If I had a dollar for every time I heard a marketer or buyer say that they’re “data-driven,” well, I’d be richer than I am now. Of course, data is the foundation to understanding the impact of ad campaigns. But too often I see ad effectiveness pretty much stop with data. A podcaster, network, agency or advertiser gets a report full of charts and graphs representing data – stats about what happened – and that’s just about it.
But data is only just the start, and isn’t what drives real business objectives.
It’s much more important to be “insights-driven.” Insights are where the rubber meets the road. Or, more precisely, where data meets meaning and purpose. It’s where we find the story that explains the outcome, and informs the roadmap.
Data Tells You What Happened
In ad effectiveness research there are data, analytics and insights. Data are the results telling you what happened.
Let’s look at a real-world brand lift study. A national advertiser debuted new messaging with an audio campaign across streaming and podcasts. Logically, a top KPI was message association – gauging if listeners who heard their ad also associated this new message with the company.
After one quarter of the campaign, we found that 29% of streaming listeners made this connection, while a smaller percentage of podcast listeners did – just one fifth. The overall results were not surprising, since it takes time for a new message to sink in. But still, it begged the question: is this a good result? And, what should the advertiser do based on it?
On its own, data doesn’t provide these answers. What you need are insights.
Analyzing the Data
An essential element of the brand lift analysis is comparing the scores of listeners who heard the ad – the exposed cell – to those who didn’t hear the ad – the control cell. In this case only 17% of the control cell connected the new message to the brand. That means the streaming ad generated a lift of 12 points, while podcasts only generated three.
After running statistical analysis, we found that the streaming lift was significant, meaning we’re confident that if we re-ran the same study over and over we’d continue to see that lift in message association 90% of the time. In other words, it’s a good result.
The podcast results? While not terrible, they’re not nearly as good.
With this analysis we had a clearer understanding of what happened, and how to evaluate the results, but it still wasn’t clear why things turned out this way. The brand might wonder if podcasts just aren’t a good fit, and even question if they should continue advertising on the channel. But this data and analysis don’t brightly light the way.
Learn the Why (and Optimize) with Insights
This is where insight comes in. We interpret the data and the analyses, and look for ways to use them in the real world.
Looking at other brand measures, like favorability and consideration, we saw the same picture as message association: significant lifts for streaming, and none of podcasts.
As this was a big campaign, there were many different podcast placements, shows and networks, and yet the performance was pretty consistent for all. It’s not unusual for some placements to perform better than others, but given our experience it’s more unusual to see such flat numbers across the board. This propelled us to dig deeper.
Brand lift is about an ad campaign’s influence on consumers’ perception of a brand. The ad creative is what does the work, so it makes sense to analyze that, too. It’s not our job to judge or critique it on its own. Rather, we examine the ad based upon what we’ve measured.
Turns out, the brand took a very creative approach with their, er, creative. That seems to have been a good bet for streaming, but the podcast ad likely sounded like too much of a departure from what listeners expect, not aligning well with the podcast content.
Listening to it we could clearly tell it was different. At the same time the copy and approach was pretty much the same for all placements, whether it was a host-read, announcer-read or pre-produced spot. All of this led us to suggest that the ad itself – rather than the show, network or channel – might be the most impactful factor.
Our recommendation was to stay the course with the streaming strategy, since that was showing clearly positive results. Instead of suggesting any changes to the podcast placements – because they pretty much performed equally – we recommended re-evaluating the creative, potentially trying a more conventional approach. The following quarter, that’s what the advertiser did.
The Impact of Insights
The results speak for themselves. In the next quarter, podcasts generated a significant lift of 9 points in message association, actually surpassing streaming this time around. The change in creative strategy made a measurable impact.
To generate these insights we looked at the overall context of the study and the campaign itself, employing both our knowledge and experience with how audio ad campaigns work and perform. This allows us to discern what data is relevant, what about the analysis is important, and why.
While the overall ad creative turned out to be pivotal in this case, in other cases the story is about matching the ad creative and messaging to specific audiences or audience segments, defined by demographics, content preferences or consumer behaviors.
It’s a holistic enterprise that is not just about being “data driven” – it’s about being driven by the right data and analyses.
Those are insights, and it’s what we are focused on delivering to our clients. It’s also why “Insights” is literally in our company’s name.
