We’ve all experienced the power of uniquely tailored, relevant content and recommendations. Top-tier platforms such as Netflix, Amazon and Spotify utilize powerful content and product recommendation engines to create the kind of seamless, delightful user experience that keeps customers coming back again and again.
Most of us, however, don’t have access to tools as sophisticated as those of Amazon, Netflix or Spotify. Yet, we still want to provide our customers with useful, relevant, actionable information that simplifies their purchase decisions. But exactly WHAT that information is constantly changes throughout the customer journey. This makes selecting the content, products, and offers which will “break through” a very difficult and fast-moving target. Multiply that by your number of customers and potential touchpoints, and you’ll see that choosing content for each customer interaction quickly becomes next to impossible.
Of course, there are tools to help personalize content. Recommendation systems and content optimizers have existed for years now, but they are typically based on manually entered rules and inflexible templates. They are also driven largely by marketers’ hypotheses about what would resonate with their customers rather than what customers’ behaviors, interests and lifecycle stages reveal. Consequently, they don’t keep pace with today’s rapidly changing consumers.
And as we all know, irrelevant content is the #1 reason consumers disengage with brands. Can you really afford to base your revenue on guesswork?
That’s where AI is here to help.
AI helps marketers work smarter, faster, and more intuitively as they engage customers along their customer journey. It does so by optimizing WHO marketers should be targeting, selecting WHAT content they engage with, WHEN to engage with them, and WHERE is the best channel. This “AI Marketing in Action” series will explore AI’s impact on these 4 Levers of cross-channel marketing and quantify its impact on each lever. Our findings are based on a recent benchmark study that analyzed 3.8B marketing interactions from campaigns across various channels and verticals.
Our last post explained how AI helps you determine WHO are the best customers to target at any moment for each of your customer strategies. Now, we’ll explore how AI helps determine WHAT those customers see.
AI-POWERED PREDICTIVE RECOMMENDATIONS
Predictive recommendations surface the most relevant content—be it an offer, a product, an article—for each individual customer at a given moment. It ensures every message you deliver is unique and personalized to what each customer seeks at that stage of their journey with your brand.
It does so by analyzing—in real-time—a complete view of user profile, interest and behavior data in relation to your brand content. It then continuously tests itself, self-learning with every customer interaction and optimizing content for revenue-generating actions.
How much work is this for me? With Predictive Recommendations, you simply guide each message’s content by setting basic parameters and defining whether to base content on user actions, their affinities, other customers’ purchases and browsing behaviors, or relevant trending items.
The recommendation engine picks up from there, pulling together all user behavior event-streams from sites, apps and other sources as well as historic CRM data and your brand’s content and product information. AI then continuously analyzes the dynamic relationship between your users’ product interactions, historic brand engagement, and latest customer activity across multiple channels. As each campaign runs, AI selects the most relevant content for each customer based on their current context, crafting individualized messages for each of them without your having to lift a finger.
SHOW ME THE FACTS
Having an Amazon, Netflix, or Spotify-grade predictive recommendation engine to optimize your content is a real bonus, but let’s not forget where the rubber hits the road: ROI. And the numbers don’t lie: Our recent benchmark study found that Predictive Recommendations drive a 2.5X – 5.X lift in engagement. “
Predictive Recommendations also drive nearly 3X revenue relative to their use in the marketing mix.
THE BOTTOM LINE
With Predictive Recommendations, marketers can listen to customers’ needs, reply with the relevant, actionable content customers seek, and stop wasting critical opportunities to connect. More importantly, making your customer communications truly 1:1 not only improves your relationship with customers but also drives incremental ROI.
For the full set of findings, as well as real examples of marketers who have used AI to drive revenue by making better, quicker decisions about the “Who, What, When & Where” of cross-channel marketing, download The ROI of AI in Marketing: 4 Levers for Cross-Channel Success.
In upcoming blog posts we’ll explore AI’s impact the remaining two levers of marketing:
- “The When” with Predictive Engage Time: Optimize the delivery of the campaigns to the times when each individual customer is most likely to engage
- “The Where” with Predictive Channel-of-Choice: Deliver the campaign on each individual customer’s channel-of-choice