Gartner Conference Recap: Using Data to Map Your Customer Journey

At the recent Gartner Data & Analytics Summit, AI and machine learning got a lot of attention. While these topics are important for mapping your digital transformation long term, customer analytics are still the most relevant for today.  According to Gartner, customer analytics continues to rank highest in terms of technology investment for customer experience (CX) projects.  Further, they anticipate that by 2020, more than 40% of all data analytics projects will relate to an aspect of customer experience.

So what can you do now to start to understand the different elements of your customer experience?  Start to think about your customer journey, from initial inquiry to customer support. What makes your customers happy, loyal, and repeat purchasers. You need data to help answer these questions.

Moments of Truth

It’s important to understand your customer as much or more than your technology. You need to understand your typical buying cycle and customer buying behavior, and a customer journey map can help.  Per Gartner, there are "Moments of Truth" within activity streams that fluctuate as customers move from buying cycle to owning cycle, and back to buying.  These moments are key opportunities to make a strong, lasting impression.

Gartner describes these moments as three major customer activities that include Explore, Evaluate and Engage.  The graphic below shows how the cycle works, with moments of truth preceding the “off ramps” where customers could abandon your product or service if they have a bad experience.    

Source: Gartner

Source: Gartner

 

Explore and Evaluate

Customers want transparency and choice. And they want to research products and talk to experts to help them make informed decisions.  This is where trust is established, with the customer determining whether they are being manipulated or empowered, or inundated and confused.  Building customer segmentation data based on records of past purchases across your customer base can help match product and price that is appropriate for each customer. It’s not about manipulation, but helping them quickly and easily finding the right product or service at a price point that meets their expectations.  The more you know about them, the better you can help them make an informed choice. Using segmentation and customer archetypes helps jump start the process by presenting them with the appropriate products, at a price they are willing to pay, that returns acceptable margins to the business.

Engagement

In the Gartner model, engagement is the most prominent in the “Owning” cycle, where customers consume products and services, and develop impressions of the overall experience. As the graphic above shows, abandonment can be a result of a poor experience at moments of truth. Again, customer analytics can help you understand customer needs and behaviors post sales, as they consume or use your product or service.  Using past customer data, you can look for patterns of use and behaviors that may lead to abandonment.  For example, there may be a spike in support calls from a customer followed by a reduction in purchases. Or perhaps there are seasonal considerations for a certain customer segment that create inventory and staffing challenges that need to be anticipated. 

Source of Customer Data

So where might you find the data to better understand your customers at each leg of the journey?  Gartner suggests the following:

  • Direct feedback surveys such as relationship, transactional or special purpose survey.
  • Indirect feedback such as text, speech and interaction analytics for customer care.
  • Operational data from CRM systems, call center software and marketing analytics to infer customer perceptions.
  • Market research such as marketing department studies gathered to define and understand the target audience.
  • Qualitative research including focus groups, online research communities, and ethnographic research.

For most B2B firms, the operational data from CRM and transaction systems will likely be the best place to start, followed by direct and indirect feedback from surveys and call centers. These data already exist or can be easily obtained and could help accelerate your journey mapping exercise to identify areas of dissatisfaction or missed opportunities.  

Recommendations

In her session, Maximizing Value along the Customer Journey, analyst Melissa Davis offered up the follow recommendations:

  1. Identify high-priority customers – Identify the highest impact customer segments (likely your top 20% of customers that deliver 80% of your revenue and profitability).  This is your starting point for customer analytics.
  2. Identify high-priority moments— Identify places on the buying journey that disproportionately create or destroy customer loyalty and advocacy.  Look at conversion, abandonment and churn around these moments.
  3. Identify high-priority investments in customer analytics— Work with LOB leaders and IT to create an inventory of data analytics competencies and develop a road map of key data analytics projects.

While advanced analytics (AI, machine learning) may be on your road map for the future, make sure you are focused today on customer analytics that will create demonstrable value along the customer journey.