Data Principles That Could Help Brands Regain Trust with Consumers | Industry Insights | All MKC Content | ANA

Data Principles That Could Help Brands Regain Trust with Consumers


Despite my reluctance to socialize, I have been running an experiment at dinner parties. In half of these parties, I introduce myself as a chief data and technology officer in advertising.

In the other half, I simply say I work in advertising.

In the latter case, I am asked: "What brands do you work for?" or "Did you make that ad?" In the former, the conversation begins with the other person trying to understand what data is doing in advertising and ends with them saying, "I hate what you guys do with my data," or "You folks practice the dark arts. You track me and abuse my privacy."

We have all seen the surveys that show the importance of creativity, the irritation of people who see the same ads repeatedly while binging a show, and the toxicity in social networks that has led consumers to consider them untrustworthy.

Most advertising decisions are driven by cost efficiency because we cannot get attribution or measurement right. Therefore, we spend more on paid media than we probably should, and don't focus much on horizontal optimization across platforms.

In the end, it's the CMO whose demise is predicted. Between the rise of retail media, CTV, and the various cause quagmires that lie for brands, there is a temptation to optimize for performance, a nebulous word that rules in favor of data-driven platforms.

But people don't like it. They're voting with their wallets, willing to pay for no-ad services on streamers, especially if they can afford to. And regulators are turning the screws through privacy policies. But it doesn't have to be this way; we can make data-driven marketing more equitable for brands and people.

Three principles are crucial in changing ad experiences for people. The first is what I call "less is more." Over time, we have allowed the Big Data approach to guide our thinking. Big Data is data led and looks for insights without any assumptions. Prior to that the approach was based on hypothesis testing; it still exists but our data strategies are led by the Big Data approach. The Big Data approach works with volume; hence the focus of most organizations has been on collation of data and investment in infrastructure to process these large datasets.

I've heard CMOs tell me how they are either drowning in data or having debates about what to do with what they have.

But what if we reversed the approach and asked ourselves a different question: What is the least volume of data (along with other characteristics like variety) that we need, to answer the most important questions for our brands?

In some areas, there are hypotheses available that need to be proved or not; in others, we aren't sure. For example, we may have a hypothesis for why our brand is losing share in a market but when it comes to tactical media optimization, it's best for the data to go find opportunities. The data strategy set for the organization is vital because it mimics the strategy of the brand. This seems obvious, but in many cases, we seem to have let the data strategy dictate and even overwhelm the brand strategy.

The artificial intelligence (AI) brigade would say we need more data. Not necessarily true. There are AI approaches like reinforcement learning and supervised machine learning that work with small data sets. Their only requirement: human input and learning by doing. Isn't our industry about people? In any case, with consumer signals going down and the platforms neither sharing data nor passing it to other platforms, we will be forced to rely on these AI approaches.

A less is more approach to data could free us to think about people and our brands. We need to cut bloat in data and become precise with it (the irony!).

The second principle is simple: Speak in plain understandable language when it comes to privacy. Use plain English to describe data usage. The New York Times privacy policy is a good foundational example. Why is this important? As a study published by the University of Pennsylvania showed that 88 percent of Americans don't think it's right for companies to collect information without their consent even if they were given discounts. And on consent, Americans don't feel that they understand what is being collected and how it's being used. This fuels overzealous policies that don't solve much but create more hurdles, such as the California Consumer Privacy Act, which will let consumers request that all data brokers delete their information.

The third principle is to drive horizontal optimization. People hate multiple ad exposures with no control over frequency. AI can solve this without requiring change from the platforms. Between the AI approaches outlined above and the creation of synthetic data, AI can act as the intelligence bridge for a CMO. Just not in the way the AI brigade is claiming it will work.

But, as an industry, we need to make that a priority over automation that cuts costs but doesn't drive efficacy. By using less but more precise data, having a clear privacy policy that gives choices to people to opt-out, and using AI approaches that better control for frequency of exposure, we can start to win back the trust we have lost with people. We would also save a pile of money, make our brands successful, and I can finally go back to attending dinner parties without having to worry about how I'm introducing myself.

The views and opinions expressed are solely those of the contributor and do not necessarily reflect the official position of the ANA or imply endorsement from the ANA.

Arun C. Kumar is the author of the upcoming book The Data Deluge: Making Marketing Work for Brands and People. He is the former chief data and marketing technology officer for Interpublic Group with a proven record of developing products, managing technical operations, and setting data and technology strategies that drive business growth. Kumar has 25 years of experience in driving digital development, with global roles across APAC, Western Europe, India, China, Japan, Brazil and Mexico. He is a member of the Forbes Technology Council and the CNBC Technology Executive Council. He earned a bachelor's degree in Journalism from the University of Delhi. He completed his post-graduate studies in communications at the Mudra Institute of Communications Ahmedabad in Gujarat, India, and his executive education through Wharton's Advanced Management Program at the University of Pennsylvania.