Deeper Relationships Require Intelligence

If you track all of the emails that your marketing and sales reps send, all of the phone calls they make, and all of the meetings they schedule, you might think you have everything you need to get a complete picture of the relationships they have with customers and potential customers.

Before you get too complacent, though, this is just a part of the information you need in today’s business environment. You also need to discover the relationships your reps have yet to develop, the risks associated with deals that might already exist in your pipeline, and the strength of the relationships they have. This is more insight than basic activity tracking can provide.

Relationship intelligence, a new type of technology that combines data collection, artificial intelligence, and analytics, will allow you to connect all the dots to ultimately make more informed decisions and to provide the appropriate coaching where necessary.

Experts agree that Big Data, culled from a variety of different and sometimes unconventional sources, is the prime element in building relationship intelligence.

“Organizations are increasingly investing in a data lake approach that aggregates incredibly large volumes of transaction data, customer information, market intelligence, and activity signals that, when taken together, paint a highly detailed and actionable picture of the state of buyers,” says Geoff Webb, vice president of product marketing at PROS, a digital commerce solutions provider.

“Big Data and cloud services are the usual methods for delivering the foundational elements, but the advent of cloud-based AI has finally enabled businesses to unlock the potential of the data they collect,” he adds. “Machine learning-based models, tuned and optimized by AI engines, can now evaluate and look for underlying patterns and trends in massive quantities of data, and they can do it fast enough to keep pace with the speed of digital commerce. Not all brands have the access to all of the data that they need.”

To say that the amount of data companies have today is massive might be a huge understatement. There can be as many as 10,000 data events providing relationship intelligence tied to every purchase decision that a consumer makes, according to Lewis Gersh, founder and CEO of PebblePost, a marketing platform provider.

Ninety percent of these data events can be found online via Google Analytics and other means, but there are important pieces of information that are available from other sources, Gersh says. A division of Mastercard, for example, sells card purchasing data that includes demographic information.

While much of the initial research is conducted online—increasingly via mobile devices—and e-commerce continues to grow, 90 percent of actual purchases are still made at physical locations, according to Gersh; so data from these locations can be helpful as well.

Companies can augment this data from various resources with automatically triggered customer surveys, adds Will Wilson, CEO of Bloom Intelligence, a marketing and customer intelligence provider.

But for it to provide any real value, all of this disparate data needs to be centralized, according to Webb. “There’s a lot of options for technology stacks out there. Some are aimed squarely at B2C, some are B2B, and some are capable of extracting insight and intelligence for any kind of business. And while a lot of this technology has been deployed so far with marketing organizations, more and more of the customer and market insight tech is being delivered to sales teams, too.”

By enabling all parts of the organization to have a common perspective and view of customers and their behavior, they have greater alignment and can better focus their resources toward delivering the common goal of a great customer experience, according to Webb.

Likewise, machine learning is enabling companies to measure customer behavior, churn rate, and similar data to identify triggers for when to communicate with customers and what types of communications to send them, Wilson says.

Data and analytics can further segment company databases to guide the timing and type of offers. Wilson recommends using this information to build customer personas, such as loyal customers, ones who are ready to churn, ones who will respond to certain types of offers, etc.