Predictive hyper-personalisation is the ability to predict customer behaviour, needs, or wants and then precisely tailor offers, products, and messages to each recipient across channels and touchpoints. 

These messages differ greatly from traditional manual segmentation and personalisation tactics as they are based on insights revealed through automated data-driven algorithms, not just past behaviour, and are continually optimized through machine learning and artificial intelligence.

While traditional personalisation tactics will boost conversion rates by 10 % on average, the average lift (according to Forrester) for predictive AI, and machine learning hyper-personalisation revenue is 22% and increases CTR by a whopping 83%. Some solutions have considerably higher returns. Here are proven predictive personalisation methods to implement that will drive a significant increase in engagement and revenue.

Predictive hyper-personalisation of email marketing

Called “the future of marketing” by Forrester, predictive hyper-personalisation software identifies the product most likely to be purchased next by each consumer, and sends details of that individual’s unique selection by email.

It provides customers with highly precise, contextually relevant products at machine-learning speed and scale. Email hyper-personalisation is acknowledged to achieve the highest of all marketing returns – Forrester, Forbes, Statista etc. As your customer base evolves, predictive models automatically learn and provide the best assessment of what future product purchases each customer will take. 

• Quickly build your highest-performing customer analysis, uncovering hidden opportunities by identifying subscribers who may have been left out of previous segments that used traditional activity filters
• Segment based on predicted lifetime value, likelihood to purchase, brand or category affinities, discount affinities, and much more
• Acquire new customers using lookalike audiences based on predicted customer lifetime values and affinities
• Save customers who are at risk of churning using enhanced, automated post-purchase and loyalty campaigns

Hyper-personalised Product Recommendations

It quickly leads customers to merchandise they love the most – and they’re most likely to purchase. Advanced machine learning algorithms provide the ability to layer deeply customisable merchandising decisioning, predicting the most relevant, data-driven, and personalised products to influence the unique customer journey toward the path to purchase.

• Leverage anonymous browse data to personalise recommendations
• Show products that haven’t been purchased or previously suggested with intelligent recommendations
• Open-time optimisation ensures you never recommend an out-of-stock item
• Use across multiple campaigns and channels: email, on-site, display ads

Predictive Send Time Optimisation

Using machine learning, send time optimisation allows a sender to make sure every customer receives an email at the time they are most likely to engage. Predictive algorithms determine the right time to deploy messages based on previous email engagements and measure the type of interaction, including opens or clicks, mobile or desktop, whether or not a purchase was made, etc. Knowing when a customer is most likely to buy will allow hyper-personalisation solutions to automatically deploy that individual’s selection of products, delivered as an optimum times email message.

Hyper-personalised Content Recommendations

Strengthen readership and improve engagement through context-aware content recommendations. This is a game-changer that enhances customer experience by identifying and delivering the exact content that will make the biggest impact while helping customers remain engaged and active during the sales journey.

• By applying natural language processing, artificial intelligence, and machine learning algorithms to your website content, Predictive Content automatically interprets and understands the context of your content

• Dynamically adapt and personalise your recommendations to each user’s preferences, brand or category affinity, and real-time intent

• Automatically discover new content • Maximize your content investment

• Use machine learning and probabilistic modelling to determine the best time, personalized to each subscriber, for optimal engagement activity

• Could also base deployment on subscriber’s time zones for specific, time-sensitive messages, such as flash sales

• Replenishment Campaigns are also driven by predictive delivery algorithms to determine the re-purchase cadence of individual products across your customer base.

AI-Powered Customer Journey Automation

Marketers have been challenged with understanding the connected customer journey across all channels and touchpoints and how to use that information to maximise interactions on a personal level.

As CRM platforms continue to break down the data silos and put more insights directly into marketers’ hands, the entire customer journey has shifted. For instance, the welcome message is no longer the first step of the sales cycle as anonymous customer data captured during visits before the opt-in can be used to create a fuller, more accurate picture.

Other improvements include the ability to accurately capture and use data across multiple devices, channels, and touchpoints. You’re no longer limited to past page views, email metrics, or purchase data to inform your campaigns. You can now use data collected from apps, mobile engagement, social sites, stores, customer service centres, display ads, and any other data point. This complexity is the reason why AI can unleash so much value across

• Observe, collect, and synthesise data from disparate data sources and at every touchpoint across the customer journey to capture critical customer signals, link them to a single, unified 360° customer profile and create highly actionable customer data
• Uncover new business opportunities by identifying trends and relationships that were previously unnoticed
• Deliver highly-personal retargeting campaigns across multiple channels, or specifically in the customer’s preferred channel
• Deploy win back and loyalty campaigns designed to recapture latent intent before a customer churn