Table of Contents. Executive Summary... 3 The Power of Information How Contact Analytics Works... 4

(1)

(2)

It’s no secret that excellent customer service is the key to increased business and revenue. According to Gartner, a mere 5% improvement in customer retention can increase business profits anywhere from 25% to 125%.1 Contact centers have emerged as an important strategic asset for many companies since front line agents have an immediate and significant impact on customer experience.

Contact analytics takes the raw data that is trapped in customer interactions like phone calls, emails, and chats and turns it into meaningful information that can be used to improve the customer experience and agent performance. Analytics systems provide incredible insight into all aspects of sales, marketing, customer service, and operations. Automatic scoring can reveal which agents are performing well, which are falling behind, and which customers are at risk for churn.

The case study featured in this paper will discuss how a healthcare insurance provider used contact analytics to identify and remarket to potential “at risk” customers. Additional benefits of contact analytics will also be discussed, including a reduction in average handle times, a reduction in call volumes, and the delivery of performance feedback directly to agents.

The Power of Information

There are several ways contact analytics or speech analytics can help improve the customer experience. First, analytics can reveal the most common reasons for calls and the most commonly asked questions. Based on this information, companies can reevaluate how customers are educated and find areas for improvement. In addition, these tools can reveal the reasons behind long call times, frequent holds, or repeat contacts. Finally, analytics can help pinpoint why customers are dissatisfied and perhaps most importantly, reveal which customers are most likely to churn. According to Gartner, “pervasive, advanced analytics will become necessary for leading organizations that want to gain competitive advantage.”2

“pervasive, advanced analytics will become necessary for leading organizations that want to gain competitive advantage.”

(4)

The importance of providing business intelligence information all the way down to the front line through continuous feedback and training cannot be overlooked. Agents in sales and customer service are in the best position to turn the insights gained from analytics into actionable results, because they are the ones interfacing directly with customers. These departments can’t afford to be underserved with analytical insights, as “the Best-in-Class are more likely than all other companies to deliver BI to departments like sales…and customer service.”3

How Contact Analytics Works

Contact analytics is the process of taking unstructured data trapped in the audio of recorded calls, emails, chat transcripts or other customer interactions and turning it into structured data that can be searched and analyzed. The first step of the contact analytics process involves pairing conversations from the source system (call recorder, VOIP stream, email systems) with associated metadata such as which agent handled the interaction, what day and time did it occur, and who the customer was.

The audio undergoes a speech recognition process where sounds are turned into text. At the same time, acoustic signals such as agitation and silence are extracted. Text transcripts are also normalized into a consistent format – for example, chats may have system generated messages at the beginning and emails have quoted messages within the body. These nuances in the different formats need to be dealt with in order to use a single system and process for analyzing contacts across all channels.

Mine intelligence from your contact center conversations...

(5)

Fig 1.—Speech analytics translates raw data into structured, consumable information

The next step of the process is automated categorization. Categorization is not the act of separating contacts into discrete buckets but rather the tagging of contacts that have similar characteristics. At this stage the goal is to uncover certain language patterns in the conversation: What was the reason for the call? How did the participants behave? Was there language suggesting the customer may be at risk of cancelling services?

The final step of the process is to automatically score every contact for various business metrics leveraging language patterns and other metrics. Every call can be automatically scored for Agent Quality, Customer Satisfaction, Compliance, and even Churn Risk. Through this process, very raw unstructured data in the form of calls, emails, and other communications has been converted into very structured information that can be searched, trended, analyzed, and used to automatically measure performance across any number of dimensions.

(6)

Case Study—Using Speech Analytics to Predict Churn

MainTrax, a speech analytics professional services company, specializes in developing and refining customized audio search libraries for marketing and operational purposes. MainTrax was brought in by a healthcare insurance provider to help the client use their speech analytics software to identify Fixed Indemnity policy holders who might be prone to churn.

The client’s research had shown that policy holders who called were significantly more prone to churning. In fact, they already had created a keyword set of cancel-related phrases to help catch the most obvious “at risk” customers. The provider thought that if they could better understand the more subtle churn signals, ones that weren’t already on their list, they could better train their agents to spot them and modify some of their call handling strategies.

The client also wanted to use speech analytics to identify individual “at risk” customers on an ongoing basis. This way, they could proactively reach back out to them and attempt to retain the customers as quickly as possible. The key was to determine which phrases, or combinations thereof, were the best predictors of churn.

The first step was to understand who might be considered “at risk.” MainTrax met with the client, reviewed their call handling procedures, and listened to a small set of archived recordings. The goal was to see if MainTrax’s analysts could pick out which callers went onto churn. While listening to the recordings, the analysts documented which words and phrases they based their conclusions on. They noted who said what in addition to when and how often those phrases occurred. The results of the exercise showed that about 80% (37 of 46) of the callers the MainTrax analysts identified had in fact gone onto churn within two months of the time they contacted the provider.

Key Fact:

80% of customers manually identified cancelled within two months of calling.

(7)

The next step was to automate this process of identification and replicate the results as best as possible. MainTrax took the library of churn ‘super phrases’ that the analysts spotted within those calls, reviewed them, recognized a few patterns, and divided the phrases into three categories: Miscommunication, Frustration, and Impending.

The ‘Miscommunication’ category contained phrases related to being misled or deceived.

The ‘Frustration’ category contained phrases related to being upset.

The ‘Impending’ phrases included phrases that suggested that the policy holder might be taking further action, such as “I’m going to make a few calls” or “I may need to make some decisions before long” or “I think I’ll shop around because this isn’t a fit.”

Each category also contained several subcategories. For instance, within the ‘Miscommunication’ category there was a ‘Misinformed’ subcategory with phrases like:

“I was told it would be cheaper.” “That’s what he told me.” “Why wasn’t I told?” “Not how it was explained.”

(8)

The churn library amounted to about 100 ‘super phrases’ across the three main categories and 10 subcategories. MainTrax’s client then took the library, embedded the phrases into their speech software, and ran them against a month’s worth of inbound calls.

The results:

About 35% of the calls had the client’s cancel phrases present About 59% had no “at risk” phrases present

About 6% of that month’s calls contained one or more churn ‘super phrases’ but didn’t contain any of the client’s cancel phrases. These were customers who gave indication of churn on the call but didn’t flat out say it, otherwise known as “silent switchers.” MainTrax’s client then looked at the lapse rates of the different control groups and found that 23% of the “silent switchers” churned within two months of their call.

As a standalone category, the ‘Impending’ (or ‘Shopping’) phrases generated the highest percentage of those who churned. Narrowing the focus, MainTrax’s analysts leveraged the client’s speech analytics correlations tools and identified a number of different combinations that generated especially strong results. The best performing combination was found to be a specific set of ‘Miscommunication’ and ‘Impending’ phrases that increased the prediction rate to 38%.

(9)

These insights are now being used for training and process improvements. The client’s next steps may include expanding their library to include comments about the Fix Indemnity product itself.

As for remarketing, the client can test and compare their retention strategies with different segments within the “silent switcher” group, such as New Policy Holders, Policy Holders of Long Standing, and customers who also have Major Medical with them.

The client is currently in the process of developing and executing strategies to retain these customers. Measured results are already available, however, from another MainTrax client in communications that has had significant success:

Of the “silent switchers” they reached by phone within a few days of their call:

 20% had their issues resolved and were no longer considered “at risk”  76% were reported to be considered “safe” by the agents calling them.

The agents claim that it was the call itself that seemed to make the difference.

 Only 4% remained “at risk”

The churn rate for “silent switchers” that weren’t reached was 16%.

This case study illustrates the power of speech analytics. By developing the appropriate library of language patterns and pairing it with speech analytics technology, clients can dramatically increase customer retention rates. It’s one of several ways that a company can utilize these tools to realize significant business returns.

Key Fact:

96% of identified “at risk” customers were saved by getting their issues resolved or receiving a callback.

(10)

Improving Customer Satisfaction

In addition to predicting churn, contact analytics can be used to automatically score every contact for customer satisfaction. Common language patterns, when matched with other metrics, can indicate positive or negative experiences. Compared to limited survey data, companies can get a far more accurate and timely picture by scoring every call. Satisfied customers are less likely to churn and the cost of retaining existing accounts is far less than the cost of acquiring new customers.

CallMiner created a Net Promoter Score index for a large company to assist them in automatically identifying promoters and detractors. More importantly, it also showed why customers fell into either of those two categories. In addition to measuring satisfaction, contact analytics can also help determine the root cause of customer dissatisfaction so actions can be taken to correct the cause.

Optimizing Contact Center Efficiency

Contact analytics can help optimize contact center efficiency in two ways. The first way is by reducing contact volume. Categorization and automatic topic identification help qualify and quantify what is driving high volume calls. Understanding what is driving these contacts allows companies to better service them with more effective routing and better agent training. It also provides the opportunity to improve self-service information either in the IVR, through the web, or other channels.

Best in class analytics systems will allow the identification of repeat contacts. By following the customer journey, even across different communication channels, the root cause for repeat contacts can be determined and preventative actions can be taken.

(11)

The second component of improving operational efficiency is reducing average handle time. In order to keep costs in check, it is essential that agents handle as many calls as they can. Average handle time can be quickly improved with analytics. Automatically categorizing every call, capturing contact metadata and generating the full contact transcript, removes agents from having to disposition calls or write detailed call notes. Analytics can also help determine root cause of high silence times or percentages, which can be caused by agent training issues, process issues, or even system issues. Large silence usually represents an opportunity to reduce average handle time.

A client using call analytics identified very long silence blocks in all of their billing calls. These blocks were occurring while agents were waiting for the billing system to respond. Providing this feedback to their IT team, the billing system was tweaked and overnight the client reduced their average handle time by 11 seconds.

The Importance of Agent Feedback

When it comes to affecting change in the organization and the experience of your customers, feedback on performance needs to be delivered continuously, automatically, and directly to agents. The agents are the front line and the ones that ultimately have to change their behavior and performance to make a difference. Any delay through manual processes or intermediate evaluators means a delay to improved performance.

CallMiner’s recommended best practice is to deliver a single view of all key performance metrics directly to the agents. Manual processes are slow and inaccurate, while automated scoring can be applied to every call providing a much more accurate and fair evaluation. It’s also important that information be delivered in a fashion that is easily understood and acted upon by agents.

Another emerging practice in automated performance feedback is to motivate through gaming concepts or competition. The goal is to move away from driving agents to a target performance metric. Instead, motivation is delivered by letting agents know how they rank amongst their peers and providing direct feedback on if they are improving or not. With this information, agents strive to be the best within their group instead of doing just enough to meet their targets.

Key Fact:

A client using call analytics was able to reduce AHT by 11 seconds by identifying the root cause of blocks of silence.

(12)

Conclusion

Companies today are under significant pressure to deliver excellent customer service. It is important that contact centers and the agents staffing them are performing at a high level. Contact analytics can help ease this customer service pressure by:

 Revealing the most common reasons for calls and the most commonly asked questions.

 Targeting the reasons behind long call times, frequent holds, or repeat contacts.

 Pinpointing which customers are dissatisfied and at risk of churn.

 Automatically measuring customer satisfaction to aid in retention and improve quality ratings.

 Delivering direct feedback to agents and improving performance.

Speech analytics can be more than just a tool to improve contact center performance, however. As illustrated in the case study, the rollout of an advanced voice of customer analytics package, along with a commitment to customer service and continuous process improvement can have immediate and measureable impacts across the entire business.

(13)

References

1

Gartner Group and “Leading on the Edge of Chaos”, E. Murphy and M. Murphy 2_{Rita L. Sallam and David W. Cearley, Advanced Analytics: Predictive, Collaborative} and Pervasive, 2012

3

Michael Lock, The Business Value of Pervasive BI, 2009

About CallMiner

CallMiner is the leading cloud-based conversational analytics solution for improving agent performance across all contact channels (voice, social, email, chat), by automating Performance Management. Unlike complex analytics that require a sophisticated fulltime analyst, CallMiner Eureka pushes actionable insights directly to the people who need and can act on the data, from the VP who manages contact centers and/or BPOs, the Supervisor who manages a team of agents, and to Agents themselves. CallMiner has solutions tailored for improving sales effectiveness, driving positive customer experience, and for monitoring compliance.

CallMiner, Inc.

12730 New Brittany Boulevard, Suite 200 Fort Myers, FL 33907

http://www.callminer.com (239) 689-6463

About MainTrax

MainTrax provides speech analytics professional services that help clients maximize their existing speech technology to capture project-specific business intelligence and actionable insights. By focusing on each client’s specific business objective, MainTrax develops customized audio search libraries for marketing and operational purposes such as churn reduction and script compliance. Teams of analysts are trained to learn the actual language used between an organization’s agents and their customers, quantify the impact of specific phrases relevant to the business issue, assign numeric scores to each phrase, and test/authenticate for accuracy. MainTrax easily adapts to any industry or company’s initiatives whether the scope of the project is significant or abbreviated.

MainTrax®

442 Hayward Ave North Oakdale, MN 55128 http://www.maintrax.com 612-817-4090