CX Measurement at a Crossroads

Surveys have a long and valuable history which some experts say date back to the beginning of the 20^th century. Back then, they were used by researchers to determine the extent of problems like poverty in our society. But as soon as marketers realized its worth in gauging customer experience (CX), they too employed surveys and capitalized on the information gathered to improve their campaigns.

Results from a 2020 analysis by McKinsey, however, questioned whether surveys can still meet the needs and demands of today’s marketers. McKinsey’s survey, which was conducted in 2020 in partnership with Alphasights and the Gerson Lehrman Group, reported that only 16% of marketers polled felt that surveys provided them with enough data to satisfactorily meet the underlying reasons of most CX problems. Just 4% said their existing system allows them to calculate the ROI (return on investment) of their actions.

Most surveys sample 7% of a brand’s customers and 93% of marketers said they still rely on them. Yet just 13% of marketers polled felt confident that the results are representative of their customer base. And while almost 66% listed quick responsiveness to CX issues as one of their top three concerns, only 13% of the marketers felt confident they could act that swiftly with existing data.

These issues and concerns have given rise to greater consideration for the use of predictive analysis of customer data. With the customer information gathered today, along with financial, behavioral and personal data, more marketers are beginning to feel that they can rely more on this wealth of information to plan better campaigns. Plus, any marketer’s database with unique identifiers and analysis must cover all customers and not just a select few.

By using machine-learning (artificial intelligence) algorithms for analytics, brands can see predictive scores for each customer. Deeper results not only predict customer satisfaction, but also their value outcomes like cost to service, loyalty and revenue. The result is better insight into ROI on each customer.

A successful transition to predictive analysis requires some important steps. Since the vast majority of marketers still employ surveys, this will require a change in mindsets, not just among marketers specifically, but other staff as well. Not only will people in marketing need to be aware, but those in IT, those in sales, and senior management need to be updated and sold on the concept, but education on interpreting the output of results will also be an important factor. In some companies, silos will need to come down and cross-functional teams built.

Because the output of data will be much larger, it’s advisable to start off by analyzing and sharing the most important information first, data that delivers the most value. Don’t overload staff. As they become more comfortable and familiar with the output, more data and deeper analysis can then be introduced. Throughout the entire journey, brands must also be sure to ensure customer data protection as a priority.

The future of CX may be headed to data-driven predictive analysis but human interaction is still vital. It’s likely that a combination of both will foster better CX. Many customers like to offer their feedback via surveys and also relish any rewards that sometimes come with them. And while a data-driven system may encompass all customers, surveys can help validate any analyses and predictions arising from the data.

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