Understanding the Best Machine Learning Models to Use for Marketing
Marketers face a growing challenge in building meaningful, one-on-one relationships with millions of customers across dozens of complex touchpoints. And we know that customers expect brands to know them on a more personal level even from their first interaction. So, instead of sending countless batch-and-blast email campaigns and hoping something sticks, marketers need to engage customers with messaging that makes them feel recognized and valued.
The pressing need to reap value from enormous collections of customer data becomes increasingly challenging and time consuming as businesses scale, and this data scatters in silos throughout many technologies and systems. The sheer volume of this data is impossible for humans to make sense of, which is why many companies are investing in machine learning (ML).
Machine learning uses algorithms and data models to make more accurate predictions about customer behavior based on a series of patterns and previous interactions. By breaking down large audiences into specific segments and demographics and applying machine learning models to reveal specific customer preferences and attitudes, marketers can better align their actions and campaigns to generate meaningful results and optimize each customer interaction to drive future sales and brand loyalty.
Sounds like machine learning is a magic wand for marketers but how does it look in practice?
Why choose a CDP with a machine learning framework?
Previously, it was hard for marketers to access the technology and data needed to practice predictive analytics techniques. To collect, analyze, and process the enormous quantity of customer data coming in from multiple channels and regions, organizations often turned to third-party providers. These external data warehouses made it more difficult (and often more expensive) to access customer insights quickly and deploy them as relevant customer experiences.
Then customer data platforms (CDPs) came about. By collecting and unifying data across channels and systems, this enterprise software acts as a single source of truth for customer data. And, in a CDP, like Acquia's, predictive analytics and machine learning technology is available to everyday marketers through a machine learning framework that allows them to apply predictive models to their data and anticipate the next best action to take.
All models in Acquia’s machine learning framework leverage the company's unified, cleansed, de-duped data in a single profile standardized and stitched together from any data source. The CDP ingests customer data flowing across all other systems (like a CRM, ESP, POS, the website, and so on) and brings it together in a unified customer profile.
Let’s look at potential use cases for how these different models can be applied to marketing to provide better customer insights and improved results.
One of the most familiar purposes of predictive analytics is to anticipate a customer’s future behavior. By observing behaviors over time, brands can more accurately judge lifetime customer value and create more relevant experiences. Here are just a few examples of the types of prediction models the CDP can create:
- Likelihood to engage: ML can predict the odds a customer will open an email or subscribe to a newsletter, informing which segments to send certain offers to.
- Likelihood to buy: This model predicts if a customer is at the point in their journey to make a purchase. This information is valuable when knowing which users to nurture more and which to offer time-sensitive discounts.
- Likelihood to churn: This model identifies at-risk customers so an organization can be prepared for a loss of income or focus on a win-back campaign.
The Persona family comprises a series of clustering models. Clustering is an unsupervised learning technique where the machine learning algorithms create customer segmentations based on many different variables. Unlike marketer-made segments, machine learning models can take into account many more customer dimensions. A few types of useful clustering models for marketing include:
- Product Clusters: Knowing which types of products certain segments regularly purchase helps improve targeted campaign efforts.
- Behavioral Clusters: These clusters reveal things like preferred channel, average spend and average time spent browsing vs. buying so marketers can better anticipate how, when and where to engage.
- Seasonal Clusters: Many retail brands use seasonal segmentation and data to detect patterns of when customer demand is high for certain products and inform when they start “summer sales” or begin heavily discounting season-specific items like overcoats and snow boots.
Today’s customers are constantly inundated with new marketing messages from brands, and the influx of new digital touchpoints means that the stakes to create memorable connections and earn customer loyalty are higher than ever. Personalization empowers marketers to create a holistic marketing strategy that covers the who, what, where, when and why of customer experience. Here are some examples of how personalization models can be used to reach the right audience with the right content on the right channel at the right time:
- Next best product recommendations -the “what” a customer wants to buy)
- Send time optimization for email campaigns - the “when” customers want to receive messages
- Next best channel - the “where” customers prefer to interact with brands
Some well-known examples of this are Spotify’s personalized “Discover” playlists or Amazon’s “other products you may like” feature. By tailoring an experience to each customer’s preferences, marketers are able to boost up-sells and cross-sells and keep people within their brand ecosystem longer. And as a bonus, the more actions the customer performs on your site or products they purchase, the more customer data is given to the algorithm to continue personalizing their next best experience.
The benefits of a machine learning framework
There’s a growing interest in adopting customer data platforms across all industries as the number of customer interaction points and the amount of data coming in continues to rise. “CDPs offer a very efficient solution to leverage the massive amount of unstructured data brands have accumulated over the past few years. This data, which comprises both known and unknown customer information, has been increasingly difficult to manage for brands; it is coming from a lot of different sources — sometimes in real-time, sometimes in batch, and it rarely shares the same structure. Modern martech systems need to be built with AI in order to create contextual meaning from this massive amount of data. Customer data platforms with built-in machine learning capabilities like the Acquia CDP are perfectly suited to leverage these complex challenges,” said Arthur Galibert, Lead Marketing Consultant at SQLI Switzerland.
1. Freedom to leverage machine learning for any data source
Marketers use machine learning models to leverage insights from all available data regardless of the quantity of records. This distinction means marketers can draw more accurate, precise conclusions than if they were only working with models built from a smaller sample of data.
All machine learning capabilities aren’t built equally. Every brand has their own unique goals for how to meet the needs of their audiences. Just like your customers want a personalized experience, the machine learning models you use to build and orchestrate these experiences need to be customized to deliver personalized outcomes.
2. Configurability and accessibility
Unlike common static machine learning models that don’t allow for customized use cases, Acquia’s CDP embraces configurations in machine learning so that brands can adjust models to suit their specific needs. This is accomplished by a layered, robust machine learning framework that allows all teams to directly access, configure and run the tests they want to inform their business strategy.
3. Explainable ML models
Marketers need to be able to understand and trust the predictions generated by machine learning rather than blindly rely on predetermined scoring systems assigned by black-box models. By giving marketing teams direct access to all of their customer data via a CDP and the ability to design what tests they want to run through configurable machine learning, teams will have a deeper understanding of what each prediction means and be able to interpret results into strategic actions.
To find out how to meet the complex digital demands of today's consumers, download the free O'Reilly report, Transforming Customer Data Into Insights: A Chief Marketing Officer's Guide to CDPs.