6 Questions Every Brand Manager Should Ask to Maximize the Value of Big Data

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First, you have to ask the right questions. For brand managers to maximize the value of big data, they first must know what questions to ask.

Without real, valid intelligence, creating a strategy for a brand is just guesswork. Data science is a potential source of intelligence, a scientifically sound basis on which to build a strategy. But the answers you receive are only as good as the questions you ask. So how do you know which questions are the right questions?

1.       Make sure your questions are framed with a tangible business goal in mind.

Many brand managers ask analytics teams and/or data scientists questions like, “How many clicks did my last email campaign generate?” or, “How much traffic was on brand.com last month compared with the month before?” Those sorts of questions don't take full advantage of the value of data science.

A client recently asked, “What can we do to generate $1 billion by 2020?” Intouch used data science to answer the key questions – such as “What?” “So what?” and “Now what?” – so the client had a real, mathematically sound roadmap to reach blockbuster status.





2.       Ask about your own data before spending money on someone else's.

Many pharma companies spend money on third-party data while letting their own valuable data go unexploited. For the client who asked how to grow sales to $1 billion, a significant portion of the data we used came from one of the company's own unbranded disease-awareness sites.

The lesson: treat every patient- or HCP-touching tool you build as a potential data-producing asset, and pay attention to the data it produces.

3.       Don't just ask about what your customers are doing; ask about what you are doing, and whether it's working.

Companies rarely look at the data from their entire portfolio of customer-touching assets to calculate the relative impact of each. A good data scientist with access to all the data from those assets can provide decision-makers with an equation that shows, based on the data, “if I invest fewer dollars in asset A and X amount of dollars more in asset B, I can reasonably expect outcome Y.”

4.       Ask how much each customer is worth.

Would you rather spend marketing dollars on a 25% probability of getting $100 or a 90% probability of getting $1? The answer is self-evident, but the question isn't asked nearly as much as you'd expect in our industry. A good brand manager should use every data input available to calculate the expected value of each potential customer, both in the short and long term, and then adjust spend and targeting accordingly.

5.       Don't be afraid to ask.

In our industry, we rightly worry about overstepping into the HIPAA danger zone. However, data science is powerful enough to draw conclusions and make predictions without using any personal health information at all. So fear not: A good data scientist with access to everything, but PHI can use data to tie actions and behaviors to probabilities, and still draw a remarkably accurate picture.

6.       Ask what to ask.

Your data analysts watch the data roll in every day, and they're trained to divine its significance. Ask them what they think looks odd, interesting, or seems to be impacting sales in an unusual way. That's what we're here for.

Sam Johnson is director of data science at Intouch Solutions.

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