We explore why data-driven marketing needs a rethink as brands start to recover from the COVID-19 crisis
But while COVID-19 has clearly wrought significant changes, there are many behaviours proving more resilient to the unprecedented circumstances. According to general manager for data quality and targeting at Experian in A/NZ, Steve Philpotts, many data points remain constant.
“The key to understanding the extent of the impact of a change in data is to understand what data is used to make decisions. For instance, is it stable or volatile, and to what extent does your business model rely on human behaviour?” he asks.
“If we take clothing retail as an example: People will still have the value of taking pride in their appearance. A children's clothing retailer is still going to target parents with children, female fashion labels are still going to resonate with females of certain age groups. The data around who their customers are remains fundamentally unchanged.”
What businesses do need to take into consideration now is the ‘how’ and ‘where’.
“Such as, how will people shop in a 'new normal', or where should our products be located - a boutique holiday location, CBD location, or in suburban shopping centre,” Philpotts says.
That places huge emphasis on data quality, and the need to analyse what data might be incorrect or missing. It means validating contact records, incorporating data collection as an engagement strategy, and leveraging third-party data providers to back fill information.
“With the latter, this will incur a cost but will quickly fill the gaps and any cost should be outweighed by a strong customer return,” Philpotts says. “The key here is to make decisions that will future proof your database.
“If you are filling gaps in behavioural or attitudinal data due to COVID-19, it is important to note there hasn't been enough time to fully assess if these behaviours have been embedded. Also, the collection of data may still be changing and could be problematic, as you could amplify one-off changes, which could lead to incorrect decision making in the future.”
Philpotts says one lesson emerging quickly is the need for organisations to have the agility to work with data to reach decisions quickly, and be able to work with data from multiple systems, or of unknown accuracy. That, in turn, means models need to be sufficiently robust and flexible to handle these new requirements.
“Driving cultures of innovation and learning are also great strategies to test and learn, in turn providing a more holistic view of how to engage with customers,” Phillpotts says. “Ultimately, the successful organisations that come out of this environment are the ones that have the agility, and the ability, to drive authentic engagement with their customers to foster stronger relationships.”
That concept of agility has also become more important for director of strategic consulting for APAC at data technology company Silverbullet, Tim Beveridge.
“A fast-moving situation requires an approach to data science that can give you a fast-moving read on those signals,” he says. “Most of the modelling done in marketing is on the assumption things are slow moving and predictable and follow patterns. Rather than using the approach used in the past and try to make that work, I would be getting my data science team to explore other methods for using faster moving signals.”
One very obvious area for change has been in out-of-home advertising, especially that connected to public transport.
“That public transport signal would add more value whereas previously it would have flatlined,” Beveridge says. “It is about resetting how those models are built, resetting what feeds into those models, and building more than one model to figure out the best way of moving forward.
“Some of the variables in models marketers are using that would have been excluded for not being very helpful should now be included. Our recommendation is to go back to your assumptions and your models with your data scientists to rerun things to see if those models describe the reality of the world in a different way.”
Organisations that have geared themselves towards agility are going to be able to recut models a lot faster, implement and operationalise those models more easily, and communicate about those models a lot better, Beveridge continues. “Agility is a key facet to all of this.”
So while the world might be different, and the data it generates along with it, the fundamentals of good modelling and analytics should hold true. For the team at Dentsu Analytics, what is required now is exactly what good modellers do all the time.
“We are always trying to account for the sensitivity in the market to any given variable,” Price says. “The conversation we are having with clients is that this is business as usual for your model, which means if you are doing it right, you are continuously updating those models so you always have the latest data feeding into that.
“Models are built by looking at a longer history over time, understanding all those sensitivities, and doing the best to predict what happens if demand drops or stimulus increases. For most of the brands we are working with, there is the knowledge that their category hasn’t disappeared, but consumers are demonstrating a heightened sensitivity to what has happened through COVID-19.
“So if you think about cars and energy and insurance, there is a tightening of the wallet, so from a modelling point of view, we need to update the models to better understand that sensitivity.”
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