Much of our discussion of digital transformation is focused on impacting consumers’ experiences by anticipating their needs and delivering offers, service and communications that are “relevant.”
For data scientists, the challenge of relevance is to design, construct, and continuously improve predictive models. Predictive models take into account what large numbers of consumers have done in the past and project what people who share those traits or behaviors are most likely to do in the future. Predictive modeling is mathematically sound and is a surefire method of delivering meaningful incremental changes in campaign performance (near-term) and customer satisfaction (longer-term), when all points of contact can be optimized around a predictable vision of their needs.
We’re working with a very large global retailer who recently implemented a membership program built on the premise of “customized offers.” The idea is that big data and data sciences are able to consistently deliver a set of offer choices that each consumer can embrace as “just what I want.”
The magnitude of that proposition keeps us challenging ourselves on exactly what does customized or relevant offers really mean to a consumer. For example, when the measure of relevance is actually scored, to what extent do consumers view their ability to proactively shape their experiences, offers, product choices based on their own desires, and digital tools that empower that shared dialog?
A recent study by Deloitte points out that only one in five consumer is “happy for businesses to use their personal information to offer them more personalized products or services.” Instead, Deloitte confirms that consumers empowered by social networks and their digital devices are increasingly “dictating what they want, when and where they want it … becom(ing) both critics and creators.”
Consumer-driven relevance can take on many forms, such as providing preference centers that allow consumers to set the frequency and nature of email and other digital communications. Too often, consumers are not given that option until they’ve hit an unsubscribe page and are given a softer exit option. The next step is to extend preference to products and offer types (e.g. discounts, early access, exclusives, BOGOs). Marketers have to be willing to put aside—or at least balance—their merchant goals with consumers’ explicit choices. If not, the bond of trust will be broken and the effort to offer control will have backfired.
Finally, consumers are looking for a way to iteratively use their engagement experiences to fine tune their relevance. Every email clicked (or ignored), every offer converted, every product “liked” provides a continuous flow of personalized data that augments predictive models and refines them from “people who generally resemble me” to “me.”
Self-customization can deliver the highest degree of perceived relevance. In addition to actually improving the predictive accuracy of statistically driven engagement, it provides a means for consumers to play a proactive role in their relationships with brands and further solidifies their loyalty. The keys are to provide the means of input, make it simple and transparent to do so, and acting upon consumer provided input in a noticeable and meaningful way.
Missed the first of this blog series? Check out CEM Top Ten #1: Customer Experience Dominates Digital Transformation. Next time, we will dive more deeply into the power and impact of predictive analytics. In the meantime, attend our Destination: Digital series of roundtable webinars to explore the business and technology requirements, best practices, and fast-track steps for transforming to Digital Business. Learn more and register here. Looking to begin your journey to digital transformation? Register to attend TIBCO NOW 2016, May 16-19, in Las Vegas.