The 360-degree view of the customer is back, but is it for real?
If you're like me, you can't turn around these days without hearing a new pitch about the 360-degree view of the customer. Almost every vendor in the in the customer experience space is touting a new approach to attacking the customer 360 problem. From data lakes to data model enrichment to simple data integration, everyone has a new tack on how to achieve the 360 panacea.
Since the dawn of CRM, tech companies have been touting the holy grail of the 360-degree customer view. In an ideal world, we would know everything about our customers, from their purchase histories and complaints to their shoe sizes and ice cream preferences, and all that knowledge would enable us to deliver a stellar customer experience, regardless of channel.
Let's get real. The goal of a 360-degree view has been around forever, but has anyone attained it? Not really. And it's only gotten more complicated lately. Most of us are still struggling with having a complete view of customer interactions across the channels we support, let alone internal data from accounting or external transactional, social, and behavioral data. And new channels and data sources are popping up every day. It's no surprise that we're still far away from a true 360 view.
In fact, while 60 percent of companies have invested in some integration between sales, marketing, and service, fewer than one in three have extended integration efforts beyond CRM walls. One in five have made little or no integration effort at all, instead focusing on trying to just get sales, or service right on their own.
This has largely been because of the cost of not just bringing all the data together but keeping it updated and accessible in a meaningful way for service, sales, and marketing. So what makes the new pitch for customer 360 different, and what has changed that will make it successful this time? Four things have to happen.
First, any data integration or data lake effort has to have a dramatically different cost structure than previous efforts. The costs of integration and data storage have fallen significantly in the past few years; however, it's not just the cost of integration or data mapping, it's the way it's accomplished. New efforts must have scalable, reusable components and no-code or low-code capabilities so that business users (not developers or consultants) can manage the customer model on an ongoing basis and rapidly adapt it as needs evolve.
Second, it has to be intelligent. Modern customer 360 needs to leverage artificial intelligence to reconcile conflicts, enrich records, and ensure the timeliness and accuracy of data on an ongoing basis. AllSight, for example (recently acquired by Informatica), leverages machine learning algorithms to link and resolve entities at scale and learns from new inputs to better identify relationships between data and present multiple views of customer data based on business users' specific needs. Static rules-based resolution is nice, but not enough: the real application of AI is in training the model over time to continuously improve data.
Third, it has to be fast, in three ways. It must be relatively quick to deploy (look for a payback in months versus years). Once deployed, it must deliver real-time access to timely data in a usable format so users (or AI) can act on it immediately. On an ongoing basis, it must be able to rapidly adapt to incorporate new data sources and channels as they emerge.
Finally, it has to be trusted. With increasing scrutiny on how data is being gathered, used, and shared, transparency is table stakes. The leadership role in new customer 360 efforts for both solution providers and companies managing their customer data are 10 percent technology and 90 percent trust.
Rebecca Wettemann is vice president at Nucleus Research.