Customer Data & Analytics Blog

The Shocking Cost of Data Decay - How Bad Data Causes Loss

Sandeep Koul | 3 minute read

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One universal fact about data is that, like many perishable things in the world, it goes bad over time (unfortunately pretty quickly). 

There are many reasons for this—companies are acquired, people change jobs, relocate to new places, or assume new roles.  It’s inevitable. In the B2B world, contacts and accounts can “go bad” (a complexity that B2C simply doesn’t face).

There are a couple of ways to quantify the rate of data decay.  

  1. Turnover rate – One way to assess data decay is to identify the employee attrition rate (or turnover rate) of different industries present in your TAM (total addressable market).  US companies had an average annual turnover rate of 22%, which equals an average of 1.83% turnover in a month.  With an 88% increase since 2010, turnover is expected to continue to accelerate due to a strengthening economy.
  2. Email bounce rate – Another way to quantify decay is through email bounce rate.  One email marketing study of 200 million emails found an average bounce rate of between 5-15%.  As we marketers know, even with a client list, there are always hard bounces.

For the purposes of this article, we’ll use turnover rate.  Keeping these figures mind, it’s imperative to understand how the cost of data decay impacts your overall sales and marketing plans.  Below are few sample calculations that illustrate the business impact of bad data, but keep in mind that while this illustration represents a moment in time, we’re continuously adding new data, so this issue compounds over time.  

Cost of data management, reputation, and replacement

Suppose you have 1,000,000 records in your CRM.  If you purchase one such record, you can assume it will cost you approximately $1 (that’s the going rate these days). Therefore, the total cost of your data equals $1 x 1,000,000 records = $1,000,000.

If left unchecked, costs continue to rise over time.  Utilizing the above employee turnover rate as reference, this means about 22% of data becomes bad annually (you’ll remember that the U.S. had an average annual turnover rate of 22%).

So essentially, out of 1,000,000 records, 220,000 are not usable after one year.  That’s a lot, and just one “lost” contact could have been one of your most engaged leads.

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Bad data not only needs replacement (assuming that same $1 replacement cost per contact) but is also associated with a reputation cost & storage cost (to keep it as active in your CRM, for example). Let’s assume $0.48 as reputation cost & $0.15 storage cost. This means that the total cost of each bad record is $1.63 ($1+$0.48+$0.15).

Now, if we multiply each bad record cost ($1.63) by 220,000 bad records, we get $358,600.  That means dirty data costs $358,600 annually.   

Opportunity cost

So far, we have just covered the cost of dirty data.  In this section, we take it one step further and explore the impact of dirty data on your sales pipeline.

Here is a sample sales funnel:

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Again, assume that you have 1,000,000 records in your CRM.  Suppose that 20% of those are ideal customer profiles who have a high probability of conversion (20,000 contacts).  Imagine you convert 20% of those contacts to opportunities (4,000 contacts) and you convert 40% of those opportunities into deals (1,600 contacts).  With an average order size of $100,000, your revenue = $160,000,000.

Now, let’s consider those numbers with data decay factored in.

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Starting from the top again.  Assume that you have 1,000,000 records in your CRM and 20% of those are ideal customer profiles who have a high probability of conversion (20,000 contacts).  With 22% data decay factored in, that number falls from 20,000 to 15,600. If you convert 20% of those contacts to opportunities (3,120 contacts vs. 4,000 without data decay) and you convert 40% of those opportunities into deals (1,248 contacts versus 1,600 contacts without data decay), with an average order size of $100,000, your revenue = $124,800,000 (versus $160,000,000 without data decay).  That’s an annual revenue loss of $35,200,000.

Taking this seriously yet?

Total cost of data decay

Combining the cost of data management, reputation, and replacement and opportunity cost, you end up losing approximately $35,200,000 + $358,600 = $35,558,600—or 22.2% of your annual revenue.

Conclusion

Data decay is a reality and, as you can see, presents a huge cost for organizations.  While it seems like a few bounces here and there or aligning with industry standards (like GDPR, etc.) are a non-issue, the cumulative effects should cause concern.

CDPs, coupled with data cleansing, appending, and more, help solve this problem for good.  They sync with your CRM, marketing and sales tech, and other important sources of data for your organization to keep your customer information updated and usable on a continual, unaided basis, and most importantly, keep data decay at a minimum. 

Topics: Data Quality