4 Steps To Leverage CX Analytics To Boost Revenue Retention

Pace of technology is enabling competitors to quickly replicate products and services of successful companies. Differentiation by price is no longer a viable option as this creates a race to the bottom as several retail and e-commerce businesses could painfully attest to.

As sales organizations grapple with new revenue recognition challenges they are starting to pay increased attention to optimizing revenue expansion through customer retention, up-selling and cross-selling strategies. In addition several research studies have shown the high cost of acquiring a new customer as compared to retaining an existing customer highlighting the importance of customer retention.

In financial services, for example, a 5% increase in customer retention produces more than a 25% increase in profit.” – Bain: Prescription for cutting costs

A study, by The Aberdeen Group, called “The Business Value of Building a Best-in-Class VoC Program” discovered that companies that invest in customer feedback through Voice of Customer (VoC) programs experienced increases in customer retention while also reducing operational costs associated with customer support.

Source: The The Aberdeen Group

As organizations start to invest more in VoC programs to unlock the revenue potential they are running into several technological and organizational barriers.

Key challenges in deriving CX insights

Untapped customer feedback data from digital channels

With increased exposure of brands via social media, it is becoming vital for marketing teams to be aware of customers’ experiences across an entire customer journey and touch points. It is no longer enough to measure CX through traditional metrics like NPS and CSAT as customers could provide feedback across multiple channels. Omni-channel measurement of customer experience (CX) is becoming more important to marketing strategy today than it was in the past especially for B2C companies.

However, several enterprises especially small and medium ones are not equipped to tap into the hidden trove of customer feedback data from digital channels (chatbots, social media, etc.) in a comprehensive fashion. This is due to lack of cohesive approach for tracking customer feedback, resource constraints and organizational culture.

Inability to close loop on customer feedback between Sales and CX management teams

Most sales organizations use CRM tools like Salesforce to create new leads, identify new opportunities or close deals. Once the deal is closed managing customer experiences becomes the responsibility of a different group like customer experience team or in some instances completely outsourced.

In majority of instances the systems and process used by sales, marketing and customer experiences teams are totally different and not integrated to offer a seamless view of how a bad customer experiences might be correlated to revenue leakage through customer churn. Due to this sales teams lack visibility into customer feedback coming from different channels that tie into their CRM systems. This creates blind spots for sales personnel preventing them from proactively reaching out to customers to prevent or minimize customer churn.

Ineffective customer segmentation: When it comes to targeting existing customer base, sales & marketing teams typically target customers based on objective terms like past purchase history, deal size etc. They typically do not have knowledge of how customers are feeling about the brand/product at any given time which is usually expressed via feedback through channels like email, chat, contact center calls etc. By not paying attention to recent customers’ feedback, marketers will not be able to create optimal personalized campaigns running the risk of increased customer churn.


1.Track customer feedback at journey level across multiple channels

Customers especially in the B2C sectors interact with businesses through multiple channels depending on the journey they undertake. For example customer initially opening a bank account journey will interact with the bank by going to physical bank. The same customer will be using online access to complete his transactions. So it is critical for enterprises to track customer feedback at journey level to truly understand their experiences.

Source: McKinsey CX impact study, 2018


With the advent of natural language understanding (NLU) and Machine Learning (ML) it has become practical for even small and medium sized businesses to analyze unstructured data like customer feedback at scale. Using NLU techniques, unstructured it is possible to extract customer intents, sentiments & emotions from unstructured customer feedback. Similarly using ML techniques, this data can then be correlated to structured customer transactional data (ex: Sales transactions) to create business impacting predictive models like churn prediction, NPS outcomes etc. This will help CX and sales teams to pro-actively address customer needs creating enhanced customer experience and a competitive advantage.


Majority of traditional customer experience analysis models ignore customer segmentation and misclassification cost, which reduces the rationality and prediction accuracy of model like customer churn prediction. Based on business domain customers could be segmented into pre-defined clusters into which customer experience metrics can be mapped to. Customer Experience metrics like customer sentiment, intent and emotion play a very important role in identifying how customers feel about a firms product and service offerings.

Through NLP/NLU analysis customer intents/sentiments/emotions could be derived from both structured feedback like NPS scores, CSAT surveys and unstructured data sources like customer comments in public forums, social media, chats, emails, call control records etc. For example customers could be segmented into happy/un-happy customers based on sentiment analysis. Similarly Net promoter scores (NPS) can be used to segment customers into promoters, detractors and passives.


Once customers are mapped to specific segments based on insights and intents derived from the feedback data it is critical to make the CX analysis actionable. Majority of the enterprises use some form CRM system for managing sales pipeline for customers. Similarly organizations use ticketing systems like Zendesk to track and address customer issues. By tying CX analytics originating from multiple systems from different business units to CRM systems the impact of customer feedback on top line revenue becomes immediately measurable. This integration enables sales and account teams to have a 360-degree view of your customers feedback regardless of who in your company is talking to the customer. Finally companies can create personalized and highly targeted marketing campaigns to address customer feedback proactively improving ROI on the marketing spend.


Customers leave treasure trove of data through various feedback mechanisms. Majority of enterprises are grappling with the challenge of measuring impact of customer feedback on the business top-line revenue. With latest AI technologies it has become possible even for small and medium sized companies to derive actionable insights into customers experiences and their intents.

It is very important that organizations not only create deep and meaningful insights but take steps to actionalize the findings. Not only should customer experience systems be integrated with CRM systems but CRM system should become single source of truth for customer analytics. This creates a closed loop to proactively address customer needs and increase revenue retention.

Are there other mechanisms to improve revenue retentions based on customer feedback analytics? Please comment below.

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