In recent years, the landscape of business-to-business (B2B) technology has undergone a significant transformation. Traditional models of selling software and services have given way to subscription-based models, where customers pay on a recurring basis for access to products and ongoing support. This shift has not only changed the way companies monetize their offerings but has also necessitated a reevaluation of how they measure and deliver customer experience.
To enable the transformation data science and analytics in these organizations must embrace three key imperatives that reshape their approach to customer experience:
Shifting from NPS and CSAT-driven CX to Focus on Holistic Customer Health: While NPS and CSAT surveys offer insights, they may provide a narrow view of customer sentiment. B2B tech providers should adopt a holistic approach, assessing aspects like product usage, engagement, adoption rates, and support interactions. This shift enables proactive identification of areas for improvement, driving higher retention and loyalty by addressing issues before they escalate.
Shifting from Focus on Historical Patterns to Focus on Customer’s Technology Roadmap: Traditional historical data analysis may overlook evolving customer needs. B2B tech providers should prioritize understanding their customers’ technology roadmap to align offerings with long-term goals. This proactive approach enables anticipation of emerging needs and positions providers as strategic partners in customer growth.
Shifting from Customer Experience Optimization to Customer Success Optimization: Prioritizing customer success alongside optimizing customer experience is vital for B2B tech providers. Beyond satisfaction, it involves proactive partnership, personalized support, and education to achieve measurable business outcomes. By shifting focus from experience to success optimization, companies deepen relationships, boost retention, and foster sustainable growth in the subscription economy.
Gaps in existing B2B Customer Data Foundations
The existing B2B customer data foundations typically revolve around two entities: the company itself and the decision-makers within it. This approach provides only a partial understanding of the dynamics at play within B2B customer relationships. While focusing on the company provides insights into organizational needs and preferences, and understanding decision-makers offers visibility into the individuals driving purchasing decisions, it overlooks crucial aspects of the customer ecosystem.
B2B relationships are often complex and multifaceted, involving various stakeholders with differing roles, priorities, and influence levels within the organization. Ignoring the broader context of these relationships can lead to gaps in understanding regarding the overall health and satisfaction of the customer. Moreover, the exclusive emphasis on company-level data may obscure variations in needs and preferences across different departments, teams, or geographic locations within the organization. Similarly, focusing solely on decision-makers may fail to capture the perspectives and sentiments of other key influencers or end-users who play significant roles in the adoption and utilization of products or services.
To gain a comprehensive understanding of the B2B customer landscape, companies need to expand their data foundations beyond company and decision-maker entities.
Next generation customer data platforms: The birth of a 4-fold customer view
As businesses seek to navigate increasingly complex market dynamics and deliver personalized experiences at scale, the next-generation CDP must transcend traditional data silos to offer a holistic view of customers and their interactions across various touchpoints. In this context, the integration of four key entities—Company, Buying Centers, Decision Makers, and End Users—within the CDP framework emerges as imperative for unlocking actionable insights and driving meaningful engagement strategies.
Company: This entity hosts the fundamental characteristics of customer organizations, such as firmographics and market trends, lays the groundwork for targeted engagement and strategic alignment.
- Firmographics: A lot of data providers offer comprehensive detail in essential company demographics such as size, revenue, industry, and geographic location. This provides foundational context for understanding the customer’s organizational structure and market positioning.
- Priorities: Deploying advanced NLP techniques like topic modelling and document summarization on Newsletters, financial statements and analyst reports provide insight into the strategic objectives, pain points, and key initiatives of the customer organization. This enables alignment of products and services with their overarching goals.
- Industry Trends: LLM driven social listening workflows help monitoring industry-specific trends, market dynamics, and competitive landscape. This offers valuable foresight for adapting offerings and strategies to meet evolving market demands.
- Macro Trends: LLM driven social listening workflows also help in Understanding broader economic, regulatory, and societal trends. This helps anticipate systemic influences that may impact customer behavior and decision-making.
Buying Centers: Within customer organizations, distinct buying centers represent critical decision-making hubs, each with its own dynamics, budgets, and competitive landscapes, necessitating tailored approaches to engagement and relationship-building.
- Decision Makers: Most existing customer data platforms identify key stakeholders, influencers, and decision-makers within the customer organization, along with their roles, responsibilities, and decision-making authority. This facilitates targeted engagement and relationship-building.
- Budgets & Spending patterns: Advanced technographic databases capture 1P survey-based data that help in tracking budget allocations, spending patterns, and investment priorities across different buying centers. This provides insights into resource allocation and potential opportunities for upselling or cross-selling.
- Competitors in Buying Centers: Analyzing competitors’ presence, activities, and relationships within buying centers informs competitive positioning and differentiation strategies.
Decision Makers: At the heart of B2B transactions lie individual decision-makers whose preferences, relationships, and purchasing patterns influence buying decisions and vendor relationships, underscoring the importance of personalized engagement strategies.
- Existing Relationships: Evaluating, sizing, and monitoring the strength and depth of existing relationships with decision-makers enables proactive relationship management and retention strategies.
- Vendor Affinity: Assessing the customer’s affinity towards existing vendors and competitors provides insights into potential opportunities for displacing or expanding vendor relationships.
- Active Contracts: Tracking active contracts, contract terms, renewal dates, and health indicators (e.g., renewal probability, satisfaction levels) facilitates proactive contract management.
- Purchase Patterns: Analyzing historical purchase behavior and patterns helps identify cross-selling or upselling opportunities and tailor offers to meet individual needs.
End Users: While decision-makers drive purchasing decisions, the experiences and needs of end users ultimately determine product success and customer satisfaction, highlighting the significance of user-centric insights in product development and support initiatives.
- Support Needs: Understanding end users’ support requirements, pain points, and satisfaction levels informs service delivery and support optimization efforts.
- Product Adoption: Monitoring product usage, feature adoption rates, and user engagement metrics provides insights into product effectiveness and areas for improvement.
- User Experience: Assessing user satisfaction, usability feedback, and user experience metrics helps optimize product design and functionality to enhance user satisfaction and retention.
- Support Experience: Tracking support interactions, resolution times, and customer feedback on support experiences enables continuous improvement of support services and processes.
- Product Success: Evaluating the extent to which products or features contribute to customers’ business objectives and desired outcomes helps measure product success and value realization.
Brining this customer data platform to life
Bringing the next-generation customer data platform (CDP) to life involves adopting innovative approaches that harness the power of data and technology to drive meaningful insights and actionable outcomes. Here’s an expanded look at key strategies:
- Global Data Integration: Companies should prioritize integrating diverse datasets from global sources to enrich their CDP with comprehensive and diverse information. This may include demographic data, market trends, industry insights, and competitive intelligence gathered from various regions and markets. By harnessing global data, organizations can gain a more nuanced understanding of customer behavior and market dynamics, enabling targeted strategies and personalized experiences.
- Advanced Natural Language Processing (NLP) Techniques: Implementing advanced NLP techniques allows organizations to extract valuable insights from unstructured data sources such as customer feedback, social media conversations, and online reviews. By analyzing text data for sentiment, themes, and sentiment, NLP enables organizations to uncover hidden patterns, emerging trends, and customer sentiment in real-time. This enables proactive decision-making and rapid response to customer needs and preferences. Example: The customer data platform identifies customers and prospects that are aiming a major digital transformation exercise for the year. This is then used as input to identify the right solution offerings, personalized marketing messages and contextual lead generations.
- Social and Market Listening: Companies should leverage social media monitoring and market listening tools to capture and analyze conversations, mentions, and trends relevant to their industry and target audience. By monitoring social media platforms, forums, and online communities, organizations can gain valuable insights into customer sentiment, brand perception, and emerging topics of interest. This enables companies to engage with customers in real-time, address concerns promptly, and capitalize on opportunities for brand advocacy and thought leadership. Example: The customer data platform identifies companies that are expanding offices in a specific geography.
- LLM-Powered Workflows: Leveraging large language models (LLMs) powered by advanced AI and machine learning algorithms, companies can automate and streamline workflows within their CDP. LLMs enable organizations to process and analyze vast amounts of textual data, generate insights, and execute actions based on predefined rules and criteria. This includes automating tasks such as data categorization, sentiment analysis, customer segmentation, and personalized content generation.
By focusing on these innovative approaches, companies can bring the next-generation customer data platform to life and unlock the full potential of their data to drive business growth, foster customer engagement, and stay ahead in today’s competitive landscape.
