Leveraging Data Analytics To Boost Customer Service Satisfaction

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Alex Ross is cofounder & COO at Hire Horatio CX. Horatio CX manages the CX & other customer needs for today’s fastest-growing brands.

Historically, many companies used customer service surveys to “close the gap” and assess a customer’s overall satisfaction levels. Pre-internet, customers were mailed short forms or handed a stock card in a store to rate their experience. Often these were just numerically scaled, but the “advanced” surveys of years past also would ask open-ended questions where customers could fill in missing gaps that weren’t covered by the questionnaire.

While many of us find these antiquated customer surveys to be charming as they hint at a slower time when customer care was seemingly more personalized, these surveys often were discarded and the questions asked did not lead to actionable insights or change.

According to a 2021 survey from McKinsey and Company, when it comes to customer surveys, “executives increasingly recognize that survey-based measurement systems fail to meet their companies’ CX needs—although surveys themselves are an important tool for conducting research.”

McKinsey’s survey of more than 260 CX leaders from U.S.-based companies also found that 93% use a survey-based metric as their primary means of measuring CX performance. This might include a customer satisfaction score, for example. Still, only 15% of leaders claimed to be “fully satisfied with how their company was measuring CX” while a mere 6% “expressed confidence that their measurement system enables both strategic and tactical decision making.”

Peter Drucker famously coined the phrase, “You can’t manage what you don’t measure.” This statement, which hints at the importance of surveys, also translates to today’s digital world in which data analytics are paramount in assessing customer satisfaction.

The key difference between surveys of yesterday and customer analytics as we know it today is that CX analytics allow businesses of all sizes to collect customer data through customer touchpoints or omnichannel experiences. This allows brands to assess this feedback throughout a customer’s journey, not just at the end of the transaction. Here are some other key ways data analytics can help boost consumer satisfaction.

Using Data Analytics To Personalize The Customer Experience

CX data analytics involves gathering and analyzing customer data across multiple channels to gain insights. In addition to traditional surveys, this data can come from sources like social media feedback and AI-powered tools such as chatbots.

The most important feature of harnessing all data is to make sure that the questions asked help lead to data that can further enhance or optimize the customer experience. Further, companies that take these findings and tailor them to update individual customer profiles that are aligned with their preferences increase their chances of surpassing competitors.

An example of a company that does this well is Amazon. In 2023, Amazon launched Amazon Personalize, which the company stated, “enables developers to improve customer engagement through personalized product and content recommendations—no ML expertise required.” Amazon is an example of a brand that is transparent about using data to help its customers with purchasing decisions, and this personalization may increase brand loyalty as well.

How Omnichannel Data Analysis Sets Brands Apart

In a deep dive that explored the customer experience in the age of AI, The Harvard Business Review stated that “The first requirement for building an intelligent experience engine is constructing a 360-degree view of each customer, using the expanding range of possible ways to capture new signals from each one.” This data can then be leveraged to predict preferences and future recommendations can be made accordingly for each customer.

Making sure that the data collected results in actionable change is also important. A notable example of this is the Four Seasons, which famously catalogs feedback from customers and makes sure their concerns are heard and needs are met on following trips.

If a customer said they were missing towels, the Four Seasons will leave extra towels each night of that customer’s next stay. The Four Seasons is also an example of a legacy brand that is adopting technology to track customer preferences through omnichannel support. In 2019, the brand launched mobile-first technology and stated “Since 2018…guests have exchanged more than 5.7 million messages using Four Seasons Chat, 1.3 million of which were sent via the Four Seasons App.”

Conclusion

The brands that prioritize their customer experience are making it an evergreen goal to constantly evolve how they collect and assess customer data. Ultimately, these companies using their insights to come up with actionable solutions position themselves to outshine the competition.

I believe that offering hyper-personalization and a multitude of ways to communicate with your brand will result in next-level customer experiences that lead to optimal brand loyalty. For all of these reasons, in 2024 and beyond, companies should be extra thoughtful about the technology and general practices used to assess customer data analytics.


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