With the media buzzing around COVID19, and many numbers are being quoted it is very surprising how wrong their projections and calculations are.  

You may wonder how statistics that the media and some organizations such as world health organization is getting something like the death rate wrong? Simple, they are calculating total known cases and total number of those that died. How this is wrong? Well, of those that are currently sick some will still die therefore, bring the total percentage of those that die from COVID19 up. Now, this maybe done on purpose to reduce panic, but statistically it is not accounting for time delay.

How would you report on your campaigns? By calculating the length of time it takes someone to make a purchase, you can have more accurate conversion rates. For example, some one can visit your website on Tuesday but only make a purchase on Friday, therefore, looking at conversion rate of those that visited the site on Tuesday but did not make a purchase is going to give you a wrong conversion rate for that campaign.

Another mistake is to calculate the amount of deaths based on total closed cases which is what a website such as worldometers is reporting. In theory this is correct, however, in reality unless the virus is done spreading the death rate is actually being reported on a much higher percentage than in reality as there are going to be way more recovered people that are not being included due to the long length of virus being in their system.

The simple solution for marketers is simply to measure the results of a campaign once it is finalized and allow enough time for purchase post the campaign prior to analyzing results.

The comparison between the flu or other pandemics is also being misinterpreted. Some people are comparing estimated death rates of the flu versus COVID19. While not enough data is known on the estimate number of those infected by the COVID 19 when comparing it to the years of different studies on number of flu cases per year. Another variable is that some countries are testing more while others only based on certain criteria, this means that there is really an unknown number of those infected. Therefore, it is not a fair comparison between the flu and COVID-19 due to lack of data.

The simple way to avoid this mistake in your marketing is to make sure you use the same framework when benchmarking. For example, the length of a campaign or the timeframe of a campaign can make a big difference of your definition of success especially when using metrics such as revenue or profit. It is also not fair to your campaigns to compare Cyber Monday deals with Easter Deals.

Finally, China’s methods of testing has been changing making it impossible to predict the future. Changing how you report your cases and how you measure in the middle of a pandemic makes it harder to predict the outcome. This is currently the case, while it looks like the peak has passed for China with the number of new cases drastically decreased it is impossible to say at what timeframe the peak occurred therefore, making it hard for other countries to predict and prepare.

Making sure your reporting is consistent is key for future projections. For example, if you have lead scoring or customer score and you change your scoring methods this will paint a different picture when looking at aggregate measurements.

In conclusion, accounting for time lag in purchases, comparing the similar campaigns only when benchmarking and being consistent will not only make your marketing analytics accurate but will help predict future sales and improve overall marketing performance.