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There are few groups who need each other more than data science and marketing. However, the communication between the two is a lot harder than it sounds because they understand the business from two different perspectives. Metrics are an area where the two often meet and clash because data scientists are focused on one type of metrics, utility, while marketers are focused on a different set, perception.

Utility metrics are how a data scientist measures value. Perception metrics are how a marketer measures value. The communication problems between data science and marketing teams often start with this simple difference. I want to explain value from both perspectives as a starting point and then teach you how to reconcile the two. Marketers and data scientists will work together better just understanding this simple difference.

Utility Metrics

Data scientists love utility metrics. These are measures of value like ROI, supply, demand, elasticity, cost per feature, incremental value per feature, and many others. These metrics measure the real impact on the business and customers. If we were all rational actors, these are the metrics that would guide our decisions.

That’s why data scientists are into these numbers. Utility or loss is a concept that’s drilled into us. It requires structured research and some heavy math to build the case for. We use them to prove that a business, product, or feature has value and is worth investment.

The conflict comes into play because utility metrics don’t always drive buying decisions. Customers are irrational actors. Analyze anything touched by people and you’ll find the influence of irrational thinking. Irrational doesn’t equal wrong. People make the right decision using irrational heuristics every day. The decision making process isn’t somehow broken in most people. It’s driven by factors outside of traditional utility metrics.

Perception Metrics

Unless they’ve studied behavioral analytics or psychology, perception metrics aren’t on the radar of most data scientists. Behavior is driven by a whole host of factors from past experiences to influencers and many others. Closing a sale or generating a lead from an advertisement is an effort at triggering the buying behaviors for an individual customer. A thorough presentation of utility metrics seldom has the desire effect.

Perception metrics are the result of a customer behavioral model or psychographic profile. When marketers ask for help shaping a campaign, they’re less looking for a presentation of data points that support a product and more asking for what triggers a customer to buy. Think about selling a steak. If the commercial showed a raw piece of meat and vital statistics like available weights, price per pound, cuts, and fat content…would that entice a customer to buy? Those are great utility metrics but not ideal perception metrics. Perception metrics show that people make their decisions about what to buy for dinner between the hours of 4:30 and 6:30. The sound of meat cooking triggers a 40% increase in choosing meat when given the choice of meat, chicken, or fish.

Perception metrics are the “sizzle.” Sizzle sells steaks because the sound triggers buying behaviors. You could also say that the smell of steak cooking sells steaks, but that’s not as concise or memorable. Perception metrics have staying power so customers can become evangelists spreading that simple, powerful message again and again.

Perception metrics tell a story in a meaningful way. Just like sizzle sells steaks does. There’s a metric in there; something like the 40% increase in choosing meat when given the choice… Then that metric gets expanded out to determine a larger pattern in marketing. That’s something good data science teams excel at; turning correlation into causality. Now it’s time to take that causal relationship and tell its story in a clear, concise manner. That’s a difficult translation, from numbers to stories, and it’s a key driver for the collaboration between data science and marketing.

Bringing Both Worlds Together

Utility metrics are what prove the product isn’t garbage. They prove customers will actually use and love the product. It’s not always why customers will buy the product but it’s a vital part of why customers will stay loyal and become promoters of your product. First time buyers don’t care about utility metrics. For repeat customers, it’s all they care about. Without utility, all sales are one and done.

Perception metrics are what get new customers interested and provide a clear message customers can share with each other. The product can be as useful as a knife and fork. Without perception metrics, we’d all still be using our hands. This is why so many useful products fail while a copy that comes along later succeeds. Perception metrics made the iPod a success while the Zune is something you’re going to have to Google.

Products start with utility metrics. The best products are born from problems hidden in utility metrics. The expenditures associated with research, development, and go to market are all justified by utility metrics. However, at go to market, perception metrics come into play while planning the marketing strategy. Utility metrics aren’t any less important, but the business need arises for data that indicates how marketing will get customers to take a look at then initially buy. Marketing needs to trigger buying behaviors with perception metrics. Then it becomes cyclical. New utility metrics drive new features. New features appeal to new customers with different buying behaviors. That means new perception metrics are needed.

Marketing can play a key role in the discovery of utility metrics that few data science teams take advantage of. Customer problems, complaints, wish lists, etc; utility is hidden in these anecdotes. Every problem is an expression of loss. A customer with loss is a customer whose utility, and by extension value, hasn’t been maximized.

It’s only when marketing and data science are in sync with each other, that these type of opportunities can be identified. Otherwise businesses are leaving a lot of value on the table.

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