Wise Insights

Personalized Marketing: From Promise to Reality with Machine Learning

Personalized Marketing: From Promise to Reality with Machine LearningPersonalized marketing has long been the holy grail for marketers. For years, the growth of customer data has driven the potential for hyper-relevant marketing, but the reality has always been just beyond most marketers' grasp. New machine learning applications can finally enable the micro-segmented marketing that yields better customer engagement without the significant time and resources required to set up and maintain such a personalized approach.

Limiting factors of traditional segmenting

Companies have traditionally counted on marketing and sales teams to take a best guess at developing new segments. Many still lean on carefully crafted customer surveys and other organizational initiatives to create differentiated offerings that will hit (at the very least) close to their mark.  This approach is a human, mostly manual exercise relying on intuition, gut analysis and customer feedback, which limits the precision for making marketing decisions and measuring campaign results.

This classical approach to customer segmentation limits the upside potential because it:

  • Sacrifices content relevancy - All customers within a segment are treated alike, while in reality no one customer or customer buying path is ever exactly the same.
  • Misses opportunity for data analysis - It's nearly impossible to use the full range of available customer data when segmentation is only implemented as a human exercise.  Humans have a hard time processing and analyzing multi-variable and disparate data.
  • Requires a lot of resources – It takes a lot of time and manpower to develop, complete, and maintain these initiatives.
  • Stays static over time - A lack of ease and affordability means assumptions stay static until the next refresh can be budgeted, approved and executed, which typically happens annually. Customer behavior and buying decisions, in fact, likely change more frequently and in more nuanced ways than once a year.

Machine learning takes segmentation to the next level

Machine learning enables more relevant personalization so companies can interact and engage at every stage of the customer’s lifecycle. With the sophisticated segmentation allowed by machine learning, companies can focus on the right customers with the right message at the right time. This notion of micro-segmentation is powerful because it lets marketers treat customers differently, even if they look the same on paper.

In reality, no two customers are the same. What message will move them? At what time? On which day? On what device? If you are going to be able to get a customer’s attention when they are most receptive, you have to be able to keep up with each individual “what” “when” and “where.” By treating the customer differently, communication and outreach is more relevant to the customer and the company sees better results. Those who pair micro-segmentation practices and machine learning experience predictable outcomes, greater engagement and even more return.

A multidimensional approach allows richer customer engagement

Companies have a great deal of information they can leverage to understand how customers actually behave including data about order frequency, lead times, product usage, etc.  Machine learning can find patterns in this data to analyze what leads to great customer outcomes and to open pathways for increasing the relevancy of content and extending the conversation.

What signs and customer behaviors point to the likelihood they’ll take one action over another? What’s happening just before they turn away from the conversation or click to make another purchase? Machine learning extracts patterns from every individual customer interaction and easily analyzes and interprets a wide range of data to determine which characteristics and behaviors are truly predictive of an outcome for a particular customer. It takes a multidimensional approach to identifying these various “tells” to predict like behavior of a customer in a similar situation, enabling marketers to scale and enhance those inherently personalized customer interaction decisions.

Not only can machine learning assess and flag predictive behavior, it can do it over and over again, taking new cues from changing, current information and behaviors. For a marketer, that means never having to refresh a segmentation initiative or rely on outdated, static information. With machine learning, computers automatically learn by example and continuously adapt to the shifting sales and marketing world. 

With this kind of detailed intelligence in hand, marketers can take action that is far more informed and has much greater influence on creating relevant content for a conversation personalized to each individual.  With sophisticated micro-segmentation through machine learning, customer twists and turns don’t throw you—they are captured and built upon for continued engagement and success.

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Topics: Machine Learning, Customer Success, Customer Support