Just Because It’s Data, Doesn’t Mean It’s Right

Why might this concern me?

It’s because I know the dirty little secret of the digital marketing world...

For most of us, marketing data has serious issues and limitations and used poorly will deliver us far less than we think. Sometimes, it will do as much harm as good.

Marketing data experts and most seasoned marketers won’t be shocked by this statement. They use data, often brilliantly, despite these issues and limitations.

The real victims are the everyday marketers and executives who believe the often simplistic marketing pitches of data-driven agencies, the case-studies from giants like Google and Amazon or the feel-good marketing articles that tell us that data holds the clear and simple answers to our increasingly complex marketing questions.

If I didn’t lose you at the first mention of data, read on! I’ll cover the 5 common issues I see working on campaigns for a wide range of mid-sized businesses often with long and complex sales cycles.

Most companies use Google Analytics on their website. They also run campaigns where data is collected in Facebook, Google Ads and many other platforms. The first mistake many marketers make is in not actually reviewing this data on a regular basis. If it’s not analysed, all the data in the world means nothing. Honestly, when was the last time you checked your analytics package in detail?

Data must also be accurate for it to be meaningful. When our data specialists audit new client campaigns, they often find issues with how data is being collected. Roughly half of the campaigns our specialists analyse have significant configuration or tracking issues—Google Analytics has been incorrectly installed, it’s double-counting key metrics, it’s not picking up goal completions or it’s tracking goals that are largely meaningless.

We see plenty of campaigns where marketers and executives are obsessing over the wrong metrics or numbers that simply don’t matter for the business. It’s always tempting to focus on ‘vanity’ metrics, whilst ignoring the more challenging metrics that will drive future revenue. But before you focus on a metric, you need to ask yourself if you’ll be able to make a business decision based on what the metric tells you. If you cannot answer this question, you’re probably focussing on the wrong thing.

Attribution simply explained, is how you credit separate channels, or touchpoints within those channels, for the role they played in a prospect’s path to purchase or goal completion. If people visited your site a single time and then either converted or left for good, attribution would be simple. The channel that drove them to the site, let’s say Google Ads, would be correctly attributed the full value of the purchase or goal.

But what happens in the real world, where most paths to purchase are complex and involve multiple visits on possibly multiple devices? Which channel deserves credit? Is it the one that drove them to you in the first place (known as first-click attribution)? Or is it the channel that delivered them to you when they finally reached the goal (last-click attribution)? Alternatively, does it make more sense for you to equally value all the channels that drove a particular person to your site prior to them converting (this is called linear attribution)? These are only three of many ways to attribute credit to different marketing channels whenever a goal is reached.

Attribution is a critical part of making decisions, but you need to know what attribution means for you, keeping the full buyer journey in perspective. Chances are that your analytics platform is defaulting to a last-click attribution model.

Ask yourself: How complex is your buyer journey? How likely is it this model undervalues the earlier stages in the buyer journey and over-values the final stage? How is this affecting the way you judge the performance of each channel? What impact is this having on how you set budgets and select future channels?

It is very easy to understate the complexity of data and overstate the insights the average business can receive from the relatively small amounts of data they have access to. A lot of articles you’ll read talk about how powerful data is, but those articles often base their conclusions on large companies with highly sophisticated data sets. Many small and medium-sized businesses don’t benefit from this, as they simply don’t have the volume of data required.

Then there are the many valuable actions never measured. A prospect might see an ad and be influenced by it, but not click on it. Someone else might receive and read an email from you and then later manually type in your website or search on your brand name. It’s impossible to measure the value from these sorts of activities. Yet we regularly see marketers making major decisions based on data alone.

I am not for a second suggesting that as marketers we burn our Google Analytics logins and storm the barricades to get to the people who create our flawed marketing reports. Quite the contrary - I’m a huge fan of data and believe that a good marketer needs to be strong in this area.

What I’m calling for is a more mature stance when it comes to data. We need to clearly understand what it can and cannot do. We need to understand when it’s time to think and act like marketers solving complex strategic and creative problems. And when it’s time to think and act like a data scientist. Most importantly, we need to know that most of us, in fact, are not data scientists.

As a first step, take some time to sit down with your team and agree on a small number of metrics that will genuinely give you insight into how your marketing activities are contributing to company profits. Then work backwards to develop and improve campaigns to get those metrics moving in the right direction. But don’t forget, consistency is key, and to start with, keep it simple and sceptical.

And remember, just because it’s data does not mean it’s right.