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Intimates Stories: Looking Back at the First Wave of Selling Online

Looking back on the successes and lessons learned from one of the world's first online lingerie retailers.
Intimates Stories: Looking Back at the First Wave of Selling Online | EDITED

I co-founded in 1999 at the beginning of the first wave of online retail.  It’s hard to imagine quite how different the internet was in 1999:  the majority of customers accessed the site via dial-up modems, AOL was our main source of traffic, the site was written on Microsoft commerce server, and the dominant browser was Netscape. 

Most retailers thought the internet was an irrelevance.  Looking back 23 years later, below are some reflections on lessons learned, and what I wished I’d known...

1. Growth driven by customers

Everything starts with an achievable plan, and it’s critical to understand how growth is driven in terms of customer acquisition and retention.  Back in 2000, figleaves was about three times the size of ASOS (about 3 years later, ASOS was three times the size of figleaves).  What we failed to understand was that our growth was being fueled by new customers, and whilst our overall retention rate was good, the purchase frequency of our retained customers was low (the loyal figleaves customer bought c. 3 times a year vs. the loyal ASOS customer who bought c. 20 times a year).  This was not a problem per se, but our failure to understand this dynamic led to the board setting an unrealistic growth target (“you grew at 70% last year, why not go for 100% next year!”).  Unfortunately,  we failed to properly understand the dynamic and had committed to inventory based on this growth target, which led to overstocks, promotions and to a vicious spiral in which we delivered the top-line but trained customers to wait for promotions.   

Lesson 1:  Build a customer-centric plan.

2. Customers are not Created Equal

There is no such thing as an average customer, and we spent a lot of time at figleaves understanding the difference between those that were high and low value.  In particular, we were the only online retailer in Europe to sell La Perla (a premium Italian lingerie brand), and we saw that this was a great way to acquire high value customers - i.e., customers who bought La Perla on their first purchase and then went on to buy other brands . 

A few years after I left, a new team decided to reduce the range (which was sensible) but did so by ranking brands from the most profitable to least profitable and then delisting the worst performers.  This approach sounds smart but failed to understand the underlying dynamics, and the role that different brands played in acquiring and retaining customers.  La Perla was a great example - it was a loss-making brand given its low margin, high returns rate and expensive photography costs but - when looked at through a customer lens - it was absolutely critical.   The consequence was that figleaves lost its uniqueness and whilst these actions had a small short term impact on sales, they had a massive longer term impact on growth and customer value.

Lesson 2:  Understand what’s important to your highest value customers.

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3. Revenue is vanity, profit is sanity, cash is reality. 

It’s very easy to give money away online when all the variable costs such as picking, packing, packaging, returns, marketing etc. have been fully accounted for.  We spent a lot of time understanding our profit per order but there was relentless pressure on margins driven by increasing marketing costs, higher promotional spend and customer’s expectations of free delivery.   One area we never cracked was own-brand.  The vast majority of figleaves’ sales were branded products where the intake margin (i.e., the gross margin if we sold at full price) was around 50%.  

One of the challenges of selling branded products is how easy it is for the margin to disappear with a modest discount.  For example, once products were marked down by 25%, we lost money on the order.  By contrast,  brands selling direct or retailers with own-brands typically start with an intake margin of nearer 70%.  The impact on the per-order economics is huge. Retailers can offer consumers alluring discounts and still make a very positive profit per order.  These dynamics will accelerate the demise of multi-brand retailers (think department stores), and the shift towards brands selling direct.

Lesson 3:  Understand profitability at both a customer and SKU level

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4. Focus on controllable inputs.  

The figleaves Chairman was obsessed with the site’s conversion rate.  Rarely did a board meeting pass without a discussion on what we could do to drive conversion rate.  The challenge is that the website conversion rate is an outcome of a wide range of decisions relating to price, availability, range and service [better service = more repeat customers = higher mix of repeat customers = higher conversion rate].  In 2004, we hired a COO from Amazon who sat me down after two weeks and said “you’re measuring all the wrong things.” He gave me a number of examples but one that stuck in my mind centered  around availability.  “You’re telling me that you’re 92% in-stock at the SKU level.  But it doesn’t matter.  What matters is whether we’re in stock of the products customers are looking at.”  

We thought we were a pretty analytical leadership team but measuring “viewed availability” had never occurred to us.  We started looking at this new metric and discovered - to our horror - that it was around 70%, driven by a range of factors including digital marketing that was driving traffic to products that were sold out, highly fragmented or sometimes didn’t exist!  In addition, we had lots of clever on-site recommendation engines but they typically didn’t handle SKU-level availability very well.  Finally, our merchandisers simply looked at whether products were available or not available, rather than understanding SKU-level availability. 

But having identified “viewed availability” as a critical metric, what became clear is that no one team owned it - it was the intersection of the merchandising, marketing, digital and supply chain teams.  And this continues to be a massive challenge for retailers in 2022 - critical measures of the customer experience are un-ownable in a traditional functional retail organisation.  We’re now seeing retailers increasingly move to a trading squad model where a cross-functional team can orchestrate the required actions.  

Lesson 4:  Understand the controllable input metrics that drive action. 

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5. The perils of automation. 

Figleaves had both a fraud problem and also a backorder problem. As we were growing so rapidly, we were constantly challenged to stay in stock of core basic products, and in the early days of the internet, all retailers were subjected to continuous fraud attempts. We became quite sophisticated at identifying fraudulent behaviour based on looking at customers' previous purchasing, the products they were buying, the email address they were using, etc., and we had a good process where orders were either automatically accepted, or “referred for review”.  We found that automatic rejection would simply encourage the fraudsters to try harder, whereas if we put their orders on hold for a few days, they decided it was easy to go and rip off someone else.   

On the reordering, we developed a clever automated tool that would look at the sales velocity, our current and predicted stock, and would then automatically send a reorder to the brands to keep us in stock.  In particular, that meant that if we'd taken significant backorders on the website, we would immediately create purchase orders so as not to continually run out of stock. However, these two processes had an unfortunate interaction.  

On one occasion we had a number of large fraudulent orders placed for several thousand pairs of HANRO boxer shorts, a wonderful, expensive Swiss brand of underwear.  Unfortunately, the reordering system kicked in before the orders had been cancelled. And before we knew what had happened HANRO had delightedly accepted our order and shipped us the products. When this arrived in the warehouse and we did our analysis, it amounted to about six years worth of inventory.

Lesson 5:  Understand the blast radius of automated decisioning.

Michael R is the Senior Vice President of Retail Science at EDITED, Co-Founder of DynamicAction and an Executive Fellow at the London Business School.

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