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Choose Your KPIs Carefully. Not All Data is Created Equal.

It’s already the end of January, which means many companies are in the midst of (or are already done) preparing KPI and revenue projections for 2014. So in the spirit of “Data Season” I want to encourage all the marketing data analysts out there to take a deep dive into what really constitutes your KPIs – because not all data is created equal. Moreover, if you don’t do this, you run the risk of working less efficiently, and misappropriating your time, energy, and resources, in order to reach your department and company goals.

Let’s envision a scenario to illustrate this narrative.

The marketing departments at two companies, let’s call them Company A and Company B, have each been tasked with increasing new revenue by 25% YoY. That’s a pretty big jump. And the data analysts at Company A didn’t do thorough analysis of how the leads their marketing department generated translated into revenue that year. So the marketing department at Company A believes they must increase new leads, sales appointments, and revenue by 25%, without 25% more budget or resources than the year before. With little to no insight into the performance of the previous year’s marketing programs, their only recourse is to work 25% longer (or more!) in order to make up the difference.

Meanwhile, the team at Company B was busy dissecting how leads generated from various marketing channels impacted the sales funnel. What they discovered was that MQLs (Marketing Qualified Leads) from different lead sources did not represent a 1:1 value to bottom-line revenue. Let’s dive into why this is the case.

Conversion Rate. While leads from the first channel they analyzed converted at a rate of 4% from MQL to SQL (Sales Qualified Lead), leads from the second channel converted to SQL at 16%. We can consider leads from the second channel to be 4 times better quality than the first!

Deal Value. Not only were leads from the second channel moving through the beginning of the sales funnel much more efficiently, but the closed business from those leads was worth an average of 50% more per SQL as well.

Ultimately what this means is that comparing MQLs from each of the two channels 1:1 would be greatly undervaluing the second channel. In fact, in this case, an MQL from the second channel could be worth 6x that of the first in terms of revenue!

Armed with this information, the marketing team at Company B could allocate more resources to the most profitable channel in their marketing mix – enabling them to work smarter, not harder. I hope you take the same approach to understanding your metrics to get the most out of your marketing this year.

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