Data can help determine business success

Quantifying how successful your service is at helping customers starts with one basic question: what does success look like?

Data can help determine business success

We’ve talked a number of times in this column about Dressipi’s journey from a personal styling service for consumers into a fashion recommendation solution for retailers. Over the past few years, we’ve adapted our business model to reflect the needs of the market, updated our technology continuously and got some amazing clients along the way. And we’re now at that time in any busy start-up’s life when you have to start answering a very difficult question: ‘how do you measure success?’

Measuring the effectiveness of what you do is key to every enterprise but it’s particularly acute when you – like us – get your revenue from selling services to other companies. Unless you can justify to your clients that what you are doing for them is improving their business, they’ll stop paying you. As a principle it’s simple: in practice, however, it’s fiendishly difficult to get right. As we’ve found many times, what looks like a success for you as a service provider might not resemble anything of the sort for your customer.

For example, our customers – fashion retailers – want their online businesses to grow. They know that while their overall revenues creep up by about 3% a year, their e-commerce can grow by as much as 20%. Their challenge, however, is that fashion e-commerce can be tremendously inefficient. Retailers might get millions of website hits a month but conversions are often quite low and factors like returns can significantly eat into their margins. 

When we first started out as a B2B service, we decided to measure the effectiveness of our service by looking at how Dressipi changed some of the key pain points for retailers. We set out to prove that people using our service spent more money and returned less product. Additionally, we made a case for how much customers valued our service by measuring what proportion of a retailer’s customers completed a Fashion Fingerprint on our site.

What we found from taking this approach was that, although these measures are relevant, they did not take the whole picture into account. We found that retailers were keen to work out the overall impact, including conversion and the actual difference it made across their entire business. They loved what we were doing but they wanted to see the undeniable benefits in cold hard cash terms.

So we did what start-ups always do: we went back to the drawing board. Instead of focusing in on the details, we went for the bigger picture. Now, when an e-commerce manager asks us how we’re going to improve a business, we don’t talk in terms of reduced returns or even how we might drive up average basket sizes with one of our Fashion Fingerprints. Instead, we talk about how we’re using our data to increase average revenue per visitor. And that’s a metric retailers find easy to measure but hard to argue with.

It’s taken us two years to get here. But, like all solutions that seem so simple with hindsight, it’s a problem you can only solve using time, experience and sufficient data: all the things we’ve spent the last four years assembling. 

Sarah McVittie
Sarah McVittie

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