Make It Count: Distilling Data Into Value Metrics

How many followers does your brand have on Twitter? A hundred? A hundred thousand? Does it matter?

The answer: it depends.

The traffic you get to your Twitter feed, website or Facebook page isn’t as important as what you want that traffic to do for you.

  • Do you want them to buy something?
  • To learn something?
  • To sign up for something?
  • To get others to sign up for something?

Look at it another way. You hold an open house for your business. Over 500 people attend, but no one signs a contract. On the other hand, you printed up a thousand brochures about your products, and they are all gone.

Whether you were successful depends on what your expectations were for the open house. If you were looking to make sales, the open house didn’t work because you didn’t make any. If you were looking to publicize your business, it sounds as if you made your goal, at least if we measure that in terms of both how many people attended and how many brochures people took with them.

Value Metrics

That’s basically the concept behind value metrics. Data only has value in terms of what it measures and whether what it measures aligns with your objectives. Data without context is just a number.

ClickZ reports on one company’s experience in using high-profile celebrity Kim Kardashian to tweet about its products. While Kardashian had 15 million followers, only 1,200 ever visited the company’s website. And only 30 placed orders, with an average order of $30.

social media followers

Fifteen million followers on Twitter sounds like a lot. But there was actually little value in that number. There was considerable value, however, in knowing how many followers turned into actual purchases. As it turned out, the value metric revealed how little Kardashian and her followers added to overall sales.

So while the result showed there was little value in Kardashian’s Twitter followers, the insight gained was highly valuable. Namely, it was a waste of money and resources to hire this celebrity spokesperson in hopes of spiking sales.

Related Article: Metrics that Matter: 4 Data Points You Should Consider Measuring

Finding the Right Value Metrics

As Jeff Berry points out, everyone is overloaded with data. The problem is figuring out what data is relevant to what you want to do. He defines three steps to determine if the data you’re looking at is the data you should be looking at.

Business Objectives

1. Does the data align with your business objective?

  • What is your objective (e.g., to increase market share, sales, brand awareness)?
  • What is the timeline (when do you need to see results)?
  • Who is the target group (new customers, customers in a certain demographic, resellers, early adopters, etc.)?

You can, of course, have multiple objectives. But for each objective, think about what kind of data you need to collect to show you how well you are achieving that objective.

2. Decide on data points that measure what you want to achieve.

Does it matter how many hits you get on your website? It does if you are promoting a new website and want to gauge the effectiveness of a marketing campaign that announced the new website launch.

However, the number of hits doesn’t matter if you are trying to determine if people are buying as a result of print or social media marketing. Instead, you’d want to look first at the conversion rate (page viewers who eventually make purchases). Then you’d want to look at the customer survey email that’s triggered with every sale that asks if what prompted a purchase is due to a print ad, Twitter announcement or something else.

Related Article: 11 Best Tools for Setting and Tracking Goals

3. Properly collect and optimize data.

Historical data tells you where you were. Real-time data tells you where are. Comparisons can be useful. Data can also be queried to segregate for different characteristics. For example, you might want to look at and compare sales in certain geographical regions, demographic groups or different times of year. 

Collecting data isn’t that hard. Figuring out what data to use and how to use it takes a little more work. It’s easy to get overwhelmed by the quantity of data that you collect every time a customer pays with a credit card, visits a website, opens an email, uses your app, fills out a response card or scans a frequent buyer card.

But, as Berry points out, “If you keep customer benefit top of mind and define your objectives prior to data collection, and remember—more data isn’t always better—you are on the right path to delivering relevance to your valued customers.”

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