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No one has the ability to capture and analyse data from the future, however, there is a way to predict the future using data from the past. It’s called predictive analytics, and organizations do it every day.
Big data analytics is mostly associated with the features of data namely variety, volume, velocity, veracity, and value. Variety refers to unstructured data in different forms such as customer emails, social media data, audio and video data, volume refers to large amounts of data, velocity refers to how fast the data is generated and how fast they need to be analysed, veracity refers to the credibility of data or how reliable it is for making crucial decisions, value, the most important V of Big Data, refers to the worth of the data stored by different organizations, especially contact centre. Call centre is an important part of customer relationship management which consists of people, processes, technology and strategies. Service quality of a call centre is a result of comparison of actual service performance and customer expectations. Evaluating the service quality which is offered by customer service agent to customer is more difficult than evaluating the product quality. Performance evaluation in call centres is generally performed through listening randomly selected calls from recorded calls, and evaluating the words one by one in the related conversation. However, such an evaluation has its drawbacks in terms of time and costs since actual people are required to listen to the recorded calls and only a few of the calls can be evaluated. Speech analytics uses big data technologies to store all of the recorded conversations and analyse them using distributed text analysis methods.
Speech analytics – also known as customer engagement analytics, interaction analytics, and voice of the customer analytics – can analyse conversations via phone, email, text, webchat and social. The software transcribes 100% of conversations and turns them into searchable, structured data that businesses can use to gain insight into what customers feel or think. Speech recognition software has been around for many years but only in the last decade have commercial-strength applications been developed. There are three different approaches to the underlying technology: Phonetics based, Text to speech and Grammar based.
How do you decide if there is a need for speech analytics within your customer service operation? Think firstly about what your organisation is missing:
Analysis of speech and conversation will not only help answer the questions above it will also improve operational efficiency, enhance customer experience whilst improving bottom-line contribution. Typical use cases for Speech Analysis usage in a Contact Centre
A key consideration when selecting a vendor is the speed to business insight. How long will it take them to process your audio? How soon can you expect to see results? How quickly can they drill down and accurately clarify your core problems across all your agents? Listening to thousands of hours of recorded audio shouldn’t take thousands of hours. Large call centres with hundreds of agents making thousands of calls per week should expect to get deep and meaningful intelligence within hours or a few days.
A speech analytics solution needs to be able to summarise and consolidate large volumes of data, it needs to be scalable to your particular requirements, and be able to present the results in a visually intuitive format that can be easily understood by call centre managers or senior executives across the enterprise.
It is important to be able to evaluate the potential return on investment, possibly with an initial engagement that can understand the issues and deliver short-term results. Flexible deployment options such as a hosted or on- demand solutions could give you all the benefits of powerful analytics while eliminating expensive hardware costs.
With any emergent technology, the skills needed to maximise its use are not in-house. The ability to turn detailed call analytics into meaningful business insight is a developed art. It is vital, therefore, to ensure that the vendor chosen will be able to work alongside your team, combining analytical skills with your business knowledge to deliver meaningful business intelligence that will drive effective organisational change. These skills will ultimately need to be brought in-house, so that speech analytics can deliver ongoing insight and value.
By ensuring that the speech analytics solution has a high recognition rate you will be able to measure effectively both the level of emotion of the caller and the agent. The contact centre can select the words which the software recognises, including positive words that signal a caller’s intent to purchase or words which suggest the caller is dissatisfied, such as swear words.
For example, if a customer displays buying signals, a pop-up script can be enabled on the agent’s desk prompting them to close the sale. In addition, supervisors can also make use of the whisper function, either to advise agents how to finalise a sale, or to offer guidance through a difficult call. In the most severe cases, an alert can be sent to a supervisor to intervene in difficult calls to resolve the situation.
The final thing to consider is the ease with which it can be configured, as this will have considerable bearing on the cost effectiveness of the solution. By choosing a solution that can be configured by those within the contact centre you cut down on administration costs as well as making it easier to react to ever-changing business circumstances. If a speech analytics solution can provide accuracy, real-time intervention and a cost-effective solution then it can help firms to increase customer satisfaction and reduce their churn, something which is pertinent in today’s economic climate.
With many companies having thousands, if not millions, of calls the main requirement is the ability to search for individual or groups of calls that match certain search criteria. There are two fundamental search techniques; one that is based on word or phrase recognition, and more advanced systems that use phonetic search techniques. The latter has the distinct advantages of being more flexible, quicker to set up and search times tend to be faster. On top of basic search facilities, nice-to-have features include the ability to recognise tone and speech volume, to recognise calls during which the caller becomes angry, for example.
As well as identifying individual or groups of calls, it is important to identify patterns and trends. Coupled with root-cause analysis techniques, these allow companies to go beyond picking out calls and begin analysing common causes of calls and frequent content. These can be used to identify the need to change back-office processes that result in unduly high volumes of calls. As with any other analysis applications, the system should be capable of producing the results in highly graphical formats that make it easy for users to grasp what is going on.
Research has shown that digitizing customer care can significantly raise customer satisfaction while reducing costs simultaneously. Therefore, organizations use advanced data collection, speech analytics, and AI to provide valuable insights about customers and improve the overall contact centre experience. CyFuture is committed to providing a fully integrated, modern customer experience platform that eliminates the delays, hassles, and high costs associated with traditional contact centre operations. By analysing your contact centre data, we’ll be able to quickly improve your processes and call handling patterns. Collaborate one-on-one with our technology leaders to help unlock competitive advantages and achieve your goals. For further queries and consultation, please get in touch with us on www.cyfuture.com or email us on [email protected]. Our team of subject matter experts will assist you achieve your targets in the most customised and optimal manner. We are looking forward to working with you.
