Consumer Behavior

Cross-sell and upsell: How data analytics optimizes customer experience

Roy Castleman, founder and managing director of EC-MSP Ltd., offers up six steps for applying analytics to customer experience.

Photo by istock.com

February 25, 2021 by Roy Castleman

Customer experience has surely suffered a lot of vague definition for being one of two main factors determined to be the 'shapers of our near future.'

It's easy to become lost in defining CX as 'the tech paraphernalia and processes retailers now employ to facilitate people coming through the door' — literally or figuratively — but CX is simpler (and far more nuanced) than that.

CX is about perception.

It's how customers perceive your brand: what customers see, feel, or hear of you.

And if there's one, over-arching dictate about CX — it's that it needs to be seamless.

More than just funky biz speak, "seamless" means that when customers make a call to your helpline, shop in-store, shop online, pay an account, employ your products or services, they need to feel the same caring ethos you said you provide. Seamless CX is simple, but detailed. Everything down to site microcopy evokes an emotional response in customers. Smart retailers want that emotion to be positive because that's what's good for business.

It's sometimes easy to assume customer expectations around tech, payment gateways, and overall digital ease of use, but ask a professional IT support company how much of their time is spent redirecting tech towards more logical and pleasing aims for retail clients, and you start to get a clue how best to apply analytics to enhance CX.

Here's the whole CX philosophy in a nutshell (with apologies and thanks to the medical fraternity):

First, do no harm.

Put another way, if you start out with the massive 'basket of tech' and other goodies you can employ to better serve your customers, start out by never assuming, never alienating (increasing the distance between you and your customer), and never limiting their movement and freedom to choose.

6 steps to apply analytics to CX

Optimizing CX means putting customers first, and leaving out any other enchantment you may experience about how systems can help facilitate your daily behavior.

Efficiency is an in-house consideration — it doesn't mean much to customers. In fact, efficiencies are often the very reason why CX diminishes. Skimming a bit there, chopping staff here — these might save costs and make a company seem more streamlined, but they might also cost that company its very future.

Effectiveness is the core of CX.

How effective are you in pleasing clients and making sales?

Here is where data analytics can become a very useful tool. Analytics finds one of its prime applications in interpreting retail behaviors, which is never a simple task. When every touchpoint in a customer's journey needs to encourage them to continue and return, you need a detailed and accurate interpretation of analytics to guide you.

Especially now that digital life has become mainstream, it's easier than ever to look for the right data and apply it successfully. This isn't new science — analyzing customer behavior to better serve them and grow your client base faster is as old as the hills. Data analytics just makes it more accurate, and fun.

1. Document your existing customers
Make sure that your existing customer inventory is extrapolated for individuals' referral origins, behavior online, frequency in-store, personal tastes, and even their helpline use. All of this detail forms the base from which you can better tailor existing clients' experience of you, while also more appropriately attending to new customers.

They shop online and were last in a store two years ago? Make sure your approach to them fits that behavior. They frequently call and ask about product applications? Make sure that you ping them with your new blog post, which lists several important and useful tips that detail the fundamentals of your most popular products. That's the kind of thing that cements customer loyalty.

2. Don't get lost in a sea of facts and figures
Having already done the initial profile building, take each individual's data and use it to build a complete view of them. The wonderful part about data analytics is that it eliminates a lot of the guesswork that legacy surveys and similar retail approaches tried to address.

The data is unambiguous: the customer did that, said that, then did that again. Those are the facts — use them to build a correct picture of your customers regardless of what you think or feel about the products — or processes — they employ or eschew.

3. Find and fill the gaps
Once you've built a customer profile, there's a mountain of data available from peripheral sources that can augment what you already have. Data analytics almost intuitively leads retailers to ask the right questions — why does this client do this, or why did that customer repeat this behavior before abandoning it completely?

Data analytics will show you where the gaps are in your picture of your client as you view the available intel.

When you start saying "Hey, wouldn't it also be nice for customers if we…", that's going to be because analytics has given you a good, 3-D picture of that customer, and ways to enhance CX with them are going to fill your imagination. You'll see the gaps, so you can fill them.

That's a precious ability that analytics provides.

4. Map customer data regularly
Data is fluid; it changes over time, and data analysis is not a once-off task. Stay current on what your customers want and you'll easily optimise your relationship with them.

Once you've built a customer profile, don't abandon it as a static asset. Map the data on a regular basis — for example, by comparing different data sets against a customer profile.

That's the joy of analytics — it allows you to refine your CX further than ever, which in turn allows you to enhance your relationship with them.

It's also OK to get it a little wrong in this process; as long as they perceive you're trying to provide a better service, they'll understand the occasional mismatch.

5. Bump up business intelligence
Formulating and testing a hypothesis based on available customer profiles is one arena where dedicated research can seriously impact revenue.

For instance, if you identify a group of people who came close to a defined set of customers, yet behaved differently in the final analysis, that key information allows you to mine that group and identify who is most likely to:

• Become one of your best customers.
• Return as a regular customer.
• Shop infrequently as their visit was a once-off.
• Recommend you to their peers through good old-fashioned word of mouth.

Thus, you can cost-effectively apply your marketing budget toward those identified as being potentially high lifetime value customers, as opposed to the shotgun approach of mass mails or newspaper ads.

6. Use your data analytics' end results to measure
Finally, such intimacy makes for good future growth, allowing savvy retailers to upsell customers and even cross-sell into future service or product range expansion.

Measuring your performance in delivering CX is not hard — the data will be staring you in the face if you've followed the steps above.

The cycle needs to be:

• Collect data.
• Take action.
• Measure its effect.

If you take logical actions based on your analytics — actions meant to enhance CX — and then measure them for their success, you're working toward the best customer experience you can provide.

Customers will know it, see it, and experience it.

In short, their perception of you will improve, and that's exactly what CX is — the customers' perception of who you are, and how much you value them.

Roy Castleman is founder and managing director of EC-MSP Ltd.