Finding Your M.O. | Part 18: Driving Performance Through Data

In the next instalment of her on-going series, Finding Your M.O., Áslaug Magnúsdóttir, co-founder and CEO of fashion-tech start-up Moda Operandi, addresses the importance of collecting, mining and reviewing data. Nosafashions would like to state that we claim no rights to the copyrights of this material. This is strictly for educational purposes.

NEW YORK, United States — As a founder and CEO of an e-commerce company, I have come to appreciate the importance of tapping data to drive decision making. Now, that may seem obvious to some, but many of the fashion businesses I worked with previously did not effectively collect and harness data to help steer their business. In the early days of Moda Operandi (M’O), we lacked adequate systems and people to properly track and analyse data. Today, data is a big focus within the company. We are constantly collecting, mining and reviewing data to check and adjust our strategies and consumer offering.

But why is data so important to an e-commerce company? What kind of data should be tracked and analysed? And how often should data be reviewed? As I often say, there is no right or wrong answer. Every company is different and will have different data needs. Here’s a snapshot of how we look at data at M’O as a key input to decision making.

WHY IS DATA SO IMPORTANT?

Clearly, companies need data to stay on top of key performance metrics, such as revenue, margins, operating costs and cash balance. But why is it important to track other types of data?

Business trends. Data allows a company to assess, on a rolling basis, whether the business is performing better or worse in a specific area than previously. You need trend data to react and adjust course accordingly. For example, one of the key things we track at M’O is the total number of unique visitors to the site in a given month. Since we know our business is, to some extent, cyclical, this metric helps us stay on top of how we are doing compared to seasonal expectations. For example, we might see a significant spike in the number of customers visiting our site during the months of October and March when we have the most merchandise available from the runways of New York, London, Milan and Paris. And we would not be concerned to see a drop in unique visitors in the off season, from October to November. But we would be alarmed if we saw a drop in unique visitors from October to March, or in March of this year versus March of last year. So data keeps us on top of whether we are performing according to our targets.

External benchmarking. Data also allows a company to compare its performance to other companies that are considered best of breed in specific areas. External benchmarking allows a company to spot key opportunities and areas for improvement. For example, we find it useful at M’O to compare our conversion rates (purchases made as a proportion of unique visits per month) with other e-commerce sites. If a company discovers its conversion rate isn’t competitive with other best-in-class businesses, it needs to dig into the data to understand why and respond accordingly. Could the issue be the product mix? The price points? The user experience? The data can often reveal this.

But to be clear, data can also be misleading. You need to make sure you are comparing “apples to apples” when performing external benchmarking vis-a-vis other companies. For example, comparing the cyclical rhythm of M’O’s unique visitors to that of other fashion e-commerce sites would be an “apples to oranges” comparison due to the unique nature of our pre-order business model.

Objectivity. One of the beauties of data is that it doesn’t lie. As a result, it can help bring objectivity to decision making. For example, data can help determine what to do when team members disagree on how to approach a certain aspect of the business. At M’O, the question came up recently about whether inventory purchases should be based on historical sales information versus editorial purview. A simple data-driven analysis of the performance of each approach can be implemented and measured to help guide buying in future seasons.

Accountability. Because data tells the truth, it can also help create clarity and accountability among team members. At M’O, our performance management system contains both quantitative and qualitative metrics. Having clear quantitative metrics helps focus individual team members on priority areas of the business. And since team members can tap data to stay on top of how they’re doing, they can leverage it to perform their roles well. For example, as the CEO, I might intuitively sense that the team is getting faster at processing product into our warehouse. But by looking at the data, I can quantify how much better we have gotten at doing this within, say, a given 24 hour period of time versus, say, the same period of time last month. Having this data helps me ensure we are staffed with enough team members in the warehouse. And having this data helps our operations team feel empowered and responsible for their jobs.

Reporting to investors. Although the primary reason for tracking data is to use it to drive business decision making, having data readily available also makes reporting to investors much easier. Investors tend to like quantitative updates that include myriad metrics and facts. They want to look at the company’s performance from lots of different angles, so the more data, the better.  Similarly, when it comes to fundraising, data is king. Preparation of the investment deck and any follow-up due diligence is much easier when you have key data points compiled and at your fingertips in advance.

WHAT DATA SHOULD BE TRACKED?

The kind and volume of data tracked by a typical e-commerce company should be sufficiently diverse and extensive, so as to allow the team to stay on top of all areas of the business. And while the data a company monitors is often standard across a given industry, some data requirements need to be specifically built for that company’s unique or special line of business.

Here are some of the key metrics we track at M’O.

Traffic data:

• Unique visitors – Are visitors to the site increasing season on season?
  
• Unique visits – How often are they visiting the site? How long are they on the site in a given visit?
  
• Bounce rates – How many visitors are we losing? Immediately or after a period of time? Why?
  

Customer data:

• New customers – Is the number of new customers increasing in line with expectations?
  
• Repeat customers – How are we doing on customer loyalty? Are customers coming back to shop again?
  
• Frequency of purchase – How frequently are customers buying? Is the frequency increasing the longer the customer has been shopping with us?
  
• Lifetime value of customer – How much margin does each customer contribute to the business on the average? (This can help guide how much to spend on acquiring new customers.)
  
• Channel – Where are our customers coming in from? Editorial, social media, customer events, search engines? Where are our VIP customers coming from? (This can help guide where and how to spend marketing dollars.)
  

Order data:

• Average order value – What is the average basket size the customer is buying and how is that changing over time?
  
• Average unit value – What is the average value of the different products customers are buying? How does it differ by category (apparel, jewellery, accessories)? How does it differ across business units (i.e. pre-order trunkshow versus our in-season Boutique)?
  
• Average units ordered – Do customers typically order only one piece at a time, or multiple pieces? Is the company taking advantage of opportunities to cross-merchandise and cross-sell?
  

Merchandise data:

• Boutique sell through levels – What percentage of uploaded/ available product has sold?
  
• Average sales per trunkshow – Is the average revenue per trunkshow increasing over time? (This is a somewhat unique metric for M’O. We carefully monitor how this develops overall and per category.)
  
• Another metric that is specific to M’O is the correlation between items sold in trunkshow and items sold in Boutique. Are Boutique customers shopping the same brands as trunkshow customers? Are they reacting to the same pieces?
  

Financial data:

• Gross revenue – What is the total order level from customers?
  
• Net revenue – What is the company’s income minus returns and cancellations?
  
• Gross margin – What proportion of the revenue contributes to operating expenses? What is the profitability of the business?
  
• Operating costs – What are the fixed costs of running the business? What are the variable costs?
  
• Cash position – How much cash does the company have? What is the cash burn?
  

Of course, these metrics are only a subset of the total volume of data we track at M’O. We also track operational data (such as time for inventory receipt, shipment and return processing), technology performance data (such as average upload time per page), employee related data (such as retention rate) and much more.

The bottom line is: data is invaluable to any e-commerce business. It provides companies with a brutally honest self-awareness that is ultimately empowering. The fact that data may be underutilised in fashion should incentivise companies to collect and harness data to best less savvy competitors. But to be clear, data can only serve as one part of a company’s overall decision making process. Human thinking must temper even the most telling data analysis. Indeed, sometimes objective facts must take a backseat to subjective gut. But it’s only by pitting hard cold data analysis against industry instincts that best of breed business decision making is made.