How to Use Conversational Analytics Software Effectively

Conversational analytics software does much more than simply creating transcripts.

It gives deep insights into customer conversations, helping businesses fine-tune their strategies across various departments. It interprets and analyzes a conversation’s context, which makes it easier for companies to make informed business decisions.

It’s advisable to focus on conversational artificial intelligence (AI) tools that comply with privacy regulations and follow best security practices. Since the software directly deals with customer information, non-compliance leads to violations that might result in a data breach.

This article will guide you through using conversational analytics software effectively, enabling you to get the actionable insights you need to deliver a stellar customer experience while remaining legally compliant.

Conversational Analytics Software Across the Enterprise

Sales, marketing, customer service, product, and UX teams all have a use case for conversational analytics, though each department uses it for different purposes.

How a sales team uses conversational analytics

A sales team uses conversational analytics software to preserve context from customer interactions. This software gives managers accurate customer insights and allows new reps to quickly take over ongoing deals.

Sales reps often forget to enter all the necessary information in their customer relationship management (CRM) software, leading to inaccuracies. When inaccurate or incomplete data is supplied to a new rep, they spend most of their time connecting the gaps and trying to absorb the context of discussions.

Conversational analytics software automatically:

• Records customer interactions and performs conversation analysis.
• Transcribes these discussions and adds a summary of key takeaways.
• Feeds the data into a CRM.

Automation reduces human error, making the data more accurate for new salespeople to work on.

Teams use conversational analytics software to set best practices and benchmarks. The software enables you to coach team members to become top performers based on insights from past conversations that led to successful deals.

You can also see what went wrong during customer calls and give prescriptive suggestions, helping your team improve. The tool can help you establish a benchmark and track agents’ performance KPIs and metrics to ensure they trend upward.

Conversational analytics software allows you to create bite-sized training materials for new reps. Using these guides, reps become better equipped to execute winning strategies on repeat.

Here are a few notable areas where conversation intelligence tools benefit the sales team:

• Automates note-taking: Salespeople focus more on the conversation and ask better qualifying questions on sales calls when conversational analytics software takes care of notes.
• Forecasts deals: The software studies signals from conversations on phone calls or any other channel and helps users understand where the deal stands and how to approach it.
• Provides competitor insights: It lets users track and compare win vs. loss rates among other solutions and services on the market.
• Qualifies deals: The software understands a deal’s progress and gives it a lead qualification score, making it easier to prioritize.
• Offers predictive sales intelligence: Based on the conversation’s context, the software highlights upsell and cross-sell opportunities for your team to engage prospects.

How a marketing team uses conversational analytics

A marketing team uses customer interactions to understand buyer motivations and concerns. They refine marketing personas consistently as insights come in, helping them tweak their messaging accordingly.

Conversational analytics software picks up customers’ sentiments from their interactions with the company. Marketing teams craft messaging that complements the sentiment analysis and customer feedback. Such conversations often reveal unmet customer needs or product gaps, helping you discover new competitive opportunities for your product. If a gap frequently emerges in conversations, you can share it with the product team for consideration in future sprints.

Source: Deloitte

Here are a few ways conversational analytics platforms help marketing teams increase personalization opportunities: 

• Keeps track of new trends: The software records what customers talk about and what they might need next. It helps companies better plan their go-to-market initiatives.
• Adjusts content strategy: The software’s valuable insights show information gaps you can bridge through content, such as creating an onboarding guide to increase product adoption.
• Provides sentiment and product analysis: Understanding the feelings customers have toward products and services helps your messaging resonate with your customers.

Conversational analytics helps marketing team members to keep up with change and make sure their strategies are improving. It enhances and optimizes customers’ experiences, fueling business growth.

How customer service and customer success teams use conversational analytics

Intelligent virtual agent (IVA) solutions quickly pick up FAQs and common issues based on customers’ historical interactions with contact centers. This lets the customer service team create a reliable knowledge base and encourages customers to self-serve.

When integrated with a contact center platform, conversational IVAs recognize common concerns and either respond directly or give support reps tips on overcoming these problems effectively. They can also arm agents with the knowledge and communication skills needed to fill gaps in a call center.

Here are some common ways customer service teams use conversation intelligence platforms in their workflows:

• Speed up responses: Conversational AI makes it easier for you to respond to frequent queries automatically. Customers get a response faster while your team looks into more critical issues with improved focus.
• Personalize support: With a holistic understanding of a customer’s preferences, history, and behavior, your agents can better personalize support interactions and suggest other solutions for more upselling and cross-selling.
• Provide agent training: The software lets agents identify and receive training on the parts of conversations they struggle with, improving their skills.
• Improve first-contact resolution rates: With conversational analytics software, you can easily see and understand how a support person closes a ticket and achieves a first-contact resolution in real time. By using the same process to train existing or new agents in a company, you can save costs by resolving queries faster.

How product and UX teams use conversational analytics tools

The product team uses insights from customer support interactions to understand user frustrations or pain points with existing features. Conversational analytics makes the process faster and more scalable for the team.

These insights allow product team members to understand how customers use features and to identify areas for improvement.

Here are some ways conversational analytics software helps product and UX teams:

• Reveals the actual customer journey: The software details a customer’s journey to purchase a product or service based on insights from sales conversations. It lets you discover customer engagement opportunities, offering a better user experience and increased customer satisfaction.
• Flags customer issues: The software allows you to investigate a customer’s issues deeply. It helps you spot common problems in the product and fix them before they turn into deal breakers.

Security and Privacy Considerations of Conversational Analytics Software

Conversational analytics software processes highly sensitive and confidential customer data. Make sure you check with the vendor about their practices to maintain data privacy and comply with regulations.

Confidentiality risks

In a survey, 39% of respondents felt that using public generative AI could leak conversational data.

Public AI models may not be sufficiently secure to trust with confidential customer conversations. Any exposure to such sensitive information can lead to data breaches or legal consequences. These models train themselves through natural language processing and machine learning using the data you input.

For example, ChatGPT’s model training FAQs clearly state, “We also use data from versions of ChatGPT and DALL·E for individuals.”

Before onboarding any software tool, discuss the vendor’s data security practices with your IT and security teams to ensure you keep your customers’ data confidential.

Data governance

While handling confidential information, it’s advisable to enforce data governance policies. Simply put, you must anonymize any sensitive or personally identifiable information processed by conversational analytics tools.

This will help you prevent unauthorized access to customer data, keeping customers and the company’s interests safe. 

Compliance

While processing customer data, make sure you don’t violate privacy laws like the General Data Protection Regulation and the California Consumer Privacy Act. These regulations advise on data collection, storage, and usage guidelines. You also need to adhere to their advisory to avoid hefty fines or damage to your reputation. 

From HIPAA to PCI DSS to FINRA to non-discrimination, there are many different kinds of compliance when it come to contact centers.

Best Practices for Using Conversational Analytics Software

Making the most of conversational analytics software involves much more than setting up the tool. Here are a few tips that will help maximize the value you get from your investment while keeping your customers’ data safe:

• Choose secure and compliant software: Consider conversational analytics software with robust security features to keep customer data confidential. Ensure that it has everything you need to comply with industry regulations and protect your business from legal damages or from ruining your reputation.
• Give proper data handling training: Anyone using the analytics software should be trained in appropriate data handling practices. They should know what they can or cannot feed into the software and how to anonymize sensitive information to secure customer data.
• Put insights above transcripts: While reading through specific details of customer conversations might be infinitely tempting, the insights you gather from conversational analytics software add real value.
• Integrate with other data sources: Integrations let your conversational analytics software work with other CRM and data platforms to unify customer data. When you see the complete picture, it becomes easier to make data-driven decisions.

Nextiva: A Secure Conversational Platform

Nextiva offers a streamlined customer conversation solution with enterprise security features to safeguard customer and business data. It adds a protective layer over customer data and seamlessly integrates with CRM and customer support platforms to offer a holistic view of customer information.

Nextiva offers valuable insights from customer conversations, helping you make informed business decisions. It gives you a competitive edge in creating customer-centric products and services with a unified conversational strategy.

Related: Conversational AI Solutions: Benefits, Challenges & Best Practices