In this three-part series I want to get to the heart of what makes a great customer experience – and how best to use your data analytics to help you achieve this.
As retailers are battling to hit revenue targets, some will reach the ‘promised land’ of omnichannel perfection, with a loyal following of satisfied customers – while others will hit the headlines for all the wrong reasons. You know the story : share price down, mournful shots of boarded up shops and tragic odes to the loss of the great British shopping experience. We’ve seen it time and time again.
So, in such a competitive arena where consumers have a choice and they are more informed than ever before, how do retailers win business and maximise wallet share?
The customer is king or queen – but what do our majesties want from us?
I want to focus on customer service. In particular, is a good customer service about being personal and relevant to the consumer – or do they care more about getting an efficient user experience? Or, spoiler alert: perhaps it depends who they are?
So, let me get one thing straight. We’re bombarded by industry buzz terms such as ‘customer 360’ which has led to every Tom, Dick and Harry having their own understanding and opinion of what it actually means. I want to move away from this and explore how one defines a positive customer service and customer experience.
We all know that if a retailer is more personalised with their offerings and relevant in the way in which they interact with you across all channels, there is a higher chance that you will make a purchase – or at least associate the retailer in a positive light. On one side we see organisations like Amazon, Netflix and Eventim thrive as a result of smarter and more personalised recommendations yet we still see retailers making the same lazy mistakes.
Happy birthday John…I mean Jake….Sorry, Jill?
There’s nothing that will make your customers feel more like a faceless dustbin for your marketing than a piece of misplaced personalisation. For instance, If I buy a garden shed on a retailer’s website it isn’t likely that I am going to want to purchase the same shed again. So, it was baffling to me this week, when my in-boxed pinged with an advert for yet another man shed the moment after I bought one online.
Does this mean that we shouldn’t personalise?
Not at all. But before we go any further it’s important that we move away from trying to define a one-fits-all approach to a positive customer experience. It can mean different things to different people. Let’s take a look at two different customer types:
As a millennial myself, I believe we’re after the best product at the best price – and we’re not particularly interested in forming a relationship with the staff in store. Sorry shop assistants. Personalisation for millennials is about offering the right product at the right time and providing an efficient ‘in and out’ experience.
It’s worth pointing out that I’m not saying there is one ready-made template for the unique human experiences of any so-called demographic of people. But, it’s fair to say that in general our grandparents are far more used to face-to-face interaction. When they go the local shop each morning to get their paper, they are far more likely than me to stop and chat – especially as I’ll be gorging on news from my mobile. Likewise, their weekend shop will be more of a social event rather than a few clicks for an online delivery. In fact, in some cases the conversation they may have with members of staff could be the only interaction they have with people all day. So, for them, the personal human touch is what keeps them coming back over and over again. And any notions of efficiency are pretty redundant, in this case.
And this is where data comes into play
It’s clear that a one size fits all approach doesn’t work – and you don’t need a super computer to work that out. But it’s interesting to see how retailers are using their data to better profile and understand the different consumer groups.
Timing is key. I can have the best product at the best price but unless it’s attractive to a consumer and meets a need at the optimal point in time they won’t make a purchase.
That’s why it’s important for organisations to move away from only looking at the data on the net result – what product or service they bought – and move towards understanding the steps and triggers which lead to their decision to buy. If you can understand why a consumer bought a product and the unique journey the customer went on before they made the purchase, you can make positive change.
The question is: how? This is what I’ll be covering in part 2.