Covid-19 and predictive models: transitioning to a new normal

Published by Wan Hsien Heah, Allan Engelhardt on

Estimated reading time: 6 minutes


Does your business rely on predictive models for churn management, attribution modelling, renewal offers, cross-sell campaigns, claims management and the like? If so, you face a particular test in keeping these models relevant and effective during this period of massive external change.

The transition from the current crisis mode brought about by Covid-19 to some, yet to be understood, new normal will be challenging. 

Can you rely on the past to predict the future?

The past is no longer a good prediction of the future, simply because the present is so different from the past. This will remain true until we have reached some form of steady state in a new normal situation. We don’t yet know what that will look like or how long this transition will take. In some areas we may never return to “normal” as the pace and scale of change increases.

Therefore, all the models that you have built using primarily your customer relationship management (CRM), sales, claims and web data will be out of date. This means that your predictions and your forecasts are likely to be wrong. You may well find that your planning is so far from reality that you may be better off not planning at all! This is a challenge we have given some thought to below. 

Are you keeping your models up to date?

In a fast changing world your models need changing equally fast. Critically, you need to supplement them with more data and with external data that is faster than your backwards looking corporate data and more reflective of the sentiment in your buyer and user groups.

The US Federal Reserve understands this. Despite all the data at their disposal, they are still travelling across the country, trying to get a sense of the economy by interviewing people on the street. 

The Bank of England does this regularly as well, because they understand that forward predictors are not always in the historical data. Traditional indicators are less reliable and less predictive during a period of disruption like Covid-19. 

There is no need to give up. On the contrary, this is the time to double-down on data and analytics. Use it to transform your organisation and spark behavioural change, making your company agile and relevant in a fast changing, complex world.

You need to focus on three areas:

  • Refresh and adapt — be fast and furious
  • Use more data — both external and emotional
  • Embrace uncertainty

You must refresh your models, together with your plans and your processes, much faster than you have ever done in the past and at a pace that likely exceeds your existing capacity to do it efficiently.

Is your data flowing well?

You need to make sure that data is flowing through your organisation so you have clear visibility of what is happening now. This is nothing new, so take the current situation as a wake-up call to prioritise this now. 

This is not just “hard” data but also the softer information: What are your customers saying? How are they feeling? What are your competitors doing?

Ask yourself the following questions. Do I have the current data at my fingertips? Is it driving behaviours in my organisation? Are people and processes changing when the data is changing?

Are you involving the right people?

You also need to think about whether you have the people skills and capacity to model and challenge models in real time, day after day, week after week.

Make sure you get the right people involved. You need human intelligence, not the artificial variety. You can’t just follow the data blindly when the world is rapidly changing in ways you have never seen before.

Do you think . . . “business first, models second?”

With limited resources, focus on getting the data flowing and leading the business change. This is work you are better placed to do than anyone else. 

If the data flows and is understood and used throughout the organisation then you have solid foundations. 

You may then choose to get external support to help with the (near) continuous model refresh while you continue to challenge the models and drive the business impact.

What about emotional data?

Customer sentiment may now be one of your fastest and most important sources of model data. In a time of change, emotions trump habit. It is time to make the most of those customer experience programmes. Use the insights as a driver in your renewals and churn models.

Can you process text and sentiment data efficiently and use them in your customer models? Can you run your customer experience programmes much faster than you do now? Do you understand how to make these insights commercially relevant to your business in real time so you can drive your retention, cross-sell, service, and acquisition processes?

What external data should you be using?

You will need much more market data if you hope to understand the transition period. Again this is nothing new, but takes on a new urgency during the transition. 

Track the economy, including employment; politics, consumer sentiment (e.g. consumer confidence), the sentiment of your customers’ customers (if you are in B2B), health data, competitor actions and general market data (e.g. footfall in the high street). Much of this matters and, frankly, always has.

Do you have access to the data to understand the society and environment in which you sell and operate? Do you have the skills to pick the information that matters to your business? Do you use it in your models and forecasts? Do you know how?

Other countries are well ahead of us on the journey of return to the new normal. We do have data on what that future holds if we are willing to look. So look at some of the countries in East Asia and also look beyond your industry.

Do you have the skills to apply the lessons from other countries, markets, industries, and cultures to help you plan better and forecast more accurately? Do you have agile scenario planning capability?

Why is it so important to embrace uncertainty?

Before, data was never good enough. Not accurate enough. Not complete enough. Now you have to make decisions with the data that is in front of you. In truth, you always did. 

There is no such thing as perfect information, for you or for your competitors. Good enough is good enough, if it gets you to the right decisions at the right time for the right reasons. So you need to learn to love “bad” data.

In volatile environments, you must move from static business plans to scenario planning, which requires more decisions and faster decisions. Plan for changes so you can respond, not react, to events.

Be the market player who is the most agile and the most responsive. Through history, more opportunity has been wasted by indecision than by bad decisions.

The world is changing. Are you?

Our work with clients

At Barnett Waddingham, we help leaders make better decisions in uncertain times with incomplete information.

Our unique approach combines the skills of actuaries with data scientists and business consultants; we call it Management Decision Analytics. Talk to us about unlocking the value of data and improving your decision process.

Our model factory uses a combination of automation and human intelligence to keep your models and insights fresh and up to date. 

For decades, our clients have relied on our expertise in data and analytics to keep their models current, allowing them to focus on their business, secure in the knowledge that they have the best possible insights available.

We have deep expertise in modelling and operations through our work in the Insurance Sector and cross-sector teams dedicated to Risk Advisory and Analytics and HR Analytics and Consulting who can accelerate your value delivery in these areas.

If you would like to talk about the topics discussed, please get in touch with your usual Barnett Waddingham contact to find out how we can support you. Alternatively, please contact the authors below.

Visit our Covid-19 hub for all the latest analysis on the impact of coronavirus on insurance, business, pensions and investment.

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