What is customer intelligence?
Customer intelligence is the collection, identification, and analysis of customer data, behaviors, and activities so you can deliver great customer experience and make informed decisions. It involves identifying individuals across channels and applications at every touchpoint so you can deliver the timeliest and most relevant next best offer and personalize it at a granular level.
Why is customer intelligence important?
Customer intelligence links everything you know about a customer and applies data science and enterprise analytics so you can answer the complex questions that help you better understand what your customers and prospects want. It lets you tailor those customer analytics to the unique perspectives of marketing, sales, service, and other departments so they can visualize relationships, hierarchies, networks, and households in the ways that best meet their strategic needs.
Although B2B and B2C organizations store and use customer data in almost every system across the enterprise, that data tends to be incomplete, inaccurate, and siloed. Therefore, that data isn’t as useful as it could be for initiatives that drive superior customer experience. To achieve the highest impact and greatest value from vast amounts of customer data, companies need to turn what they know and can infer about their customers into a true360-degree customer view.
What is a customer intelligence platform?
A customer intelligent platform (CIP) is the next stage of customer data management. It serves and connects business users in sales, marketing, commerce, and service, linking billions of data points across disparate data sources to uncover insights.
A CIP leverages AI and machine learning to understand, resolve, and evaluate structured and unstructured data across the enterprise, including:
- Resolving and de-duplicating core master data about prospects, customers, accounts, locations, transactions, preferences, and associatedreference data
- Matching data entities and new record types
- Enriching the data with inferred or derived indicators such as engagement, sentiment, value, and journey
- Allowing a wide range of users to perform complex analysis via an easy-to-use interface
- Generating multiple unique customer views in real time
A CIP creates the single customer view that enhances customer experience and business agility. Adding intelligent process automation helps reduce customer friction points by streamlining core data-driven tasks, allowing you to track and measure every stage of the customer journey across a variety of use cases.
What types of insights can a CIP deliver?
When a consumer visits a retailer’s website for the first time, a CIP can begin to identify that consumer’s unique characteristics. It then supplements that visit data with related information from external sources so the retailer already has a high understanding of who the consumer is before they make their first purchase. After the purchase, the CIP can track which products the customer bought, what they paid, which offers they responded to, which products they considered but didn’t buy, where and how they use their purchase, and a torrent of other information used to nurture the customer relationship.
This data guides the retailer’s next steps, such as:
- What products or services should I cross-sell and upsell?
- How frequently should I present offers, and in which channels?
- How should I phrase or craft my offers for maximum positive impact?
The CIP also ensures that service and support staff give relevant advice, enables them to offer proactive suggestions to prevent problems, and speeds response time when problems occur—all while giving customer support agents the context they need to adapt their interactions to whomever they’re speaking to.
Understanding patient behaviors can help a hospital system plan demand for services, staffing levels, equipment purchases, and medical supplies, from vaccines to platelets, by location and season. Because a CIP lets the hospital relate its decisions to individual patients, it can help administrators understand how changing those decisions will impact business outcomes and quality of care.
For example, a healthcare provider can see how additional outreach could improve post-surgical follow-ups. They can also get insights to create more efficient schedules and get suggestions for more efficient ways to allocate resources. A CIP can also improve the hospital’s ability to compete in a crowded market by turning data about current patients into profiles that suggest how to market to prospective patients with similar conditions. With more patients taking ownership of their healthcare choices and expecting high-touch, highly personalized experiences, these insights from CIPs become even more valuable.
Using a CIP to identify patterns in individual customer behaviors lets banks put specific events in context, making it easier for them to identify unusual or suspicious activities that might indicate fraud or assess other types of risk, such as bad debt, in the broader context of what they know about the customer.
Banks can also use aggregated customer data to see and recognize new fraud techniques as they emerge. And by combining individual and aggregated data, banks can predict churn and determine which actions are most likely to retain which individuals. This allows them to make the most cost-effective offers, limit offers to customers who are likely to be considering leaving, and design effective loyalty programs.
What is the future of customer intelligence?
Many companies have invested significant resources in analytics, but are just realizing that they must also invest in data governance and data management or they risk basing decisions on bad data—which leads to bad results. To deliver the more automated, more personal, and more predictive services that customers expect, organizations need a CIP driven by AI and machine learning to manage and analyze data from multiple disparate sources at volumes and speeds no humans can match. AI will also be necessary to automate the back-office processes and customer-facing interactions necessary to deliveromnichanneland digital experiences that are sufficiently accurate and personalized.
These customer expectations are driving companies to seek out advice from consultants and analysts about how best to implement a customer intelligence platform to achieve insights into individual customer behavior. Contextual data about where consumers are, both in the buying process and in their broader lives, will help them further tailor the end-to-end customer experience, from marketing offers to sales interactions, in person or online, with goods or services, after the sale and for renewals. The more accurately companies can identify and remove friction points in their internal and external processes, the better they become at creating experiences that deliver what customers want when—and maybe even before—they know they want it.