DATA IN A CHANGING DIGITAL LANDSCAPE: The information you need to know and a guide on how to assess it.

Author:Smith, Jordan
 
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There is a lot of data available today for franchises to leverage when making decisions about their business. With so much information abound, it can become confusing to know what to pay attention to and what to ignore. In an ever-evolving digital landscape, the increasing number of marketing channels and opportunities only compounds this cloud of confusion.

To cut through the noise, there are key performance indicators that will help any franchise brand gauge the success of their marketing strategies. These should be measured holistically with the understanding that the sum outcome of an integrated marketing plan is greater than its parts.

Critical key performance indicators franchises should consider:

* Total Revenue

* Number of New Customers

* Number of Repeat Customers

* Cost per Acquisition

* Cost per Lead

* Advertising Reach & Engagement

Advertising, spending and planning.

If these KPIs are performing well, chances are your marketing strategies are working harmoniously. But what if some KPIs are on target while others are off the mark? This is where I'd like to introduce a fundamental marketing maxim, and that is that nothing you do in advertising and marketing is done in a vacuum. It's a simple truth, but is easily missed.

Mailers, radio spots, Facebook ads and video ads are all examples of conduits that can influence the successes or failures of your other advertising channels. Is it worth spending $1,000 a month on digital display ads if it produces zero leads, but drops your PPC campaigns CPL by 20 percent? Does your average conversion rate increase by five percent so that three new customers are converting each month?

That's why it is so important to understand that advertising channels coexist in an ecosystem and to plan, execute and measure all your initiatives as a unified advertising strategy.

It's how you use the data.

In grad school, I had an advanced statistics class that talked about how good data, applied incorrectly, can lead you to bad conclusions. For example, there is a classic story of correlation without causation when measuring the number of people who eat ice cream and the number of people who drown while swimming. Data shows that ice cream consumption and drownings both increase during a typical summer. Does that mean eating ice cream causes drowning? We know that is ridiculous and that eating ice cream doesn't cause you to lose your swimming ability. We can make the same faulty assumptions with our advertising...

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