6 ways machine learning can boost your marketing processes

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Everyone is rushing to deploy machine learning (ML) in their marketing processes in hopes that it will bring unprecedented power to outshine the competition. Marketing, after all, is highly dependent on data and communications, and it's changing so rapidly that many programs are obsolete by the time they're ready to deploy.

ML increases both the speed and flexibility of many marketing processes, but it is not a one-size-fits-all solution. Some functions benefit powerfully from a good dose of ML; others only marginally. To get the most out of any ML investment, it helps to know which is which and how the different types of analysis apply to a given situation.

For most marketing applications, data analysts typically use three basic approaches:

Descriptive - applied to past event data Predictive — used for forecasting and planning; Prescriptive - used to determine optimal courses of action.

Of the three, predictive and prescriptive are the most commonly used to create ML algorithms, while descriptive analytics is mostly applied to reports and dashboards. Depending on the size of data streams and overall data accumulation, some companies can spend up to two years accumulating data to properly analyze consumer behavior and personalize customer relationships.

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Even then, ML must be applied strategically in any marketing process, and experience has shown that it provides the greatest benefit to six key functions.

Product recommendation

When integrated with a prescription analytics and personalization model, product recommendations aim to increase conversion rates, average order value, and other key metrics. Experience has shown that when targeted offers are made using data from previous experiences, revenue can increase by 25% due to the increased relevance of the product or service to consumer needs.

Going further, organizations can use collaborative filtering and other tools to identify similarities between users, and this data can be used to provide relevant product recommendations across multiple digital properties. ML, combined with a unified customer profile, can take into account online and offline customer preferences, including products purchased and product interactions such as wishlists and views. This can then be used to create recommendations without having to rely on specific user histories. This way, marketers can make instant recommendations to new users even before their profiles are established. Organizations can also use collaborative filtering to predict user preferences based on socio-demographic variables, such as age, location, and preferences.

Churn rate prediction

While most churn models work very well without ML, a dose of intelligence goes a long way in honing the ability to leverage reliable customer insights, which can then be used to build customer retention and marketing strategies, such as attrition rates and proposed schedules. However, to do this effectively, the ML model requires access to some very specific predictive data, such as recent purchase history or average order value. With this in hand, the model is able to analyze and rank customers based on their p...