Data Discrepancies: Strategies for the Business Killer
Nothing quite kills progress in business impact quite like data discrepancies.
The CMO leans back in his chair and lets out a loud sigh.
"I can’t approve this new budget - what about the mismatch between our transactions and the analytics data? I just can't trust the data."
The marketing manager opens her mouth to disagree. The analyst just opens her hands wide as if to say, "I don't know what to tell you."
And the new initiatives meeting ends without any decisions made. A few months later, the marketing manager gets pulled into her boss's office to have the 'why hasn't the revenue increased' talk.
Sound familiar? Maybe with different characters, different excuses - but distrust in data because of discrepancies- are among the biggest killers of business growth. One of our most common project requests is to validate data or deep-diving into implementations.
We want to let you in on a few secrets and hard-to-swallow truths. So take a deep breath, leave your outrage for the end and hear us out:
The average discrepancy between financial systems and analytics platforms is 15-25%.
It's impossible to have your numbers completely match between your different ad platforms, analytics, and your financial data.
Web and product analytics isn't a hard science or even a social science.
Facebook, Google Ads, your optimization tool, and pretty much every other web data tool count metrics slightly differently - from bounce rate to how they attribute transactions. You will never force them to get along.
If you're spending more than 15% of your marketing and analytics team's time addressing data quality, you're losing money.
Why are Data Discrepancies Not As Big of A Deal?
As long as you have properly implemented your tracking code, do your best to implement UTMS for campaigns, and audit your discrepancies about once a quarter, this isn't all that much more you can be doing to get your data to match up more. With the rise of user privacy (Like ITP and other solutions), some tracking and counting of web data will change and potentially get less accurate. But here's the kicker - very few companies are actually leveraging all of that data enough to see a big impact in their return from these changes.
We've been working with major publicly traded companies, growing mid-sized companies, and scrappy startups for over ten years. Out of those 100+ companies, how many of them were hyper-analyzing their advertising data to squeeze out every bit of performance? One. And even they had room to grow with their existing data. So the truth is trends are more important than exact numbers, calculating profit is more important than perfect attribution, and doing something is better than doing nothing. What if, instead of launching another data audit, you invested in a project to determine your customers' lifetime value and apply that to your advertising strategy? Or launched a UX testing program to boost your conversion rates?
How to Build an Impact-Based Data Strategy?
Ok, so maybe you believe us now and will halt the crusades of your web dev team about why you're missing web transactions. How do you get the rest of the company on board and start making data-driven decisions? Creating a strategy is never simple, but there are a few key pieces that will help you move on and break up with your discrepancy fear:
Set up a quarterly audit for discrepancies. Cancel all other discrepancy tasks. If you have your analyst pulling a discrepancy report every week - stop. They will love you for it.
Educate key stakeholders on the nature of web analytics.
Begin using your financial system/backend database data for more decisions and models - use web data for on-site behavior and trends.
Create a decision-making culture, and evaluate data projects by which ones can create new revenue. Make sure more of your projects are intended to create revenue than are intended to report, clean up, etc.
The Bottom Line Goal
Your main goal should be to get over the data-worry phase as quickly as possible. If data discrepancies or data-distrust have been an issue in your company for quite some time, the issue isn't going away overnight. Instead of regular reports, excuses, or more quality projects, try to decide how you're handling this moving forward - no excuses.
Here are some ways to do that:
Create a data-discrepancy fact sheet for your business. Present it and get buy-in about the state of your implementations.
Challenge any derailments in meetings related to data-discrepancies. Say, "we have a picture of how our data performs - we need to decide in this meeting regardless."
Check-in with your analysts regularly. Help them prioritize projects, so that impact projects come first - challenge stakeholders who ask for data discrepancy or other reporting that doesn't create growth for the company.
And if you want help with any of it, Insight Lime Analytics is here to help.
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