The Ugly Cost of Bad Data

When the cloud-based data broker, ZoomInfo, went public earlier this year its initial offering raised more than 900 million. To be sure, a nearly billion-dollar IPO is notable in its own right, but the sum is significant for another reason as well: it underscores the undeniably important role data plays in today’s business world. 

Largely used to fuel sales and marketing activities, bygone are the days of using the yellow pages to find new prospects. On the contrary, sales data has emerged front and center as today’s most essential sales tool — and essential it is; 89% of today’s sellers say that quality data is ‘important‘, with another 63% saying it is ‘very important.’ 

So important, in fact, a standard subscription for ZoomInfo comes with an annual sticker price of $24,000. But with so many options for reaching buyers, including more economic options such as LinkedIn SalesNavigator, why are sales executives willing to foot such a large bill for the best data? The obvious answer that better data will yield more sales only tells half the story; the other consideration — what happens when my team uses bad data?

From Macro to Micro 

To zoom in on the costs of bad prospecting data (no pun intended) we will analyze a fictitious inside sales team composed of three full-time Sales Development Representatives. For the uninitiated, a Sales Development Representative (SDR) is a specialized inside sales rep who focuses exclusively on creating top of funnel opportunities, typically through a high volume of cold calls. For the sake of simplicity, we will assume the SDRs are making a flat $50,000 salary and average 12 dials per hour, 8 hours a day. We will also assume an average of 21.83 working days per month. 

In this example, a 98% accurate prospecting list would still produce approximately five inaccurate leads per day for the team, amounting to 29 minutes (.48 hours) of wasted calling time. Not great, but a certain amount of inaccurate data is expected and five inaccurate records is far from catastrophic. But, what happens when we recognize a conservative data-decay of 2% per month? 

In this example, by the end of the year, the SDR team is wasting 317 minutes (5.28 hours) each day calling inaccurate leads! Over the course of December, the same SDR team will have wasted more than 115 hours of time — roughly equal to $2,750 in wages.

When calculated annually, the figures become more worrisome with this hypothetical SDR team spending 780 hours (about 12% of the total working hours) calling on inaccurate data. This translates to $18,600 in compensation for work which has a zero-chance of driving positive sales outcomes. 

Justifying the Cost

As we demonstrated above, the cost of bad prospecting data quickly begins to outweigh any potential savings from using low-quality sources. Although the annual wasted wages don’t outright pay for the cost of a ZoomInfo subscription, it comes close ($18,000 vs. $24,000). 

Likewise, there are other costs associated with low quality data which are hard to quantify in our model. Examples include, opportunity cost, low morale, employee turnover, and a damaged brand reputation. Considering the average cost to hire an employee is $4,129 and the typical turnover time is more than 40 days, these “hidden” costs can quickly add up. 

At the end of the day, sales leaders must carefully consider all the costs of using sub-par tools, including their sales data — and if data is the substance that fuels your sales team, sales leaders would be wise to consider the old saying, “you are what you eat.”