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In this episode, I had the pleasure of speaking with John Napoleon-Kuofie, a senior business intelligence analyst at Farfetch. In this conversation, we talked about how John’s career led him to Farfetch, a traffic dashboard he’s built for his stakeholders at Farfetch, and how Excel was his gateway into SQL and the wonderful world of data. One of the reasons I enjoy conversations like these is because you get to learn from someone who is in the data trenches, as it were.

The path to Farfetch

Farfetch is an e-commerce company focused on boutique fashion companies. Before landing at Farfetch as a customer insights analyst, John was studying mathematics at university and thought he was going to work at a bank after graduation. He ended up working at a media agency where he help built statistical models demonstrating the value of advertising for the agency’s clients.

What’s so great about this part of John’s career (and many entry-level analysts) is that you get to do a little bit of everything. John was working in Excel, R, and other bespoke tools. During this phase of your career, you are constantly learning and experimenting with new tools to figure out what type of career you want to end up in. John wanted to stay focused on analytics for his company’s customers.

Storytelling may be more important than the data itself

Data storytelling is both and art and science. It’s not just doing the number crunch and creating the analysis, but pulling the salient points out and creating a compelling story with the data. This skill is so important that big news outlets like the New York Times have created data bootcamps to help its journalists become more proficient in data analysis.

John discusses working with a telecoms client at his former media agency, and the client was cycling through different creatives in their online ads. Each ad had a different celebrity, and John noticed that the efficacy of their ads could be improved. Using data and a bit of marketing, his team convinced the client to adopt a more consistent advertising strategy with one celebrity instead of multiple. In marketing speak, this led to stronger brand recall and the numbers backed it up.

I think many online classes teach you how to use all the knobs and switches in Excel, R, SQL, and Python, but the real value analysts can provide is creating these data-driven stories to make decisions. (I’m really passionate about this subject and have an online class about this topic).

Self-service “traffic” dashboards

In order to help its clients generate sales, Farfetch utilizes multiple marketing strategies including pay-per-click advertising, affiliate advertising, and SEO. In order to help internal stakeholders figure out the proper marketing mix to maximize sales, John created traffic dashboards in Looker and Tableau. The key to these dashboards are that they are self-service so that his colleagues can slice and dice the data they way they want.

An example of a metric these dashboards track is website visits. The dashboards allow people to find out which channel is driving the most traffic so you can figure out whether to invest more or less into that channel. Revenue, costs, and conversion rates are additional metrics you can find on these dashboards.

John brought up an interesting problem that many marketing teams would be envious of: Farfetch gave the marketing team an unlimited marketing budget.

Of course, this freedom came with one big condition: the marketing team had to achieve a specific cost per sale.

Thus, the marketing team needs to pull different levers in order to maximize ROI on every dollar it spends on Google Adwords, Facebook advertising, and other channels.

In order to build out these dashboards, John had to triangulate multiple data sources and “make them talk to each other.” These sources include Google Analytics, app providers, data from Facebook/Google, and of course 1st party data from Farfetch’s own database. This is one of the most challenging types of projects because multiple data sources have their own definition of a “Customer” or “Sale,” and it’s your job to do all the VLOOKUPs and custom SQL views to unite these data sources together.

The next-level questions these dashboards could solve might include which products should be promoted in which regions? By incorporating cost of goods sold (COGS), the dashboard might be able to identify a product that is selling really well but is ultimately unprofitable for the business because the shipping costs eat into the entire profit margin.

Excel is the gateway drug to SQL

John talked about how when he first started at the media agency, he thought he knew Excel inside out. After seeing how his colleagues were using Excel, he realized he was still a beginner in the tool. John picked up Excel from analyzing the files his colleagues and from simply Googling when he didn’t know how to do something (one of the key skills I mentioned in my 2020 lessons learned post).

As John became more proficient in Excel, he started picking up SQL as well. He discusses a pattern I’ve seen with other analysts who are learning SQL: querying small datasets in SQL that you can also query with formulas in a spreadsheet. Jumping straight into SQL can be difficult, so by using a visual IDE like Excel, you can double-check your work to ensure the SQL query returns the result you expect.

At Farfetch, John is using BigQuery along with Connected Sheets so that analysts can quickly see data from BigQuery directly in a Google Sheet.

Moving on up from analyst to manager

John has moved from an independent contributor to a manager role. To John, being a manager has its own challenges, but there are times he misses being “close to the data”:

As an analyst, you like touching the data and doing the work. You like the satisfaction of having a problem to solve and using the tools you have to solve it. As a manager, problems are more abstract. It’s all about passing on the wisdom. Your focus shifts to creating the right environment for other people to excel (no pun intended) at their job.

Despite being a manager, John’s passion for data and learning new skills has not waned. He is currently learning Python for IT use cases, and hopes to apply some of his new Python skills at Farfetch. His company has created a Python package to automatically spit data into Excel so that others can quickly build reports off of the data.

In closing, John said you still need to earn your stripes in Excel before you move on to other languages (like Python). I couldn’t agree with him more. Build a solid foundation in a spreadsheet before picking up the latest tools.