Drive Business Impact with Proactive Customer Retention Strategies

Multifaceted interactions and transactions between customers and organizations often make it difficult to evaluate individual and collective experiences, identify churn signals, catch churn indications during an interaction, and proactively address them, while the customer is still on the line. But, within this complexity lies great opportunity to forge stronger, more lasting bonds with customers—that is, for companies that can harness their customer dynamics to reduce churn.

“Eighty percent of executives believe they deliver great customer experiences, whereas only eight percent of their customers second this fact,” says Dr. Vasudeva Akula, Co-Founder & Head of Data Science & Customer Analytics Practice, VOZIQ.

“There is a huge gap between what the company thinks and what the customer sees, because companies fail to understand what the customer is trying to accomplish.” -Dr. Vasudeva Akula, Co-Founder (Head of Data Science & Customer Analytics, VOZIQ)

How VOZIQ Predicts Customer Churn?

Most organizations fall short at maximizing the utility of unstructured data, which is typically huge in volume, dynamic, and distributed across multiple touch points in the contact center, such as post-call agent notes, satisfaction surveys, call tags, IVR logs, and so on.

VOZIQ leverages advanced text analytics and predictive algorithms to convert unstructured customer interaction data into a deeper intelligence about churn risk for every customer:

  1. Ingest every contact center customer data source, structured and unstructured.
  2. Apply NLP to understand effort, sentiment, and intent of the customer.
  3. Link this intelligence to what happened on previous calls.
  4. Understand context of an interaction by linking it to past transactions and interactions.
  5. Calculate churn risk, based on sentiment and effort scores.
  6. Build predictive models to forecast future customer behavior, based on past behavior and actions.
  7. Loop back the churn predictions into contact channels, using built-in APIs for proactive retention activities.

“Instead of a survey based approach to understand customer experience – i.e., asking the customer “how well are we doing, and tell us where we can improve” – an organization can leverage the call center as a listening post to understand expressed as well inferred churn indicators,” says Dr. Akula.

Enabling Proactive Customer Retention Strategies and Driving Business Impact

VOZIQ’s Predictive Churn Prevention Solution provides dashboards, reports, and APIs to drive actions, based on predictive churn intelligence. Here is how the solution drives actions:

  • Finds the root causes of cancellations by leveraging multiple customer interaction data sources and text analytics.
  • Predictive churn models using expressed and inferred churn risk by using customer sentiment from agent notes, call reasons, and surveys scores.
  • Optimizes churn program through A/B testing of multiple models and tracking the success of each outreach campaign.
  • The organization, using predictive text analytics, will also get access to churn management dashboards and reports that include:
  • User configurable dashboards with volume and trends of churn propensity and sentiment scores.
  • Out-of-the-box churn root cause reports by key issues, products, customer tenure, geo-locations, revenue groups, etc.
  • Role-specific scheduled reports with weekly, monthly, and quarterly trends in churn propensity and customer satisfaction scores.

Delivering Significant ROI to Clients

VOZIQ provides quicker time to value and an outstanding ROI compared to most other solution providers.

“At VOZIQ, we put an extra emphasis on making the predictive text analytics very affordable and extremely easy to use. This significantly brings down the total cost of ownership of the solution,” said Dr. Akula. “When combined with the positive impact on revenue, it means that our solution delivers huge ROI to any mid-sized recurring revenue business.”

VOZIQ also provides professional consulting services; its team of talented data scientists works with customer teams to jumpstart the analytics program, reducing the starting hassles for the customer, while minimizing the cost of hiring and managing contact center domain experts.

Driving Proactive Customer Recovery – A Customer Success Story

“When one of our clients, from a subscription-based technology service industry, needed a customer retention solution for minimizing their customer churn rate, we used contact center interactions as a differentiator, by identifying customer intent, effort, and sentiment. We then applied predictive algorithms on this unified data to create churn propensity scores that were more accurate and dynamic in comparison with churn risk calculated only on the basis of customer identity in the traditional approach,” says Dr. Akula.

The biggest success factor, according to Dr. Akula, was the way they delivered recovery actions on likely churners. The VOZIQ team worked with the client and its Interactive Voice Response (IVR) system to re-route likely-to-churn customers to special recovery teams. These teams were tasked with retaining the customer proactively by delivering qualified offers, newer products, and higher levels of service.

With this approach, the solution delivered a whopping 500 percent to 1000 percent ROI, resulting in increased annual revenue.

See how VOZIQ’s Predictive Churn Analytics Maximizes the utility of latent interactions in contact centers and delivers significant ROI by boosting revenue and cutting customer care costs!

Originally appeared on LinkedIn.

Prescriptive Analytics: The Solution to Reduce Churn through Definite Actions

More and more recurring-revenue businesses are looking at previously untapped sources of customer data from contact centers to create deeper intelligence about the drivers of customer experience. This deeper intelligence helps these companies to manage customer experience effectively and create a competitive edge.

By gathering customer perceptions of their experience, these businesses are able to use the data to get an insight into how to improve the quality of the customer relationship to improve customer loyalty. Such recurring-revenue companies use the data to identify where customer experience improvement efforts will have the greatest return on investment (ROI).

Business analytics in increasing customer loyalty

Business leaders often find it difficult to use customer feedback in the form of unstructured text for identifying loyalty improvement and ROI optimization opportunities. Here comes the crucial role of business analytics, which generally includes three phases: Descriptive, Predictive, and Prescriptive:

  Descriptive Analytics Predictive Analytics Prescriptive Analytics
Which questions are answered? What happened?

How many customers?

Where revenue is less?

Why it is so?

What will happen next?

What trends will continue?

What if we change pricing?

What is the best course of action for a given situation?

Which offer is most likely to be accepted by a particular customer?

How it is done? Use of KPIs, dashboards, and charts Use of statistical methods and predictive modeling to understand the relationships in input data & predict the outcomes. Use of advanced statistical optimization and simulation techniques with inputs and constraints to recommend what actions to be taken.
General examples How many customers have churned? Why did they churn?

How many customers will churn in the next few months?

What actions need to be taken to retain these predicted churners?


Prescriptive analytics in preventing churn

According to Gartner, prescriptive analytics are the next step in the value chain after predictive analytics. Often referred to as the “final frontier of analytic capabilities”, prescriptive analytics involves the application of mathematical and computational sciences to suggest decision options to take advantage of the results of descriptive and predictive analytics.

With the capability to constantly gather new data to re-predict and re-prescribe, prescriptive analytics can automatically improve prediction accuracy and prescribe better decision options to business leaders.

Prescriptive analytics ingests hybrid data, a combination of structured (numbers, categories) and unstructured data (IVR Logs, CRM Textual Notes), and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities

By using prescriptive analytics in a churn situation, business leaders can decide which type of remedial actions can be used for different customers likely to churn.

For example, a leading global airline was delayed on the tarmac for a half hour. As soon as the flight landed, all the business class passengers received an email from the airline apologizing for the delay and offering a $50 voucher towards their next flight. Similar actions can be taken in contact centers where high profile customers who are on the verge of leaving due to some glitch can be identified and a marketing plan is executed to retain them.

Automate decisions with prescriptive analytics

In contact centers, prescriptive analytics play an important role in providing the instructions to contact center leaders about what to do or what not to do. Particularly when there are too many options, variables, constraints and data for a person to evaluate without assistance from technology, prescriptive analytics enables contact center managers to take the lead. In contact centers, agents often have to depend on prescriptive analytics to know the appropriate options, amount, and under what conditions, a prospective customer can be extended varying levels of credit.

In business where the customer experience matters the most, prescriptive analytics can provide key decision makers with the capability to automate actions. With prescriptive analytics in place, it is easy to automate instructions and suggest the best options for the person to act upon.

Particularly in contact centers, prescriptive analytics can be put to use to automate decisions in churn situations like high risk callers who can be automatically diverted to special agents or emailed special retention offers.

Prescriptive analytics

Source: Gartner


With prescriptive analytics in place, decision makers can quickly extract actionable knowledge from the available data and define effective actions for the future. Based on advanced machine-learning, technology prescriptive analytics provides actionable rules and insights, which are inferred automatically from data coming from multiple heterogeneous sources. This helps leaders to approach and solve the problem in three ways – understanding why customers churned in the past (rules of behavior, ranking of drivers), forecasting who is going to churn and undertaking specific actions (marketing and sale) that prevent each individual decision to churn.

Churn Reduction Solution



5 Ways to Create Customer-Aware Contact Centers

Contact center leaders and managers face a real challenge when it comes to reducing costs without compromising on the levels of efficiency, quality and customer experience. While contact center leaders are expected to keep customer satisfaction levels and NPS high, they are also expected to look for ways to cut the cost-to-serve customers. If you look at it, it would appear that cutting down the cost-to-serve may actually negatively affect the ability to serve the customer better, thus leadingto poorer customer satisfaction levels.

However, this isn’t really true. Increasing customer satisfaction while decreasing abandoned calls and escalations is vital for contact center executives today. While high-quality support is critical, poor responsiveness can drive customers and prospects to competitors.

In this context, let’s see how contact centers can boost efficiency and cut costs, while still serving the customers better by utilizing customer and operational insights.

Contact center inefficiencies hindering customer awareness

According to research by Capgemini, the cost-to-serve in contact centers is a consequence of three inefficiencies:

  1. Ineffective and complex call center processes
  2. High customer contact ratios
  3. Non-ergonomic IT user interfaces

Another critical piece in the whole picture is the call center agent who actually deals with the customers. The three inefficiencies listed above directly affect the performance of the call center agent, thereby affecting not only the costs but also customer satisfaction levels. It is crucial for organizations to understand that customers are increasingly expecting a personalized experience while interacting with a call center agent.

Deploying analytics to understand customer intent

Contact center interactions present an enormous opportunity for businesses to make decisions based on real customer perceptions, needs and issues.

However, these customer interactions are seldom put to effective use due to various reasons. These interactions are spread across various customer-facing functions and several data silos across the organization. A large chunk of these interactions are in the form of unstructured data, and turning this unstructured customer feedback into useful insights is an uphill task for any organization. Typically, contact centers fail to synthesize and fully utilize all this data that they capture so meticulously.

Customer interaction

An analytics solution that does the job of integrating and analyzing the unified customer dataset can uncover unique opportunities to manage the contact center costs, all the while actually boosting customer satisfaction levels.

customer satisfaction


Create Customer-Aware Contact Centers with Text Analytics

Cloud-based text analytics technology provides significant opportunity to convert unstructured customer interactions into transformative insights about customers and their experiences.

Text analytics solutions have the power to deliver a significant ROI very quickly, by eliminating time-consuming, on-premise software deployments. Let’s take a look at the five unique ways in which text analytics can boost contact center efficiency and cut the cost-to-serve:

  • Call reasons analysis: Call centers often fail to identify call reasons accurately. Millions of agent notes captured after each call hold a key to understanding and categorizing the exact intent of a customer contact. Applying text analytics to agent notes leads to identifying customer intent at scale. Customer intent capture in turn leads to several opportunities to improve processes and agent performance. customer Intent report                                                              Customer Intent Report
  • Call center activity analysis: Call center activities, such as call volume, transfers and repeat calls, directly add to the overall cost-to-serve customers. A text analytics solution can help in correlating the activities with call reasons as well as customer segments, thus identifying opportunities to improve first call resolution rate (FCR), decrease call transfers, and transfer more interactions to self-service channels.
  • Root cause analysis: There are millions of agent-created notes in call centers filled with human-interpreted data about every call received. Apart from customer intent, this data can also shed light on the root causes of customer experience issues as well as customer sentiments. Understanding the root causes of repeat calls, call transfers, and call volume leads to a focus on alleviating customer irritants as well as on process improvement opportunities.
  • Agent performance analysis: Connecting call reasons, call center activity and root causes to agent performance leads to analyzing the agent performance and identifying both top and bottom performing agents. Based on this analysis, data-driven strategies can be designed to business-driven quality management and agent coaching.
  • IVR analysis: Tracking, benchmarking and improving IVR performance is key to designing better IVR. Deploying IVR analytics to measure IVR performance, then integrating the insights with the data from the analysis of agent notes leads to optimization of the IVR performance with a focus on transferring customer contacts either to self-service channels or the best equipped agents. The text analytics insights can also be fed back into the IVR system via APIs in order to improve IVR performance.


Improving contact center efficiency is crucial not only to managing costs and improving performance but also to addressing the root causes of customer satisfaction issues across the enterprise. Applying text analytics on customer interactions data aggregated from IVR, agent notes, CRM and billing systems equips contact center leaders with actionable insights into the best opportunities to improve customer experience and boost revenue as well as reduce operational costs.

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Thank you, Customer Service Experience 2016!

Customer Service Experience 2016 ended on a high note on 25th May! It was a wonderful experience being at this year’s Customer Service Experience at Washington, D.C., from 23-25 May. Co-located with CRM Evolution 2016 and SpeechTEK 2016, the main focus of this year’s event was on how organizations should master the art of deriving information about the customer from each communication channel (social, mobile, phone, web, email, etc.).

Thank You! Customer Service Experience 2016

VOZIQ receives great response

VOZIQ was proud to be a Gold Sponsor of this year’s Customer Service Experience conference and a corporate sponsor of the CRM Evolution Conference. We had a packed schedule this year with a considerable presence in VOZIQ’s booth as well as a speaking engagement. VOZIQ was represented by its Founder & CEO, Dr. Vausdeva Akula, Director Dr. Sunil Issar, and CTO Suresh Akula at the event.

It was our first year at this event, but we were overwhelmed by the number of visitors to our booth that came to meet our team and claim a free trial of our award-winning customer experience analytics solution. It was an enriching experience to engage with industry leaders and contact center executives who visited our booth to discover how our solution could dramatically improve contact center ROI.

Voziq CEO Vasu Speech at Customer Service Experience 2016

Voziq CEO Vasu Speech at Customer Service Experience 2016Our Founder & CEO Dr. Vasudeva Akula, who was representing VOZIQ at the conference, was fascinated by visitors’ interest in predictive text analytics and about its potential in analyzing unstructured data. Here is what he has to say about his experience at the event:

“We participated at the Customer Service Experience conference for the first time and were delighted to see a very good response from visitors. As part of an exclusive promotion, we offered $25,000 worth of Predictive Text Analytics totally free to the attendees of the conference. It was really great to see many attendees accepting our promotional offer and sharing their positive feedback about the solution. Besides this, it was great interacting with top customer experience professionals and showcasing before them the capabilities of our award-winning predictive text analytics solution. The organizers have done a great job and we would be glad to be part of the next edition.”

Voziq CEO Vasu Speech at Customer Service Experience 2016

Speaking opportunity at SpeechTEK 2016

On May 24th, VOZIQ Director Sunil Issar spoke at the SpeechTEK 2016 about how to improve IVR self-service by analyzing unstructured call center data and combining it with IVR logs, customer profiles and billing data. Sunil’s talk was met with a good response from delegates who flocked to the room to understand more about IVR optimization. The speech was then followed by a discussion about the potential of adding unstructured data to the call center data mix. Be sure to take a look at Sunil’s presentation at the event.

VOZIQ Director Sunil Issar speaking at the SpeechTEK 2016


Missed our Free Trial offer at the conference? Here is your chance to see VOZIQ solutions in action.


VOZIQ Featured in Top 50 Customer Analytics Blogs

VOZIQ’s Customer Intelligence Blog has found a place in a list of Top 50 Customer Analytics Blogs compiled by NGDATA. Other blogs in the list include Harvard Business Review, Forrester, Yale School of Management Center for Customer Insights, and Wharton Customer Analytics Initiative Blog.

On VOZIQ’s Customer Intelligence Blog, we always strive to bring to you all the best practices, best resources, and our original research to help companies offer stellar customer experience.

We will be more than happy to receive your feedback as well as expert contributions for the blog!



Transforming Your Business with Cloud-Based Text Analytics

Typically, contact centers are viewed in any business as a cost center. Such businesses are focused on acquiring new customers, and contact centers that serve existing customers feature low on the priority list of the CXOs in the organization. However, an important fact is often overlooked by such businesses: customer retention is far more valuable than customer acquisition. A retained customer is not only already paying for the services, but is also a source of future revenue. Further, if the retained customers are happy, they bring in higher customer lifetime value, thus improving the bottom-line.

Contact centers can play a crucial role in helping you offer a superior customer experience, as well as retain your valuable customers. Let’s see how this can be done.

Why Focus on Contact Center?

Contact centers are pivotal inshaping the customer experience. Throughout the customer journey, customers turn to contact centers to voice their grievances, seek more information, or share their feedback. These customer interactions are rich in insight about customer sentiments, customer preferences, and customer’s perception of your business.Though, contact centers collect all these interactions in their databases, their full value is never extracted for generating customer intelligence. In this sense, contact centers offer a great opportunity to transform not only customer service, but other functions by basing decisions on real customer intelligence.

Power of cloud-based text analytics for contact centers

Cloud-based text analytics offer a highly cost-effective and powerful solution for realizing the full potential of customer interactions. The customer interactions are a typical set of big data: They are huge in volume, they are highly dynamic, and they are unstructured. A full-blown text analytics solution to analyze customer interactions entails a lot of dependencies, for example, on IT department to install and maintain the solution on company servers. Then there are privacy and security concerns. Further, to make sense of all the data, a team of data scientists also needs to be in place.

A cloud-based text analytics solution removes all these dependencies and helps you realize a solid return on investment. Let’s see how –

  1. Customer intelligence gold mine of call center agent notes: Customer transactions and demographics do not tell a complete story of the customer. Customer surveys rely on only a sample of your customers. Speech analytics lacks the accuracy at scale, as the technology is still evolving. Call center agent notes, on the other hand, not only offer the largest possible sample of customers but also are human-interpreted notations on each customer conversation happening at the contact center. When combined with the other sources of customer information, call center agent notes offer the richest customer insights.
  2. Make real sense of customers with text analytics: Text analytics converts the unstructured text from agent notes and transforms them into structured categories. This structured data can then be combined with CRM and billing data, as well as customer surveys and IVR menu choices to create the richest and a large pool of customer data. Slicing and dicing this data will paint a full picture of customer sentiments, customer journey, and customer experience. You can also segment customers by geography, sentiments, issue types, and so on to create a complete map of customer experience transformation.
  3. power of cloud: Deploying cloud-based text analytics means you remove the hassle of a solution installed on company servers. All the costs and dependencies that come with a server-based solution are eliminated with a cloud-based text analytics solution. This means that the ROI is realized sooner than non-cloud deployments.Furthermore, cloud-based deployment also offers best-in-the-class security, agility, and scalability.
  4. Create interventions, not just insights: Based on the insights generated from contact center interactions, contact center leaders can collaborate with other business units to offer superior customer experience, remove operational gaps, and improve marketing and sales effectiveness. 


The power of contact center interactions, especially those captured in the agent notes, is often neglected. Cloud-based text analytics offer a very cost-effective solution to convert these interactions into insights, as well as actions, and to realize transformative benefits across an enterprise.

(Read the original article here: Transforming Your Business with Cloud-Based Text Analytics for Contact Centers.)

Customer Experience with Text Analytics

[Infographic] Solution to the Customer Experience Puzzle

Customer experience (CX) is the main competitive differentiator. As per a McKinsey research, customer experience actually trumps product performance as the main indicator of business success. However, analyzing customer perceptions and offering them a stellar customer experience is a complicated task due to the proliferation of social media, multi-channel customer contact and so on. How do businesses get the critical insights needed to shape delightful customer experience?

The answer may lie in your contact centers which collect critical voice of customers in the form of millions of agent notes.

This infographic illustrates the critical yet puzzling nature of customer experience and the way contact center analytics can play an important role in solving this puzzle.

Here are some key stats from the infographic:

Customer Experience is Critical

  1. 4x A customer is 4 times more likely to buy from a competitor due to a customer service problem rather than price or product related. (Bain & Co.)
  2. 2020 By 2020, customer experience will overtake price and product as the key brand differentiator. (Customer 2020)
  3. 82% customer say that quick resolution of issues is the top factor to a great customer experience. (LivePerson)
  4. 60% Customer service managers think customer satisfaction is the most important call center metric. (Ovum)
  5. 89% of companies expect to compete mostly on the basis of customer experience by 2016. (Gartner)

Customer Experience Puzzle

  1. 80% CEOs believe they deliver a superior customer experience, but only 8% of their customers agree. (Bain & Co.)
  2. 75% Companies recognize service as a competitive differentiator. (Dimension Data)
  3. 4 Years CSAT levels are down for 4th consecutive year. (Dimension Data)
  4. 60% Consumers decided not do a transaction due to poor service experience. (AmericanExpress)

The Key to Customer Experience: Contact Center Interactions

  1. 92% Customers judge an organization based upon the call center interactions. (Thetaylorreachgroup)
  2. 62% Organizations view contact center customer experience as a competitive differentiator. (Deloitte)
  3. 45% Customers are looking for personalized services. (AmericanExpress)
  4. 48% Consumers prefer speaking with a ‘real’ person on the phone for complicated inquiries. (AmericanExpress)
  5. 34% Contact center leaders identified Customer Satisfaction as a priority followed by customer effort (28%). (BT)

Check the infographic for more stats –

Contact Center Solution to Customer Experience Puzzle

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Call Center Solution to The Falling Customer Satisfaction

As per an ASCI report, customer satisfaction across industries has fallen in Q1 2015 and it is the lowest it’s been since 2007. At a time when most industry leaders are concentrating on customer experience, this report seems baffling. So what is causing this slide downwards?

Why Customers Are Dissatisfied?

The answer is not straightforward. Take the example of the dilemma faced by a leading TV provider that kept on losing customers in bulk for three straight quarters. As company leaders started digging around the data, they uncovered the root of the problem. Most customers weren’t fed up with any one phone call, field visit, or other interaction—in fact, they didn’t care much about those singular touchpoints. What reduced satisfaction was something few companies focus on: the cumulative experiences across multiple touchpoints and in multiple channels, over time.

Customer Experience is Cumulative of All Touch-Points

Customers can, at any given point in time, engage with your brick-and-mortar storefronts, your digital storefronts, your social channels, your on-domain communities, your customer service team, and so on. And those are just your owned and operated properties. Add to that the myriad third-party touch points that your customers regularly tap to engage with your brand indirectly, and that customer experience net you cast becomes even wider.

Now, you may not have access to third-party touch points, but what about the data already present with you (especially your call center data)?

What People Do Not Like about Call Centers?

We did a quick study on Twitter data to identify what people are saying about call centers on Twitter and how the themes and topics are related to one another. What we discovered was that, apart from product issues, call centers were a major source for customer dissatisfaction.

Customer Satisfaction Factors

In the above graph,

  • The size of the bubble denotes volume for the topic
  • Curved lines denote a relationship between topics
  • The thicker the curved line, stronger is the connection
  • Bubbles have been color coded for simpler understanding and grouping purposes
  • Issues related to products have not been considered

As you can see, customer service teams seem to be a major contributor for customer dissatisfaction. And your call centers should be the epicenter of the customer experience revolution as far as the processes are concerned. At call centers, customers are proactively providing valuable information free of cost. This valuable source of insight could be used to improve the process and increase sales—but most importantly it can be used to identify pain points for a seamless customer experience.

How Do You Transform Customer Experience?

  1. Organize around customers: To deliver an exceptional customer experience, rethink how your business addresses it. Marketing plays a part, but true customer experience management means organizing your entire company around the customers by using the right processes and the right technology.
  2. Deploy text analytics to mine unstructured CX data: As several studies show, customers prefer to call the customer care when their issues remain unresolved on other channels. The call center agents create their notes on the interaction after each call. These notes hold the key to uncovering the underlying customer experience issues. Using advanced text analytics technology will help you extract the rich customer experience insights hidden in these post-call agent notes.
  3. Let the insights be real time and be available: We live in an era of extreme customer expectations. A real-time view of the insights which can be accessed easily is an important part of the process of meeting customer expectations.

So it’s critical that your frontline agents, support, and POS systems are in lockstep with your enterprise-wide CRM systems to ensure that information and insights can both travel faster and be accessible anytime, anywhere across your organization.

By aligning your systems around customer experience and deploying text analytics to understand what the customer really wants, you can make managing a single streamlined and consistent customer experience across multiple touch points exponentially easier.

Customer Experience with Text Analytics

Predictive Analytics to Improve Your Customer Retention in Contact Centers

Even a small reduction in customer churn leads to a considerable impact on the bottom-line by helping companies retain their valuable customers. Further, proactive engagement also leads to increased customer satisfaction levels. Dynamic and proactive customer retention strategies based on predictive analysis go a long way to increasing the effectiveness of your customer retention drive by enabling enterprise-wide actions.

A loyal, paying customer is the best success any type of business can have. Everybody knows this fact. However, when we start getting into nitty-gritty of retaining best customers, it starts getting complicated. Customer retention is often driven by overall customer experience, which itself is shaped by several distinct experiences a customer has throughout the customer journey. Most importantly, these customer experiences are not about rational experiences a customer has. How the customer feels after an interaction or a call to the call center probably defines the customer’s experience a lot more than what standard KPIs are being reported.

So, how can companies ensure that their customers feel valued and keep using their product or service?

A Contact Center is Critical in Shaping Customer Experience

What drives the customer sentiments towards a business? Our research using sentiment analysis of social media mentions how different brands confirmed that overall positive mentions of a company show a strong correlation to the positive mentions of “customer service” of that company. The following graph shows this correlation for various industries –

How to Ensure that Contact Centers Drive Customer Retention

The answer to this question lies in the interactions that happen at the contact center. The contact center is the most visible and approachable part of any business for a customer. Over the course of the customer’s journey, he or she approaches the contact center multiple times, for multiple reasons, and via a multitude of channels like emails, website chats, customer service calls and so on. Through all these interactions, customers voice their concerns, share their experiences, give their feedback or register complaints. Further, the customers also expect to get a satisfactory response when they contact the business.

All these interactions are a treasure trove of data about the needs and wants of the customers, their behaviors, pain points and expectations. If mined properly, these interactions reveal very crucial insights about the intention and behavior pattern of customers.

Typical Retention Approach – Performance Management

The typical retention program companies deploy are reactive in nature. When encountered with a customer who wants to cancel their services, the call is typically routed to a ‘save desk’ which creates an offer for the customer and tries to win them back. By managing the performance of the save desk agents with various training and coaching tools, contact centers aim to keep customer retention numbers high. However a segment of customers can never be saved with this approach if they already signed up with a competitor before the save desk attempts to retain them.

Typical Customer Retention


If we inspect typical activities that happen prior to a customer deciding to cancel, typically a customer calls to get their issue resolved from the service side of the contact center. Over a period of time, some issues are still left unresolved, resulting in dissatisfied customers. Often even these disgruntled customers give the company a chance, and express their dissatisfaction in their calls and expect quick resolution to their issue. However, if the issues this customer is facing are unresolved after a few calls, he or she finally decides to cancel the services and switch to a competitor. Until this point, call centers make no differentiation between this customer and others. When an actual cancellation request surfaces, the customer is then routed to the save desk. This team is responsible for convincing the customer to stay with the company by offering special discounts and deals, which is often a very expensive way to retain customers.

Besides the cost of retention issues, it might also be too late to ensure that the customer is retained at the time the save desk makes an offer. The focus here is on trying to minimize the customer churn, which is a reactive and less effective approach because the customer has already decided to cancel prior to this, and in some cases, they might’ve already signed up with another service provider.

A Better Approach –Predictive Customer Retention

Applying predictive analytics to mining contact center interactions uncovers new opportunities to approach customer retention effectively.

Predictive Customer Retention

Identifying a dissatisfied customer

All the interactions a customer has with a contact center have enough clues about their satisfaction or dissatisfaction. Typically businesses fail to leverage these interactions as a source of customer intelligence. Predictive analytics enables identifying these clues and categorizes the customer as ‘satisfied’ or ‘dissatisfied’ based on the their previous interactions, and then assigns a propensity to churn score.

The typical indicators of an at-risk customer are:

  • Large number of calls
  • Expression of dissatisfaction
  • Competitor mentions and comparisons
  • Enquiry of alternative price plans
  • Customer experience issues

Using interaction analytics and predictive churn modelling, a churn score can be created for each customer based on dissatisfaction identifiers like the ones mentioned above. When the churn score is above a pre-defined value, the customer is tagged and then various recovery efforts can be made either within the contact center as part of future interactions or, even better, a proactive outreach to the customer to resolve their issue.

Proactive customer retention strategy

The next time a customer calls, the call is automatically routed to expert agents with higher skills and empowerment on the basis of churn scores. With their skills in empowerment, empathy, resolution abilities and communication skills, the agent stands a better chance of offering a satisfactory solution to the customer. Note that this effort takes place even before the customer decides to cancel the service.

As you can see, the customer retention approach becomes proactive instead of reactive with the application of predictive analytics. The proactive retention efforts aim at identifying a dissatisfied customer and offering a timely solution to his or her concerns before it is too late. These programs can be implemented with very little additional cost, with intelligent segmentation and routing within the call centers based on risk scores.

Improving customer experience

Big data analysis of the customer interactions has another benefit – fine tuning operations to enhance customer experience. With the approaches discussed above, not only do customer churn scores go down, but other contact center KPIs such as satisfaction scores, first contact resolution scores, etc., typically increase as well resulting in a good return on investment (ROI).

In addition to improving contact center specific KPIs, this same intelligence from contact centers can identify the drivers of customer experience and the root causes behind them, as most customer experience issues lead to a contact center interaction. These insights into the customer experience help the contact centers to collaborate with other business units with clear action plans to enhance the overall customer experience across many other touch points without involving contact centers.

(The original article was published on

You can also download PDF version of this article here

The Power of Text Analytics-Driven Segmentation

Big Data is discussed everywhere today. Big data IS everywhere today. The idea of big data is compelling. In many cases big data is overkill. Although big data can help in identifying WHAT trends exist, the main question will always remain- WHY. Small data enters the picture here. What’s small data? Small data connects people with timely, meaningful insights (derived from big data and/or “local” sources) . Often visually organized and packaged, small data is accessible, understandable, and actionable for everyday tasks.

Data-driven Segmentation

Data-driven segmentation derives small data from big data by relying on the ability to create and identify actionable data. This data demonstrates the worth of a customer/prospect to a company, how often they interact with the brand, their purchasing habits and preferences, their possessions and, most importantly, their feedback. Organizations armed with this information can drive the message more acutely and create an exponential effect on marketing efforts as well as improve their customer experience tremendously.

Segmenting the data by any number of factors and parameters allows you to listen to your customers effectively. When you regularly review your VOC program in a way that incorporates data segmentation according to customer journey topics, you give yourself the ability to refine and optimize the strategies.

Restaurants and Text Analytics

We recently carried out a research on the restaurant industry using twitter data. We employed the following methodology:

  • Gather twitter data of the restaurants (TGI Fridays and Chili’s) for a period of 90 days (Q1, 2015).
  • Apply Voziq’s unique categorization technique to filter out noise and segments data into useful categories – Data Segmentation
  • Use Voziq’s visualization techniques to correlate these categories (data segments)
  • Also Using Voziq’s sentiment analytics engine to get sentiments for user comments.
  • Compare and spot the trends.

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What did we observe?

  • Through Sentiment Analysis:
  1. In general, people like the quality and taste of food in both the restaurants
  2. Customer service has not fared well.
  • Through Category Correlation:
  1. Customer Service issues center around wait time and staff behavior in both the restaurants.

Now these are pretty specific and targeted problem definitions. The purpose of data analysis in business is to hunt for the correlations that identify issues and drive sales. Once you find these correlations, you can determine what to emphasize in the future for each segment. The more you segment your data, the more opportunities you have for correlating segments with better accuracy, timing, format and content.

Text Analytics-Driven Segmentation Drives ROI

To compete in today’s digitally-driven world, businesses across all industries must achieve a 360° view of the customer in order to successfully market to their specific needs. This in turn drives revenue growth. Such a comprehensive vantage point rises out of integrating data across all platforms and from all available sources to realize true data-driven segmentation. Investing in data unification solutions to achieve data-driven segmentation and a superior customer experience, regardless of the device used to access information, is the next step toward building a better VOC engine and achieving the ROI.


Image Credit: Flickr


Customer Experience with Text Analytics