Interaction Analytics: Meaning, Advantages and Tools

"Analytics" is one of the most popular terms in the corporate world, and it's becoming popular in the call centre sector as well. However, as with most new technology, fascinating discussions about the possibilities tend to take precedence over the fundamentals. 

 

The following is the easiest method to illustrate the importance of analytics in your contact centre: With customer interaction analytics, one can now see the entire picture of their customer experience—every channel, interaction, and aspect of every contact point. 

 

More crucially, analytics uncovers the most relevant and actionable insights hidden inside huge amounts of client contact data—patterns and trends that would be too hard and costly to detect within your own.

 

Every day, a company most likely communicates with hundreds of clients via multiple communication channels. Each encounter has a wealth of information about the customer's worries, expectations, and even how happy they are with their provider. Interaction analytics (IA) assists in analysing all of this uncontrolled data in order to tailor service and minimise bad interactions.

 

In this post, we'll look at what interaction analytics is and how it may help businesses. Then, we'll go through seven software options that one would need to evaluate client interactions and enhance service. Finally, we'll look at the future of the IA sector and how it may enhance customer experience.

 

(Related read - Website Analytics)

 

 

What is Interaction Analytics?

 

Interaction Analytics (IA) entails employing software tools to extract raw data from sources such as:

 

• Journeys of the customer

• Experiences with customer service.

• Interactions on social media.

• Calls are recorded.

• Chat transcripts, for example.

 

This raw, unstructured data is then filtered, searched, and analysed by the programme purpose of providing structured data with actionable insights. These interaction analytics can help you find areas for development and provide a better customer experience as part of the Quality Assurance (QA) process.

 

Purpose of Interaction Analytics

 

Conversations are gold mines for learning about your clients and their perceptions of your company. While traditional polls are sampled and skewed, using Interaction Analytics to analyse phone calls, social network interactions, or Liveperson discussions shows the customer's holistic, true voice. 

 

Leveraging use of the tremendous wealth of unsolicited feedback available within their contact centres enables firms to develop critical thinking faster, saving time, money, and hassles.

 

(Recommended blog - Top Business Analytics Tools)

 

 

Advantages of Interaction Analytics

 

Embracing the full potential of customer interaction analytics may assist your call and contact centres in the following ways:

 

• Outline the customer journey to determine why the consumer is contacting you for help.

• Customers that require rapid care should be highlighted.

• Recognize significant terms and subjects in customer interactions.

• Determine the underlying reasons for consumer discontent and turnover.

• Recognize trends in consumer behaviour.

• Predict call volume during peak times.

• Ensure that agents keep to the script and follow business regulations.

• Agent performance should be evaluated using pre-defined KPIs (Key Performance Indicators).

• Keep track of each agent's knowledge base and rectify any gaps with focused coaching.

• To adjust your service plan, get real-time insights about client sentiment.

• Identify typical client questions and use chatbots, FAQs, and other self-service options to provide solutions.

 

(Related read - Big Data Analytics in Cybersecurity)

 

 

Interaction Analysis Use Cases

 

1. Marketing and Sales

 

AI-derived consumer interaction data may even aid your sales and marketing departments. Common terms might provide your marketing staff with a sign about what they're anticipating, allowing them to adjust their plans. You may also assess the success of your sales staff by evaluating consumer sentiment throughout such encounters.

 

If a salesman receives favourable feedback, you may utilise the recordings of their conversations to instruct others. Finally, you may monitor your brand's social media mentions and sentiment to gather vital customer insight.

 

2. Management of Digital Experiences

 

The digital customer experience is fully data-driven and analytics-reliant. These data can assist you in determining your clients' preferred means of contact. For example, Millennials and Generation Z prefer non-voice service, whether agent-assisted or self-service. To reach out to this rising group, you may need to extend your social media staff.

 

Customers want speedier reaction times on social media as well. You may use IA to determine how long they have to wait at various touchpoints and how this impacts overall Customer Satisfaction (CSAT) rankings.

 

3. Back-Office Functions

 

While the back-office personnel may not constantly interact with consumers, they are the foundation of your business. Some of the most critical corporate procedures take place here, including:

 

• Account conception and management

 

• Order fulfillment.

 

• Billing and payment.

 

Insights from the contact centre frequently have a direct influence on all of these operations. As a result, your corporate room falls under the purview of the customer experience. Adding analytics software to your back office can help you obtain a better knowledge of your company's operations. For example, automating regular activities like data input can help save time while also increasing customer satisfaction.

 

4. Product Administration

 

Customer input is critical in the administration of products and services. Call transcriptions can help you extract the most often cited qualities of your product/service as well as the mood around it.

 

You can also utilise IA software to monitor social media mentions of your business on YouTube, Facebook, Twitter, and Instagram. As a consequence, you may be able to locate committed and trustworthy review accounts.

 

Using this information, you may submit your product to these customers as a beta test — a group of selected end-users testing the product/service to identify problems or flaws prior to a wider release. Because these influencers have large younger audiences, they may assist your company in tapping into this burgeoning market.

 

(Also read - Augmented Analytics: The Future of Business Intelligence)

 

 

7 Frequently Used Interaction Analytics Tools

---

Interaction Analytics Tools

---

Customer information is received by omnichannel contact centres from a variety of data sources. The seven technologies listed below can consolidate all of this data into a single unified dashboard, allowing you to acquire meaningful consumer insights and boost agent efficiency.

 

1. Analyses of Phonetics

 

A phonetics-based programme searches the index file for specific sounds or sequences of sounds and connects them to terms and expressions. It basically correlates and translates audio data into pre-existing words and sentences. 

 

This tool may be customised to incorporate many languages, dialects, regional accents, jargon, and vernacular. Similarly, you may quickly add new terms to your current vocabulary, such as your company's name, service tool, and so on.

 

2. Analytics for Speech-to-Text

 

This technique, also known as Large Vocabulary Continuous Speech Recognition (LVCSR), remembers the full dialogue rather than just certain keywords and phrases. It provides a full transcript of every voice encounter, enabling root-cause analysis and a more in-depth understanding of consumer concerns. 

 

LVCSR is perfect for multichannel contact centres since it integrates easily with text-based communication techniques while simultaneously understanding audio calls.

 

3. Sentiment Analysis

 

Every client encounter is assigned a good, negative, or neutral sentiment score by AI-powered sentiment analysis. The number of favourable and unfavourable utterances in each conversation is used to generate these scores.

 

Sentiment analysis may be used to give an effective framework for agent training and performance evaluations. Positive encounters help agents learn which terms and phrases clients react to, while bad interactions teach them which ones to ignore. Negative encounters might also assist your agents to determine whether or not to contact a customer again.

 

4. Text Mining

 

Today, the majority of consumer contacts take place through text-based channels such as chatbots, emails, social media applications, message boards, customer surveys, and so on. 

 

A text mining solution examines all of this unstructured data for important phrases. The structured data is then used by text analytics to obtain quantitative insights. For example, the programme may search for price-related phrases in reviews and social media mentions, such as "expensive," "cheap," and so on. 

 

It then records the frequency of these terms, allowing you to determine whether to cut the price, provide a deal, or leave the price the same. These analytics can also assist agents in communicating with consumers by providing real-time information into evolving client attitudes.

 

5. Analysis of Customer Voice (VoC)

 

In solitude, speech, text, or sentiment analysis may not provide a full picture of your customers' thinking. This is when VoC tools come in handy. They collect client feedback across several channels by combining the capabilities of text and speech analytics. 

 

All of this information is then evaluated in order to digitally track the progress of each client encounter and uncover the core causes of common problems.

 

The VoC programmes, for example, may be used to collect certain phrases on surveys. They also supply information on client issues, complaints, and preferences. As a consequence, quality managers may make realistic adjustments based on customer feedback and improve the entire customer experience across several KPIs.

 

6. Desktop Analytics

 

Desktop analytics, also known as screen analytics, offer your firm vital information about your agent's actions throughout a client engagement. For quality assurance purposes, most screen monitoring technologies allow you to record the agent's desktop activities.

 

The recordings can be used to assess their performance afterwards. Similarly, they can discover systems and procedures that are causing workflow delays. Desktop recordings can check whether the person is following the proper procedures when assisting consumers.

 

7. Customer Journey Analysis

 

Today's customer journeys aren't only about getting from point A to point B. After seeing a social media ad, a consumer may begin by visiting your website. Following a purchase, consumers may contact your customer care via email or social media before reaching out to a phone call if the question is complex.

 

All of these distinct touchpoints and interactions are incorporated into the customer journey analytics system. Making sure that all of these channels function together smoothly and collectively may assist decrease consumer effort, which leads to improved engagement.

 

Interaction analytics provide you with a thorough view of your customer's expectations, history with your business, preferences, and much more. As a consequence, you will be able to customize your offerings, increasing consumer engagement and retention.

 

By improving workflows and automating regular operations, IA solutions may also assist enhance contact centre productivity. By detecting rising customer trends and implementing innovative technology, you may provide better customer service while increasing your profit margins.