How COVID-19 has impacted email marketing. Part 2. Why now is the best time for AI A study by Profusion & Pure360 In Part 1 of this study, we analysed the email marketing interactions of over 1 million Retail Consumers pre and during Covid-19. In Summary we found: In part 2 of this study we look at why now is the time for marketers to invest in AI The COVID-19 pandemic is forcing marketers to think of new and innovative ways to meet business objectives and increase revenue. A new normal will mean that returning to usual BAU activity will render you irrelevant. Old processes and old ways of thinking will need to adapt to ensure your business cuts through the noise and stands apart from the competition. Over the past two months many marketers have struggled to keep up with changing consumer behaviour. A struggle that may have been resolved with AI. Our study also looks at two excellent use cases that articulate why marketers should invest in AI now. 1. It predicts the changes in engagement habits and adapts to ensure you reach the right customer at the right time. In this analysis we looked at peak engagement times pre and post COVID-19. Main send times for this 3.7 million customer study are Monday, Wednesday and Friday. Peak engagement in the 7 weeks pre COVID-19: Peak engagement in the 7 weeks post COVID-19: Our old assumptions on the best day and time to send emails are no longer valid. What’s changed? Overall, we see better engagement during the period. Darker purple signifies best open rates. More concentrated hours of people engaging pre-Covid. We now see engagement across a much wider range of times including very odd hours of the night and early morning. Monday’s send receives opens into the early hours of a Tuesday morning. This is really extended during Covid period, showing that a significant number of customers are online all night and interacting with emails. For this analysis, weekends continue to be a time of rest for customers in terms their email inbox action. Using the generic time and day analysis allows us to see the changes in behaviour, but it doesn’t allow us to make changes quickly. It is also reliant on data that becomes old and obsolete quickly. Using a send time optimiser AI model, the machine is learning on the fly and makes the necessary changes in real time based on live consumer behaviour. This allows you to increase engagement and generate revenue during a time when consumer behaviour is changing on extremely regular basis. Our analysis studied retail customers who received an email at a random time compared to customers who received an email at the optimal time for each individual. Results demonstrate: 2. It predicts customer behavioural changes within everyone’s lifecycle with your brand. How the AI lifecycle model works: This diagram shows 4 key events within the email lifecycle: Our study shows that since lockdown, mid-March, we have seen a huge number of At-Risk customers move into the Active status within their email lifecycle. There is a massive opportunity here to design and test a re-engagement strategy for these previously disengaged customers. The AI lifecycle model is completely adaptable based on the outcome you wish to see. In this case we used the AI lifecycle algorithm to look at customers email engagement the same way as you could use it to look at transactional patterns at an individual level. For one major retail customer an automated Win Back programme for Churned and At-Risk customers drove £13 million in revenue over a 6-month period. Through testing a series of different incentives, we also established the best ‘win back’ incentive to get customers spending again. See the full impact of the transactional lifecycle below: Our study has shown that COVID-19 has caused a sudden shift in consumer behaviour. However due to the volatile nature of the virus that will mean a slow exit out of lockdown and lead to a potential drag back depending on a second wave, marketers must be prepared for continuous change. Businesses must be prepared for different scenarios and marketing teams must be truly agile to keep up. Marketing leaders should ensure their teams have the right data processes and are using the right technology to support this greater need for agility. Now is the right time for AI.