Customer Experience MythBusters: Data Is Scary

November 19, 2020
Customer Experience
By Lindsey Ryan Contributors: Monique Braudo, Emily Davis, Julia Smadja

A federal client of mine recently said to me, “I understand the benefits of Customer Experience and what it can provide our agency, but I don’t know where to begin on collecting the kind of data needed to understand what our customers want.” She’s not alone. I’ve been hearing about this challenge recently from many leaders across the federal, state, local, and private sectors. Here are three ways leaders can begin to engage with data and help make data feel less scary:


Become Data Literate: Understanding What Data Is. 

Data is just information and it exists in a variety of sizes and formats. There are two kinds of data:  quantitative and qualitative  Quantitative data sets are numerical and ask: How many? How often? For example, what is the amount of time a customer spends on a customer service call, or what is the customer satisfaction rating? Qualitative data sets are categorical and ask: What type? For example, what is the type of task customers want to accomplish on a website, or what were customer’s comments on the services they received?. 

As a leader, becoming “data-literate” also means creating and encouraging a culture of data literacy within your organization. Helping your teams to understand what data encourages a culture that sparks the exchange of data among your employees. Actively sharing success stories around data and establishing communities of practice and conversations around data empowers your organization to capitalize on and use data more comfortably. You can also increase data literacy by exploring LinkedIn Learning and General Assembly for courses on data and data analysis to understand the basic language of data and how best to manage it. 


Leverage the Data Analytics Lifecycle: Understanding What to Do With Data.

Once you understand more about what data is, you can begin to use it as a tool to inform decision-making and to validate assumptions about your customers. Consider using the following Data Analysis Lifecycle as a way to get you there:


Step 1: Framing – Formulate the question/problem/purpose and key outcomes. 

  • Ex. Purpose: Improving customer experience.
  • Ex. Outcomes: Which customer types are rating our services the lowest/highest? Which service types are getting rated the lowest/highest? Why?

Step 2: Gathering – Identify and access data sources. Examples:  

  • In Service: Call centers, customer service centers  
  • In Transactions: E-commerce, customer relationship management (CRM)
  • In Engagement: Chat functions, social media, email marketing, website visits, customer calls, in-person customer visits 
  • In Management: Voice of the customer programs, business analytics, business intelligence, key performance indicators, objectives, and key results  

Note: The key here is to start with what you do have, leveraging any data you have available to you.

Step 3: Understanding – Review the available data related to the question or purpose and become familiar with how each variable relates to your desired outcomes.

  • Ex. How does our data on customer satisfaction scores relate to the types of services receiving the highest/lowest ratings?

Step 4: Cleaning & Transforming – Prepare the data as needed, look for errors in the data, and create any additional variables. You want to be making decisions based on accurate data.

Step 5: Analyzing – Align analysis with the desired objective: 

    • Descriptive: What is happening? 
      1. Examine and familiarize yourself with historical data.
      2. Identify patterns, trends, and relationships.
      3. Attempt to understand variables related to events and/or behaviors.
    • Predictive: What could happen? 
      1. Identify future probabilities and trends.
    • Prescriptive: What should we do? 
      1. Identify the best outcome of events given existing data. 
      2. Guide decision making to take advantage of future opportunities or mitigate future risk.

Step 6: Visualizing & Interpreting – Find meaning from your result. 

  • Ex. Create a visual analysis to understand what the data is telling you and ask, “Does my outcome align with the questions I was seeking to answer?”

Step 7: Applying – Create solutions from your results.

    • Regroup the analysis in a thoughtful and digestible way.
    • Summarize insights from the analysis.
    • Strategize on presenting findings and providing options on recommendations for the next steps.

Step 8: Reporting – Present conclusions and recommendations to your intended audience.

    • Communicate findings to key stakeholders. 
    • Use the data to help tell a story.
    • Add visuals to help illustrate the story in a more user-friendly and meaningful way.

Invest in Data Resources: Understanding Who Can Support You.

Within the Data Analysis Lifecycle, 80% of the work is involved in Gathering, Understanding, and Cleaning the data. Therefore, it is critical to invest in people and processes to help you with this heavy-lift: 

  • Trust experts to handle this piece of the lifecycle by hiring data scientists and data analysts who can work alongside you to find and make sense of the data you’re analyzing.
  • Consider investing in a data management platform to store and filter the various formats, sizes, and sources of your data all in one place. 
  • Make it easy for your customers to provide you the feedback and data you desire, as the data you have is only as good as the data you collect. For example, you might try developing quick, pulse surveys to customers following specific interactions with your organization. Customers are more likely to provide feedback and you get access to real-time, customer information.

The first thing I told my client was, bottom-line: start with the data you have. Once you understand more about what data is, you might have more than you think. The data lifecycle can then help you make sense of even the small amount of data you have available. Investing in the right resources and tools can set you up for better collection and management of data in the future.   

Do you have any follow up questions on data or how to leverage it in your organization? Feel free to reach out to me directly at

Interested in the Data Analytics lifecycle? You can download our data analytics lifecycle one-pager here to keep as a reference for the future!