Understanding customer needs has become a major pain point for B2B companies. Buyers today have near limitless choices and ever-rising expectations for personalized engagement. However, research shows most firms struggle to capture and leverage customer insights:
- 93% of organizations view customer experience as a competitive differentiator, but only 29% believe they are consistently meeting expectations. [1]
- 90% of B2B buyers say experience is just as important as products. [2]
- 55% of businesses name incorporating customer feedback into processes and decisions as a top challenge. [3]
This widening gap between customer expectations and actual delivery threatens disruption across industries. How can B2B companies get on the right side of this trend?
The answer lies in the voice of the customer (VOC) – when systematically captured, analyzed, and activated via data.
In this comprehensive guide, we’ll explore a future-forward approach to harnessing VOC analytics in B2B marketing.
What is Voice of Customer Data?
Voice of Customer refers to various kinds of direct feedback from an organization‘s current, former, or prospective customers.
Common sources include:
- Surveys
- Interviews
- Focus groups
- User reviews and testimonials
- Customer service calls/emails
- Comments on social media
- In-application or website feedback
- Third party review sites
This first-party data provides qualitative insights into customer preferences, decision factors, brand perceptions, emerging needs and more.
Properly analyzed, VOC offers unprecedented understanding of the customer perspective – but translating feedback into measurable improvements remains a challenge. Our latest estimates indicate:
- Only 24% of businesses consistently incorporate VOC data into decisions. [4]
- 55% of analysts report dissatisfaction with existing VOC collection and systems. [5]
- Companies lose 12% of revenue on average due to lack of customer focus. [6]
This points to major upside from investments in VOC analytics.
Deriving Value Throughout the B2B Funnel
Let‘s explore high-ROI applications of VOC across each stage of the B2B customer journey:
Awareness Stage
VOC guides messaging relevancy and early relationship building. Tactics include:
- Mapping content to customer priorities based on data
- Refining personas and ICP models with research
- Optimizing channels based on engagement feedback
- Crafting messages that speak to pain points
- Incorporating genuine customer stories
Example: An IT services company surveys customers asking them to rank their top 10 biggest security challenges which then informs targeted content.
Consideration Stage
Here VOC provides transparency to advance evaluation. Methods include:
- Creating comparison materials vs. competitors
- Offering customized free trials based on usage data
- Publishing gated resources on key questions
- Enabling peer references and referrals
- Hosting customer advisory panel events
Example: A cybersecurity startup offers customized product demos by profiling visitors on their site using historical behavioral data.
Decision Stage
VOC builds confidence in final deliberations by:
- Proactively addressing concerns surfaced through analytics
- Facilitating hands-on pilots focused on major features
- Modeling ROI and TCO using actual customer examples
- Providing access to specialists to answer questions
- Offering lengthier enterprise pilots including reporting
Example: A payment processing platform creates an online ROI calculator using aggregated metrics from customers.
Retention Stage
Here VOC maintains and expands lifetime value through:
- Journey mapping to ID churn predictors
- Alert systems for at-risk accounts based on metrics
- Improvements to areas with lowest CSAT scores
- Recommendation engines based on usage patterns
- Feature releases addressing common requests
Example: An accounting software app company analyzes support ticket data to prioritize bugs and new features.
Emerging Data Sources for Comprehensive Insights
While surveys, interviews and customer service cases provide rich insights, analysts are increasingly incorporating emerging digital data sources to build 360-degree customer intelligence.
Key opportunities include:
Social Media Listening
Online conversations offer candid, real-time feedback. With AI, analysts can automatically classify mentions by topic, sentiment, competitors and more at enormous scale.
Example: A retail bank uses social listening to analyze reactions to a new digital wallet offering.
In-App Behavior
Detailed tracking in digital products reveals exactly how customers navigate and where they struggle. Heatmaps, session replays and user flow diagrams uncover pain points.
Journey Analytics
Connecting data across channels provides complete visibility into multi-touch journeys. Analysts can visualize decisions paths across paid, owned, earned media to reduce friction.
Example: A software company correlates trial sign-ups to initial website visits to better target high-intent audiences.
Using analytics, these emerging sources allow analysts to build precise behavioral profiles and predictive models.
Cutting Through the Noise with Automated Analysis
VOC data is growing exponentially across structured and unstructured sources, while stakeholder demands for insights accelerate. Waiting weeks or months for results is no longer viable.
This is where automated analysis driven by machine learning delivers transformational value. Modern techniques allow you to:
Extract Insights from Unstructured Data
Over 80% of VOC data is locked away in hard-to-process text – open-ended survey responses, call transcripts, feedback forms etc.
Using natural language processing (NLP), key topics, themes, sentiments and concepts can be automatically identified within text sources up to 90% faster than human review.
Spot Trends and Outliers
By combining historical VOC data assets with machine learning, you can uncover changes over time and unusual patterns that impact customer behavior. This provides forward-looking insights for planning.
Enrich Data for Deeper Understanding
AI allows analysts to merge CRM data, behavioral data, and first-party VOC to create unified customer intelligence for granular targeting and hyper-personalization.
Operationalize Insights Across Teams
With automation, VOC findings can update dashboards, drive workflow changes, or trigger alerts in real time. This breaks organizational data silos and optimizes decision velocity.
The combination of expanding input data and intelligent algorithms provide the foundation for the next generation of customer intelligence – delivering immense commercial value from VOC.
Building a Future-Ready VOC Architecture
To fully catalyze VOC analytics across your stack, leading organizations are making foundational investments:
Configure Workflows to Capture Data
Launch listening programs across both owned (surveys, support portals, in-app feedback) and earned (monitoring social channels, third party review sites) sources. Centralize VOC data pipelines.
Cloud Data Lakes for Flexible Storage and Analysis
A data lake model offers limitless, low-cost ability to take in new, unstructured VOC data at scale while interoperating with other systems via APIs.
Machine Learning Layer for Insights Automation
Build or leverage no-code ML tools (like MonkeyLearn) to apply the latest NLP, classification and predictive modeling techniques to VOC corpuses in the data lake.
Presentation Layer for Decision Activation
Surface VOC insights directly within existing workflows – CRM records, marketing automation, contact center consoles. Trigger alerts for sales and support.
With the right enterprise foundations in place, harnessing VOC analytics becomes vastly simpler at lower costs. Execution can match the ambition.
Key Takeaways
Here are concise best practices for analytics leaders seeking to accelerate growth through customer data:
- Applying VOC systematically uncovers growth opportunities at all stages – from messaging improvement to customer retention.
- Look beyond surveys and support tickets. Emergent sources like social media, behavioral data and journey analytics add critical context.
- Using the latest NLP and ML techniques is imperative to transform burgeoning qualitative data into timely insights.
- Operationalize analytic outputs across the tech stack – CX, marketing, product etc – driving faster decisions and execution.
For CX practitioners feeling overwhelmed by rising expectations, developing an insights-driven approach rooted in VOC provides the path forward. With investments in skills and architecture, unlocking value becomes repeatable across the customer lifecycle.
- "2021 State of Voice of Customer Report." Qualtrics. 2021.
- Walker, Alicia. “B2B Buyers Prioritize Experience Over Products.” Adobe Blog. June 2021.
- “Poor Customer Experience Deter Users From Brands.” Smartbrief. September 2021.
- ”Global Marketing Statistics 2022.” Statista. 2022.
- ”B2B analytics study.” Forrester. 2021.
- ”The ROI of Customer Experience Study." Forrester. 2020