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Formation: Injecting Machine Learning in the MarTech Stack for 1:1 Personalization

April 1, 2020

Formation: Injecting Machine Learning in the MarTech Stack for 1:1 Personalization

Brands know that leveraging customer data is crucial for creating profiles and analyzing how customers interact with the brand in a variety of ways - from buying habits to their preferences in types of products to activities. From this data, brands can gain an understanding of what motivates their customers. But most marketing teams don’t have access to all of this information, so typically develop transactional offers, like BOGOs or 20% off on product categories consumers frequently purchase.

In today’s ultra-competitive market having a single view of the customer is no longer enough. It’s what you do with the data that counts.

Brands want to develop stronger, lasting relationships with their customers, but it isn’t clear just how to do it. At the same time, consumers are seeking a more personalized brand experience, but current practices are not addressing either the brand’s goal or the consumer’s desires. The traditional transactional approach doesn’t allow brands to build that 1:1 personalized relationship their customers seek.

Realizing that they need to change their approach to marketing, many brands are turning to customer data platforms (CDPs). These solutions offer a central, unified customer database that gathers data from multiple sources to create customer profiles. The structured data can then be used by other elements in the marketing stack, including content management systems (CMS), email service providers (ESP), customer relationship management (CRM) and more.

Do You Need a Customer Data Platform or CDP?

There is no right or wrong answer to this question. Every large enterprise has unique needs, which means their MarTech stacks can differ vastly. When considering implementation of a CDP, you should ask yourself what you’re trying to achieve, and what is the best way to get there.

The answer may or may not be a CDP. For example, a CDP could be a valuable tool to gather insights and create a single view of your customers. But CDPs can also fall short, according to a Forrester report. Forrester warned that “CDPs are a Band-Aid solution to a much larger challenge. “Data has become bigger, faster and more complex, and marketers have to activate it on more channels.” The report noted that CDPs often:

  • Do not solve critical issues - CDPs "don’t offer the advanced level of identity resolution and cross-channel execution marketers need to be productive."
  • Lack key data features - Even though CDPs can act as a primary data ingestion point, marketers "need solutions that dive into and solve various data complexities.”

Organizational Challenge: Moving from Customer Insights to Action

Beyond the CDP downfalls noted by Forrester, there are also organizational challenges. In most enterprises, data scientists are the ones who have access to the customer data that is gathered from multiple siloed sources to form customer insights that benefit the entire organization.

Yet other departments, like the marketing team, typically do not have access to these customer insights, which would be valuable in creating more personalized offers. And when the data science team tries to help by creating propensity and expected spend models, they may not be dynamic or effective enough to deliver the 1:1 personalization that today’s consumers demand. This puts marketing at a disadvantage since most marketing tools were originally created for mass marketing, not 1:1 personalization. And without the right tools in their MarTech stack, marketers have no way to access these unique customer insights and develop more personalized messages, offers and rewards.

Formation: Putting Machine Learning in Marketing to Bridge the Gap

Formation is helping marketers gain more control over customer data and bridging the gap that exists between customer insights held by the data analysts, and the actions the marketing team needs to take to drive 1:1 personalization strategies.

We leverage machine learning algorithms to gather and analyze customer data, better understand what motivates customers and then determine actionable marketing insights. As part of the marketing stack (see graphic below), the Formation solution then connects these insights to delivery channels, enabling marketers to take a variety of actions and connect with customers through truly 1:1 personalized offers.

What sets us apart from other tools in the MarTech stack is that Formation applies artificial intelligence (AI) and machine learning (ML) to automate creation of 1:1 personalized offers to guide the customer journey. With Formation in the stack, you can send out millions of offer combinations that are completely tailored to individual customers. Our ML algorithms automate processes for testing offers, learning from them, then fine-tuning to best align with customers' motivations, buying habits and interests. Formation can even help predict offers based on these characteristics that will encourage customers to buy more. Additionally, with Formation, time to market leveraging ML takes days compared to months with manual processes.

Formation’s innovative solutions enhance the value of your MarTech stack. And we address the challenges of getting customer data and insights into the hands of marketers to support greater personalization, which will bring greater value to your brand by increasing customer engagement and lifetime value (LTV).


To learn more about how Formation bridges the gap between insight and action for 1:1 personalization, schedule a demo now.