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Yes, we have a three month return policy to get a refund, but we actually have a 365-day return policy whereby you can get an account credit.
The reason we have such a long return policy is because our products are made to last – they are more sustainable than any fast fashion. We’re making those pieces that sit in the wardrobe and get worn time and time again. So even beyond three months, all the way up to a year, if our products start degrading or don’t perform the way our customers expect, we want them to be able to return them. That’s the level of service we aim to provide.
Obviously, this has quite a big impact on how we manage our stock. We have a very unique approach to our clearance strategy, as we may receive products long after they were designed to sell, which we then we need to sell again. Clothes are seasonal, so trying to sell these products 2-3 months outside of their target period poses a challenge. Selling shorts in summer is easy. Selling shorts in autumn isn’t.
So we have a variety of strategies, sometimes even removing items from the site completely to resell at the next appropriate time, rather than reducing prices so significantly that it damages the brand.
There are understandably nuances around how we manage all of this: there’s a cost to holding the stock, and there’s a cost to managing it. We have a good warehouse, so we’re not limited in our storage space, and are able to explore things like delayed resale. To offer much longer return periods, we’ve had to gear other areas of the business up to support it.
The key from a data perspective is really being able to predict the return cycle and the return phasing accurately, to prevent us buying more stock for similar lines when we know we have quite a high return rate. If we know we’re going to get lots of stock back, we need to plan effectively for the returns coming back in, or we will end up with overstock. Data is going to play a big part
That is a key benefit we expect to see from having our PIM in place. We’re not quite there yet – the parts of PIM we’ve released at the moment are focussed on driving demand and how we operate our PLP to make our product more easily discoverable to the customer.
As we move into next phase of PIM, we’ll be looking at obtaining a greater level of control over our option levels (for instance, one style available in several colour options). As we roll this out, we’ll be able to change descriptive information and product titles based on each colour, to give better information to the customer at point of purchase. We’re hoping that this will lead to more customers getting the right products.
If they get the right product first time, they are less likely to return it.
Specifically in regard to PIM, we have a wide range of products that are available at option level, and we wanted to try and display as much of our range as possible.
However, we found if we put every single option product on our listing page, customers tend to get overwhelmed – there’s too much choice to scroll through, and they don’t understand how best to find the products that are most relevant to them.
What we’ve been doing is using PIM to group options effectively. So where we have one style – a cashmere cardigan for instance – that comes in 20 colours, we are able to group options together. Bright colours go into one listing panel, dark colours go into another, patterns are grouped together... it gives a clearer view of the depth of range on offer when they interact with each page, but it doesn’t overwhelm or repeat the same style too many times. We use tracking data to see how well that activity is performing, and then curate it for better conversion.
PIM is also enabling customers to discover products easily, through more intuitive filter terms and navigation.
Filtering is very helpful for those who use it. We have an interesting concept in our business, in that a lot of our content is driven by our catalogue. Lots of people browse the catalogue before coming to the site looking for stuff they have already seen. As a result there is quite a low engagement with our filters – only 20% get used at all, and the most used by far is ‘size’.
The most digitally enabled will go to the search bar, but more commonly people go to the category of the thing they have seen, then scroll through a list of options to find it.
We get a higher scroll depth than would usually be expected, which is another reason that too many options visible on the product listing page doesn’t work for us. Before we put grouping capability on our PIM, we had something like 360 women’s dresses panels. If someone is looking for something they’ve seen in print, and it’s right at the end of that list, they’ve got to do quite a bit of scrolling to get there.
I think data has played an increasing role in driving our organisation over the last five years, and I think we do use data effectively from a transactional perspective – to understand what customers are buying and how. There is still a whole wealth of things we could be doing in the future, to become much more targeted around customer behaviour, who our customers are, and where we can acquire new customers.
But I think customer loyalty really is all about providing an excellent service – be that through deliveries, returns, or how we engage with customers. In addition, the Boden brand has a very distinct voice and character to it, and that resonates strongly with some people.
There’s always been a saying in Boden: you can’t retain your way to growth. In marketing you will always have a leaky bucket, and some people will drop out. If you pour enough new customers in the top, you’ll still grow.
I do think that focus is changing, and as we move into next year, we want to look at how we can improve retention and make sure that the new customers we’re bringing in are driving growth effectively. That means getting an accurate view of where people are lapsing and how we can reactivate and engage with those people.
Similarly we need to be more active in re-engaging with customers after their first purchase, to make sure they come back. Once they’ve ordered two or three times, they tend to be engaged over a longer period of time.
Lastly, we’re using our data to develop our lifetime value model for our customers, to better understand the value of each customer and target our marketing spend effectively.
Our next step is to re-appraise how our online checkout looks and feels. It’s ok on desktop, but on mobile it isn’t a strong experience. And obviously traffic is shifting to devices – we want to make it easier for our customers to get that wonderful product that will bring joy to them, so making that checkout process smooth from a mobile-first perspective is essential.