Avoiding the hidden costs of 'data inflation'

Guest Writer's picture
by Guest Writer
Chris Martin, Chief Marketing Officer, FlexMR

How are marketers navigating the challenge of 'data inflation'? Chris Martin, Chief Marketing Officer at Cumbria-based research agency FlexMR, explores strategies to cut through the noise in an increasingly complex world of data.

Inflation, in its most basic form, is the rate at which prices increase over time. It can be affected by input costs, supply and demand, as well as a whole host of more complex economic factors.

But the result is always the same. As prices rise, the purchasing power (and, ultimately, the value) of money falls.

Since 1982, UK prices have risen by a cumulative 1,300%. Or, to put it another way, a single £1 coin in 1982 would buy you the same groceries as £14 would today. The purchasing power of the pound sterling has fallen by 95% in 50 years. Your coin is no longer as valuable as it once was. Because, as more money enters the system, you need more of it to keep pace.

While often limited to economic conversations, it’s important to remember the phenomenon of inflation isn’t unique to our financial system. And nowhere is it felt more consistently by marketers than in the volume of data we have to work with. It is estimated that the world produced 6.5 zettabytes of data in 2012. But only a single decade later, that figure is expected to reach 97 zettabytes in 2022. That’s an inflation rate of over 1,300% in 10 years, not 50.

So, what does this mean?

Well, by the end of the last decade, media mix research – for example – needed only to compare a handful of distribution channels. Today, there are ecosystems of complex behavioural data held in the walled gardens of tech giants. Audiences can choose between hundreds of TV channels, thousands of games and millions of websites. But far from making media investments easier, this exponentially growing choice and the data that each channel provides, grinds effective decision making to a halt.

Compounding this challenge is the competitive arms race for customer-closeness. Loyalty programs, digital tracking tools, behavioural indicators and personalised customer journeys are all converging to drive performance and understanding, but often at the peril of true customer-centricity.

To understand how this contradiction occurs, let’s look at point-of-purchase retail data. I’ve lost count of the number of times that I’ve sat with marketers who can explain, in excruciating detail, attribution models that determine the influence of touchpoints on consumer purchases, how messaging impacts purchase volume and frequency, the relationship between demographics and basket makeup – but struggle to concisely answer the most fundamental of questions. Who buys from you? Why did they choose your products? What is important in their life right now?

That’s the hidden cost of data inflation. The more information decision-makers have access to, the harder it becomes to zoom out from individual measurements and construct answers to the bigger questions. As we traverse further into the exponential age, where these challenges will only grow in magnitude, there are three tactics that will help ease the bite. As marketers, we must return to fundamentals first, build empathy into a commercial asset and completement big data with small data.

Let’s take a look at each of these tactics in turn.

Returning to Fundamentals

Stripped down to its essentials, marketing is a simple practice. It’s the process of taking a product or service to market. That’s it. In order to do this, we need to segment the total audience for our proposition, target the groups that are the most valuable, and develop a position that resonates with them.

The core decisions marketers make still fall into the traditional four 4Ps of product, price, place and promotion. It’s vital that these questions are answered first, as they form the baseline for the tactical complexity that comes afterwards. The fundamentals give us perspective. They act as a reminder that there is a path to follow and a filter through which its easier to determine which data points should carry more weight than others.

Empathy as a Commercial Asset

As marketers, we are not representative of our customers. Even if we purchase the products or services that we market, as soon as you sit behind the desk – your decisions are clouded by knowledge that doesn’t exist in the public domain, pressure from looming targets and the very nature of our decision-making frameworks.

But that doesn’t mean we can’t understand the feelings and experiences of our customers from their frame of reference. All too often, customer-centricity is boiled down to knowing customers. But that can quickly feel like a static state; an activity to be done. Truly customer-centric companies have the confidence and humility to say: “We don’t know how our customers feel, we’ll ask.”

By building a culture that is connected to customer context and supplemented by data – brands stand to gain huge competitive advantage at all levels.

Complementing Big Data With Small Data

Finally, let’s talk about a stark reality of the future. Big data will get bigger. Decision-making will become more complex. We’re fast approaching a point where consumer datasets are so large and interconnected that AI will be better at spotting patterns than humans.

This is the inflection point that marks the greatest point of peril for data-driven marketers. Making decisions (that likely will work) without knowing why. It’s a somewhat unsettling, almost dystopian thought. But the role of why has never been the job of data in the first place. Data is descriptive. It can tell us what has happened, and what might happen. To understand why, we need to build greater capacity for qualitative insight.

Interviews, ethnographies and focus groups are the social science methodologies that have, for generations, helped marketers understand the thoughts and feelings that underpin consumer actions.

Even today, data inflation doesn’t pose a threat to them. Because, in the competitive arms race for knowledge, they will act as the gold-standard insurance against data inflation – complementing micro measurements with macro, human context.

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