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The Fast-Food Chain Data Story: Do’s & Don’ts of data storytelling.
Suppose you are a marketing analyst for a fast food chain, and your task is to create a data story to help the company understand the behavior of its customers. After conducting some analysis, you've identified three key insights:
1. Customers who order online tend to spend more than those who order in person.
2. Customers who use the restaurant's mobile app are more likely to order meals with add-ons, such as drinks and desserts.
3. Customers who order delivery are less likely to visit the restaurant in person.
Armed with this information, you create a data story using Power BI that includes a variety of visualizations, such as a bar chart showing the average order value for online vs. in-person orders, a heat map showing the most popular add-ons by app users, and a line chart showing the change in customer visit frequency over time.
Here are some Do's and don'ts to keep in mind when creating a data story with this scenario:
DO's:
1. Define your audience:
In this case, your audience is likely to be the company's marketing and operations teams, so you need to tailor your data story to their needs and goals.
2. Choose relevant data sources:
You'll need data on customer orders, online vs. in-person ordering, app usage, delivery frequency, and other related factors.
3. Use clear and concise visualizations:
Make sure each visualization supports the insights you want to convey and is easy to understand.
4. Identify patterns and insights:
In this case, you've identified three key insights that will form the basis of your data story.
5. Use interactive features:
Power BI's interactive features, such as drill-down capabilities, will allow your audience to explore the data in more detail and draw their own conclusions.
DON'T's:
1. Overload your audience with too much data or too many visualizations:
Stick to the most important insights and visualizations that support them.
2. Use difficult-to-understand visualizations:
Make sure each visualization is intuitive and easy to interpret.
3. Force your data to fit a preconceived narrative:
Let the data speak for itself and avoid cherry-picking data to fit a particular story.
4. Ignore the context surrounding the data:
Be sure to provide any additional information or context that may impact the insights you've identified.
5. Assume your audience has the same level of understanding of the data as you do:
Use clear language and provide explanations for any technical terms or concepts.
By following these do's and don'ts, you can create a compelling data story that will help your audience understand the behavior of your fast-food chain's customers and make more informed decisions.
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