Tomorrow is usually pretty much the same as yesterday. That is not how Andrew MacMillan sees 2016.
“There has been a huge shift in organizations,” MacMillan said. “They are focusing on renewal and customer lifecycles.” He observes that businesses offering goods and services on a subscription model are changing their outlook on customer relations. “The model dictates that the customer must be successful.” he said. All sales are not final, as the merchant must “find and keep that dollar for a really long time.” This is not done by repeat sales, so much as engagement, MacMillan pointed out. How instrumental is the product to the customer? Does the vendor understand how the customer uses the product?
Act-On’s 2016 road map calls for changes in its architecture to include multiple sources of signal data, multiple scoring sheets to produce more sophisticated sales leads, and some way to combine that data through algorithms to yield a greater degree of customer engagement. This is where the combination of CRM and marketing automation makes itself felt. Act-On already scores a potential customer’s interactions with a variety of channels, be web site visits, webinar attendance trade show attendance and such. Marketers use that information to develop and sharpen sales leads, and hopefully convert them into sales
It is at this point where “predicting the future” comes in. Act-On’s 2016 strategy is to sharpen this data further to look for what is “indicative” or “predictive” in the data, MacMillan said. That means acquiring data from different sources, scoring that data differently to produce insight, and running it through a different algorithm to bring about that customer engagement, he explained.
This is now entering the realm of big data, which sets up another challenge: making complex tool sets easier to use, so that a marketing executive can operate it. Right now, data analysts are harder to find, and experts can spend a lot of time gathering data from many spread-out sources and consolidating it into one place, where it can be worked on, MacMillan said.
Algorithms can sift that data to produce more correlations. It is not enough to know that a customer bought one thing. An algorithm can look at that customer’s sales history, knowing how many times the firm closed a sale with that person, knowing the behavior that led to that closing, and even checking e-mail and social media for clues, MacMillan explained. In the end, a company can know every step a customer took to find out more about a product or service, from first query to final sale, and from there repeat business.
