According to an IBM infographic I recently viewed, 23% of an organization’s data is bad data (incomplete, out of date or inaccurate).
When I first read this statistic, I was shocked. But that shock quickly turned to sadness when I realized that I’m sure it’s true, and I wasn’t quite sure what to do about it.
As a marketing manager, I spend much of my day strategizing how to bring in new leads, make customers happy and engage with prospects. And I know my sales team is spending their day a little further down the funnel trying to do the exact same thing. But our best efforts (or 23% of them) are being thwarted by inaccurate, unorganized or mishandled data.
So how does bad data affect our workflow? More importantly, how are we trying to fix it?
Bad data example 1
We had a hugely successful event where we met lots of new prospective customers and partners. Everyone is eager to call these new leads, but they soon see that the contact data is incomplete. Many attendees never provided a direct phone number. Due to this incomplete data, our sales team needs to spend hours searching for the correct information – even after these contacts have indicated they want us to reach out to them!
What can help
This situation is tough because there were no errors on our end that led to the incomplete data. So what’s the best way to handle it?
In these situations, using a third-party company information search tool to find accurate contact information is a must – especially because you know these leads want you to follow up. Some of these tools have gotten more sophisticated by using crowdsourcing (where people share their databases) or web/email scrapers (collect information from email signatures and websites) to find the most up-to-date contact or company information. Make sure you pick a tool that integrates with your CRM to eliminate wasted time.
Some of the inaccuracy can be avoided by having one person upload the contacts to make sure they all have the right lead sources and are associated with the right campaigns. Have that point of contact then divvy up the leads appropriately. This will avoid internal human-error that comes with multiple people trying to do the same task.
Bad data example 2
The sales dashboards we have projected on big-screens around the office aren’t always accurate. They should track demos booked, deals closed, revenue brought in and other metrics, but members of the sales team sometimes complain that they are wrong. This is frustrating for sales and for management who are trying to keep track of their team.
What can help
This problem stems from a lack of consistency when filling out fields in your CRM system. Make sure your team is trained, not just on using the CRM, but how your company reports from that data. Make sure the necessary fields are mandatory. However, try not to overburden your sales team with too much menial work, which can be difficult. Try to establish what reports are crucial and make sure the corresponding fields needed to populate those reports are required. Don’t make it too complicated! That’s bound to lead to more errors.
Also, create naming conventions so all members of the team are filling out fields in the same way. Use drop down options as much as possible.
Bad data affects the whole business, and we’re always experimenting to find better ways to avoid errors. If taking these steps reduces your inaccuracy from 23% to 13%, you’ll have much better insight into your business.
If you have any good suggestions, we want to hear them!