If the typical (and ideally continuous) customer lifecycle is modeled as a sort of wheel, then AI works as a protectant that coats that entire wheel.
Not only does it prolong the lifetime value, it also prevents burnout (churning customers), enhances campaign performance, and drives (eh hem, pun intended) success for the whole ride.
Alright, enough with the analogies. And if you’re already using AI — as 40% of marketers are, according to a DemandBase study — then you know how it systematically, automatically enhances performance across use cases you’re already working with.
Forrester just released “The Forrester Wave:™ Cross-Channel Campaign Management, Q4 2019” in which the independent evaluator provided Emarsys with the highest rating across 34 criteria, citing its industry-focused AI marketing tools and comprehensive offering for CCCM as a nudge above the rest.
AI predicts when customers are about to churn, become inactive, or intend to make a purchase, all in real time. AI can also predict segments, content, channels and timing to deliver one-to-one personalization. Let’s explore how this works.
AI Enhances Cross-Channel Communication
AI works across the whole customer journey… not in isolation, not on one channel, or only with certain segments.
A centralized, unified customer profile and personalization engine to back it up is critical to being able to execute across a multitude of use cases. Therefore, brands need an end-to-end marketing platform for AI to work best.
In this scenario, the most important thing to understand — and the mindshift needed — is that AI isn’t just “sprinkled onto” a channel here or a campaign there. The AI serves as an underlying, integral element of the code, woven into the fabric of the platform itself for application across the entire lifecycle.
“#AI works best as an underlying layer woven into the fabric of a #marketingautomation platform” says @mjbecker_ CLICK TO TWEET
A customer dictates the pace at which they move through their lifecycle, and they always will. The beautiful thing is that AI works across all channels whenever and wherever an interaction takes place. AI also predicts when, where, what, and how much — here’s a few resources about the predictive capabilities of AI:
- Using AI to go from Reactive to Proactive Marketing: Use Cases & The Future – Raj Balasundaram [Podcast]
- How E-commerce Companies are Predicting Customer Behavior Like Never Before
- From Reactive to Proactive: Unlocking New Dimensions of Marketing with AI
Benefits to this approach are important to understand because it marks a new paradigm for marketing teams and a new dynamic which allows for more creative freedom, time, and energy.
While AI handles the complexities of the customer journey, you can focus on collaboration.
Data Powers AI Algorithms for Better Customer Experiences
Without rich datasets, AI algorithms are virtually powerless. The neural network and underlying code may be wonderful, but without gasoline, nothing will happen. That’s why data is actually the most important piece of any AI project.
Good data and good algorithms together help companies scale one-to-one interactions across every channel, no matter when or where customers interact. Specific AI use cases for e-commerce include:
- Buyer Predictions
- Lead Predictions
- Revenue Predictions
- Send Time Optimization (STO) and Open Time Content (OTC)
- Replenishable Products
- Banner Personalization
- Product Affinity / Recommendations
- Incentives / Incentive Usage Prediction
- Email Engagement
- Web Engagement
- Mobile Engagement
- In-Store Engagement
- Channel Priority Management
Specific use cases for #AI include incentive usage prediction, channel priority management, buyer predictions, & revenue prediction CLICK TO TWEET
Companies using first-party data to power these use cases are driving real results with AI.
“By leveraging AI and automation against its rich vertical first-party data… brands like ours can generate greater insights into consumer behaviors, create consistent, personalized journeys and deliver them across all of our consumers’ different touchpoints – whether online or offline, within one single platform. Our partnership [with Emarsys] has not only transformed how we communicate with our customers, but also allowed our marketing team to spend more time creating better engagement for consumers wherever and whenever they engage with our brand.”
Given quality data, AI can decipher user intent, understand when contacts are browsing or buying on your website, and make inferences about what individual consumers need to keep them from churning.
If you’re just getting started using AI, begin on small projects with the biggest impact. Choose the best method of deployment (in-house, outsource, vendor-based or hybrid) for you. Always deploy on ROI-based projects, and test with historical data before deploying. Lastly, make sure you always do control vs. treatment statistical testing to optimize results.
Using AI to predict, scale, and automate contextual customer experiences across platforms and devices, marketers can open new revenue streams and unlock new dimensions of their marketing. We believe that AI is the key to not only compete, but to succeed, in the next age of marketing.
➤ Learn more about how AI enables predictive marketing in our latest white paper.
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