{"id":10512,"date":"2025-10-28T15:54:12","date_gmt":"2025-10-28T21:54:12","guid":{"rendered":"https:\/\/attentionmedia.io\/?p=10512"},"modified":"2025-10-28T15:54:12","modified_gmt":"2025-10-28T21:54:12","slug":"customer-sentiment-and-risk-are-hidden-in-the-emails-in-your-crm","status":"publish","type":"post","link":"https:\/\/attentionmedia.io\/?p=10512","title":{"rendered":"Customer sentiment \u2014 and risk \u2014 are hidden in the emails in your CRM"},"content":{"rendered":"
\"A<\/div>\n

I\u2019ve been exploring the impact and importance of leveraging unstructured data in my previous articles for MarTech, and for this piece, I\u2019m digging into perhaps the biggest source of insights and<\/em> risk: our email.<\/p>\n

Much of the genAI email discussion is centered on the use of GenAI to produce better targeted, personalized mass email campaigns<\/a>, with the aim of improving the effectiveness of outbound campaigns.<\/p>\n

In this article, I\u2019m going to dig into genAI use cases for inbound emails captured in the CRM. These messages include one-to-one messages and one-to-many reply-all emails. I am assuming here that the organization integrated its team\u2019s individual inboxes with the CRM, as is common among today\u2019s marketing, sales and customer teams.\u00a0<\/p>\n

Every email that goes out or comes in is tracked and stored as part of the activity record. As a result, what were unstructured, scattered email conversations are now part of that business\u2019 central repository of data, and can be analyzed using a CRM\u2019s embedded AI and natural language processing (NLP)<\/a>.<\/p>\n

This allows us to move the discussion from tracking outbound engagement metrics, like opens and clicks, to instead analyzing the content and sentiment of the emails in the CRM,\u00a0 particularly as their capabilities becomes more common in leading CRMs like Hubspot<\/a>.<\/p>\n

From email campaign metrics to conversational insights<\/h2>\n
\n\n\n\n\n\n
Use Case<\/strong><\/td>\nExample KPIs<\/strong><\/td>\nKey questions answered<\/strong><\/td>\n<\/tr>\n
Analyzing traditional \u2018outbound\u2019 email metrics<\/strong>.<\/td>\nOpen rate, click-through rate, conversion rate<\/td>\n\u201cDid my campaign perform well?\u201d<\/td>\n<\/tr>\n
Generating Insights from genAI analysis of \u2018inbound\u2019 emails<\/strong>.<\/td>\nSentiment scoring, intent classifications, response to content\u00a0<\/td>\n\u201cHow do our customers feel<\/em> and what do they need<\/em>?\u201d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n

Dig deeper: The future of the martech stack and marketing operations is \u2018unstructured\u2019<\/a><\/em><\/strong><\/p>\n

Insights and concerns<\/h2>\n

A more complete email analysis takes you beyond the high-level \u201cwho\u201d (email senders) and \u201cwhat\u201d (subject lines) to the underlying sentiments of the email. Let\u2019s break this down further into the opportunities for marketing, sales and customer-facing teams, and cover new considerations around privacy and compliance.<\/p>\n

The pros: Opportunities for customer-facing teams<\/h2>\n

Enhanced customer insights<\/h3>\n

Using NLP<\/a> to analyze email interactions reveals deeper customer sentiment and pain points, leading to more personalized marketing and sales strategies.<\/p>\n

Emails contain detailed customer questions, specific objections and information requests. By using NLP or even AI-driven analytics, marketing teams can extract patterns from emails to improve the messaging and tailor follow-up content approaches.\u00a0<\/p>\n

This approach allows us to understand more about individual customer\u2019s preferred tone and content formats. By applying NLP techniques, organizations can analyze the emotional tone of incoming emails and classify them as positive, negative or neutral. This sentiment analysis can be combined with the intent of message sequences \u2014 such as \u201cdemo request\u201d or \u201cpricing question\u201d \u2014 to help determine deal momentum or blockers.<\/p>\n

Improved marketing-sales-customer collaboration<\/h3>\n

A centralized analysis of email activity enhances collaboration among teams, going beyond the stored history of customer interactions. Extracting insights was limited in the past due to sheer volume.<\/p>\n

Recognizing when additional stakeholders get added into email replies could lead to more insight and automation around buying roles and decision makers. Matching this information to additional meetings tracked for RFPs, requirements and more helps drive correlation analysis to overall funnel stages, lead statuses and deal pipeline stages and improve those processes.<\/p>\n

Data-driven decision making<\/h3>\n

Using an LLM to analyze emails potentially informs decisions, allowing teams to respond quickly to trends and customer needs.<\/p>\n

Email sentiment analysis helps inform target accounts and contact scoring beyond simple personas. Traditional lead scoring models relied too heavily on structured data like job titles, which alone are also unstructured data<\/a>.<\/p>\n

Rather than waiting for milestones and pre-programmed actions to trigger surveys and extract sentiment, we can now monitor sentiment along the journey.<\/p>\n

The cons: Key concerns and challenges of analyzing unstructured email<\/h2>\n

Email conversations are much more casual than other communication forms. We are likely more cognizant of what we say in a meeting that\u2019s being recorded, than what we quickly fire back in an email. That creates some level of risk.<\/p>\n

Privacy and consent<\/h3>\n

Teams will need to revisit customer privacy and appropriate consent measures, especially when dealing with sensitive information.<\/p>\n

We need to revisit email footers created long before the latest genAI LLM capabilities were released. Although these were generally structured as confidentiality disclosures that protected against risks, they may not include the appropriate language to indicate that they will now be fed into an LLM model for additional predictive analysis.<\/p>\n

Teams may need to consider new processes, like aggregating insights without connecting them to specific individuals. Similarly, another concern that may arise in typical B2B use-cases when contacts move across accounts regularly. Does an individual\u2019s email from their prior employer still represent their views now that they\u2019re at a new employer?\u00a0 Can you count on this to be their consistent position, or do you need to reset their sentiment toward you?<\/p>\n

How does this change when teams enable the CRM connectors with ChatGPT <\/a>and have broader implied use cases?<\/p>\n

Regulatory compliance<\/h3>\n

Hubspot made news last year when it released its sensitive data and HIPAA compliance<\/a>. However, because it\u2019s likely that Hubspot customers had enabled the centralized email tracking prior to then, organizations may need to revisit their email policies and processes.<\/p>\n

These new capabilities also illustrate the nuances of structured vs. unstructured data. Sensitive data classifications were typically structured around defined form fields that could be categorized as such. It\u2019s not as clear how those guidelines apply if sensitive data elements are included in the body of unstructured emails.<\/p>\n

Access controls and internal policies<\/h3>\n

All of these newly embedded capabilities may lead to organizations adjusting access controls so only authorized, trained users can view email activity. This is a significant change, as centrally captured emails were previously available with company-wide access. By definition, it\u2019s impossible to predict what\u2019s going to be part of an email conversation. Some organizations may choose to take a conservative approach rather than allowing the LLMs to analyze all email.<\/p>\n

Platform considerations<\/h3>\n

Marketers need to ask their platform vendors for more help navigating these risks. New features roll out quickly.\u00a0<\/p>\n

Vendors can also help users understand what should be activated by default. For example, Hubspot\u2019s Breeze AI Assistant is still labeled as beta<\/a>. But when HubSpot released its new AI capabilities, the customer conversation data was on by default \u2014 on the broadest possible set of data for call transcripts, emails and more.\u00a0<\/p>\n

\"AI