Mobile app, web, brick and mortar, WhatsApp, social media, beacons, smart kiosks, email… The litany of channels you must keep within sight at any moment seems to be stretching into infinity as we welcome another year—so does the data and communications that come with all those touchpoints. Anything that can alleviate the sheer enormity of the task at hand will be a welcome prospect, and artificial intelligence has been incorporated into a variety of solutions to offer precisely that relief.

The Difference AI Can Make

To be precise, AI, in the context of customer engagement solutions, analyzes massive streams of data at the brand’s disposal to generate actionable insights on audiences and drive responses that can maximize growth. It allows marketers to pull off that much-heralded trick—providing the right customer with the right experience, at exactly the right time, channel, and lifecycle stage.

AI delivers personalization done right and at a considerable scale, with a fraction of the time and resources usually required of such efforts. That’s a tantalizing proposition, but what does it actually entail?

1. AI enables marketers to surpass superficial metrics on reach, engagement, and conversion, giving them access to actionable, accurate insights on audience behavioral patterns, propensities, and other traits.

2. AI-driven segmentation goes beyond demographic and transaction-based targeting. It can distill data on individual customers’ preferences in terms of time, channel, offer, and more, all while automatically deriving data from engagement. This allows marketers to be far more proactive and strategic in their customer engagement efforts in real-time.

3. AI, embedded in a robust data-driven customer engagement solution, makes it far easier to achieve, at scale, the identification, creation, and enrichment of singular customer identities—indispensable to the delivery of seamless, consistently contextualized customer journeys across channels and over time.

The DelphAI engine powers features throughout RESUL to enables a wide range of use cases designed to streamline customer engagement processes, proffer more valuable metrics, and optimize performance outcomes.

• Auto segmentation: RESUL uses cluster analysis—identifying common attributes in audience data—to suggest the right campaigns for a given target segment and even discover hidden new segments from data consolidated by the solution’s CDP.
• Lookalike targeting: RESUL scours data it has unified for additional contacts with characteristics similar to those of the target segment that has been selected for lookalike targeting. This allows the marketer to enjoy a greater reach and thus potential acquisition.
• Personalization in real-time
• Audience propensity analysis: RESUL’s propensity models analyze audience behavior to find individual customer most likely to respond, engage, and/or convert in terms of time, offer, channel, and content. It also calculates the likelihood of a customer to churn to trigger timely interventions.
• Automatic cross and up-selling recommendations: RESUL examines a customer’s past purchases to determine what products are most likely to be cross and/or u-sold to them. It also identifies products with the greatest chances of being purchased together by this customer.
• AI-powered customer journey generation: A main communication method in RESUL, this feature allows marketers to simply configure the basic parameters of a customer journey, after which the solution’s AI engine will automatically create a customer journey with the best chance to succeed.

Want to learn how AI can put you ahead of the competition beyond the buzzwords and headlines?