Common customer service frustrations persist across organizations: endless hold times followed by repeated identity verification through security questions, detailed troubleshooting recitations, and then call transfers requiring complete restart of the entire process—account numbers, equipment serial numbers, model specifications, purchase dates cycling through again.
These scenarios occur countless times daily across enterprises of every size. What makes comprehensive customer recognition and information consolidation so challenging? Why can't organizations capture basic information, append it to customer records, and seamlessly transfer that context across departments?
Fragmented Information Across Enterprise Systems
Real-world experiences illustrate these challenges. A personal banking example appears in The AI-Powered Enterprise. After refinancing a second mortgage through my bank—an initiative they promoted to encourage loyalty—I received subsequent refinancing offers from that same institution for the loan just completed. When approaching them about automobile financing, I learned the bank didn't finance vehicle purchases. Yet I discovered later that my car dealer used that very bank to finance the loan I ultimately secured. This represented poor customer experience and organizational inefficiency. The bank wasted marketing expenditure targeting a customer who'd already refinanced while losing a direct loan opportunity that could have strengthened customer relationships.
Pursuing Comprehensive Customer Understanding
Addressing these missteps requires comprehensive customer visibility. This approach ensures every organizational division recognizes all customer interactions, behaviors, experiences both positive and negative, and prior purchases or equipment ownership. Surfacing this intelligence enables smoother customer experiences, reduced support expenses, and enhanced capability for serving customers with additional products and services.
Large enterprises typically maintain distinct business units, functions, products, and solutions. Companies frequently expand through acquisitions, making system integration more challenging than initially apparent. Digital transformation programs pursue silo integration, but legacy systems, custom applications, and complex customer experience technology stacks create individual and collective difficulties. New offerings might launch as market tests before full integration, sometimes using one-off, ad hoc, or software-as-a-service technologies that complicate comprehensive customer view development.
System Disconnection Creates Customer Experience Gaps
In other scenarios, as marketers introduce new tools, insufficient time and investment gets allocated to ensure seamless integration. Results manifest as organizational disconnection and disrupted customer experiences. Service representatives may lack needed information or responsibility for new offerings, impacting both costs and efficiency.
Customer data platforms address these challenges. These systems integrate and consolidate information from numerous customer interaction systems including website visits, social media engagement, online advertising, email marketing, and more. The objective: creating unified customer databases enabling complete interaction visibility and harvesting signals—digital body language—as customers traverse their journeys doing business with organizations.
Distinguishing Customer Intelligence Platforms from CRM Systems
You might think customer relationship management systems serve this purpose. Partially correct. CRM systems focus on sales with account structures supporting sales interactions. Customer intelligence platforms emphasize individual interactions across diverse channels and technologies potentially unintegrated with CRMs. These platforms are architected for integration. While many CRMs provide sales and support process visibility, they typically don't consolidate multi-system data and drive marketing interactions from that intelligence. Multiple reasons explain this: sheer touchpoint and tool quantities, integration complexity, and CRM data utilization capabilities. For example, website behaviors generate intensive data. Making sense of click path behavioral data within CRMs would overwhelm systems. Purpose-built tools make such data more actionable.
CRMs leverage customer intelligence platforms to unify disparate sources and customer identifiers avoiding duplicate records. For example, someone might appear as J Smith in email systems, Jon Smith on Facebook, and Jonathan Smith in CRMs. Platforms would track these interactions as one person. Complete customer understanding and needs assessment requires synchronizing all customer touchpoints.
Signals from multiple customer-touching systems like social media listening, email marketing, campaign management get integrated and consolidated in platforms, which then personalize marketing messages and allow service representatives to see problems from customer perspectives rather than from siloed processes or departments.
Implementation Considerations and Value Realization
Exercise caution regarding vendor return-on-investment claims, but don't hesitate examining these platforms judiciously. Building needed infrastructure with customer intelligence platforms represents non-trivial efforts, but benefits prove significant: improved efficiencies, lower support costs, and happier, more loyal customers.
This article by Seth Earley was originally published on Customer Contact Advisor on January 22, 2021.