“The customer is not a moron, she is your wife.” This less famous quote by David Ogilvy is about 70 years old, but has lost none of its relevance.

In fact, it is directly in line with Apple’s Tim Cook’s appeal at CPCD on January 29. Coverage of it has been very one-sided, focusing on Cook’s alleged criticism of Facebook. However, Cook gave a tour d’horizon of using technology for good, and the resulting corporate responsibility and positioning of Apple.

David Ogilvy couldn’t have known any of this 70 years ago, but he urged even then to take consumers seriously, to respect them as people who are intelligent enough to see through over-the-top advertising and who had better not be bored to death. “You cannot bore people into buying” in 2021 also means that you don’t buy the data of umpteen marketers together, in order to then track a consumer via targeting across the most diverse applications. In the 20s of this century, as in the 50s of the past, you need content: advertising that fascinates, that interests, that can generate resonance on its own.

But we no longer live in the time of Ogilvy or Bernbach. We live in the time of technology and data. If Ogilvy understood creativity as a measure of courtesy to consumers, modern marketers must face the challenge of how the demand for this courtesy plays out in their own strategy on data and technology. Or within Tim Cook’s logic, how to live up to one’s social responsibility as an advertising company—and thus as a service provider in this field. 

Not everything should go through the cycle of it emerging, being misused and consequently banned before we look at it critically and allow it to be possible with the right effect. The events surrounding elections in once democratic fortresses, or the division of societies through the spread of fake news should concern everyone who uses these media or uses them commercially for themselves. This is not solved with a one-time boycott as an advertising partner of Facebook, as effective as the #StopHateForProfit initiative was. 

But even then, criticism was mixed in with the applause, and questions were raised about financial or moral motives, about one-time restrictions or permanent consequences.

There was little discussion, however, about whether it was enough to point the finger at the social media giants or whether the company should reassess its own handling of customer data. In Germany in particular, the discussion about customer data usually only takes place in connection with legal initiatives, i.e. the DSVGO. What is allowed and what is not seems to be more important than what is right and what is not. The sudden abandonment of cookies is understood as an obstacle in the same way as the advent of adblockers five years ago.

However, in 2020, the year of COVID, two other major developments are significant that put the issue in a different light. One is the debate around purpose for brands. More and more marketing decision-makers believe their brand needs to communicate what role it wants to play in society, what it should stand for. One can argue whether candy bars need a socially relevant role or whether such a question should be decided in marketing and addressed in communication. But one can hardly argue whether brands that claim such a strategy for themselves also need to provide answers about their responsibility in handling and using data. This topic is causally located in marketing and is a direct question of communication. 

The second major issue in 2020/21 is that of direct customer access. D2C (direct to consumer) was one of the big winners at a time when many brands felt that the absence of a strategy in ecommerce; the dependence on a few platforms can make business very cumbersome, to put it nicely. The investment of a billion on the part of Dr. Oetker was not about a few leased delivery trucks, but about owning the last mile, bringing access to customers and the use of data for assortment and sales planning.

And while this deal was one of the big headlines in Germany during COVID, it’s important to note that most marketers have a purpose strategy in place rather than a data strategy. The Marketing Tech Monitor 2020 suggests that this strategy isn’t even in the drawer—no, it’s mostly not even in the planning stages.

It is becoming increasingly difficult to engage with customers because their media behavior has changed so massively. And even if many still carelessly release all cookies with every single website visit, marketing that continues to rely solely on the data policy of third parties will be too expensive in the long run. An idea about direct access to customers, about a first-party data strategy and on which technology this should be mapped, is becoming more and more essential. Technology for the benefit of people, as Tim Cook put it. And how do companies position themselves in this regard, what is their responsibility, who wants to be “a good corporate citizen in a tech world?” Marketing has to answer these questions, because the customer becomes pickier, but never becomes stupid. As David Ogilvy described it 70 years ago.

This article was originally published in German at Horizont. You can find follow-up coverage from Horizont here.

What is significance?

Making decisions based on data needs the support of a robust measure of confidence in the data.

Off the back of an event of some sort (campaign starts, new app feature, global pandemic), if we observe any change in our data we need to be confident the "thing" that happened was actually responsible for the change in data—not just a correlation. We need to be able to demonstrate that had this thing not happened, the data wouldn't have changed.

Then we can infer a causal relationship between the event and the change in the data. Remember—it's still a probability, we can never prove causality in a categorical sense, but we can be highly confident (and it's way better than guessing!). We can remove emotion and unconscious bias from decision-making. We don’t eyeball data or use our gut—mathematics informs the decision making process.

Here's the full chat and slides from last week's "Live with MightyHive" episode (scroll to the end for the slides):

How does it work?

The technology behind the Google CausalImpact R package that was demonstrated in the episode constructs a Bayesian structural time-series model and then tries to predict the counterfactual.

Simply, the mathematical model uses data prior to the event to predict what the data would look like had the event not happened. Important: the prediction is actually a probabilistic range of values. If the historic data is noisy, then the accuracy of the prediction will change. See the screenshot below from the demo walk through linked above. In the image below, the blue shaded area is the prediction (synthetic control estimator) from the model. If the observed data falls outside the blue region, we have significance!

The blue region gets bigger with noisier data. The broader the blue region, the more extreme the observation will need to be in order to achieve a significant signal.

Using Google CausalImpact

You can use the CausalImpact package with as little as three lines of R. R Studio is open source or you could try it out using rstudio.cloud.

Be advised, if you install the CausalImpact package locally, due to dependencies, you'll need at least v3.5 of R. I updated Linux on the Chromebook to get the latest version of R and R Studio via this very useful article and the package installation was very straightforward.

There's another option thanks to Mark Edmondson from IIH Nordic. Mark wrote a great Shiny app front end for CausalImpact that's free to use, so you can explore significance in your own GA data.

Using significance to establish causality and take action

We used the package to analyse client data to confidently answer key business questions that arose regarding KPI changes since the UK was locked down.

As well as considering YTD data (setting the 'event' as Jan 1), we use pre- and post-lockdown (Mar 9) date periods. Data shows clear patterns in purchase behaviour for retails sites. Media sites appear to exhibit explosive growth. However, the specifics regarding growth areas of content are highly informative—not what you'd expect to see by just eyeballing the data from afar.

For retail and media clients, the ability to identify current and future growth areas with confidence is a highly valuable tactic. At a strategic level, the forecast output from CausalImpact is highly actionable in driving campaign content, budgets, and timing.

While tactics for the current global situation include "managing," there is a clear near for preparation as well. Making decisions on current data and using forecasts with confidence proves to be valuable for our clients.

Additional Resources

Thank you for reading! The slides from the episode can be accessed here:

 

Watch the CausalImpact R package introductory video here (mandatory viewing!):