Monsoon Project MAX & eReceipts overview

We start our issue with RichRelevance(now Algonomy) in 2013. And initially of recommendations worked very well for us going early traction customers about this 13% of our sales on the web have through recommendations to realize that had relevance to our customers and residents to our customers. So we decided to integrate that into relevant touch points. So Max is what we call monsoon accessorize extended its internal project name. What it does, it’s an assisted sell tablet, they use iPads in 102 stores in the UK. And what we do on the assisted sell tablets, we do effectively three possible scenarios.

One is a very simple extended ourselves, a customer comes in product isn’t available in that store in the size of two product category. And you can buy that very simply online.

The second potential scenario is the products in that store, you can do a simple tilde transaction on that device.

The third possible scenario is that the product is available another UK store. And again, you can process it through on the same device. What’s neat about the solution is that’s all integrated into one basket.

So you do all three of those scenarios in one basket with the customer. What we learned quite early is as we launch this into our stores, so we got quite a big UK set of about 300 stores, monsoon accessorize 100 of those have got this device running in the stores you got we’ve got a plan to extend that 280 Next year, we realized it actually the product knowledge of our store teams around wardrobe building wasn’t as mature as perhaps we would like it to be. So we saw an opportunity to use RichRelevance(now Algonomy) to help our store teams drive incremental sales to our customers. So as they were going through this sale transaction with customers, we could help the customers by understanding how you can build a wardrobe around the particular product that customers interested in.

What’s also quite interesting is when we look at returns. So this is physical returns coming back as a result of those transactions, returns for this max Transaction Search is about a third again, less than we see on a typical online transaction. Typically, online you see about between 35 and 40% returns on a max transactions is his cell tablet in store typically see about 11%. This is quite interesting. It’s quite a profitable sale, obviously so lower customer, and we’re driving multiples instrumentals there. And certainly it can be no harm having the recommendations. So we’ve seen that as it is quite a success. Linked to that is obviously receipts, the receipts are becoming increasingly popular, we give the customer options with the receipts, meaning you either have any receipt, you don’t we think that’s appropriate for customer, particularly giving up across valuable data, in this example, an email address, and it’s got to deliver value, you know, so why give up this data. So what we decided to do, again, in a relatively early stage of this, we know who the customer is, we thought well, rather than just issuing a receipt, we should issue it and giving value back to the customer. So giving them recommendations simply. So we saw the opportunity again with RichRelevance(now Algonomy) team. And we decided to include recommendations on that. So what’s the advantage of this? The simple advantages is what’s the open rates, the average open rate in an email is about 19%. Yeah, from the emails that we’re sending, ie receipt emails we’re sending through we get an open rate of about 63% which is very, very high and it’s extremely high open rates. Yeah, so that’s good in itself. The other interesting one is the click through rates. So typical click through rate we have an email anywhere between two and 3% approximately 15 to 3% as a standard email for our website. When you on these particularly receipts, we’re getting a click through rate of 9% three times and so would suggest that the data we’re sending that it seems tricky engagement customer. So those two initiatives that we’ve grown, as a result of experience, have initial recommendations and working closely with RichRelevance(now Algonomy) team.