One aspect of COVID-19 has been the amount of poor data and misinformation circulating. The signal to noise ratio is not good.
Aside from some very dodgy health advice given by some who appear not to have read the warning label on household cleaning products, getting accurate numbers on infection and deaths rates is hard. The data issued by governments is often not comparable across countries, and reporting definitions change according to policy within a country as well.
The reliability of economic data is suffering. For example, the latest inflation report from the Office of National Statistics notes difficulties in collecting information on prices of some products. Tricky to observe the price paid for flour when it's out of stock. And because a chunk of economic data is survey based we are unlikely to get a sensible read on key indicators like inflation and GDP for 6 months. It could be longer given seasonal patterns are messed up as well.
The latest Bank of England Monetary Policy Report notes the impact of COVID-19 will be "particularly uncertain" with the governor stressing that the latest outlook is a scenario rather than a forecast. Apple has not even entertained such guesswork stopping short of giving any financial guidance for Q2 performance in its recent earnings report.
Then there is some very noisy financial market data. At the start of the crisis stock prices fell sharply. In some cases this triggered a scramble for cash and had a knock on effect of a sell off in safer equities and gold. So prices of these, relatively safe, assets took a bashing temporarily distorting their underlying value. We've also seen somewhat counterintuitive stock price increases for companies that have (quite rightly) given away or reduced profit margins on products relevant to helping in the pandemic.
So much noise means we have work harder to detect the signal and ensure we interpret it correctly. Actually it's always been vital to understand what lies behind the data. Planning co-creators Stanley Pollitt and Stephen King knew this. Despite strongly believing advertising development should have more scientific underpinnings, they brought together experts in creativity and data to help find the best idea.
Yet, in recent years, marketing has become so data driven that in some cases it's replaced expertise. The models never give us a complete picture of the consumer (they are models after all) yet we have become hugely reliant on them. Now consumer behaviours and attitudes are in shock from the pandemic and the models are 'structurally broken'. But decisions still have to be taken now and the craft of marketing needs to be in the driving seat.
When you lose your signal you move to try and pick it up again. Marketing during COVID-19 needs small scale experimentation, where outputs are validated through fast data, often intermediate digital metrics. Tracking a range of social trends can help evaluate sentiment. Building a dashboard of 'clean' metrics through to e-commerce transactions will help improve the signal to noise ratio.
It's also a great time to automate test and learn, for example, using elements of programmatic creative. And we are also more likely to find innovative answers when we combine with wider perspectives, interesting collaborations and partnerships.
Above all we need to move, to change our perspective of what is happening and use our intuition to find more innovative solutions. One of the effects of the pandemic will be for marketing to move back to placing more emphasis on understanding the consumer and why something works, or not. That will be a positive.
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