You’ve probably heard the hype for a few years now. But did you know that the majority of CMOs plan to add e-commerce AI and machine learning to their marketing strategy over the next three years?
Artificial intelligence is primed to truly revolutionize the retail and e-commerce space within the next 12-24 months, and merchants are beginning to experiment (and open new revenue streams) with AI, automation, intelligent algorithms, and machine learning.
Here are 5 use cases you can implement right now.
1) AI + Personalization: Individualized Incentives
Individual incentives mean that each customer receives their own, unique discount code designed for them.
Incentives should be presented to each customer based on his or her propensity to buy, affinity for certain products, and likelihood to purchase.
Automating the delivery of these kinds of specialized offers requires marketing technology–powered by AI–that can distinguish who’s who. It needs to take everything into account including previous purchase history, preferences, engagement level, and more. People don’t have the time or propensity to review all this data, but AI can.
AI-enabled incentive management is a win-win for you and your team, too. While you can expect revenue to go up from more targeted offers, you’ll also save time and resources by partnering with the machine which will handle everything besides content creation and strategy.
2) Predictive E-commerce AI for Buyers and Leads
Until now, marketers have generally pushed campaigns out, and simply hoped for the best–wondering and waiting to see what happens, and adjusting on the fly. This is reactive marketing, and it’s the way of the past. Today, marketers can predict campaign performance and adjust before launch.
AI is a tool that gives us more granular foresight into who will respond and how they’re most likely to engage… all before anything actually goes out the door. This is proactive marketing, and it’s the way of the future.
How would you change your approach if you knew who was likely to engage with an email, at what time, and with what content? How would your segmentation strategy change if you knew at what price threshold individuals would no longer be willing to make a transaction, or how steep of a discount was necessary to incentivize action? These are the kinds of predictions that are possible with artificial intelligence.
3) Next Cart Value, AOV, and Revenue Predictions
AI does more than make predictions about what customers might want. It enables truly proactive marketing by projecting how much customers are likely to spend.
Next Cart Value and AOV
AI unlocks insights that can help set expectations about how campaigns will perform on an individual level. Next cart value, for example, helps inform discounts, product recommendations, and even creative appeal that an email should include. AI can also indicate average order value (AOV) to help you understand whether customers are spending more over time.
Additionally, buyer forecasting can give an incredibly accurate depiction of how valuable a customer will be over the long haul. Across your database (active/engaged contacts), revenue projections can be made for segments, campaigns, or the business as a whole, allowing marketing teams to anticipate their contribution to the bottom line months in advance.
4) Web + Email Recommendations
Marketers have limited time and space to really jolt their audience. Engagement predictions and product recommendations are helping retailers pack a remarkably accurate punch right where it matters.
By connecting an amalgamation of disparate data points, AI can synthesize all available information about customers to understand who they really are and what they really want (even if the customer doesn’t yet know) based on their specific lifecycle stage.
Recommended items can be used in any format, and the more personalized they are, the more enticed buyers will be to act.
5) Deliver Right-Time Content Per Individual with Send Time Optimization
The expectation for hyper-personalization of today’s complex consumer needs to be met by us, by marketers, working to give them that amazing CX.
Optimizing send time is one piece of the puzzle that wasn’t so important 5 or 10 years ago. Today, it could be the difference between an email being read and followed through on, or ignored and deleted!
Same-time, mass sends to segments are actually not enough… especially when AI is capable of doing more. A Bayesian model can determine the best send time for each individual, and determine what time a message is most likely to be acted upon across all devices.
Paired with up-to-the-moment OTC (Open Time Content), the right content recommendations, and the best CRO (Conversion Rate Optimization) tactics, marketers using STO (Send Time Optimization) add a new dimension to their marketing and stand to gain a clear edge.
Final Thoughts: AI is the Answer for E-Commerce Brands
E-commerce marketing teams are embracing artificial intelligence as an enabling technology that can deliver the true personalization customers are craving.
AI-enabled martech can understand all data points about customers, and then scale the delivery of 1:1 content that marketers can’t do manually, even across hundreds of thousands of customer profiles.
At Emarsys, we offer a natively integrated CDP (customer data platform) with an AI prediction platform and channel automation capabilities as one seamless solution, out of the box. AI-based turnkey tactics are fully populated with business logic to personalize communications on a 1:1 level. Marketing teams load in content, set their strategy, and choose their segment.
The platform handles campaign execution and all of its components. With embedded AI and knowledge from 2,000+ clients and 4.2 billion contacts, the Emarsys Omnichannel Customer Engagement Platform has the ability to predict customer behavior over a timespan of 360 days.
➤ Learn more about AI adoption in our new e-book, 5 Steps to Artificial Intelligence Marketing Adoption.
About the Author
Raj Balasundaram is SVP of AI at Emarsys, where he helps leading brands leverage their digital platforms and data to out-maneuver competitors and achieve superior financial results. Raj has delivered presentations and talks at a variety of global conferences and road shows including #DMWF Expo Global, evangelizing the art of data-intelligence-based marketing. Prior to Emarsys, Raj worked at both Oracle and ExactTarget.