Something strange is going on in the world of marketing… Analysts say they have tons of advanced tech, but marketers don't seem to know about it.
In an earlier blog, we found out that marketers want martech that tells them what to do, and how to make better decisions. Analysts, on the other hand, prioritize tech that allows for more advanced analysis.
But our latest report uncovers that, beyond disagreeing on what tech they need, it seems marketers and analysts can’t even agree on what they already have access to.
Analysts are much more likely to say they have advanced capabilities
Remember when we found out that manually integrating data is the top challenge for analysts? Well, it turns out that only 59% of analysts have access to a centralized data warehouse — which might explain in part why they have such trouble getting all their data in one place. What’s more worrying is that for marketers, this number falls to just 43%.
You might be thinking, perhaps we should cut these marketers some slack — they don’t necessarily need to be interacting with data warehouses if their analyst is on the case. And maybe they don’t even need to know what a data warehouse is.
But even if that were true, (and I’ll explain why it isn’t in a second), it doesn’t explain why we see a similar story with reporting tech.
This is tech that marketers absolutely interact with, and need to understand. While 64% of analysts say they use a BI tool to visualize their marketing data, only 47% of marketers agree.
In fact, far more marketers say they build reports in spreadsheets (56%), despite the fact that BI tools and data dashboards offer much more efficient, effective, and accurate marketing reporting than spreadsheets.
But by far, the biggest disagreement between analysts and marketers is on whether they have access to predictive analytics. Although 60% of analysts say they have predictive analytics, only 42% of marketers agree.
“The disconnect between marketers and data analysts is only going to get bigger which will cause greater impacts on the business bottom line, as marketing performance and operations are not aligned.” Says Harriet Durnford-Smith, CMO of Adverity. “Businesses actively need to start changing their attitudes towards data and this starts at the top.”
If your marketing tech is gathering dust instead of turning data into insights, then something's gone seriously wrong. To get marketers and analysts back on the same page, we need to figure out why this gap exists.
Why does this gap exist?
There are a couple of reasons marketers might under-report their team’s capabilities. Let’s take predictive analytics as an example.
One reason predictive analytics might not register on a marketer’s radar could be that predictive modeling isn’t actually impacting their marketing operations. Sure, in theory, the capability is there — analysts have the training to run predictive models, and they’re keen to use it. But oftentimes, analysts lack the tools to make predictive analytics a sustainable part of the marketing strategy, meaning it has no practical impact. And we already know that marketers are obsessed with practical impact.
“Marketing data analysts are being hamstrung by old processes and outdated technologies. Their skillsets are being wasted.” Says Harriet Durnford-Smith. “At the same time, the marketers that are meant to be benefiting from their insights are not getting the most value due to this lack of foresight in the evolution of martech. For any business that considers itself analytically mature, this has to be the number one priority in solving this issue.”
Marketing data analysts are being hamstrung by old processes and outdated technologies. Their skillsets are being wasted.
Harriet Durnford Smith, Chief Marketing Officer at Adverity
Besides a lack of tools, another possible diagnosis is a lack of communication. Predictive analytics needs input from both analysts on the data capability side, and marketers on the strategy. Marketers might not be data-savvy enough to know what questions to ask, or what kind of brief to give an analyst when it comes to predictive analytics.
Analysts and marketers can’t rely solely on a profound knowledge of their own roles, because like it or not, they’re in this together. If their knowledge is sitting in two separate silos, then it is much less valuable.
Bridging the gap between marketers and analysts
What marketers want most when it comes to data is to know what actions to take, and whether they’ve paid off. This is what people mean when they talk about data democratization — everyone in an organization is getting access to the same data.
Our challenges and priorities report already showed us that measuring the impact of decisions is something marketers in particular really struggle with.
Analysts, on the other hand, prioritize more technical features. It’s something we can see when we compare top data priorities for 2022. While marketers are most keen to achieve a single unified view of marketing data this year (58%), 69% of analysts see predictive analytics as the top focus in 2022.
Data democratization might be a buzzword, but it is still of enormous value to businesses.
Harriet Durnford Smith, Chief Marketing Officer at Adverity
Before marketing teams charge ahead with more advanced analytics, they need to take stock of their current tech stack and consider this gap.
Is there tech here that isn’t having a practical impact — and why might that be? Whether it’s a miscommunication, a problem with adoption, or a lack of tools and time to get accurate data, all of these issues will continue to crop up and cause bigger problems as you add in more tech.
“Data democratization might be a buzzword, but it is still of enormous value to businesses. By communicating and working together, not only can the business truly take stock of all the capabilities in the business, but start building advanced layers on top of that. If everyone continues to work across different platforms, technologies, processes, and concepts there will never be a way of becoming data mature.”
So if you’re going to bridge the gap between marketers and analysts, then it’s going to have to be from the ground up — otherwise, that shiny new predictive analysis tool might end up just sitting there, gathering dust.
