The profusion of smartphones and tablets in recent years has changed the retail landscape, contributing to an ongoing shift in the balance of power firmly towards the hands of the customer. Armed with a relatively inexpensive mobile device, shoppers have instant access to facts, opinions and reviews and can compare prices on just about anything they might be interested in buying – be it from their local corner store or an online retailer operating out of giant warehouses on the other side of the world.
The combination of mobile device and data plan effectively gives customers a “Big Data license”, bringing ready access to a wealth of product and retail information.
One survey in the US for example found that 59 per cent of consumers said they regularly use their smartphones while shopping in retail stores to compare prices for the same or similar products.
But of course it’s not only prices that can influence purchase choices. Having a “Big Data license” also allows customers access to a broad swathe of other information, including:
- Social media sentiments about the product in question
- Rumours of upcoming versions
- Friends’ views on the items
- Recent celebrity endorsements
- How a specific the store’s sales staff treats people
- Pros and cons of competitor or alternative products
With this depth of information at their fingertips, customers are now in a stronger position to know more about specific products than the salespeople in the store.
Selling better
In the space of just a few years then, and with relatively little conscious effort, customers have changed their processes and technology, leveraging Big Data to enable them to answer the ultimate purchase decision: “Do I buy this here and now?”
One survey earlier this year found that nearly half of consumers in the 18-49 age bracket felt they could locate product information more readily and reliably using their mobile device than asking a store associate for assistance.
With so much data-driven power in the hands of customers, the obvious question then is where does this leave the salesperson?
Do they have the right information to sell their products to the customers better? And, if they don’t, how do they go about getting it?
Given the volume and value of customer information offered by big data, the salesperson should also know all the details about customers as they walk through the door.
Instead of judging by the customer’s outward appearance, the salesclerk can know:
- What is the value of the customer and their purchasing power?
- Are they a repeat customer at our store, or other outlets? If so, what do they normally like?
- Who are your customers’ friends and influencers? What products are their friends interested in? What have they bought previously?
- What did they tweet, Facebook or Instagram about before, during or after shopping at your store?
- Do they normally shop at a competitor and is this a possible conversion opportunity?
- What country do they come from and what is their preferred language?
Let’s take a couple of examples that offer important lessons for retailers in terms of the way they connect with their customers.
At one end we have traditional owner-operated retailers, such as the long-running wet-market stalls across Singapore. In my own neighborhood the Chan family-owned fresh chicken stall has served customers for 50-plus years. They’ve developed a comfortable familiarity with their customers and are able to tailor their sales to specific customers.
On the other extreme, we have the online mega-retailers such as Amazon.com or Singapore’s Q0010.sg, who monitor and utilise customer data very carefully, making recommendations to the customer when they return or sending targeted, personalised emails to bring them back for more.
In both examples, customisation has led to increased sales and service levels.
The challenge for a chain operation is that the sales person is different at each store. That raises the question, can – and should – customer information be shared to provide personalised service?
Serve me better… but don’t intrude on my private life
Of course, retailers with customer data need to balance building a great image with the delivery of personalised service against the risk of being seen as invasive.
 The Starbucks experience |
In the US, Starbucks has created a customer loyalty card management app which:
In exchange for this convenience, Starbucks in return gets to know
But why not develop this app further to collect more valuable data that would enable the firm to amplify the customer experience? When a customer carrying the app sits in a particular Starbucks outlet, the app can also collect information such as:
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Some of us are willing to spend a little extra time going to the cosmetics artist we trust, a few extra dollars to go back to that particular restaurant or travel that extra mile to that nice coffee shop that’s kind of on the way home.
But more often than not, there are days when your favourite make-up artist isn’t in or the familiar coffee barista is on a day off, or times when you are just at a different outlet? You lose all the usual benefits, the warm welcome and most importantly, the connection and status of a regular customer.
What if this “personalised human touch” could be applied to all retail stores across all of one chain’s outlets and maybe even across the world?
When the service person was known to us, even a friend, we can be comfortable with the personal touch. But what about when it is someone we know we have never met before?
Imagine walking into the Starbucks on the other side of town. The store assistant is able to welcome you back with a warm smile and address you by name, even though you have never set foot in the store before. And, even before you show them your Starbucks card or credit card or tell them your order, the assistant asks whether you would like your usual coffee.
How would you feel? Impressed like a special guest with a special connection and a sense of familiarity? Or invaded because a perfect stranger knows your habits?
It’s a fine line between the personal touch and the overly familiar, and where that line is varies enormously from person to person. It is down to the retailer to figure out if and how recognising a regular customer in other retail outlets improves customer connections.
Getting started
Nothing mentioned in this article requires a sophisticated algorithm. What retailers can start with is basic blocking and tackling, a big-data capable device in the hands of the sales team with fast visual answers about the customer in front of them. There also needs to be a new sales engagement process to use the facts to enhance the customer experience.
Consider setting goals to capture real, observed facts recorded in basic human terms that are easy to understand. Set rules on how to engage the customer without offending them.
So what do you need to deliver results?
- A vision of how well you want to know and serve your customers
- A strategic business question to focus on
- Focus, to quickly deliver monetary results as a proof of concept
- A fast visualisation tool like Qlikview to translate Big Data into actionable intelligence and a database like SAP HANA to store the results in.
- A smartphone or smarttablet to access facts fast
- A team of sales people trained to act on actionable intelligence, enhancing the customer experience
- A security review to ensure customer privacy is protected
- And finally, a close attention to the human touch and careful consideration of where to draw the line.
If we as customers see the benefit of leveraging Big Data and Actionable Intelligence to make on-the-spot decisions and amplify our own experiences, then why don’t we as business leaders do it too?
