of surprise. Organizations that consistently find themselves surprised by their customers should seek new data solutions to lessen unpredictability. Reducing uncertainty while maximizing relevance indicates a data-driven understanding of customer base and puts the element of surprise back into the business' hands.