Conversation Intelligence
|
Jul 11, 2024
Competition in today’s market is fierce. Customers have infinite options to choose from. And as a result, brands must be able to offer customers what they need, when they need it.
The numbers say it all: more than two thirds of companies compete with other brands solely on the quality of customer experience, according to Gartner.
Immediacy and quality customer service is the name of the game. Businesses need better tools to deeply understand their customers—and it starts with analyzing customer conversations proactively.
With the rise of AI and large language models, unlocking value from customer conversations has never been more accessible. With countless solutions on the market today, it can be hard to decide which conversation intelligence vendor makes the most sense for your business needs, and where to even get started. We’ve got you covered.
Whether you’ve tried traditional contact center analytics tools in the past and have been let down by results, or you are ready to dip your feet into the water with AI for the first time, we’ve got you covered.
Table of Contents
How Conversation Intelligence Works
Conversation intelligence gathers and interprets customer interactions across various communication channels. Relying on Natural Language Processing (NLP) and machine learning models, conversation intelligence tools perform three broad functions:
First, they capture information from spoken and written conversations, even when they're messy and unorganized.
Then, they match this information with structured metadata about the interaction.
They also apply AI models to analyze customer intent to surface desires, needs, opinions, and expectations of customers.
Conversation intelligence ingests call recordings and runs this data through speech-to-text AI models, followed by analyzing text data from emails, chats, surveys, social feeds, and more to make sense of the context of the conversations.
Generative Conversation Intelligence (GenCI)
A recent advancement in conversation intelligence involves using generative AI to surface insights. GenCI uses multiple large language models (LLMs) to analyze millions of customer data points simultaneously, providing robust, AI-generated insights on critical business risks and opportunities. Then, contextual summaries from these insights are generated to provide users with full context on things like intent, resolution, sentiment, and contact drivers. GenCI autonomously identifies topics, themes, and keywords at a level that is unmatched by other traditional conversation intelligence tools on the market.
The significant upgrade that GenCI offers over traditional conversation intelligence is the elimination of the tedious step of configuration. Most conversation intelligence platforms today still require manual input of keywords, tags, and user-driven category creation, which places the burden on the user to build an insights infrastructure. While this approach works for already-known issues, it makes it challenging to proactively find insights that businesses don’t know to look for, leaving hidden insights unseen.
Core Use Cases of Conversation Intelligence
Conversation intelligence is primarily used to gather context on conversations to make business decisions, whether it’s to improve sales, customer service, marketing, or operations.
Whether it's identifying emerging trends, predicting customer needs, or optimizing marketing campaigns, the ability to tap into the collective wisdom embedded within conversational data is proving to be a game-changer.
Below are a few of the core use cases powered by conversation intelligence. You can gain a deeper snapshot of how businesses are getting value from conversation intelligence on our Path to ROI guide.
Decreasing Churn
Customer churn is one of the most important metrics that businesses use to gauge health.
The cost of acquiring a new customer is 5 times higher than retaining an existing one, so retaining the existing base is important.
Companies must focus on enhancing customer satisfaction and loyalty, sure. Quick response times, personalized interactions, and effective issue resolution can significantly improve customer satisfaction levels.
Conversation intelligence allows businesses to gain 100% visibility into interactions to deeply understand churn drivers, and act automatically to address them. It enables businesses to take a closer look at every customer data point in aggregate to validate theories behind customer discontent, churn drivers, and even competitive blind spots.
Increasing Conversions
Meeting acquisition goals depends heavily on lead quality.
One way to improve funnel progression is by analyzing conversation data to detect intent signals. This information can be used to enhance AI models that determine the likelihood that leads will progress through the funnel, and in turn help businesses identify worthwhile targets.
Leveraging these findings, businesses activate workflows or campaigns in customer engagement platforms to capitalize on time-sensitive customer signals, optimize advertising spend, and even enroll customers into up-sell or cross-sell campaigns.
Boosting Sales Performance
By recording and analyzing sales conversations, managers can identify areas where sales representatives excel and where they need improvement. This data-driven approach to coaching ensures that training is targeted and effective, leading to a more skilled and confident sales team.
Some conversation intelligence platforms provide real-time feedback during sales calls, offering suggestions on what to say next, which objections to address, and how to handle specific customer concerns. This immediate assistance can significantly enhance the effectiveness of sales conversations.
Improving Agent Behaviors
Preserving customer service standards lies in how quickly agents can be intercepted. Having a close pulse on how agents are performing plays a major role in your ability to make necessary changes to customer care processes, scripts, and more.
Conversation intelligence can be paired with automation capabilities to automatically score agent performance, enabling business leaders to have data at your fingertips to make sure your agents are set up for success. With automated quality grading across all conversations, agents can learn from every missed opportunity to connect, engage, and sell.
Businesses can stop random sampling and instead ensure every interaction that needs to be reviewed gets it.
Reducing Operating Costs
Harnessing AI and automation isn't just about cutting costs—it's about maximizing output.
By automating the most repetitive tasks in customer service, employees can focus on strategic, high-impact work.
Conversation intelligence analyzes every customer interaction within minutes. This eliminates the need for manual review, saving businesses valuable time previously spent identifying reasons behind call surges or complaints.
Generative insights are also used to automate quality scoring and acting on insights, which also relieves the burden of having to communicate alerts manually across departments.
Setting Business Requirements
Before making an investment in conversation intelligence, it’s important to figure out exactly what your business needs. Setting clear goals and requirements helps you choose the right tool that best fits the needs of your company. This way, you get the most out of your investment and avoid spending money on features you don’t need. By knowing what you want to achieve, you can make a smarter decision and see better results.
What is your timeline?
Identify how quickly you need to be up and running with a market intelligence solution to set realistic expectations in vendor conversations.
Who else will be involved?
Identify key stakeholders on your team and other internal teams so you can get them involved from the start.
When evaluating a market intelligence solution, the buying committee often includes:
A business approver, like your CTO or CXO
Collaborators such as customer support, IT, QA, marketing,
What is your total volume of interactions today?
This information is critical to be able to accurately determine just how much data you need to have ingested, and how much it will cost to analyze your customer conversations.
What channels do you need analyzed?
Not all vendors will have the ability to analyze every channel you need, so it’s important to have a clear requirement around the key channels and supplementary channels you need insights on.
What is your company's purchasing process?
If you haven't purchased software at your current company before, it's a good idea to understand the layers of approval before diving into any conversations. This includes speaking with your legal and finance teams.
What is your budget?
It’s critical to understand budget ranges in the early stages – if there is only a certain amount of budget allocated for tech spend, what capabilities are absolute must-haves, and which are nice to have? This information can be documented in your evaluation tracking sheet.
Evaluation Criteria
If you’re considering a purchase decision for technology that can help you analyze customer interactions, it’s important to first define your criteria for evaluating vendors. By defining evaluation criteria, you can assess the quality and reliability of the vendor's technology, including factors like accuracy, performance, and reliability. Here are a few important areas of focus to consider.
Technology - Choose a vendor that uses a multi-LLM approach
The power of a generative conversation intelligence platform lies in how accurate its insights are. That’s why it’s so important to partner with vendors who have built a system that taps into the power of dozens of LLMs, each designed for unique analysis points like specific business questions, intent, and more.
By using multiple LLMs trained on different datasets, the analysis pipeline can capture diverse perspectives and nuances within the data. This leads to more comprehensive, accurate insights that drive better business decisions.
Questions to ask:
What are the specific areas or domains in which each LLM is trained, and how do they complement each other?
What metrics do you use to evaluate the performance of each LLM, and how do you ensure accuracy and reliability?
How scalable is your platform in terms of adding or replacing LLMs as our data or analysis needs evolve over time?
What measures do you have in place to ensure consistency and quality across the insights generated by different LLMs?
Customization - Choose a vendor that offers customized models for your unique needs
Custom models offer greater flexibility compared to off-the-shelf solutions, and they can be adapted and refined as your business needs evolve or as new challenges arise. Custom models are trained with your company's data, allowing them to recognize patterns and trends unique to the organization.
With these vendors, you can use existing data to create hyper-accurate models for complex or high-stakes analysis, like deciding when to send a big promotion.
Questions to ask:
Is it possible to customize language models to better suit our specific needs or industry requirements?
What is your approach to developing customized models for clients?
What measures do you take to address data quality issues and ensure the reliability of the model outputs?
Are there any specific data requirements or considerations we should be aware of?
Data Inputs - Choose a vendor that ingests more customer data sources
When it comes to understanding your customer, the range of customer data being analyzed matters. More data sources mean a richer understanding of your customer, which can lead to better decision-making. This is where analyzing reviews, surveys, and even social media in addition to tickets and calls can elevate the quality of insights you get.
You make use of diverse perspectives and trends, ensuring that you're not missing out on valuable information. Additionally, having access to multiple data sources reduces the risk of relying too heavily on a single channel, making your analysis more robust and reliable.
Questions to ask:
What data sources does your platform support? (e.g., social media, web analytics, CRM systems)
Are there any limitations or restrictions on the types or volume of data that can be ingested?
Can you handle both structured and unstructured data from various sources?
Can you provide examples of how your platform has leveraged multiple data sources to deliver valuable customer insights?
Integrations - Opt for a vendor with a strong integrations network
Extensive integrations mean customer and conversation data are synced seamlessly.A vendor with a robust integrations network can seamlessly integrate customer insights with your existing systems and workflows, ensuring insights are interconnected and sent to your data warehouse. By leveraging the capabilities of multiple tools through integrations, you can create more comprehensive solutions that address complex business requirements.
Questions to ask:
Which systems and platforms does your solution integrate with?
Do you provide APIs for integrating your solution with other systems?
How do you ensure data integrity and privacy across integrated systems?
What future enhancements or integrations are planned for your solution?
Workflows - Choose a vendor that ties insights to actions
Insights are only valuable if they’re put to use. Even with highly accurate learnings about your business, if you can’t apply them to improve every area of your company, you’re not making the best use of them. That’s why choosing a vendor that makes insights actionable, and impacts the broader organization, plays a role in getting the most out of your investment.
Choose a vendor that offers a way to quickly act on transformational business insights, enabling you to optimize marketing, operations, and supply chain efforts.
Questions to ask:
How does your technology platform translate raw data or insights into actionable recommendations or strategies?
What integrations do you have and how do they make insights actionable?
What mechanisms do you have in place to connect the insights generated and the actions taken by my team?
Can you provide examples of how your solution has helped previous clients turn insights into tangible actions or outcomes?
Implementation - Prioritize a vendor that implements in weeks, not months
Time to value is critical if you want your investment to impact your business in less time. Lengthy implementation schedules demand IT staffing efforts from your team, taking time away from meeting critical KPIs. Vendors that are built on newer generation technologies don’t require long time frames to go live–and if they do, the burden should not rest on your team’s shoulders.
Choose a vendor that can say with confidence the implementation of their solution can be completed in a reasonable time frame, ideally thirty days or less.
Questions to ask:
Can you provide an overview of your typical implementation process, from initial engagement to full deployment?
What level of involvement do you expect from our organization during the implementation phase?
How do you ensure that the process aligns with our organization's timeline and budget constraints?
What post-implementation support and maintenance services do you offer, and what is the process for accessing them?
White Glove Customer Success - Go with a people-first vendor
Choose trustworthy partners you can trust. When vendors prioritize people, they tend to focus on building long-term relationships based on trust, collaboration, and mutual respect. This can lead to more stable and beneficial partnerships over time, with both parties invested in each other's success.
It’s important to consider the team you’ll be working with – how involved are they in your success? Will you be charged additional professional services fees for additional support?
Questions to ask:
What do you do to ensure successful onboarding? What does your process look like?
What communication channels and reporting mechanisms do you have in place to keep us informed on progress?
How do you ensure that our team is adequately trained and prepared to use the new solution effectively after implementation?
What measures do you take to ensure open and transparent communication throughout the vendor-client relationship
Proof of Concept - Choose a vendor that shows value with your own data
Testing with your own data allows you to assess the accuracy and effectiveness of the vendor you’re evaluating in real-world scenarios. A Proof of Concept provides valuable insights into how well the solution performs with your data and whether it meets your expectations. Seeing insights in action with your own data also builds confidence among your own internal stakeholders, including executives, users, and investors. It demonstrates capabilities and potential value, fostering buy-in and support for the final decision, maximizing its potential impact on your organization.
Questions to ask:
Do you offer a proof of concept?
Is there any additional cost associated with getting analysis with our own data?
Making the Buying Decision
Finalizing your decision will ultimately require alignment across stakeholders and a clear understanding of the goals you are looking to achieve, the team you have at your disposal, and the resources available to you.
As you navigate the process of selecting software that will help you gain a deeper understanding of your customer, having clearly defined criteria and expectations for what you are looking to gain will play a key role in choosing the right vendor for your unique business needs.
Leverage customer feedback, request demos, and trial periods to ensure the software aligns with your needs. Remember, this decision is an investment in your organization's success. Choose wisely, and empower your team with the tools they need to drive growth, improve communication, and ultimately enhance your business across every layer of the organization.
Learn More
Ready to get started with your evaluation process? Set up some time with us to see what Echo AI can do for you.
