Customer Portfolios' Blog

Creating the Algorithm for Success

algorithm for success

We’ve seen time and time again new products that we think will save us all. Think margarine, processed food, plastic, and big cars. After a certain amount of time, the sheen wears off, we wizen up, and realize the negative side of these “wonder products”.

This is the current thinking of algorithms – formulas used in data-based marketing to analyze, automate, and make our lives easier. We have a plethora of algorithms that remove human decision making, freeing up resources for other, strategy-focused efforts. Sounds great, right?

That’s not always the case. Think about some ways you have interacted with an algorithm from the customer perspective. The most common example is retargeted ads that follow you around the web. From the business perspective, the algorithm is doing exactly what it’s supposed to do: remind you about a product or brand that you recently looked at so you make a purchase. But from the customer reality, this experience can be annoying at best, and intrusive at the worst.

This example algorithm is working for a very specific decision at a very specific point in time. But outside of that specific function, there’s a negative trickle-down impact. We’re working in a much bigger universe than just that function, but the algorithm can’t possibly have the full holistic customer view or emulate a human experience.   

Success Comes from Personalization 

To balance these problems and make algorithms less annoying and intrusive, algorithms need a human component. At the end of the day, we’re talking about Customer Relationship Marketing, and a relationship needs human engagement on both ends. 

An algorithm example that does succeed is one that recognizes when a customer is at risk of attriting, then sends a notification for a human to step in. 

Here’s what I mean: your database analysis reveals that a customer makes a purchase every six months. You can set up an algorithm to take an action at the best time to get that customer to purchase again. At 5 ½ months, do we put in an engagement campaign? At 6 ½ months, do we push a notification to the store associate to call the customer? That’s a decision for the marketer to make, but it will help build the algorithm into the human experience. Let it inform the process. Don’t let it drive a human experience.

A good use of an algorithm enables things that make sense at a human level to be done at scale. To continue the example above, remembering each and every customer’s 6 ½ month anniversary and calling them that day is too overwhelming for most sales associates. However, if the associate receives a daily notification with the targeted list of customers, their phone numbers, and a suggested prompt, it speeds up the process while still allowing for the personalization that customers crave.

Algorithms can provide immense value, especially as we collect more and more data about our customers that can be used to deliver targeted and relevant messaging. Yet these algorithms cannot operate in a vacuum. By incorporating data-driven personalization and humanization into your algorithm, you create the optimum customer experience and the algorithm for success.

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