Artificial intelligence (AI) is revolutionizing customer relationship management (CRM) by making sense of unstructured data, that is, information that does not fit perfectly into databases. Sources like customer emails, call transcripts, social media posts, chat logs, and product reviews are full of information that can transform the customer experience and help drive business growth. What if the key to customer experience transformation lies in the data you’ve already collected but haven’t yet explored? Here’s how AI and unstructured CRM data can help organizations unlock their growth potential, and how applying the Rumsfeld Matrix, a decision-making framework dividing the “knowns” and “unknowns” into four categories, can guide the process of exploiting this information.
Better understanding of the customer
Unstructured data can reveal valuable details about customer preferences, frustrations and expectations. AI tools can analyze various sources, such as customer emails, call transcripts, and social media posts, to identify patterns that organizations can use to refine their strategies.
For example, AI can study product reviews to spot common issues or sift through call recordings and emails to uncover recurring support issues. The result? Businesses get a clearer picture of what their customers value, allowing them to tailor their offerings accordingly.
- Known Known: AI analyzes unstructured data to understand customer sentiments and preferences
- Known unknowns: The exact impact of this information on customer satisfaction and loyalty
- Unknown Known: existing data models that have not been fully exploited
- Unknowns Unknowns: New insights from future data analysis
Improved customer journeys
Understanding the customer journey requires inspecting interactions across multiple touchpoints. AI examines data from sources like chat transcripts, support tickets, and call recordings to create a 360-degree view of the customer experience.
For example, an AI system could detect that customers often call after receiving a particular email campaign, indicating confusion or interest that could be addressed proactively. By studying the interaction between these touchpoints, businesses can alleviate areas of friction and help improve overall satisfaction.
- Known Known: AI maps customer interactions across channels to improve journeys
- Known unknowns: Potential gaps in understanding the emotional impact of touchpoints
- Unknown Known: Information buried in underutilized CRM data
- Unknowns Unknowns: Innovative approaches to enrich pathway analysis
Customer sentiment analysis
AI tools excel at gauging customer sentiment from unstructured data sources. Whether it’s reviews, social media posts, or call recordings, these tools can identify changes in tone and sentiment that could indicate satisfaction or dissatisfaction.
For example, mining support chat logs and call center transcripts can reveal growing frustration over delivery delays. Armed with this information, businesses can act quickly to alleviate negative feelings before they worsen.
- Known Known: AI detects sentiment trends in unstructured data
- Known unknowns: Challenges in accurately interpreting cultural or contextual nuances
- Unknown Known: Sentiment factors hidden in historical data
- Unknowns Unknowns: New feeling patterns or triggers may emerge
Predictive analytics for proactive strategies
AI leverages unstructured data to predict customer behavior. Call transcripts, combined with email and social media data, can help organizations identify trends that indicate churn risk or upsell opportunities.
For example, one technology company used AI to survey both social media mentions and support call data, discovering that customers discussing competitors were at higher risk of switching providers. This knowledge allowed the company to intervene with targeted loyalty campaigns.
- Known Known: AI predicts customer behavior based on historical trends
- Known unknowns: The consistency of these predictions across various demographics
- Unknown Known: Predictive models with untapped potential for refinement
- Unknowns Unknowns: Emerging behaviors and market conditions
Automation and efficiency
Unstructured data often requires a lot of time and effort to analyze manually. AI automates these processes, extracting actionable insights faster and more accurately.
For example, AI can process hundreds of customer call transcripts as well as chat logs and emails, classifying them by issue type and priority. This can significantly reduce the workload of human agents and enable faster response times, thereby improving operational efficiency.
- Known Known: AI automates the analysis of unstructured data
- Known unknowns: How well AI can handle more complex tasks
- Unknown Known: Automation features within existing systems that are underutilized
- Unknowns Unknowns: New possibilities for automation as AI advances
Improved data integration
Integrating unstructured data with structured CRM data can give organizations a holistic view of customer interactions. AI can help bridge the gap between unstructured and structured data, combining call recordings, purchase histories and social media interactions, to provide key insights and a unified customer profile.
For example, a retail company could use AI to merge call center feedback with loyalty program data, detecting that longtime customers appreciate personalized thank you notes after their purchases. This information allows you to refine loyalty strategies for better engagement.
- Known Known: AI integrates various data into an overall customer profile
- Known unknowns: Challenges of harmonizing inconsistent data sources
- Unknown Known: Integration opportunities are not fully explored
- Unknowns Unknowns: Innovative techniques for deeper integration
Real-time insights for agile decision-making
AI processes unstructured data in real time, enabling businesses to act on emerging trends. Social media monitoring allows businesses to dynamically track potential customer sentiment, while summaries of recent call center conversations can identify pressing issues.
Imagine detecting a wave of complaints about a new product. Using AI, an organization can respond quickly, adjusting marketing messages or issuing clarifications to address customer concerns.
- Known Known: AI provides real-time information to facilitate decision-making
- Known unknowns: The reliability of these insights in high-stakes scenarios
- Known unknowns: latent information from real-time data streams
- Unknowns Unknowns: Future Opportunities for Real-Time AI Innovation
Use cases
Here are examples of how organizations have leveraged AI and unstructured CRM data:
- A retail company identified an increase in complaints in call center transcripts regarding delivery times. By eliminating logistical bottlenecks, customer satisfaction and loyalty were improved.
- An e-commerce platform used social media data to detect negative sentiment around a confusing promotion, allowing it to clarify terms and rebuild customer trust.
- A technology company combined email and call data to identify common troubleshooting issues, streamlining its self-service portal for an improved user experience.
- An entertainment service analyzed chat transcripts and user reviews to improve recommendation algorithms, thereby increasing engagement and retention.
How Forvis Mazars can help you
AI tools can effectively harness the power of unstructured data. When combined with other CRM data, AI can revolutionize the way organizations interact with current and potential customers. By drawing insights from call transcripts, social media posts, emails and other sources, businesses can better understand their customers, streamline their operations and predict their future behaviors. Applying frameworks such as the Rumsfeld Matrix can provide a strategic approach to leveraging AI technology, helping organizations identify opportunities, mitigate risks and remain agile in a competitive landscape. Start by identifying the unstructured data sources already available to you. Then explore resources that can help you integrate AI and unstructured CRM data to uncover insights. For forward-looking organizations, the potential of modern CRM lies in the ability to leverage unknowns to gain competitive advantage.
To help you start your AI journey in CRM, Business Technology Services at Forvis Mazars has dedicated resources. Our technology consultants are experienced in implementation, design, upgrades and ongoing support services for Microsoft Dynamics 365 and Salesforce CRM applications. Connect with us today to learn more or request a personalized demo.
