Ask anyone across marketing and they are going to sermonize the importance of data and analytics in the decision-making of their organization. They espouse data-first or data-led, or boast their innovative use of machine learning, predictive analytics, or even AI to optimize their decision-making, innovation, optimization, or overall operations.
If this is the case, why are so few decisions actually being made using insights derived from data?
In late 2020 Gartner released their annual Marketing and Data and Analytics Survey, capturing the pulse of hundreds of senior marketing executives and their view of the effectiveness and value they are receiving from their marketing data and analytics investments. Surprisingly, over half of the senior executives polled admitted that their investments were not bearing the promised fruits of knowing everything there is to know about everything.
But is it really surprising? I don’t think so.
A study of the top reasons why data isn’t used to drive marketing decisions (see below) might suggest that data isn’t as great as it is touted to be. Poor data quality jumps out as does lack of actionability (nodding to “No clear recommendation” and “Not Actionable”).
But the first and the fifth reasons claimed on the list is where it all starts to make sense.
“Data findings conflict with intended course of action” and “Decisions are driven by our trading/promotional calendar.”
Data dumped into organizations is far different from data-led organizations. A speaker with me on a panel once said, “In order to succeed with data, there needs to be a company-wide culture of data,” and I couldn’t agree more. Just because an organization has data doesn’t mean that an organization is prepared to use data. But, as always, the devil is in the details. So what do I mean by “use data?”
Marketing data comes in several forms. From analog qualitative group research data to AI driven eye-tracking data. But again, as stated before, it isn’t that there is a lack of data available. Where the rubber hits the road is how the data is analyzed to give marketers a better picture of what direction their organization should go.
For example, let’s take three types of common advertising data. Segmentation data: Data that is to inform you of a marketer’s ideal customer. Digital performance data: the number of people who clicked and/or converted from your last digital campaign. Physical performance data: Footfall or number of people who purchased at your physical location. All of these data points are important to a brand, but in structure they are apples and oranges. The standardization of unique data sets to tell one story is required to tell data stories that drive change and impact an organization. Especially when one data set is more highly regarded—sales data—than another—persona or behavioral data.
Many companies are selling data, but what marketers need is less data and more companies with the science and engineering chops to deliver data as a story. Until that happens, we will keep seeing the same reports.
We connect marketers with the data most crucial to their cause. Our comprehensive social data helps achieve new perspectives on customers, and our segmentation tracking allows marketers to follow the most crucial data to advance strategy. Get in touch today to see how our data can help you make better decisions: email@example.com.