As the ongoing growth debate between customer retention and acquisition continues, one thing is for certain: managing the entire “customer lifecycle” is arguably the most important aspect of marketing today. Analyzing the customer lifecycle allows you to better prepare your marketing, sales, and customer service teams to turn one-time purchasers into loyal promoters. Improving customer experiences is at the top of the agenda for most CMOs but the same can’t be said of their agency counterparts, as measured by Bombora Company Surge. Agencies appear to be significantly less focused than brands on the customer lifecycle.
Instead, intent data show agencies are using their time to read up on topics like “customer loyalty.” Arguably a brand’s most valuable asset, customer loyalty implies the mastery of customer behavior strategies to earn loyalty and lifetime value. It goes beyond CX to deliver truly personalized interactions and deliver on unmet human needs. It’s critical, however, to consider the entire customer lifecycle and experience ecosystem, from the research stage through to post conversion marketing, all while making good on promises made.
Finally, “cognitive computing” showed a strong rise in interest by brand marketers last week. A subfield of artificial intelligence, cognitive computing is the simulation of human thought processes in a computerized model. With new platforms, systems, and technologies emitting customer signals everywhere, it’s become more important than ever for marketers to engage with potential and existing buyers at the right time, with the right message, with consistency, and at scale. AI and machine learning have evolved to where it is now possible for most companies to implement AI as a tool, because it enables personalized services on a massive scale — and customers increasingly demand it. The past 15 months have been difficult for many businesses. Even those on the leading edge had to adopt new technologies to meet evolving demands. Fortunately, businesses can use cognitive computing for myriad tasks, such as augmented analytics, the identification of trends and patterns in order to deliver actionable recommendations, natural language processing, real time risk assessment, and data mining.