Customer Analytics: The New Source of Value Creation
Remember the time you bought your first iPod? Was the decision influenced by a TV ad? Or did someone you trust, perhaps a technology enthusiast, gave you unsolicited advice and perhaps a quick demo? Did the same happen with a Kindle when it first came out? How fast was the progression from awareness to consideration to purchase to loyalty?
What happened to most of us was that we became a part of an authentic and powerful brand advocacy cycle. That's something that's been around for ages for sure, but I would argue that social networking has accelerated its pace and impact. It should come as no surprise that peer advice is now the primary driver of a consumer purchase decision more than 85% of the time.
This means that companies will need to focus on customer experience more than before. Marketing will go through a fundamental transformation where they'll spend more energy on delivering the brand promise vs. thinking of creative ways to communicate it. The successful CMOs will be the ones who will drive change across the enterprise to ensure that every touch point is building brand equity instead of diluting it.
Companies will also need to start paying attention to a whole new set of metrics in this new world. They need to understand who their brand advocates and detractors are, and what shapes their attitudes and drives their behavior. They need to keep track of near-advocates, and what type of interactions will turn them into advocates.
Similarly they need to keep a close eye on near-detractors, and what type of actions can reduce the probability of them turning into active and vocal detractors. Again, the concept of Net Promoter Score (NPS) isn't new. It's just that social networking has accelerated the pace and impact of it.
The good news is that algorithms can help companies understand and predict customer behavior, and improve the NPS. This type of customer analytics builds propensity models for each customer to determine the next best action they can take -- at every touch point -- such as call centers, web sites, branch offices and retail stores.
A telco company used this approach to turn 23% of its detractors into promoters. A financial services company used this approach to turn its call center into a revenue producing channel, which generated €30 million in 12 months. These examples demonstrate that the outcomes driven through the adoption of customer analytics are significant and are starting to go mainstream.
In my next blog, I'll talk about "how." How, and with what types, can we use algorithms to build propensity models? How can we use and find certain types of data to analyze? How can sentiment analytics and social networking analytics help extract insights from peta-bytes of unstructured data? How can some of these analytics be run in real time, and when does it make sense to do so? Finally, how can we take actions that are based on individual propensities without eroding customer trust, which in this brave new world will be a more important currency than ever before?