The evolution of the omnichannel experience

Real-time engagement

The temporal aspect of omnichannel communication with customers is exemplified by low-latent responses. There are several dimensions of such interactions. According to Tara DeZao, Pega’s director of product marketing for adtech and martech, “Customers today expect brands to engage them in whatever channel they choose at the moment to have data activated across the martech stack and have it be real-time.” Implicit to this paradigm (and to omnichannel in general) is the cloud. Customer resource management (CRM) and other sources, some of which can include streaming data applications, rely on cloud technology for real-time engagement.

“With the cloud, you can have everything in one central place and pull from it,” Ojala stipulated. “You can integrate with many different platforms and have all the data from all these channels. Now, customers can have a 360 view of what’s working, where, and why.”

The capability to integrate data from a variety of sources (and channels) is integral to responding to customer data events with negligible latency. In some cases, those events could involve geolocation data. Almost all instances, however, entail “delivering a real-time trigger as a result of the behavior in the channel you’re collecting and responding in the context where the consumer prefers to be [to experience your brand] and has those expectations,” Bolduc commented. Predictive or prescriptive analytics involving cognitive computing can decrease response times.

“As soon as a transaction is made, or terms and conditions are agreed to, or when you’ve updated your contact information, AI can take that info and make relevant updates and push data to the next place on the customer journey it needs to go,” DeZao mentioned. For example, a cellphone provider might detect that customers are looking at their terms and conditions on its website as a potential indicator of churn—which might be alleviated via a timely offer for a free upgrade or lower payments.

Comprehensive customer views

With the imperative for real-time responses, a widening array of customer channels, and interactions inside and outside the purchasing cycle, the data management demands for omnichannel experiences may seem daunting. Nonetheless, there are numerous solutions that are designed to address these very needs within the cloud framework, Ojala said. Nearly all of them incorporate integrations with different sources through APIs or other approaches. These are what the most pertinent ones entail:

♦ Customer data platforms (CDPs): CDPs centralize data about customers from numerous sources, which might include CRM, applications, and cloud data stores. They’re imbued with capabilities for data quality and basic analytics. This tool “is bringing together all these different signals across channels and enterprise systems and understanding who the customer is, in real enough time to do something about it, and providing that cleansed, accurate, up-todate view of customers to systems that care about it,” Zisk explained.

♦ Multichannel marketing hubs and digital asset management (DAM): These tools provide centralized places to curate information about consumers, products, and content. Organizations can use them to compile comprehensive views of customers and content that can be propelled through different channels. With DAM capabilities, “people can make content searchable,” Ojala said. “They can ensure the metadata and tagging is up-to-date so people can find what they’re looking for and get good content.” Compared to CDP, multichannel marketing hubs focus more on the frontend customer experience and less on the back-end data management requisites.