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The Modern Marketing Blog covers the latest in marketing strategy, technology, and innovation.

How to Tell Good Data from Bad

Peter Daisyme
Co-founder of Palo Alto

Marketers waste too much time chasing and interpreting bad data. When you base your strategies and campaigns on faulty information, you not only waste your time and money, but risk sending mixed messages to your audience.

Excessive availability of marketing technology further clouds marketers’ ability to distinguish between good and bad data. You should be focusing on information that helps brands build deeper relationships with customers. But how do you identify that information —  and what should you do when you find it?

Reliability vs. Relevance

Just because data is accurate doesn’t mean that it  is useful. You might know the location of every freckle on your target customer’s left arm, but that information won’t get you any closer to making a sale.

Marketers don’t just need reliable data — they need relevant data. By identifying information that correlates strongly with purchasing behaviors, you can focus your collection efforts on areas that lead to increased sales and ignore the background noise that accomplishes nothing.

Relevance varies from one moment to the next, however. You may be more interested in aligning your brand with a social cause than with increasing sales through a new campaign. In that instance, you would want to know how audiences perceive your brand, which you could learn by conducting surveys and asking questions of association.

Just as you can’t use irrelevant yet reliable data, you can’t depend on unreliable yet relevant data. If you have information that says your customers desperately want a blue option for your product, but that data comes from a 12-person survey conducted by the Americans for More Blue Things Society, you can’t ensure the accuracy of the findings.

Good data blends reliability and relevance to provide an accurate, clear picture of information that can help your brand grow. Relevant data speaks directly to your goals, while reliable data comes from dependable sources. Think carefully before making decisions based on data that fails the test in one of these two categories.

Vanity Is a Sin

“Vanity metrics” has become a slur in the marketing world. Anyone looking to discredit the strategy of another needs only to accuse the competition of prioritizing vanity metrics to ruffle feathers.

What distinguishes vanity metrics from useful data, though? The answer depends on the strategy. A young company looking to get its brand noticed might love a social post with thousands of likes and shares, while a more established brand would be more interested in an engaged comment section and high click-through rates. 

In this example, neither company is in the wrong. The small brand needs exposure to grow an audience. The larger brand, which already enjoys familiarity with its target audience, needs new leads and conversions more than it needs fame. Someone else’s vanity metrics might be your most important KPIs.

Don’t let others tell you what your marketing strategy should be. Only you know what your company needs. Those needs will evolve over time, so adjust your strategy accordingly to keep your data collection efforts aligned with the reality of your brand’s influence.

How to Identify Good Data for Your Business

Having tested your data for reliability, relevance, and vanity, however, doesn’t mean you can’t still fall victim to bad data. Here are three things to watch out for:

1. Don’t force the curve

Your expectations won’t always align with reality. Never force your data to present the truth you’d like to see. If you do, you could ruin otherwise valuable data and base campaigns on false pretenses.

This sounds obvious on paper, but biased interpretations occur frequently in practice. For instance, let’s say you expect to see a standard distribution of consumer excitement about your new product, but your measurements reveal almost no excitement. You can’t adjust your curve and treat people who rated themselves a 4/10 on the excitement scale as your biggest fans. Treat valid data with the respect it deserves.

2. Evaluate your sources

Before you consider what the data says, think about who says it. Does the person or entity on the other side have an agenda? Don’t excuse your own organization from this question. Marketers, like everyone else, naturally gravitate toward data that justifies their work and expectations.

Businesses rarely measure data that doesn’t benefit them, so don’t discount all profit-motivated studies. Consider the source and potential motivations, then keep that information in mind as you decide what to do with the provided data.

3. Assign connected criteria

Reliable, relevant data should provide you with predictive information. You can’t survive on data that tells you what you already know — you have to identify and leverage data that allows you to predict and alter outcomes that will benefit your company.

For marketers, meaningful criteria typically include new leads, conversions, and engagement rates, depending on the goals of their strategy. Look at the available data and connect that data with its prospective uses. Try a few small-scale tests. If the data consistently influences outcomes in the way you expect, you have good data.

No matter how much bad data you have or what you do with it, you can’t convert bad data into good. Avoid relying on incomplete information by considering the relationship between relevance and reliability. With a bit of caution and foresight, you can ensure your company only uses the best data available to achieve its goals.

                                                                                                     

Data drives success in marketing. Read “The New Breed of Analytics: Put Data in Motion” about how to best put your data to good use.

Take a look.

 

 

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