Wednesday, July 17, 2019
CX Tech

What Is Data, Anyway? 4 Types of Crucial Customer Information Your Company May Be Ignoring

Benjamin Hunting
May 02, 2019

6 minute read

“What is data?” It might seem like an unusual question in an age where that word is constantly used in almost every conversation about improving a company’s prospects in the cutthroat world of digital commerce, but it’s certainly one worth asking — because like many business terms, its definition often depends on who’s asking as much as who’s answering.

While some types of customer data might seem obvious, such as the gathering of names and email addresses alongside basic demographic information like ages, genders, and geographical locations, there’s an entire universe of data that remains uniquely available to businesses yet is not always leveraged. Specifically, the interactions that a company has with its customers and what can be learned from not just the results of those encounters, but also the processes associated with them.

Each and every time a customer reaches out to your business in the form of a sale, email, or website visit, there’s an opportunity to learn something about the market at which you are aiming your products and services — at both the individual and the aggregate level — and improve your sales model. Even seemingly negative interactions, such as the loss of a sale or a visitor leaving your site without moving forward through any of your sales channels, is a valuable data point to be gathered.

Let’s check out 4 types of customer data your company might be currently ignoring that could have an important impact on your future success.

Data point #1: When does the customer walk away?

Companies are notoriously excellent at keeping track of, and celebrating, their successes, but seem far less likely to expend the same amount of energy digging deep when they lose. There are a number of reasons for this, but understanding why one didn’t win a client’s business is often just as valuable as figuring out what did work.

What does this mean from a data perspective? One of the first steps is to analyze points of contact where the loss of a sale occurred. Sometimes this can be done automatically, such as tracking when a customer abandoned their shopping cart on a website , or at what stage of the purchase process a transaction was canceled.

Other times, it’s going to take a more human-oriented approach through the surveying of sales staff to delve into when and where a sale went south. Formulating a standardized survey that’s filled out by each sales representative at the end of each customer contact — regardless of whether it was a sale or not — can give management the insight required to understand what aspects of the experience are routinely throwing up roadblocks between customers and closing.

Finally, consider the customer perspective, too. It’s not always easy to get a non-buyer to provide feedback about a transaction that didn’t happen, but it’s worth the effort to at least provide the opportunity to give feedback. It’s important, of course, to make this as non-intrusive and opt-in as possible, as there’s no value to be gained in goading a potential client immediately after they’ve decided not to purchase your products or services. A more fruitful dataset may lie in questioning existing customers as to whether they had, in the past, decided against an earlier purchase, and if so, what caused them to change their minds.

Data point #2: How often do they buy?

Is there anything better than a repeat customer? It’s a certainty that your organization keeps track of which clients have returned more than once to fill your till, and it’s even likely that your sales organization pays special attention to these buyers so as to ensure their future loyalty. There’s another layer of analysis that’s possible here, however, and one that could further direct how your sales team interacts with past customers.

“The key to driving higher sales is to understand your buyers’ purchasing behavior,” Obinna Ekezie of travel site Wakanow.com told Inc Magazine. “You should classify buyers by ‘active’ and ‘dormant’ status.”

Ekezie goes on to give the example of dividing past buyers into “bought in the last 30 days” and “older than 30 days” groups, labeling one dormant and the other active.

Depending on your own business cycle, and that of your industry, there’s no reason to limit yourself to just these criteria, nor to the labels of dormant/active. Taking into account factors such as seasonality, capital expenditure, consumption, and growth, there are many different methods to organize the chronology of when your customers buy from you, each one providing insight into not just that particular client but how they compare against the overall habits of your customer list as a whole.

Data point #3: How do they behave on your website?

It’s a given that an organization pays special attention to any and all website activity that leads to conversions. Understanding which online sales funnels are effective and which are not is a key aspect of building a successful online sales strategy.

What about everything else that a customer might do on your website prior to clicking on the “buy” button? There’s a treasure trove of data waiting to be mined and analyzed for companies that are willing to take a more granular look at client behavior on more than just the purchase page. It’s a compelling answer to the question “What is data?” when looking at sources you may not have previously considered.

“Which pages do customers seem to come and go from? Are they pleased with the page that each of your links leads to, or do they revert away from them? Paying closer attention to these details will allow you to understand and reach your customers better, while also making their online experience more enjoyable,” explains Miles Jennings of Recruiter.com in an interview with The Next Web.

It’s only natural to focus on the end result of any online transaction — but the path that a customer took to get from first learning about your company to deciding to hand over their hard-earned cash is well worth your attention, too.

Data point #4: What do other customers like them want to buy?

Giants like Amazon.com routinely score secondary sales through their recommendations engines, which unobtrusively provide customers with items they might also be interested in based on the product that they just purchased. This can increase the average value of an order, while also keeping a customer inside your online ecosystem for additional purchases they may have instead made elsewhere.

Leveraging the power of a purchase in real-time is obviously a valuable tool for encouraging repeat business. Using tools such as artificial intelligence, however, it’s also possible to compare a customer’s activity against that of past site visitors or sales rep interactions in similar product silos. It’s not just what a single client bought, but rather how that purchase places them in the aggregate of your entire customer list that can empower you to make effective suggestions for future purchases.

Opening up new channels

What is data? It’s all around you, flowing through your business on a daily basis, reflecting the constantly evolving state of your market, your buyers, and your practices. While there are many insights to be derived at the surface level from interactions with your customers, next-level growth is made possible when you take the time to analyze the deeper trends associated with all aspects of the data you have gathered from both sales and non-sales — and, of course, those that may only be sales-adjacent — by using the technological tools that are increasingly available to organizations of all sizes.

Benjamin Hunting
Benjamin Hunting
Benjamin Hunting has covered science, medicine, and technology for a wide range of publications, and has also been published in the Journal of Medical Economics. He coded his first computer program at the age of 8 on a Commodore VIC-20 and still has the audio cassette he saved it on hanging around somewhere in his office.
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