Generative AI Offers Significant Customer Success Benefits

Customer success teams spend much of their time trying to understand and communicate with customers and using content to help them achieve their goals, functions that generative artificial intelligence can do more quickly and efficiently, Forrester Research concluded in a recent report.

Generative AI can provide significant efficiency and creativity gains, the research firm said, but users need to be proactive to prevent generative AI from negatively impacting customers’ experiences.

“Customer success works at the account level to retain and grow customers by ensuring they realize value from the products and services they purchase,” Forrester said in the report. “Much of this activity involves the exchange of a large amount of unstructured natural language [content] between customer success managers (CSMs) and contacts at the accounts they serve.

“The interpersonal nature of these interactions also makes the function challenging at scale without adding head count, a factor that increases the potential for generative AI to help customer success teams,” the firm said.

When it comes to customer success, generative AI can help in the following ways:

• Extending the human touch. A rule of thumb is that an additional CSM is needed for every $2 million of recurring revenue or contract value, according to Forrester, which noted that for fast-growing companies, that kind of staffing expansion is unsustainable. Generative AI supports the company growth without the need to add so many CSMs to support new business.
• Delivering a more consistent brand experience. Pushing out content for new buyer acquisition to current accounts quickly loses its effectiveness unless teams customize the content to fit each account’s particular situations, such as historical purchases, industry, geography, and other account-specific factors.
• Abiding by customer communication preferences. New and existing B2B customers expect more personalized experiences today. An earlier Forrester report found that customers making additional purchases have different preferences compared to first-time or renewal buyers.
• Reducing friction points. Generative AI can use contextual tips and anticipatory hints to create content that eases customer frustration. For example, if a customer is using an app, generative AI can recognize when that customer is struggling and offer suggestions as part of the in-app workflow. The technology can also alert the proper departments to possible changes and even suggest work-arounds.
• Personalizing content. Generative AI can pull together the right components to customize existing marketing messages or other content without having to maintain huge databases of prebuilt content items. Additionally, generative AI can change messages during the customer journey and time communications about new features once customers are ready to use those features.
• Fast-tracking of customer insights. Large language models are particularly good fits for CSM interactions that deal almost exclusively with content or conversation-based data. Generative AI can analyze and summarize vast quantities of natural language interactions to pick out areas of concern, novel use cases, and other issues while the occurrences are still relatively small, long before they become major problems.
• Capturing information from CSM conversations. Generative AI, combined with transcriptions of worker-customer interactions, can provide instant summaries, saving agents from having to write these summaries while also enhancing the accuracy of the summaries. With those summaries, sales teams, for example, can use conversational knowledge capture to generate follow-up recommendations. The technology can automatically surface these recommendations when similar situations occur during subsequent interactions with other customers. The technology can also capture best practices to recommend next best steps.