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Advanced Analytics: The Future Of CX
Despite the recent challenges in overall experience quality seen in Forrester’s Customer Experience Index (CX Index™) benchmarks, customer experience (CX) remains a priority for many organizations. Unfortunately, these organizations have struggled to realize tangible benefits from their CX programs. CX programs need to leverage more advanced quantitative analytics to drive action, increase financial impact, and prepare for a more analytics-driven future.
CX measurement programs report that their most common challenges are driving action to improve experience quality and proving the financial importance of CX. One primary cause is their reliance on soliciting customer feedback, usually through surveys. Surveys don’t often provide definitive root causes that compel business functions to make changes, and the relationship between survey scores and financial performance remains theoretical in most organizations.
While a survey-reliant CX strategy is holding CX programs back, we aren’t advocating that they stop surveying customers. Instead, they should reduce their reliance on surveys and use that feedback data as part of a more comprehensive quantitative approach.
Integrating advanced quantitative analytics into their strategy helps CX programs drive action and prove value. This involves shifting from treating survey score metrics as their primary output to using feedback data as an input to more advanced techniques. When CX programs combine customer feedback data with other metrics like operational interaction data, financial outcome data, and additional non-survey perception data, these inputs to advanced analytics can produce more actionable and financially connected insights than survey feedback alone.
After discussions with dozens of CX leaders, top vendors, and service providers in CX analytics, we found a consensus on several steps organizations must take to implement advanced CX analytics successfully. Among the five key components presented, two demand considerable attention:
For our recent research, we have defined advanced CX analytics as “advanced analytic techniques — including diagnostic, predictive, and prescriptive machine learning — that identify how customers’ experiences affect their behaviors.” The terms “advanced analytics” and “predictive analytics” are used somewhat loosely in the CX ecosystem. While useful, language analytics, conversational and digital intelligence, and sentiment analysis differ from advanced diagnostic, predictive, and prescriptive methods. CX leaders should ensure that they understand these differences when pursuing quantitative CX strategies.
Another variation in CX analytics is leveraging machine learning models to predict common CX survey metrics like Net Promoter Score℠ (NPS) or customer satisfaction (CSAT). While novel, most organizations would be better served predicting the actual outcomes of customer behavior with direct financial impacts rather than making the effort to develop these capabilities only to reinforce challenges associated with relying on customer perceptions to manage experiences.
While advanced analytic techniques are uncommon in CX practices today, CX programs and leaders should challenge themselves and find a path to facilitate, collaborate, or expand the CX mandate to pursue a more quantitative approach that will prepare them for the future of CX.
Register to attend CX Summit North America from June 23 – 26 in Nashville to delve further into these topics. Reserve your spot to join the conversation.
This post was written by Senior Analyst Rich Saunders and it originally appeared here.