Marketing Discrepancies: Organizations’ Analytics Are Less Advanced than Leaders Report
By Samantha Beavers
Big data is bigger than ever. Consumers are flooded with new messages every hour. And any serious marketing department realizes that if they’re going to compete in a crowded marketplace, they need to stay on top of it – driving improvement to their campaigns with valuable consumer insights and strategic, data-driven decisions.
As it turns out, though, most organizations’ marketing analytics aren’t as advanced as leaders would like to think.
Adverity’s Marketing Analytics State of Play 2022 report dives into an interesting phenomenon: most companies identify themselves as data-driven and mature in their analytics capabilities. But when it comes down to it, their analytics don’t measure up.
Talking a big game
This means that most organizations still have a long way to go in order to stay competitive – and few, it seems, already have what it takes.
Let’s look at the data, shall we?
The report shows that 66% of respondents claim their marketing teams are analytically mature. Interestingly, though, 77% of those same individuals don’t have a dedicated place to view their marketing data and 68% primarily build their marketing reports with spreadsheets. Further, nearly half of those who identify as analytically mature still rely on manual data wrangling.
“This involves cleaning, structuring and integrating raw datasets in order to make the data usable. It’s a very time-intensive process, but the larger concern is that these manual processes can corrupt your data,” explains Rishika Rishika, director of Poole College of Management’s Master of Management, Marketing Analytics (MMA) concentration.
Automated data integration and centralized data lakes aren’t necessarily required for advanced analytics – but those hoping to enhance their marketing performance with timely insights can’t afford to settle for anything else. Specifically, these are table stakes for predictive modeling – which 61% of respondents hope to implement this year and 69% of analysts say is their top priority for 2022.
Can predictive modeling be done without them? Sure. Is it sustainable, though? Not so much, Adverity says.
Analysts who wrangle data waste significant time and effort they could expend interpreting the data and using it to make strategic, real-time decisions instead. And with the amount of human error introduced in these manual processes, running predictive models with them may not be worth it. After all, those who rely on manual data wrangling are almost four times more likely to doubt their data’s accuracy.
Taking a closer look, it seems that many who identify as analytically mature have overstated their analytics capabilities. While they may take their data seriously, they have some serious catching up to do before they can be considered advanced.
Different vantage points
Both analysts and marketers can overplay their analytics maturity, but interestingly, analysts do this to a greater degree – with 72% of analysts identifying as mature compared to 60% of marketers.
Meanwhile, chief marketing officers (CMOs) seem less confident in their analytics capabilities. Of the 63% who say they use data to inform their decisions, the majority say they don’t have the data insights needed to guide their strategies – and nearly half lack the recommendations needed to improve their performance.
Why the disconnect between analysts and other marketers? Likely there are a few reasons.
The most obvious is that analysts have greater proximity to the data, as well as a clearer understanding of what analytics the organization is capable of. And shouldn’t they know, more than marketers who are farther from the data, how mature the organization really is?
That’s just it, though. If marketers and CMOs feel that their organization lacks the data-driven insights needed to improve their overall performance, it doesn’t really matter how much data or what tools the organization has access to. That’s why the perception of marketers probably offers a more accurate picture of the organization’s analytical maturity.
“If an organization’s analytical capabilities aren’t coming to bear on its day-to-day marketing decisions, then they must not be as far along as analysts think they are,” Rishika explains.
Another possible reason for the disconnect is that analysts may be assessing their own potential rather than their organization’s progress. Analysts may be trained in state-of-the-art analytics techniques, for example, but this doesn’t mean they’ve pulled them out of the toolbox. If they’re still inundated with manual data processes – and many of them are – they may not have the capacity to leverage these advanced tools even when they have access to them.
Finally, it’s worth noting that for a lot of organizations, the bar for analytics is still pretty low. So perhaps companies, comparing their methods to those of traditional marketers, feel as though they’re pushing the envelope – when in reality, they’re just scratching the surface.
“Overall, this disconnect serves as a reminder that being analytically engaged and analytically advanced aren’t the same thing,” Rishika says. “It also shows the need for more analytics talent, greater investment in new tools and a robust commitment to technological growth.”
In a world where every organization is fighting to be data-driven, businesses must take an honest look at their analytics maturity and take appropriate steps forward. Before jumping into more advanced technologies like predictive modeling, for instance, they would do well to start with the basics – namely, improving their data reliability.
Perhaps the most worrying reality uncovered by Adverity is that while 69% of analysts believe they’re data-driven, 41% of them struggle to trust their data. For marketers depending on data to drive their strategies and make smart decisions, this is unacceptable.
Thankfully, automated data integration tools can help. Eliminating data wrangling and putting all the data in one place, these tools give marketers a more comprehensive view of their performance and allow companies to drive their marketing strategy with timely, reliable data insights. This enables businesses to make more proactive decisions and make sustainable progress in growing their analytics capacities. At the same time, it offers analysts greater margin to implement more advanced analytics techniques.
“Investing in these tools ultimately helps businesses maximize the analytics talent under their roofs,” Rishika says. “Every organization that wants to stand out from the crowd is battling to recruit and retain analytics talent – so they have to think about how to best leverage that talent, too.”