What is Speech Analytics and Why To Use It In Retail Banking?

In today’s competitive world it is important to use every technology that is accessible to ensure better sales. One such new and fast growing technology in the field of customer service is ‘speech analytics’.

While a company may use the best call center software available in the market for recording or monitoring their calls, there are some businesses which are more specific to the customer aspect – such as retail banking – and, the use of this technology is now a necessity since it allows use of unstructured data to gain valuable knowledge about customer’s expectations and how they feel about current schemes.

To understand what is speech analytics and how it can play a vital role in retail banking to drive better results let’s try to gather more information about this technology.

What is speech analytics?

It is the process of studying the recorded interactions between the agent and the customer that are stored in your call center management software.

It uses speech recognition software to collect useful customer information from the raw data which can help in improving the service provided by the company as well as in analyzing agent’s performance.

Now that we know what speech analytics is, let us understand in-depth about its working.

Working of speech analytics tools:

To be able to understand the insights from the voice of customer the enterprise first needs to record the customer interaction and convert it into data and refine it. After that this converted data is fed into the software which then analyzes it using the following methods.

Speech analytics uses 4 different approaches to examine the information:

1.    Phonetic approach: This is the fastest approach for processing the data. As it uses phoneme as the basic unit of recognition. Since the number of unique phonemes is very low in most of the languages, the software looks for specified sounds and then tries to match those sounds with the phonetic index file.

A list of identified phonemes is saved and stored in the database which the software refers while analyzing the data.
For example, p, d, b, and t are phonemes in the English language.

2.    Speech to text approach: It is also called as full transcription, ASR (Automatic Speech Recognition) or LVCSR (Large-Vocabulary continuous speech recognition). The processing speed of this is much low as compared to the phonetic approach.

Since it uses set of words (bi-grams, tri-grams etc) as its unit of recognition. As this method requires hundreds of thousands of words to match against the audio the processing time of this method is higher.

But the accuracy of the results is much higher when compared to the previous approach. Due to more precise results, this type of approach helps in identifying business issues.

3.    Direct phrase recognition approach: It is the most common and a cost effective way to filter data. It directly analyzes the speech looking for keywords or phrases that have been pre-defined in a list and is considered to be important to the business.

Since this approach does not interact with the whole data it is generally used to identify the known issues and reviewing agent’s performance. Since, there is no loss of data this method provides most accurate and reliable results.

For example, an agent is assigned with negative points when a phrase such as ‘deactivate my card’ is detected by the system.

4.    Extended speech emotion recognition: This approach uses both emotions as well as speech recognition. The emotion recognition focuses on the change in one’s tone.

For example: If a customer suddenly starts screaming at one of the agents then with the use of acoustic and prosodic characteristics of speech the system can detect the level of emotion like anger, infuriated, irritated etc.

Since we are now aware about the speech analytics and its working, we will see what retail banking is and how the use of this technology helps in improving the productivity of retail banks.

What is retail banking?

Retail banking is also known as customer banking. It is the aspect of banking wherein the commercial banks offers services to individual customers rather than to other banks, companies or corporations.

The services offered include savings and transactional (current and checking) accounts, personal loans, mortgages, term deposits, and credit or debit cards.

Advantages of Speech Analytics in retail banking:

1.    To improve overall customer experience: The prime reason of integrating a speech analytics tools with their call center management software is that it provides banks with a huge scope for improvement in their customer service area which can greatly effect in the overall business scope.

With the use of this technology banks can study the interactions and can identify things like variation in customer’s voice like is he happy or frustrated with the agent, quality of service (QoS) based on the analysis methods, service customer is mentioning about, the level of satisfaction with service etc.

This will help them in identifying the cause of the problem and to look for effective ways to rectify those issues.

2.    Helps in the training process: The managers and the trainers can make use of the speech analytics technology to record and monitor the real-time interaction between the customer and the agent.

This can also be used to check for agent’s compliance to scripts while handling the call. Based on agent’s performance they can be trained to improve on their weak areas and increase the first call resolution (FCR) count and increase the productivity.

3.    To identify new opportunities: Along with retail banks, the call center and sales organization can also benefit a lot from the speech analytics software. As they can also use it to discover up-sell and cross-sell possibilities.

They can use it to identify how much an approach was successful and influenced to gain maximum customer satisfaction. This can also be used while trying to research the target audience market, most suitable age group or gender.

4.    Reduced customer attrition rate: With the help of recorded interactions the management can identify the keywords and phrases that indicate negative customer experience. Based on that they can look for alternative ways to try and reduced the overall customer attrition rate.

5.    Helps in minimizing customer efforts:  By distinguishing and classifying the interactions between the agents and the customers the banks can gain insight into the existing reasons of why most of the customers are calling. This helps to reduce the number of such calls as well as the efforts of multiple customers calling in for the same query and the waiting time.

To solve this problems the banks can be a little proactive and should issue a notice or a clarification about that issue on their website or through emails. This will not only reduce efforts but customer’s frustration as well since they are looking for a fix to the problem.

6.    Provide better offers: To be in the race every bank needs to come up with new and competitive offers every now and then. With the help of speech analytics, the management can identify what features or offers their rivals are offering so they can offer more exciting and beneficial deals to their customers and can prevent unwanted loss of business.

Below we will be considering a real-life scenario of a retail bank and will see how the use of speech analytics tools aided the bank in business growth.

CASE STUDY:

PROBLEM:

The case study we will be considering today is of a retail bank founded in 2002. The bank was using virtual call center management software integrated with speech analytics tool for outbound call dialing and call monitoring purposes.

While analyzing the calls the bank identified issues with their credit card business. The process of ‘customer requesting for a card’ to the ‘customer getting a credit card’ was long and tedious for the customers.

At the same time, it was causing the bank a loss in customers and increased net business cost. Due to these problems the bank observed falls in revenue and feared a bad reputation in the market.

SOLUTION:  

By identifying and categorizing the calls based on customer satisfaction levels and agents call handling skills the management was able to find out the root causes of the issue which are mentioned below:

1.    The biggest drawback causing loss of customer was due to the long process. To rectify that the management refocused and redesigned the process of issuing a credit card.

2.    They researched the market trends and came up with new innovative schemes to attract more clients and provided increased credit limit as compared to their rival banks.

3.    Another problem was with call handling and the script used by agents while offering the service. To solve this issue, trainer selected some good and bad call handling examples. This helped in improving agent’s understanding of the process. Certain new rules were enforced and calls were monitored to check for compliance.

RESULTS:

As a result of the above changes, the company saw a potential increase in the overall productivity. With introduction of new lucrative deals, the bank gained a lot of new customers.

Now that the credit card issuing process was both short and easy, they were able to reduce the overall operational cost. The CSATs (Customer satisfaction) showed improvement in the service quality which helped the bank in restoring their reputation in the market.

They were also able to identify the most appropriate target audience that helped in increasing their market reach and more successful attempts while calling customers for sales purpose.

CONCLUSION:

The key to a successful business is a happy customer. But the only way in which you can understand what the customer feels about your service is through interaction. Sometimes customers express their views directly and sometimes they show it through their words.

To truly understand that, use of speech analytic tools can be very helpful. This not only helps in identifying customer emotion but business issues as well.

So, from all the above points, we can see how practical it is to incorporate speech analytics tools in your business for more productive and systematic outbound call management.

 

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