Selling Shoes Made Me a Better Data Analyst đź‘ź
This is how my first casual job as a sneaker salesperson impacted my data interpretation and storytelling skills
While kids get their first retail experience here in Australia at the age of 15, I stepped foot into retail at 25. It was not because I needed money; I was already making a good amount working as an analyst at a supply chain company while working towards my master’s degree. I wanted to brush up on my sales skills and work at a sneaker shop to understand the business dynamics. Little did I know this would greatly impact my data analysis and storytelling skills.
In the store, I spent my early days getting to know the different brands, prices, distinct materials and characteristics that made each pair unique. Every day, I met all kinds of customers — high school and college athletes looking for light football boots, tradies wanting durable shoes, grandparents searching for their grandkids’ footwear, the list was endless. I had some really good interactions, but occasionally… not so much. Still, dealing with this lively environment helped me improve at selling shoes to just about anyone.
When working with a customer, it is important to know the needs of your audience. You wouldn’t want to sell a pair of trekking boots to competitive runners unless, of course, they were looking for them. Also, being able to distinguish between a seasoned athlete and someone who’s buying their first pair was very important. For the first case, my pitch would involve technical terms like material composition, durability, weight differences, etc., which seasoned athletes already know. However, for the latter case, instead of using technical jargon, I would resort to simpler terms to explain the features more intuitively to the newcomer.
The key information conveyed was still the same, but the mode of delivery was different. Understanding the psychology, level of expertise, and experience of the customers buying the pair of shoes was crucial to being a good salesperson.
One of my go-to pitches involved quick and smart number-crunching to create an imaginary sense of value for more expensive shoes. I would greatly emphasize how getting an expensive shoe that you like and potentially wear at least four times a week is way more valuable than a shoe on sale, which you’d wear a maximum of 10 times altogether. By introducing the dollar-per-wear concept, you can easily divert a consumer from opting for a cheaper alternative to a more expensive one. Ultimately, it’s a win-win for both parties, assuming the consumer goes on to wear the shoe frequently. Then again, this pitch will not apply to everyone. These are more effective on people who seem smart enough to grasp the numbers or wise enough to understand the value of long-term investment instead of fast fashion. At the end of the day, understanding the customer before applying a pre-meditated theory was crucial.
So you must be wondering how any of this is relevant to data analytics.
At the end of the day, data analysis is just gathering insights for a given party to achieve a particular goal. Being able to correctly understand what to look for, what questions to answer, and what goals to achieve is crucial for a salesperson, just as it is for a data analyst. I'd be wasting our time if I bombarded my non-technical boss with fancy terms like regression, supervised learning, t-tests, etc.. Instead, showing clear visualizations like scatter plots to show revenue trends, bar plots to emphasize best-selling shoes, or the time of the day that drives the most sales is what interests my boss. Showing off using technical terms holds no value if the data is not properly communicated to the stakeholders.
Always ask the following questions when working with data for a stakeholder:
- What insights should I gather from the data to create the highest value for my customer?
- How easily can they understand what I’m inferring using the data?
At the end of the day, you can blabber all you want to a customer looking to buy shoes, but if you don’t give them the right pair for their needs, you’ll end up with a bad rep and eventually get fired. Similarly, as data analysts, you must align your findings with the customer’s requirements and “sell” your data to give them the highest value. Ultimately, it’s not the shiny talk or analysis that’s important; it’s the true impact you create that leaves a mark.
