Many of the fastest growing ecommerce companies today are multichannel stores - those bricks and mortar stores with an online presence. With the notable exception of Primark - who famously have margins that don't work when marked up for delivery - nearly every high street store now has a website.
For a long time, however, it was the online-only stores that ruled the roost - the Amazons and Net-A-Porters of the online world. Why? Because they were built from the ground up for online sales, with a complete understanding of how to optimise for e-performance. Today, things are changing. The crazy growth rates of the big beasts are slowing as their market reach matures, and slower, offline brands are playing catch up.
This shift is due to a range of factors - the cost of technology, the convergence of online and offline (click and collect, for example), the wide choice of solutions now within reach of even the smallest players, and a much better understanding, across the board, of the skills needed to run an online operation that were so long the preserve of the online specialists.
What I want to look at in this post is one aspect of this 'trading intelligence' that is now within the reach of everyone - the concept of 'product level conversion'.
Part of the problem with offline stores going online is that they were - and to a large extent still are - relying on their old merchandising skills honed on the high street - where the web channel was used for clearance, or mark down, or perhaps testing new products. There was no real understanding of the different behaviours of the online shopper, and how the performance of a product on a webpage differed from the performance of a product on the shop floor. For a start, individual store owners can quickly test out different displays in the window, near the till, in the checkout queue - to see which combination worked best for sale. Performance will differ store to store. As it happens, with sophisticated a/b testing and recommendation tools, you can do the same online, as pioneered by Amazon and others - but these were skills the old style merchandisers did not have.
Doing a cursory search on Linkedin for connections who are or ever have been merchandisers, I came up with 300k results, but less than 10 per cent when looking for online merchandisers. So the job role is still new, and often the role is handled by the off-line merchandiser.
Typically the questions a merchandiser will need to answer revolve around ‘how much product do I need to stock?’; ‘which are my fast moving products?’; and ‘how can I move certain products more quickly?’. In order to answer these questions accurately when it comes online trading, they need a handle on product level conversion rates.
Product level conversion - what is it?
One of the most basic insights an online merchandiser needs to extract from their data is an understanding of which products show the most potential to drive sales. Not just those products that actually drive sales. He or she will already know this - it's in the order reports. But just because one product is selling well, it does not mean it's the product you should be selling, if you see what I mean.
Let's take an example. Product A sells at a rate of roughly 50 a week, and a price of 14.99 (discounted from 24.99). Product B sells roughly 15 a week, at a full cost of 21.99. Both products are quite similar to each other - perhaps two different pairs of trousers with slightly different styles. Now, when we look at the views each product gets, we find Product A, which is featured on the Homepage, gets 5000 views (for an effective conversion rate of one per cent), which product B (not on the homepage) gets 750 views (for an effective conversion rate of two per cent). Moreover, when you look at the margin you achieve, you find that product B makes a gross profit of £10, while product A makes a gross profit of £5.
Merchandisers who are not paying attention - or perhaps those who don't have access to the right data - would have very little idea of the potential of Product B to drive sales - and more importantly gross profit - if merchandised differently.
Part of the reason for this apparent blindness is that many retailers are still relying on a basic web analytics tool to show them what's going on on their website - and getting product level insights can be very very hard as a result. Moreover the revenue figures will never be accurate, as they won't include returns, or cost data to enable them to calculate profit - to do this they would need to stitch stats together from multiple directions, export and manipulate into Excel, and most likely need an analyst to do it all for them. Today's high growth online retailers need to to act fast, and in real time - for which they need a more powerful data tool.
As retailers get more savvy, they will also want to take a deeper look at variants and stock levels. Again, where online and offline are trading alongside, merchandisers will often aggregate weeks across all platforms, whereas it may be the case that the sales curve online looks very different to offline, and in particular the performance of what are sometimes termed 'child products' - size and colour variations of the parent. Once you have proper visibility of what colour and size combinations drive the bulk of your sales (as well as the bulk of your views), you can optimise your stock control, and if necessary fine tune your marketing campaigns. It's no good paying for adwords traffic to a product with 'fractured' stock availability, where the most popular size or colour (according to what you know about past sales) is out of stock.If retailers are to truly optimise their online product performance in the same way offline merchandisers test and optimise the in-store experience for sales, making use of growth hacking tactics revolving around data-led trading intelligence like product level conversion is vital.