Want to improve loyalty? improve the experience

I’m told that customer loyalty is the new battle ground for the retail industry. New predictive technologies allow your favorite retailer to send you updates and offers before you even need them. They will know when you’re in or near a store and update you accordingly with latest offers so you have an irresistible desire to go into the shop and part with your cash for something you don’t need.

I find it all a bit creepy.

Last weekend I was doing my ‘weekly’ and got talking to the Computer Science undergrad working the checkout. It seems we had a lot of interests in common. Anyway he pointed out the new system being installed to help reduce queues at checkout.

Asda, Walmart’s UK supermarket chain, have always been a no frills supermarket. No loyalty program, large out of town stores, extensive product range and consistently low prices, the last of which is what brings us back here most weeks. The length of time we spend waiting to checkout is testament to the fact many others agree.

Well over the last 2 years the innovation guys at Asda have been doing something about that. Their new system, Queue Clarity, tracks the number of shoppers entering the store, how long they take to fill their trolleys and predict how many checkouts they need open before the queues start to form. They’re improving the shopping experience, making it easier for me to get in there, get what I want and get out again as painlessly as possible. And no need for me to signup to a loyalty program, no concerns about what they are doing with my data and who they are selling it to.

ps. I don’t shop online, I prefer to choose my own fresh groceries and I like to go to the store for inspiration for the next week’s meals.


4 thoughts on “Want to improve loyalty? improve the experience

    • Thanks for the comment. At my last employer we worked with several major retailers in the USA using technology such as loyalty data and Complex Event Processing to attempt to predict what people wanted to buy and when. We linked this with location aware social tools so that we knew when a potential customer was near a store and automatically send them offers from that store to their mobile device. Of course this relies on consent.

      However, I believe this requires time, time for the customer to invest in signing up for this sort of program, downloading the relevant app etc. With all retailers competing for our attention how many times are you really going to do this? You could always go for something like groupon…

      I prefer the approach described above as it makes my life easier and I don’t have to do anything! 🙂

  1. Hi there!

    I know this is an old article and I’m not sure if you’ve done a follow-up, but I spent some time working with Clarity — as a matter of fact, it was pretty much all I did leading up to when I left the company — and I feel like I can give you some insight into how the system works in reality compared with how it should work in theory.

    The system itself was pitched to us as being highly intelligent: we were told that it could count the number of customers entering the store, the length of time that they would spend picking their groceries and thus predict the number of tills we should have available if we want our queues to meet the arbitrary measure of “1+1”. We were told that it could tell the difference between colleagues and reps working nearby tills (stocking the confectionery, side frames and ends); between a group of people shopping together and a cluster of individual shoppers; between an adult and a child and, crucially, between someone likely to use a main bank (manned) checkout and a self-service checkout. The virtues of Queue Clarity were expounded so vehemently by head office and regional management that we all hailed it as a potential solution to our consistent requirement for “Queue Busters”.

    This, sadly, was not the case.

    You see, the primary system we used to judge how many colleagues we required on our tills was a mixture of previous trends, sales forecasting and good old fashioned intuition. Our wage budget was controlled so tightly, however, that we were often forced to understate our needs so that we didn’t accidentally overspend. In terms of overall forecasting, the most useful aspect of having data generated by Clarity was not in rota planning, but in contract base (the base number of hours we had for any given day not inclusive of overtime). The rest was done using the above methods. So in terms of long-term planning it was useful but for medium-term planning, not so much.

    So what of the potential for short-term planning? Remember that from what we were told, we were supposed to be able to judge when we needed to call colleagues from the shop floor to assist us with queues in advance of the queues actually forming. Well, I’m afraid to say that this supposedly intelligent system was anything but useful in this regard.

    Let me explain. Clarity has a graph which displays estimated demand over a 15 minute window. It displays this information in the form of four bars which told us how many tills it had calculated that we would need open at that exact moment, +5 minutes, +10 minutes and +15 minutes. It also told us how many tills we had open (as if we didn’t already know). Now, you would have thought that the first bar on this graph would remain roughly static only moving up or down by one till at a time in accordance with the passing of time. You would also expect that the ‘now’ bar would approach the ‘+5’ bar fairly steadily if its predictions were accurate. In fact, I have seen it jump from 9 to 14 in 10 seconds when it has never predicted that we will need 14 tills on at any point before. This meant that you could be left scrambling to catch up with it… only for the demand to drop back to 10 the moment we had put the call out over the tannoy. This means that once everyone had dropped what they were doing to get to the tills, we had to sheepishly turn them away: a frustrating experience for everyone involved.

    Not only this, but Clarity is also used as a performance measure for the Services department and the store as a whole. Once an hour, we were given a percentage score for how well we kept to our arbitrary “1+1” ideal queue measure. Remember, this isn’t based on waiting time, but rather by the number of people in line at any given time. It is completely indiscriminate. I might have had two customers waiting with basket loads of shopping at one till and one customer with a massive trolleyful waiting at another. There might have been 10 items each for the two customers at the first till and 150 items for the single customer at the second till. We both know that it would be quicker for a customer just arriving at the checkouts to go to the till with the two customers, even when you factor in the time it takes to process two payments. However, if I wanted to meet my targets, I had to try to convince the customer to wait for longer at the till with the single customer. That’s good service (apparently).

    We did try to advise people with small amounts of shopping to go through self-service, but some people just didn’t want to. So why should we try to force them? The answer is that we shouldn’t. The customer is the one giving us the money for the service we provide them. It should be their choice of which till they want to go to. If the customer would rather wait three or four minutes and be served by a colleague, have a bit of a chat and have their bags packed for them, that’s what we should encourage them to do. We had customers who knew and liked to be served by a particular colleague. The colleagues themselves referred to these people as their ‘regulars’. The colleagues liked seeing these people and these people like seeing that colleague. It was good for our colleagues’ morale and it made those customers happy. But to get our number, we had to try to encourage that customer to go to another till and be served by someone else. That’s not right. That’s not good service.

    And yes, there are a majority of people who, like you, don’t really care who serves them; who want to go in, grab their stuff and get out as quickly as possible; who would not be happy to wait in line for any more than a few minutes. The thing about service is that it’s not the same for everyone. My job was to try to cater for everyone’s wants and needs and by God I loved my job, but I hated Queue Clarity. To me, customer service means a lot of things. It’s about changing what you do to suit the individual. It’s about trying your very hardest to please each and every person you see. It’s about that personal touch that makes a mundane activity memorable. Ultimately, customer service is about the customer. What it is not is a numbers game and I feel as though Clarity, and the way that the company uses it, tries to make it just that.

    Don’t get me wrong, I totally understand why they brought the system in. If it worked well enough to predict actual lane demand, it would have it’s uses. I just don’t think it should be used as any kind of measure of performance for a store, a department or an individual. The system in the way that it was used and operated was (and still is, according to my ex-colleagues) redundant. To the point that we generally only looked at it to check what our score was. Yet at the back of your mind, it was always there, influencing your decisions and failing to do what it was designed to do: improve your experience.

    And don’t even get me started on what it did to availability. That was the biggest eye-opener. What do you suppose happens when you consistently have to underestimate how many people you need to open the right number of tills? With half of the colleagues from other departments sitting on tills, who’s left to man the shop floor? Those shelves don’t fill themselves…

    • Thanks for the extensive but fascinating insight into the effectiveness of the Queue Clarity system. It is too often that we are over promised and under delivered. Sadly it sounds like the system didn’t deliver the right experience to its direct customers!

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