We are in the midst of extreme times for nearly every marketer. Covid-19 has created panic buying, shutdowns of retail, coming disruption of food supply chains, 25% correction in the stock market and a 20% unemployment rate. Ad spending is down by double digits.

As I talk with marketers via my association with the MMA, asking them about how they plan to restart marketing as the US comes out of this, some of the big questions I hear are:

• When should I turn my advertising back on?
• Should I focus on brand or performance advertising?
• How can targeting play a role in reducing ad waste at a time when every ad dollar is being scrutinized?

It certainly isn’t business as usual for marketers and it can’t be business as usual for marketing analytics teams either.  Here are three changes that analytics teams need to focus on:

• Reduce reliance on marketing mix models
• Increase reliance on user level attribution modeling and experiments
• Proving and maximizing the value of targeting

Reduce reliance on marketing mix models. The tough spot that large marketers find themselves in is that no one can (or should) trust any updates to marketing mix models for at least a year.  They model historical relationships that then rely on the principle “the past is prologue” to apply their findings to future actions. But nothing could be further from the truth. Not this year anyway.

So, under these circumstances, I think marketers need to hit the pause button on new marketing mix modeling until 2021.

Increase reliance on user level attribution modeling and experiments. On the other hand, marketers need MORE help when the past is NOT prologue.  This is the paradox that marketers need to solve.

Fresh guidance for the next 12 months, especially as marketers open up advertising again, should come from Multi touch attribution (MTA) and controlled experiments.  Why?  Aren’t they also susceptible to disrupted advertising to sales relationships?  No.  MTA and controlled experiments use data that are FORWARD looking not BACKWARD historical. MTA analyzes conversions vs. ad serving as data are unfolding going forward during campaigns and flights of advertising.  Same with experiments.  The experiment is designed and executed in the future, not the past.  The results are fresh and can be focused on the questions above.  “Should I start advertising again”? Highly testable. “What mix of brand and performance marketing works best to balance brand needs and the need to show tangible results?”  Again, highly testable.

Proving and maximizing the value of targeting. “Reducing wasted ad impressions” or “addressing advertising to consumer targets producing 2X (or more) return”, are two sides of the same coin. One is the sickness, the other is the cure. Either way, there will be financial pressure on marketing to prove that advertising is adding to the bottom line. The way to make advertising campaigns deliver each and every time is by smart targeting.

Some examples of 50% of your ad impressions being wasted that can be reduced by smart targeting:

• Ad impressions served to males for feminine protection products
• Ad impressions for smart phones served to those who got a phone within the past year
• Ad impressions served to those with no interest in your offering

Let’s say you are a QSR pizza chain. What percent have any interest in pizza from a pizza chain?  Let’s say it is 50%.  If you have access to a segment (e.g. via a data store) of pizza chain buyers, within that segment what percent is potentially interested in your offering?  It will depend on the validity of the segment.  You need to know what percent of that segment really are pizza chain buyers. Secondly, you need to know how interested they are in your particular brand.  If there are 5 segments you could license from different aggregators for programmatic targeting, my experience would suggest at least one segment is no better than random, while the best segment could predictably deliver twice the ROAS. Choosing the right segments can make your advertising self-funding AND build your brand at the same time because these are not only your current customers. Choose the wrong segment and you are back to ad waste.

Coming out of Covid-19 lockdowns, as we reboot marketing, we must reboot marketing analytics. Don’t just do what you were doing before Covid-19 hit, change practices in order to provide the right guidance.