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The first in a series, this article examines how Customer Success organizations can develop an analytics framework to help drive customer engagement and improve customer experience. Analytics is a competitive advantage that differentiates market leaders from average performers across industries. Investing in a sound data and analytics framework is a fundamental step towards not only achieving competitive differentiation but also improving the overall customer experience.

If you are an individual or leader who is looking to leverage data in your organization to improve customer experience and/or drive engagement, this is for you.

CONTEXTUALIZING THE PROBLEM

To put the problem into context, we have all experienced the desire for data in our professional roles. In every area of a company, data is central and insights are in high demand. At the same time, there is an apparent disparity in the level of effort and funding allocated to data and analytics in general compared with investments in established functions such as sales, marketing, and engineering. 

Taking a look at Customer Success as an example organization – every Customer Success professional would be delighted to truly understand how deeply their customers are engaged with their products and services to continue to deliver value and often mitigate risk. Engagement metrics are a foundation upon which you can build practices and services that help your teams become true partners with your customers. When you not only understand how your customers use your products but also understand the value they bring to the customer organization, you reach a higher level of engagement with customers and truly become a trusted advisor.

The problem statement in essence is: do Customer Success professionals have access to regular, reliable data that they can query and use in decision-making to improve the customer experience and become trusted advisors? In the context of an analytics framework, the question is whether your company’s framework supports this level of data access for Customer Success professionals and other customer-facing roles. 

WHAT ANALYTICS ARE MEANINGFUL IN CUSTOMER SUCCESS?

To understand the need for data in Customer Success organizations better, you first need to understand: what data and analytics do Customer Success professionals need to serve their customers effectively? (Please add your perspective in the comments, if you like.

In broad categories, Customer Success organizations and professionals are perhaps looking for data of the following types to drive better customer engagement (there may be many more, company-specific categories to add here):

  1. Contract and account terms
  2. Product usage metrics and analytics
  3. License utilization
  4. Customer health indicators and scores
  5. Customer sentiment 
  6. Contact records   
  7. Account team engagement with customer contacts 
  8. Changes to key contact records 

… and so forth. 

(These are basic data sets to start with; analytically mature companies may have access to many more data sets that they leverage to inform their internal and customer-facing analytics.)

With the availability of these basic and perhaps more complex data sets, from an insights and analytics perspective, Customer Success practitioners would ideally also like to:  

  1. Leverage all available datasets to create powerful dashboards both for internal use as well as sharing with customers  
  2. Derive new insights from automated triggers to drive Customer Success actions
  3. Power advanced customer analytics and perhaps monetize them 
  4. Marry behavioral and contextual data to create advanced analytics

…. and so forth.

In addition to Customer Success, other teams such as sales development, marketing, and support also need a firm grasp of customer data to deliver a personalized set of experiences.

HOW MIGHT WE EVALUATE AN ORGANIZATION’S ANALYTICS MATURITY? 

We can balance these basic analytics requirements against an organization’s ability to deliver them and start building an organizational maturity curve. In its simplest representation, a maturity model may offer the following levels into which your company’s efforts may fit: 

Entrants (Level 1):

  • Customer records are available in different systems where they can be queried (for example, CRM and Helpdesk data sets)  

Practitioners (Level 2):

  • Customer data can be combined from available data sources into a single system such that normalized customer records can be accessed and queried programmatically (for example, combining the data sets in a data warehouse or some similar service)
  • Composite records are leveraged to influence customer engagement 

Competents (Level 3): 

  • Aggregated customer data is mapped against the customer journey and the lifecycle stages (for example, in a business intelligence system)  
  • Customer analytics are available at scale 
  • Ability to programmatically identify next actions for Customer Success professionals based on the combined customer data records (business intelligence systems combined with workflow automation) 

Sophisticates (Level 4):

  • Customer experience is personalized based on data-driven customer personas  
  • Customer data sets compared against a benchmark to assess and drive towards ideal performance 
  • Customized customer-facing analytics available as an offering; potentially monetized 
  • Customer access to data and analytics available via API 

This is a basic framework and may have more nuanced stages of development. The central point remains that it is certainly a journey to progress through these maturity levels, to achieve data democracy and data-driven customer management. It is also worth noting that most companies operate at Level 1 or 2, and it takes a focused, protracted effort to achieve competency in these areas. A climb up this maturity curve, however rapid or arduous, does start with building a fundamental infrastructure upon which future growth can be positioned.  

WHAT ARE SOME FUNDAMENTAL STEPS TO BUILD AN ANALYTICS FRAMEWORK? 

Some of the fundamental steps and requirements for starting on this journey, at least in my experience, are as follows: (please add your thoughts and comments)   

  1. Build a starting vision and a strategic plan for a single source of truth to overcome data silos
    • Identify all your relevant data sources; select the critical ones your company and users need to leverage 
    • Develop a vision to combine these data sources into one single source of truth
    • Relentlessly manage towards refining this source of truth  
  2. Invest in a team to build a data and analytics infrastructure. Some of the roles in this team would span data engineering, business intelligence analysts, and program management to guide the vision forward. This team guides the realization of the single-source-of-truth vision
  3. Standardize data 
    • Without data standardization, organization data remains messy, which impacts reliability and confidence and prevents the creation of a unified customer experience
    • Invest in a Customer Data Platform (CDP) to create data governance, trust, and efficiency
      1. Provide a foundational customer data platform that allows the company to collect first-party data from sources like their website, mobile apps, customer interactions and enable other integrated systems to access the data via data warehouse connections 
      2. Without this investment, the view of a customer will remain fragmented  
  4. Democratize access to data and analytics for your colleagues and leaders, driven by your analytics team 
    • Let the analytics consumers help drive further refinement and investment in the framework by articulating their requirements and bring the voice of the customer to bear on the development 
    • Measure the impact of analytics offerings on your key metrics  

In the next article, we will look at analytics frameworks and competency in more detail. 

IN SUMMARY 

To summarize: there is a competitive advantage to be derived from customer analytics, both for internal efficiency as well as improving customer engagement and experience. The path to achieving data-driven customer management lies in architecting a solid foundation of data governance and technical infrastructure that can serve as a platform for analytics development. 

As companies move along a data maturity curve, they are increasingly empowered by the data to develop a holistic view of their customer along every step of the journey. As a consequence, Customer Success professionals, as one group, can take suitable, measured actions in anticipation or reaction to the customer journey, and help drive towards optimal outcomes. While this may seem like the ultimate goal for customer-facing teams, a path towards realizing it does indeed start with taking fundamental, architectural steps towards this goal.