Leadership
Culture
Customer Experience
Strategy
Organizations are awash in customer data, but gathering and understanding that data is another matter. Data experts Aaron Heath and Bruna Krupek spend their days helping our clients understand what their customers are telling them. Today, we’ll talk to them about the importance of understanding the information coming from your customers – no matter what industry you operate in.
Why is it Important for Leaders to Understand Customer Data?
Bruna:
Reviewing your organization’s customer satisfaction and data engagement scores is one of the best ways to understand the current state of your organization.
CUSTOMER SATISFACTION DATA IS A WINDOW INTO WHAT GOT YOU TO WHERE YOU ARE AND WHAT’S KEEPING YOU THERE. AND ONCE YOU UNDERSTAND YOUR CUSTOMER’S LIKES AND DISLIKES, YOU CAN PIVOT IN TERMS OF RESOURCING.
So, if there’s an area that’s satisfactory, what resources will it take to further enhance this? If there’s an area that’s a little bit less satisfactory, what resources will it take to improve it?
Aaron:
I would add that much of the importance of customer satisfaction and engagement data lies in what happens if you don’t have that information. Without it, effective decision-making becomes much more difficult. For example, your sales figures may look good; however, without customer data to back it up, it is difficult to know what’s driving those figures or how to improve them.
DYK? Customer-first B2C businesses see nearly double the revenue growth of non-customer-first businesses. (Forrester)
What Metrics are Used to Measure this Information?
Bruna:
In my work alongside clients, we focus quite a bit on customer satisfaction scores. These are simple – we ask if the customer was satisfied with the product or service. This can score can be numeric (i.e., rate your experience 1-5) or a phrase (i.e., somewhat satisfied; satisfied; very satisfied). This direct, one-to-one feedback is invaluable – you can identify individual customer issues and build a macro-view of the overall customer experience – especially since 73% of consumers will switch to a competitor after multiple bad experiences.
The other metric I invariably turn to is customer return (i.e., repeat customers). Measuring that churn, attrition, or retention helps us zero in on product or service quality. If the return is low, it’s time to dig in on why. If it is high, what’s driving success?
Aaron:
In addition to the tried-and-true metrics Bruna mentioned, you can get very creative and very specific when measuring. But it boils down to what Bruna shared previously: the rating scale around “are you satisfied/dissatisfied.” Once you’ve got a handle on that, you can get granular.
Here’s an example of why it’s valuable for an organization to consider the macro and the granular.
Let’s say you run a company that sells downloadable “how-to” guides. Some of your company’s best-sellers are How to Clean Your Chimney; How to Cook a Turkey; and How to Water Ski. Your site metrics say you have a million downloads, which sounds great. However, if you stopped there, you would only get part of the story. Because if you looked at customer satisfaction data, you would find that 75% of your customers report being unsatisfied. Getting even more granular, the reviews on your site are riddled with comments like “Do not use – I burned my turkey” and “I followed the steps and got stuck in my chimney.”
Once you’ve looked at the data holistically, you can begin asking a few of the questions Bruna mentioned earlier, like: “If there’s an area that’s a little bit less satisfactory, what resources will it take to improve?” Thanks to your newfound understanding of customer opinion, you know you need to adjust the cooking time in your turkey guide and add size guidance in your chimney guide.
What are Common Mistakes Organizations Make Around Data?
Aaron:
One big mistake that organizations make is confusing or conflating customer satisfaction and engagement. It’s important to recognize that these are different metrics, and that they answer different questions. For example, you could have a customer who is always engaging with your social media posts, always emailing, always in contact, but it’s because they’re very upset with your service. So, while they may be highly engaged, they’re not highly satisfied. This goes back to looking at your data holistically and remembering they are complementary metrics.
Another mistake is using a point-in-time measure of satisfaction to try to extrapolate to larger patterns. A good example of this is there was an electric company doing customer satisfaction surveys which had major implications for year-end bonuses. What they found was that at the end of the year around November, their satisfaction scores started tanking – and they could not figure it out. So, they brought in a data team to look at it. Instead of looking at satisfaction over this one year over time, they blew it up to look at the last three years. And what they found was that every November, customer satisfaction was tanking. By simply widening the time period, they were able to make the connection that it coincided with power shut-offs for lack of payment. So, they were shutting people’s power off and those affected were responding to customer surveys saying they were very dissatisfied. The lesson here is that context is key but may not always be obvious.
Bruna:
Another common mistake we see happens in the collection of customer satisfaction data. Getting customers to fill out satisfaction surveys isn’t always easy. Doing the legwork to understand the best times and the right frequency with which to solicit feedback from your customers is key. Without it, you risk over-engaging (i.e., spamming their inboxes), under-engaging (i.e., getting lost in a sea of emails), or mistiming (i.e., wrong time of day or sending the week before a holiday when engagement is already low).
How does The Clearing Help Clients in this Arena?
Bruna:
First and foremost, our customer experience experts‘ decades-worth of experience to help clients design customer feedback frameworks that get the answers they need. That includes everything from determining the right question set and audience to the best mechanism to deploy in order to collect the data.
This experience is invaluable when working with government agencies, where knowing how systems work is critical to driving survey engagement. For example, consider an agency with employees who work at secure sites that need to solicit team member feedback. They will need a specific solution to reach employees due to firewalls, security settings, and additional factors that impact what people can see during the workday or when they have access to their computers. We have former CIA, DOD, and more on staff who have lived the ins and outs of these requirements and guide us when we’re looking to make soliciting feedback a turnkey process for even the most complex environments.
Aaron:
Thanks to the experience Bruna mentioned, we are often brought in as the customer satisfaction survey SMEs. We help design the survey; we make sure the questions are actually measuring what the client wants to know; we find the property deployment tool; and then once the survey has gotten its responses we are crunching the numbers to develop easily consumable reports coming and dashboards that tell the story that comes out of the survey.
Going back to mistakes, an additional error we experience with customers is when they retrieve customer data and don’t act upon it. That’s where The Clearing’s Customer Experience and Strategy Solution Areas come in. With the data in hand, our team uses it to help clients develop the right plan of action to realize their customer objectives.
What are Emerging Trends in Customer Satisfaction and Engagement?
Aaron:
Automation and the integration of AI are the big ones. Everyone’s talking about chatGPT, Bing, and other AI chatbots. It’s not necessarily new, but the technology behind it is advancing exponentially. In the last year, it has become something that’s much more viable than what you used to get in talking with something like the Verizon Chatbot.
I believe one of the big things driving this from an organizational perspective is a focus on the timeliness of responses and immediate gratification. People don’t like waiting and or feeling like they haven’t been heard – and that is why there’s such a push for automation and AI. It replaces the need to have 1,000 people waiting and ready to answer the phone. Of course, AI comes with its own set of issues, but that’s another post for another day.
How Can Leaders More Effectively Collect This Data?
- Make it easy to engage. Be clear about where and how your customers can engage with you. Few things are more frustrating than being on a website or in some sort of transactional arrangement and having to dig to find out how to get in contact with someone. No matter what product or service you offer, make your organization’s engagement mechanism visible across device types and easy to navigate.
- Keep it simple. Don’t go overboard on survey questions. If you design a 100-question survey, you’re probably not going to get enough engagement to find the learnings you’re looking for. Reduce it to the fewest, most important things you want to know and you’ll likely boost participation.
- Be transparent. Tell your customers how often you’ll be communicating with them or asking for feedback. Or, even better, give them a choice. Overcommunicating makes it more likely the messages you really want engagement on (like customer surveys) may be ignored.
If you’re unsure where your organization stands with customers or need advice on how to engage them, we’re ready to help – reach out to aaron.heath@theclearing.com or bruna.krupek@theclearing.com to set up a time to chat. We also welcome you to reach out to the rest of our team for any of your other consulting needs.
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Improvement model, adapted from Itam
& Ghosh, 2020, focuses on three objectives: 
