Businesses today have more data than ever but don’t know what to do with it. Customer data alone has limited business benefits unless they can be turned into customer intelligence and insights. The following highlights the differences of the three:

Customer Data: This is the customer information you collected, measured, or analyzed. For example, you have the following data on your customer XYZ:

  • Revenue: $13B
  • Industry: Entertainment & Media
  • Customer since: Apr 2018
  • Product Utilization: low frequency & volume in Q1 2019
  • Purchasing amount: $250K/month
  • Helpdesk tickets: 6 complaints in Q1 2019
  • Customer service record: 23 emails & 7 calls in Q1 2019

Customer Intelligence: This is the integration and contextualization of available customer data. Customer intelligence tells you what’s going on with your customers. For example, your intelligence for customer XYZ may look like the following:

  • Tier 1 customer with high spending power
  • Product utilization reduced by 15% each month for the last 3 months
  • Customer XYZ have been having trouble logging into our product
  • Our technical support team has been slowed in resolving customer XYZ’s issues.

Customer Insight: This is the interpretation and prediction based on customer intelligence. For example, your insight for customer XYZ may be the following:

  • High priority & urgency
  • High customer churn risk
  • High negative impact on brand and reputation

As the examples demonstrated above, the meaning of customer data is obscured before it is processed into customer intelligence and insights.

How to Develop Customer Intelligence and Insights?

There are plenty of applications available to gather and measure customer data. The challenge is to what degree can you turn these data into useful intelligence or insights?

Developing Customer Intelligence

The most common customer intelligence application is your CRM, where you keep track of most of your contact and customer information. You can extract customer intelligence from the CRM dashboard as it allows users to contextualize all customer data in one location. The customer intelligence field has expanded rapidly that your CRM alone is no longer sufficient in today. Businesses frequently integrate their CRM with various marketing, sales, and data applications to get collect and analyze additional customer data.  This is often referred to as the technology stack.

For example, your technology stack may include Salesforce (CRM), HubSpot (Marketing), SalesLoft (Sales), Drift (Chat), and Clearbit (Data).  Each of the applications offers you different types of data and intelligence. The challenge is to adequately integrate all the data to form a comprehensive customer intelligence under one central application.

Developing Customer Insight

The most common customer insight tools today are basic and primitive.  For example, the lead scoring inside your CRM is a customer insight feature. In a nutshell, lead scores are derived from a rule-based formula that assigns points to various customer activities as a scoring mechanism. However, such lead scoring method is often unreliable because 1) the formula is heavily subjective to the designer and 2) the process is over-simplified to accommodate for the variations in circumstances and customers/leads.

The current phase of innovation is heavily focused on developing reliable customer insights using data science and AI technology. Here are a few benefits of using AI:

  1. Scale: It can comb through a large number of customer data
  2. Speed: It can generate customer insights in real-time
  3. Objectivity: It makes data-driven decisions
  4. Continuous Improvement: It becomes better and better over time

Key Considerations for Customer Insight Platform

AI technology is likely to transform how sales and marketing interact with customers in the near future.  Here are a few considerations you should think through before you adopt any AI-driven customer insight platform:

  1. Your Goal: You want to be clear on what you wish to accomplish. AI models are generally designed for a specific purpose and can do one task very well.  You want to select the AI application that can carry out tasks matching your purposes.
  2. Data Availability: You need to have customer data to teach AI algorithms on how to behave. Built your technology stack to acquire the essential customer data needed to drive customer insight.  Ideally, you want to have data breadth (variety) to obtain the proper context and data depth (amount) to train the algorithms.
  3. Data Agnostic: Ideally, you want a versatile AI platform that can work with various data from various applications and sources.  Different data sources often have a different data structure and cannot be processed using one set of algorithms. This is important because you don’t want to change AI-platform every time you wish to add new AI capabilities.  For instance, Salesforce Einstein’s AI engine can’t process data stored inside your HubSpot account and therefore is limited in benefit.
  4. Resources: How much resources do you need to support such an application/platform? Do you need a team to maintain and retrieve reports from the application? Ideally, you want an application that can be tweaked and used by functional users without asking help from others.

Final Note

When customer intelligence is effectively implemented, it gives a rich insight into the customer experience and behavior. Small and early-stage ventures need to focus on establishing a system to collect, measure and integrate customer data into a centralized application for customer intelligence. Large and growth stage ventures need to focus on turning their customer intelligence into useful insights that can help them better serve their customers.