Data decay: The silent killer

Like bananas, your customer data starts out green, begins to yellow, and before you know it it’s attracting flies. Some of the companies with high job turnovers in the Silicon Valley experience contact data decay rates as high as 70% per year. Sadly, like in the case of sleeper cells, we don’t realize until something explodes.
It means that the very data in your database or CRM you rely upon to make daily and key business decisions tends to become unfit for use over time. In fact, did you know that the data could be decaying at an alarming rate and you might have to invest a huge amount and commit your resources to fix it?
Your data is time sensitive. Like most things, data has a lifecycle, it comes with an expiry date. Data decay can arise due to a continuous erosion or degradation of data over time due to changes in data or human errors or validation.

 

Figure 1: Insights decay with Data over time
Image source: IBM Bigdata Hub

The bull-whip effect

Going by even the most conservative estimate – at least 30% of the overall data decays every year. It essentially means your list is getting smaller every year if you don’t generate enough new leads to balance out. Without action, over a third of your business contacts can go out of date each year, including key company information, personal details, and most importantly, accurate contact information.

 

It directly impacts your customer acquisition cost (CAC) and thereby your profit margins.

 

Figure 2: Impact of Data Decay on Customer Acquisition Cost
Source: LetterUSA

 

The rate of your customer data decay depends on several factors ranging from your consumer type to the industries you serve, their geography and more. Some industries such as entertainment, advertising, High-Tech, F&B — are highly susceptible to data decay.
Half a million small businesses shut down last year, and even more rose to take their place. One of the observations from our data scientists at Fiind is that data pertaining to company demographics or technology usages are less likely to change in short term. However, lead contacts data is one such data that has one of the highest decay rate. Therefore, when assessing the accuracy of the data in database it is key to know the inherent decay rate of the data itself.

 

Data pertaining to company demographics or technology usage are less likely to change in short term, however, lead contacts data has the highest decay rate.

 

Hard to plug the hole when leaks are everywhere

It is hard to estimate the exact cost of data decay in your organization as the costs are spread out across the entire organization. Bad data that sneaks into your processes, sucks your profit out in many ways. And more so, if your customer experience (CX) programs, loyalty programs and other engagement programs are dependent on this data.
Taking the manual route to fix the problem will consume a lot of time and resources. We come across estimates that put the annual loss to business in billions. To be honest, I don’t think you have to calculate it perfectly to know how important it is to fix this problem.

What can be done about this?

As long as it hurts when you press it, you know where to look for the solution. The good news is that you not only can prevent data decay but also can mitigate it, should you have a situation. In some companies, the responsibility of updating and verifying contact information is pushed on the sales teams alone. That won’t be enough. Data Integrity check has to be prioritized as one of the key objectives of the entire company.

Let’s see what else can be done to prevent this.

• Contact data verification is best done before the bad data reaches your databases.
• It’s easy to make regular contact data health checks a routine part of your marketing operations.
• You can centralize your contact data hygiene, then publish the clean data back to the applications and systems that use it.
• You can sync your clean contact data across systems – using data integration, data quality, master data management, or simple API synchronization.

The image below gives you an example of how a simple automated process can help you mitigate this menace.

Figure 3: ML-enabled Automated Process to refresh outdated data

 

We, at Fiind, are continuously in pursuit of solving chronic and pervasive problems with easy-to-use and reliable Machine Learning solutions. In the near future, I expect a lot of companies to use such Data Integrity and validation processes at every step of data collection and storage. Successful companies have already put this into practice – after all, we’re only as effective as our data.