Marketing's Data Addiction Challenge
The marketing profession today has developed an addiction problem. We’re hooked on data - collecting it, analyzing it and now, drowning in it. We’ve gone from storytellers to data hoarders. Today, marketing departments are spending more time managing data than growing markets. The ROI of marketing has become opaque. Teams are spending more time on spreadsheets and in analytics tools than crafting campaigns and strategies.
Last month, my team spent nearly 20 hours analysing data from ten different platforms to make what should’ve been a quick campaign decision. Only a decade ago, we would’ve made that decision in an afternoon, with good data backing it up.
With over 25 years of experience in marketing from small to multi-million dollar budgets and working with business small and large, our professions addiction to data is creating more challenges, adding to the cost of delivering good campaigns, increasing CAC costs and CLV costs over time.
A key feature that almost every MarTech platform and product highlights is their analytics capabilities. This is nice, but what’s often missed by CMOs even those in Mops (Marketing Operations) is that this means yet another API, another integration not just into the stack, but more data into the data pool. This means a cost addition, both directly in paying for more data storage, but adding workload to the analyst(s) on the team.
There is a fallacy across many business units, that more data is better. This in part is the McNamara Fallacy , also known as the quantitative fallacy. In marketing terms this translates into undervaluing hard-to-measure impacts like brand sentiment and trust. Ignoring qualitative data, dismissing the importance of intuition and and experience.?
Until the last couple of decades, marketing struggled to prove ROI, to get good data. The profession relied heavily on surveys, focus groups and lagging business data and market reports. Then along came the deluge of data at the arrival of the Digital Age. Now, marketing could prove anything by slicing and dicing.
And in many ways, it was wonderful. Our ability to collect and analyse all this data was a boon to our profession and still is. I am a huge advocate of using data in marketing and there are now many excellent tools. The problem is that now we want everything to be datafied. And at some point, more data doesn’t help.
And some studies show that data decay rates are increasing, with B2B data decaying at around 2.1% per month. Yet we rarely consider the cost of data decay, which makes managing data even more expensive. And if you’re then using Artificial Intelligence tools to help with data analysis, it means you’re either upping the data management spend or accepting greater inaccuracies in the AI analysis.
So what to do? Take a flamethrower to your data collection and management. Well, not a literal one. If you’re a CMO or head of a marketing department, look deep into your data collection, management and storage. This is often where costs are hidden, but tend to grow. Assess the data analysis tools in your MarTech stack, evaluate the cost of maintaining that data over time, considering that it will only go up in cost, not so much to collect, but certainly to manage and maintain.
Get back to qualitative research in your mix and combining that with quantitative data. Cut back, then cut back again. Have some serious debates on what metrics really matter. Work closely with whoever heads up finance and sales. Figure out the data that really matters. You’ll find that less is more and that you gain greater credibility in the C-Suite. An ideal dashboard for the C-Suite is no more than one page. Your team dashboard should feed into that overarching summary dashboard.
Just because we can get more data, doesn’t mean that it’s useful and if you’re spending more time managing the data than actually extracting value, you know you’ve got a data overload issue. We have a deluge of data and a dearth of insights. MarTech tools that emphasise more data should be a warning sign, not a feature.