AnalyticsAvoid These 3 Common Mistakes in Digital Media Data Analysis

Avoid These 3 Common Mistakes in Digital Media Data Analysis

With multiple campaigns and multitudes of data pouring in, it can be easy to misinterpret details and jump to conclusions. Consider these common mistakes when examining digital media data to avoid overkill and wasting money and impressions.

Blue BullseyeWith multiple campaigns in full swing and multitudes of data pouring in, it can be easy to misinterpret details and jump to conclusions about results without sufficient evidence. For example, media buyers may frequently be marketing to consumers who would have searched and/or bought anyway, without being hit with display impressions.

Buying behavioral, retargeting, search and other types of targeting data can make it even more likely that you are preaching to the choir. The trick is to determine whether your ads reached those consumers who truly needed to be persuaded or if they reached those who were closer to the conversion tipping point—either they already are or were likely to become customers anyway.

Certainly we want to avoid overkill and wasting money and impressions on consumers who didn’t need it. Before making a hasty assumption that may prove to be unfounded upon a deeper inspection, consider these common mistakes when examining digital media data.

Assigning a Causal Relationship Where There Was None

It can be quick and easy to assign causality when much of your data seems to point in the assumed direction. However, thorough testing of the hypothesis is required before jumping to conclusions.

For example, perhaps we have a lot of display impressions correlated with high search volume in one geographic area. Don’t assume that your display impressions caused the increased search volume. Perhaps instead there has been a general overall spike in brand interest in this market. Could offline tactics be the driver? Perhaps there was local news coverage related to your products.

To test the hypothesis that higher display impressions are driving search, increase or decrease display impressions and isolate other potential factors to see what kind of measurable impact—if any—this has on search.

Assigning Attribution for Sales Incorrectly

Particularly in markets where there’s a high likelihood that you’ll be targeting customers who are already buyers, attributing the sale can be complicated. This is especially true with site and search retargeting tactics.

Dropping a cookie on a user who visits your site or delivering ads across multiple networks to anyone who searches for your keywords can be very effective. However, in a typical purchase cycle, consumers shop around quite a bit. Absent a direct click-through-to-conversion path, it’s difficult to say that those who come to your site and viewed a banner made a purchase because of that banner. And, we don’t know what got them to the site the first time.

Are you showing ads to an audience who would have bought anyway and then attributing their buy to the fact that you showed them an ad? It’s a slippery slope that requires testing to measure the real impact.

To test your attribution theory and be aware of how retargeting might influence your results, adjust the number of impressions, frequency caps and other parameters and closely monitor and/or control for external impacts on search. When you have an overall picture of the pre-purchase drivers, you can more clearly begin to see what’s sparking the tipping point of conversion.

Failure to Consider the Big Picture

Digital media marketing through search and display don’t exist in a vacuum. Therefore, we must take a more holistic approach in determining the results. Don’t just look at click-through or search rates, but consider conversion rates, basket size, and other KPIs in relationship to these metrics.

It can be easy to say that display isn’t driving conversion if there’s no direct click-through to attribute, but how many consumers might convert with a higher basket size because of display impressions? If we look at the total number of impressions, but don’t see an increase in clicks, we might think it didn’t work, but we may be actually making more money because consumers trust the familiarity of the brand enough to make larger purchases. And, ultimately, isn’t that what we’re after?

Had we just looked at clicks or just at impressions, these results may have been obscured. To get a more accurate picture of results, we must look at all metrics from a holistic perspective to arrive at a bottom line.

Digital Media Analysis: A Double-Edged Sword

We definitely have access to hoards of data—infinitely more detailed than we could have ever dreamed of in the offline world. However, without careful critical analysis of this avalanche of information, we run the risk of jumping to conclusions without hard evidence or misinterpreting the data we collect.

Real-time technologies enable us to quickly and accurately collect data, but it is even more critical that we interpret it correctly in order to enable mid-campaign optimization. By understanding the caveats of digital media data analysis and examining relationships carefully, media planners and buyers can launch and manage better performing campaigns with accurate, proven results.


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