The holy grail of big data and marketing efforts, a single customer view across marketing channels, has increasingly become a priority among banks, insurers and retailers looking to maximize advertising and conversion efforts.
Perhaps the biggest burden for banks is collecting all the data across touch points that have not traditionally permitted a cohesive customer story. "It shouldn't matter if a customer is walking through the bank, using a call center, phone application or on the web, you should be talking to them the same way across channels," says Simon Burton, CEO of Celebrus Technologies. "The one way to do that with data is to align the customer journey, and through process and understanding of the individual over time. Banks are desperate to bring that together."
Celebrus, a feed provider of highly granular online data, is working with several international banks eager to align customer data over multiple channels. Their technology is providing rich audit trails of customer interactions, from the traditional clicks, IP address and query data down to the hover of a mouse and the cadence of keystrokes. Individual users are tracked from account opening, closing, and all interactions between, helping marketing analysts identify behaviors that preempt account closure or should trigger a targeted advertising campaign.
"Behavioral data has been collected since the beginning of the internet, traditionally through web logs," explains Burton. "People have been analyzing that for years, but that was used to build better websites, not to drive communication." With advances in technology and data capture firms are increasingly able to read data around customer experience and understand the ROI of banner ads, e-mail blasts and website enhancements.
In one case, with the granular data as a guide, the Dutch financial institution Achmea collaborated with universities to build algorithms that determine success rates of advertising campaigns within 24 hours with over 96% accuracy. "That saves a ton of money," says Burton. With the customers life cycle of engagement on hand, Achmea knows where customers came from, what they ultimately do with their assets, how often, and so on. "We can run that against a control group and see success or failure very clearly. Marketers can then tweak the campaign or drop it altogether."
In a case of timing relevant communications, two European insurance companies are now closing one third of new prospects by passing hot leads based on web behavior, including online quote abandonment, to a call center for followup.
"Banks need to make their marketing count. It's important to understand in finite detail where the customer comes from and how often they look at the products like credit cards and mortgages. Over time they build up a profile and the data drives e-mails or calls. The firms can start to communicate in a more meaningful way."
Real Time Decisions
To truly hold a conversation with customers banks must respond to the real-time triggers. Of course, deciding what changes might be most appropriate opens the door for endless trial and error.
Burton says banks are now embarking on a process retailers started a long time ago by running their own algorithms against data that show, for example, a loyal customer looking into a mortgage is less likely to respond to a banner advertisement than adjusted web text across the site.
In one Celebrus case study, Dutch insurance firm Agis saw a 24% increase in conversion rates over 6 months of using real-time personalized content delivery and performance testing. "After conversion, Agis can see what banner the customer saw, compare it to the life stage and draw relevant conclusion," said the firm's web analyst Freek Hertsenberg in the case study.
"Attribution really comes to the foreground," adds Burton. "You can understand as a marketer which campaigns you ran months ago are delivering value over time."
Celebrus says applications in retail banking have been numerous around churning and targeted sales. Although the technology has yet to expand into capital markets, Burton sees potential in risk detection, predictive analytics, timing trading decisions and sentiment analysis.