Managing customer and client relationships is one of the most expensive things that businesses do. While continued efforts have been made to automate these interactions, at the end of the day, many conversations have to be had with a human. Even the conversations that can happen with automated options require scripting, and often analysis, by a person who can optimize them.

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There are an increasing number of ways that our customers or prospects interact with our organization and brand. From email and phone call, to chatbot, text, and social media engagements - there are more channels to manage and more opportunities to maximize our relationship or to make mistakes.

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So how do we optimize the human-to-human interactions AND the automated interactions without reading through every line of chatbot text and email conversation or listening into phone calls? Leading brands are integrating AI into every aspect of their business, and that means customer success and account management. Using machine learning, businesses can evolve their teams and understanding of their customers faster than ever before, and because it doesn’t require additional staffing, it’s more affordable.

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Learning from Call Centers

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Call centers have long been analyzing their performance for decades. Many call centers are checking a sample of calls monthly to ensure compliance and optimize the performance of their teams. Even under the best circumstances, most can only monitor 3-5%of the calls, leaving a huge opportunity for improvement and revenue saving opportunities.

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What call centers have that other channels are just now working toward is a long track record of dealing with customer and prospect conversations. Call centers have experience in creating script variations, developing escalation paths, and knowing when to pivot a conversation or end a relationship.

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While we mostly think of bad call center experiences, like the cable or phone company, there are actually several examples of call centers that you have interacted with that just seem like regular human conversations. Those are the ones we want to emulate.

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Using Sentiment Analysis for Customer Success

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Customer success, customer support, and account management may be the same or different areas depending on your business. They may have just a few people or hundreds spread across different geographies, lines of business, or customer tiers. Any of these scenarios can benefit from sentiment analysis and understanding the intent behind an audience’s outreach.

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Here are a few ways that you can use sentiment analysis to improve your customer success and account management functions.

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Create Better Chatbot and Call Scripts

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Sentiment analysis sheds light on the interactions between various teams and channels across your business - and it’s grown up a lot since the days of only providing positive, negative, and neutral feedback. For example, Redflag AI provides more than 20 sentiments, including shame, disgust, gratitude, and excitement. These nuanced feelings are imperative to having successful conversations when we can’t be face-to-face.

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The source of frustration during the majority of customer service calls is two things:

1. The time it takes to get an answer. This includes having to wade through prompts and robo-menus, being kept on hold, listening to pre-recorded compliance messaging, or speaking to the wrong person because of poor routing. All of these things can be prevented through better understanding of your audience’s intention and then routing them to a properly aligned script and service channel.
2. Not feeling heard. When there's an actual issue, whether it’s a product or service related problem, the first thing everyone wants is to be heard. They need to explain the problem and feel that you understand why they are upset or frustrated. Then they want you to fix it - efficiently.

If you’re routing real problems to an FAQ section or a chatbot that has a poorly created sequence of scripts, you’re likely to lose customers and garner bad reviews. Conversely, if every question requires a long wait time via phone call instead of a quick answer available on your website, that’s also a source of frustration. Most users are willing to solve small problems themselves and want to speak to a real person quickly when it’s more complex or time sensitive.

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Chatbots, service representatives, and account managers can all benefit from better scripting. While each may be able to vary from their scripts and workflows differently, they should all have a standard decision tree that guides the conversation. Using a tool that gauges how people are truly feeling at different points in the conversation for each type of problem is key to honing the script to be not only a better experience for the customer, but also more efficient for your team.

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Enhance Your Voice of the Customer Data

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Your Voice of the Customer (VOC) data can be one of the most profitable information sources for your business.

Customers may be internal or external - and analyzing how they’re responding to your team’s work is important in all instances.

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Examples of internal customers include:

• Executive teams / leadership
• Board members
• Investors
• Other business units
• Your own employees / team members
• Teams in other geographies or regions

Understanding their sentiment and intent can tell you things about employee retention, changes in leadership, management styles, and more. You can also use this data to train teams, classify or rank your team members performance, or assess the risk of a team member.

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External customers are usually classified by their relationship to products and/or services, location, longevity, spend, and cost to acquire.

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There are several factors that can help you create a more comprehensive understanding of your customer, including:

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• Sales cycle burden - Segmenting customers by how many interactions and average duration of interaction during the sales cycle.
• Account management resources - Grouping and analyzing the amount of engagement specific clients or users have with your account management and customer support teams.
• Sentiment during sales cycle - What sentiment traits did your best customers exhibit during the sales process and how does that differ from clients who do not perform as well or stay as long? Are excitement and curiosity determining factors or is it trust?
• Sentiment and Intent trends over time - How does your customer engagement change over time by product, customer spend level, location, salesperson, account manager, and other factors?

Customer needs can usually be classified as being related to quality, cost, safety, service, and delivery — and, more and more frequently today, social responsibility. It is the satisfaction of those needs that should drive the organization. The challenge is how an organization gathers and synthesizes all the voices, many of which may be in conflict with each other.

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Voice of the customer data can be gathered in several ways, including:

• Calls to customer service representatives
• Survey data
• Focus groups
• Surveys
• Direct observation
• Complaint data
• Sales interactions
• Chatbot engagement

This information can be gathered across text, audio, and video across channels such as:

• Email
• Phone calls
• Chatbots
• Text messages
• Social media posts and comments
• Survey responses
• Text documents
• And more

Layering sentiment and intent data into your voice of the customer analysis will give you a powerful look at how internal and external audiences are impacting revenue.

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Improve Account Management and Customer Service Follow-Up

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Another area where sentiment analysis and intention modeling can benefit your business’ bottom line is in follow up communication.

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We’re often asked to rate our experience as part of follow ups, usually lacking any context from how our call seemed to go or the problem we were trying to solve. Imagine if you could add in more detail from the call and even adjust the text in your email or notifications to offer more content, sell additional products or services, or just retain your customers easily?

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You can often spot openings for new products and services during customer support and account management conversations. Leveraging intention and sentiment data, you can automate new marketing campaigns, advertising messages, and email sends. These simple updates to your existing marketing and customer engagement flows can result in millions in additional revenue.

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Ready to add sentiment and intent data to your insights? Let's talk.