George is FitVit’s first data analyst. They’re an ambitious startup building
a unique fitness and nutrition app that provides users with workout ideas and offers
a custom nutritional plan tailored to individual goals, lifestyle, and current training regime.
Before George’s arrival, FitVit operated with little information on the business’s performance
regarding growth, acquisition, retention, and user activity. Developers were too busy
building the product, while the business team didn’t have the necessary technical skills to
analyze the data collected in the database. The company’s founders have a background in finance,
but they need to get used to working with a relational database that contains millions of
rows that Excel cannot handle. The founders struggled to
answer such essential questions as: How often do users come back to the FitVit app?
What is the average session duration? Are there functions rarely used by clients?
For this reason, FitVit recruited a data analyst. George’s solid SQL skills, ability to preprocess
data in Python and proficiency with visualization tools like Tableau and Power BI made him the
perfect candidate. Once George accepted the offer from FitVit, the founders became excited as they
hoped this would soon give them visibility on the many unknowns surrounding current performance.
In his first weeks on the job, George had a chance to talk to everyone on the team and gain a better
understanding of the business. Given that FitVit is a relatively small company, it didn’t take long
to learn how they were organized and what business logics had been incorporated into the product.
A proper introduction to the stakeholders in a company is essential for every data analyst.
To tackle business problems using data, the analyst needs to understand the challenges
of the business. Moreover, George believes that it’s always helpful to build a good rapport with
other team members, as later, they’ll work together to solve actual business problems.
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to help you learn all the necessary skills. All right. Let’s continue with George’s story.
Soon after onboarding, George received his first ad hoc request. He was asked to analyze FitVit’s
checkout process - what happened once a user had clicked on ‘Buy’ inside the app.
The founders thought this was a suitable first task, as any quick wins from this
analysis would show the entire team the benefits of data-driven decision-making.
George agreed that this analysis had the potential to create measurable business value and proceeded
to explore the data collected in the database. Geroge was glad to see that developers had built
tables that collect information about all clicks made by customers during the checkout process.
At the same time, he had difficulty working with the format the developers had used—as
it was code rather than meaningful names that could be analyzed and built into a funnel.
Therefore, George spent significant time mapping and preprocessing the data—assigning meaningful
names to each type of action. Once that was done, he created a funnel visualization that
indicated how many people initiated a checkout, how many dropped out during
different stages of the process, and how many ultimately purchased. George was surprised to
discover that many customers dropped out at the process’s first (registration) stage.
The current way the product was designed asked for too much information, and 80% of
users did not continue to the second stage of adding their credit card information.
George presented his findings to business stakeholders, and everyone
agreed they could reduce the amount of information asked at registration—saving
these questions for later when clients have already purchased and invested in the product.
This led to a significant improvement interactions and gave George his first quick win.
Besides ad-hoc data analysis requests, George knew that a data analyst’s main task was to create
reports and dashboards that decision-makers and stakeholders could regularly use to satisfy their
informational needs. In his conversations with stakeholders, George understood that everyone
on the team did not know how excited users were about the app, how often they used it,
and whether they took advantage of some of its more sophisticated features.
So, George proceeded to share these observations with key decision-makers.
They agreed it would be optimal to create a dashboard enabling employees to track
user activity and give everyone on the team a sense of common purpose.
Business stakeholders defined the key questions to be answered. George continued by reflecting
on the types of visualizations and KPIs that could be observed to answer these questions over time.
George then built an ETL process to provide the source data for his dashboard. He used
internal data sources (data collected within the app) and external data sources via API.
(The company used an external payment service.) Once the data was collected, it was transformed
into a format allowing the analysis. In SQL, George created several views,
combining several data tables, which could be saved and used as a source for the visualizations
in the dashboard. To automate his report in the future, he used stored procedures,
as these jobs would be requested regularly. Before creating the actual visualizations, George
cross-referenced data from different sources to test for mistakes and ensure solid data quality.
Then it was time to create compelling charts in Tableau. Once ready, George discussed the
structure of his dashboard with team members who would later use it—ensuring it would satisfy the
informational needs of intended stakeholders. We hope you liked this story. If you did,
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