Take a moment, breath in, breath out. Whatever you do – do not panic!
Let me share with you one of those funny analytics mishaps that can turn into a life lesson.
One day, a colleague of mine discovered that the bounce rate for our pricing page was 90%. 90%! What a disaster!
She ran to the manager to report the horrendous news. Naturally, the whole company hit the panic button and started to frantically look for solutions.
In the end, it turned out that the bounce rate was incorrectly interpreted.
The 90% referred only to the 15 clients that clicked directly the pricing page and then churned. The vast majority of users who accessed the home page and the pages describing the product (around 3000 people) had a perfectly normal bounce rate.
So… no fire needed to be put out.
This was a big lesson to be learnt right at the beginning of my career.
Never panic and don’t go running to your manager! You’ll just make them panic as well. And you never want to make your manager to panic :)
Discovering striking results in your analytics reports should make you stop in your tracks and wonder if the results were correct.
Always double-check and analyze things before conveying bad news to the management.
But this is just one tip for breaking bad news gently to your superiors.
What else can you do to ensure that the message is conveyed calmly and that it does not create panic?
How to do it right?
Since it’s story time, and stories are the best way to explain things, let me tell you another one.
A company I worked with discovered they had a very low retention for the users who created a new account. Their reports indicated that their first week retention rate was 20%.
Instead of panicking and rewriting the whole future of the product road-map, this is what they did:
First, they tried to validate the data.
They realized they were monitoring the users, and not the accounts. And since an account can have more than one user, and it is the accounts that are paying, not the users themselves, this led to false results.
After this discovery, they came to the conclusion that the first week retention was 30%. But it was still very little.
Second, they tried to explain why the situation was so bad.
They realized that the app had an onboarding rate of 40%. This meant that 60% of those who made an account never followed through. They churned at such an early stage that they were not relevant in calculating the retention rate. So, they applied the retention reports to those users who created an account and also finished on-boarding. And the result for the first week retention was 50%.
Third, they made an estimate of what could happen if they registered an increase from 50% to 60%. This increase seemed realistic and it would generate 15% more revenue / month for the existing customer base.
Forth, they looked for a possible solution.
In their case, if they could identify the actions that made the difference between the users who came back and those who churned, they could create an automatic process to alert the support team, who could contact these potential clients before it was too late.
Last but not least, it was high time they broke the news to the manager.
Armed with all this data, the analyst could inform the manager about what was going on with an email that might sound something like this:
Today I had the chance to make an analysis of the churn rate and after validating the data it turned out that we have a low retention rate for all newly-created accounts (30%). However, things look much better when it comes to the on-boarded accounts. Only 50% don’t come back to our product.
So I thought that identifying the actions that are most likely to determine users to come back to our app would be very useful and I found a way to do this automatically. I would suggest to make this data available to the support team, who could contact the clients who don’t perform these actions.
If this project is successful, it has the potential to raise our first week retention score from 50% to 60%, which would translate into 15% more revenue / month for the existing customer base.
What’s your take on this? Shall we move forward with it?
1. Validate the results first.
Do not jump to conclusions when it’s too early in the game.
Check and double-check your data, look at things from all angles until you are certain of your discovery.
2. Put the results into context and then explain them to your manager
Bad news given without a context have the tendency to create panic. And nothing good comes out of it.
So, before conveying just the bad bit of news to your manager, make sure you analyze and document the entire context.
Your superior needs to understand the whole context:
- What are the causes?
- How does it impact the business?
- How relevant is this loss in the whole context?
3. Make an estimate for the future
Not knowing what the future holds and how an incident impacts the business is a manager’s worst nightmare.
People usually imagine the worst if put in a bad situation. So don’t let them fall into this trap. How can you do this?
Make a well-documented forecast about what could happen and how it will affect the business.
This way you will be able to guide your manager towards a problem-solving mindset and not get stuck in the crisis mode.
4. Offer solutions
Have you ever thought that being the one to deliver bad new might actually be that golden opportunity you had been looking for for so long?
Being the first to know what’s going on, you can take your time and come up with solutions, or at least some directions that can be further explored. This way your manager will skip the ‘dark’ early stages of the problem and go straight to the solutions. And they might even look at you in a different light.
So be the hero. Save the day – and you might even get a raise.