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5 tips to improve personalization with machine learning

Personalization engines use machine learning to optimize content for individual customers. Here are some tips on how your business can improve personalization.

Personalization is a mission-critical feature of effective marketing, as studies show that a personalized journey leads to increased customer engagement and long-term loyalty. Netflix movie recommendations, Spotify music suggestions and special promotions on Amazon demonstrate that personalized content is not only becoming the norm but a consumer expectation.

Businesses are accomplishing this task using machine learning, which is becoming the essential go-to tool in content personalization. Evergage, Monetate, Certona, Dynamic Yield and a number of other personalization engine vendors offer this functionality and are increasingly in demand. Gartner's "Magic Quadrant for Personalization Engines" 2019 report shows that personalization engine adoption is up 28% since 2016.

To improve personalization with machine learning requires a clear view of where that personalization will make a difference in customer behavior and choosing the most effective criteria for establishing content relevance. There are points in the customer journey that are optimal for adding a personal touch, and how the differences between customers will trigger a need for specific content is often a question of context.

It's also important to distinguish between personalization and customization. The marketing system performs the former for the customer's benefit, while the latter is a result of deliberate choices made by the customer to drill down to the desired content. Personalization is predictive, so machine learning is becoming essential to the effort.

Here are five tips on how to improve personalization with machine learning:

Gartner research graphic

1. Use demographic data

Sometimes demographic data can yield distinctive behaviors and preferences in customers, and often this data is easy to come by. ZIP code, for instance, can often reveal an entire socioeconomic profile for customers -- their distance from retail locations, average income, average age, ethnic ratios, youth or college student populations and sometimes even married versus single statistics. Businesses can obtain and apply this data to train and improve the predictive model, simplifying the ultimate personalization data crunch.

2. Know your social media audience

Cross-channel personalization is a high priority because a customer's social media channel of choice will determine how friendly the customer is to mobile contact. There is also essential demographic data lurking in a customer's preferred channel, because different age and social groups prefer different social media platforms: Gen Z prefers Instagram and Snapchat, while Gen X and millennials tend to congregate on Facebook.

Beyond demographic data and the behaviors of social groups, the drill-down to the individual consumer in personalization should include machine learning of the consumer's online behaviors.

3. Follow the footprints

Beyond demographic data and the behaviors of social groups, the drill-down to the individual consumer in personalization should include machine learning of the consumer's online behaviors. A user's site navigation path can say a great deal about their preferences, and their view duration can provide essential insight into their priorities. When they bounce back and forth between pages, valuable data is generated. Machine learning can soak up all of this behavior -- especially across repeated site visits -- and deliver a profile of the customer and what matters to them.

4. Relevance at scale

It's important to understand that the resolution and precision of personalized content selection isn't an all-or-nothing proposition. Netflix and Spotify didn't start out targeting individuals; they allowed their systems to evolve to that level of targeting.

Content relevance for particular consumers can begin by isolating segments of target populations based on demographics, then microsegments based on sales data and finally individuals based on online behaviors. Businesses can effectively use machine learning at each stage of this evolution.

5. Personalize cross-channel content

Finally, success in improving personalization with machine learning includes personalizing content across all channels so customers feel personally engaged wherever they are. Product pages on business websites should be dynamic, based on an individual's preferences. Predictive advertising needs to be deployed on the consumer's social media platform of choice. And email should be fully exploited as a personalized content repository, as it is easier to load an email with optimized content than it is to dynamically render a webpage.

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