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CX Leaders and Data Analytics: Boosting Customer Retention

To effectively track customer retention and ensure growth, customer experience (CX) leaders must have a clear view of customer retention as a basic statistic, as well as lifetime value (LTV).

By leveraging predictive analytics, artificial intelligence (AI) machine learning (ML), and sentiment analysis, CX leaders can proactively anticipate customer needs, personalize experiences, and mitigate issues before they escalate, thereby enhancing customer retention.

They can leverage journey analytics to measure and monitor journey milestones, in-journey signals, and success metrics to predict and assess performance for each journey.

"Advanced data analysis tools enable us to identify trends and patterns in customer behavior, preferences, and interactions across various touchpoints," said Omar Javaid, chief product officer at Avaya.

He explained CX leaders can measure the ROI of their investments in data analytics and GenAI by analyzing KPIs such as revenue growth, cost reduction, customer lifetime value, and customer satisfaction improvements.


Assessing Leading, Lagging Indicators

Daniel O'Sullivan, director analyst, KI leader, Gartner, said to effectively measure customer retention and growth, CX leaders should adopt a balanced mix of leading and lagging indicators.

Lagging indicators, like Annual Recurring Revenue (ARR), Net Dollar Retention Rate (NDRR) and Customer Lifetime Value (CLV), are essential for assessing overall success, but they need to be supplemented with leading/predictive indicators for a more nuanced assessment of ongoing initiatives.

"CX leaders should consider adopting a customer health score that aggregates various performance indicators, such as product usage frequency, adoption rate, customer satisfaction, and engagement levels," O'Sullivan explained.

This score serves as a leading indicator that predicts the likelihood of individual customer retention, allowing CX leaders to proactively gauge the effectiveness of their initiatives across different customer and product segments.

He said for the targeted assessment of specific initiatives, deploying post-interaction or post-journey surveys like the Customer Effort Score (CES) and Customer Satisfaction (CSAT) is crucial.

"These metrics evaluate discrete aspects of the customer experience and play a key role in refining CX strategies based on customer feedback," he said.

Steve Blood, vice president of market intelligence and evangelism for Five9, singled out Microsoft Power BI and Tableau as two of the most common tools for visualizing insights from many sources.

Collectively these tools enable organizations to understand more about their customers, track interactions, analyze feedback and predict customer behavior.

"However, their success is dependent on the quality of data inputs from the disparate sources," he cautioned.

He added that with solid data, CX leaders can narrow down the gaps in their CX and be specific on how to close those gaps with solutions that capture customer intent and the most efficient path to maintaining customer satisfaction and joy.

"If a CX leader has invested in data analytics, they will have an ROI already," Blood said.


Overcoming Data Silos

O'Sullivan said Gartner research identifies several common challenges CX leaders face when implementing data and analytics.

These challenges include issues with data quality and availability, a shortage of skilled talent to deliver advanced analytics, limited funding for analytics initiatives, and inadequate tools and technology.

"One of the most pervasive challenges CX leaders face is the siloing of data and insights across organizations," he explained.

Customer data is typically collected and controlled by multiple departments—such as marketing, sales, customer service, and product management—each with different priorities and technologies for data collection and analysis.

However, CX leaders can work to overcome these challenges by developing strong cross-functional CX governance.

This governance group should set unified standards for how customer data is collected, stored, and processed.

"Working in close collaboration with IT, the group should spearhead the development of a shared customer data management strategy," O'Sullivan said. "This strategy should focus on centralizing high-priority customer data and integrating it into a shared platform."

The goal is to balance individual function requirements against the need for a holistic, unified view of the customer experience.


The Power of GenAI

When used in combination with data analytics tools, Generative AI (GenAI) can act as a virtual assistant, providing actionable insights, predictive recommendations, and automated decision-making support.

"By leveraging GenAI's capabilities, CX leaders can streamline processes, personalize interactions, and optimize strategies to enhance customer retention and loyalty," Javaid said.

Brett Weigl, general manager of digital, AI and journey management at Genesys, noted advancements in CX through GenAI and modern analytics approaches can drive meaningful improvements in retention.

"Measuring cost of churn against net investments in these technologies is one simple way," he said.

Insights gleaned from solutions like journey management can be used to enhance to continually enhance GenAI training, knowledge, and data, improving its ability to produce quality outcomes, resulting in quality customer journey data—all feeding into a continuous improvement cycle. 

In a more granular sense, tracking outcomes per customer interaction can provide business leaders with not only a sense of impact ("we saved 100 customers today") but also the reasons why the churn might have occurred.

"This knowledge provides more aggregate benefits back to the business to improve underlying services and products as well," Weigl said.

He explained with the massive amount of data that comes through organizations, the opportunity to find patterns, glean insights and act upon what was learned to improve customer and employee experiences is exponential.

"With customer journey analytics, organizations can reach new levels of understanding and control, ultimately allowing them to better orchestrate customer journeys for more personalized end-to-end experiences," he said.