How Predictive Segmentation Converts Your Visitors into Customers

Data, data everywhere. As marketers, we’re used to collecting furious volumes of data for a sole purpose — to take a look into a complexity of consumer behavior and translate it into an intelligent customer interaction. And as consumers, we’ve already felt it:

  • Google autocompletes your search queries
  • Facebook news feeds are tailored based on your “likes”
  • Tesla is applying a mesh network of AI techniques where one car helps all other cars in the network learn on-the-fly

It’s no secret that harnessing large volumes of data quickly and accurately will positively affect your revenue stream through acquisition, conversion, and retention. The trick is — the rate of consumer data and intent often vastly outpaces our current technological ability to act on it.

Marketing Cloud companies have released a slew of complex products that attempt to allow marketers to harness this vast data volume. However, most technology stacks struggle to effectively enable online marketing professionals to mobilize their marketing at the speed that consumers are researching and conducting their online decisions. It becomes hard to keep up.

The result of data that isn’t continuously modeled is a significant missed opportunity. It’s a failure to connect with modern consumers in the way they expect (and demand).

Innovations are training consumers to expect more from online businesses, creating a new standard where marketers must not only understand consumer needs, but anticipate them.

But don’t reach for your crystal ball just yet — by implementing predictive segmentation and notifications, you will convert your visitors into customers by anticipating their needs.

Predictive Segmentation to the rescue

Predictive segmentation helps you to notify your visitors about any relevant offers or actions they should take based on their individual consumer fingerprint. It becomes possible due to the machine learning and AI, which are confidently dominating B2C verticals.

The AI-enabled marketer armored with predictive segments will be able to:

  • Leverage smart modeling to predict each consumer’s likelihood to perform any action.
  • Specify a set of personalized possible notifications to engage each consumer
  • Automatically adapt the journey for each individual consumer, along a predefined funnel
  • Deliver the best next product, content, or offer — every time
  • Send notifications at the right time, when a consumer is likely to engage and when a brand needs to

How can you implement predictive segmentation?

Traditionally, marketers have lumped audiences into broad groups based on attributes like location or simple product category based intent. To benefit from a smarter approach, you should segment your visitors into different buckets, as narrowly defined as possible. An effective way of doing this is to use behavioral segmentation i.e. putting your users into different segments based on their activity over one or more web sessions.

Micro targeting requires the marketer to know their audience niches and their needs. This isn’t always the case or realistic when you’re dealing with hundreds of products and categories and with different consideration periods prior to purchase. Can a predictive approach help?

Let’s take a look at an example in online retail. Online behavior can be shifted towards increased conversation rates by applying predictive segmentation to personalize shopping journeys.

How does a retail company determine discounting on active shoppers to clear excess inventory? The company typically has no explicit data on the types of customers that react favorably to discounting so it will use its customer database and predictive modeling to identify who to offer discounts to, in real-time.

Let’s follow how this works for two customers Chris and Sam using the AI based predictive marketing tool and a machine learning construct called a decision tree, inspired by r2d3 and Harvard Business Review.

Predictive Segmentation Tree

By accessing customer data such as demographics, browsing behavior purchase history and then using these variables, an AI based predictive marketing tool builds a statistical model that determines how predictive each variable is in terms of the answer the marketer is trying to learn.

For each question, the probability is calculated on the basis of answers up to that point. The machine looks for combinations of attributes that create a high level of certainty about the answer it’s seeking.

Final Model

Eventually, the probability is weighted one way or the other. The model automatically updates itself with the latest visitor information and ensures continued relevancy.

Ensured continued relevancy

Visitors to your website or mobile app are notified of what is most relevant to increase the likelihood of conversion. For example, here’s a notification that appears to the visitor who falls into a predictive segment “Likely to purchase more than one item of clothing”.

Predictive notification (top of the screen)

Thanks to predictive segmentation, visitors to your website are notified of discounts that are predicted to increase their likelihood of conversion, based on their individual consumer fingerprint.

The continuous flow of customer data translates into a greater understanding of the customer and a superior experience that builds brand loyalty. Feeling neglected or ignored by a company, or being forced to wade through irrelevant marketing, is a sure way to increase bounce rate.

How do marketers benefit from predictive segmentation?

Identity is core to marketing. Learning to read and recognize context means a more intelligent marketer and a more informed consumer. Inherent in this learning is the ability to make predictions about future behavior, to know the customer more intimately, and to be proactive rather than reactive.

*Read more: Predictive Marketing explained