Data storytelling is an important soft skill to develop as a data professional but there’s not much information to guide you on where to start. When I first became a data analyst I didn’t have a clue how to approach this. Over time it became easier and after giving many presentations since then, I’d like to share the 3-step method I use to create stories with data.
1. Pretend you own the company
One element of a successful story with data is making sure the insights are relevant to the audience. Every CEO wants to grow their company and increase revenue but what’s the best way? How do they resolve issues hindering growth? Pretending like it’s your business will help you assess if the data in your story provides any new insights that will improve business performance or solve a pressing problem.
For example, let’s pretend you own a technology startup. You need to increase new customers to grow the company. This is your top goal as CEO. What if a data analyst showed insights related to customer retention and churn rates? It wouldn’t be relevant because it doesn’t address how you can grow the company.
As the data analyst, you can still show the customer retention and churn rates but knowing new customers is a goal you can segment by the source to show where the customers came from. Let’s say the segmented data shows 50% of customers came from paid ads and they have the highest conversion rates and lowest churn rate. With this insight, your story can recommend the company increase paid marketing spend to drive more new customers. Your story is now relevant and actionable as this addresses the problem the CEO is concerned with on how to increase new customers.
How this translates into a story:
Goal— The company wants to increase new customers to grow.
Data— 50% of customers come from paid ads and they have the highest conversion rate and lowest churn rate.
Actionable Insight – Increase paid marketing spend to drive more new customers. Since they have the lowest churn rate, the company will make the most from them over their lifetime to recoup acquisition costs. The company can also create content to drive more customers from organic search to reduce acquisition costs over time.
2. Pretend you’re the stakeholder
What if the company goals are different than your stakeholders? A common company goal can be to increase revenue but a product manager may only focus on a user’s experience in the product. In these cases, talk to your stakeholders about their goals and pain points to decide how to create a story with data that’s relevant to them.
For example, I was once asked to find insights to improve free user retention. I found the more times a user used a specific feature the better the free user retention. I asked myself if I were the product manager would this information be helpful and the answer was no because it wasn’t actionable.
However, as a product manager, if knew the user had to use this feature at least 3 times within the first week of signup to show an improvement in retention then I could do something about it. These were the relevant insights I added as a product analyst to my story. The product manager could use this information to make onboarding changes to encourage new users to try this feature and work with marketing to reinforce this feature usage in the new user onboarding emails to improve retention.
How this translates into a story:
Problem— The product manager needs insights to improve free user retention.
Data -Free users using a specific feature at least 3 times within the first week of signup show an improvement in retention compared to the overall retention rate of all free users.
Actionable Insight – Modify new user onboarding in product to encourage usage of this feature and work with marketing to reinforce this message in new user onboarding emails.
3. Ask for a review
As a data analyst, I often include too much data that’s not needed to get my point across. To remedy this I summarize the key findings and recommendations for my story. Then I review the presentation with a peer or my manager to confirm the findings I summarized come across in the presentation and if anything can be modified to clarify my point.
I’ve found this step extremely helpful to identify data that are too complex and ambiguous insights that needed clarification. If you have no one to ask for a review, I suggest stepping away for a day and revisiting with fresh eyes in the morning.
Takeaways
Data storytelling doesn’t have to be difficult. Use this 3-step method to create a story with data that will be relevant and actionable to your audience.
To recap:
- Pretend you own the company. If you were presented with these insights, could you use them to drive business growth or solve your top concerns?
- Pretend you’re the stakeholder. If you were presented with these insights, are they useful and actionable to help you achieve your goals?
- Ask for a review. Did your peer or manager have the same takeaways as what you intended? Were there any data or insights that were unclear?
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