Editorial
The Gist
- Data storytelling powers decisions. CX leaders use stories to translate analytics into business action and CX improvement.
- AI accelerates insights. AI-generated data stories are coming to life with tools like ChatGPT’s ADA.
- Planning is essential. Great data stories combine context, visualization, and action in structured workflows CX leaders can lead.
The interest in crafting solid data storytelling has grown en vogue since analytics transforms raw customer experience metrics into actionable insights that drive business decisions. When done effectively, these narratives help marketing managers identify opportunities for improvement and innovation.
CX leaders are finding that crafting the best narratives from their data is becoming the primary way to keep their brand strategies and customer experiences moving forward. If you have to craft that narrative, you are in luck.
There are a few great tips you can use to plan your analysis so that your data, visualizations and messaging combine into a data story that your stakeholders can follow.
Table of Contents
- What Is a Data Story?
- Your Customer Data Story Begins
- Ask Questions That Connect Presented Data to Outcomes
- Identify Quantitative and Qualitative Data Based on Those Questions
- Focus on Customer Journey Touchpoints
- Set Up Takeaway Messages in the Visualization
- Make the Recommended Action Steps Clear for Your Data Story
What Is a Data Story?
It is an oversimplification to claim that increased analytics usage has led to an increased need for data stories; an essential part of analytics is describing trends and changes against planned KPIs to decision makers. You normally use data insights to tie the information together so that your decision makers can appreciate the information and take the next appropriate steps.
However, it is straightforward to note that those connections must be made faster these days to meet the scaling needs of business information.
The Role of AI in Modern Data Storytelling
Fast forward to today. The rise of AI capability has opened the door for automated insights from data. Advanced technologies rely on data to interpret an environment. Including AI into an analysis workflow, such as turning to ChatGPT’s ADA for data exploration, can handle more observations and provide creative solutions for many marketing endeavors, such as drawing insights from data for creating even better customer experiences.
The technological evolution has also created an urgent need among CX leaders to upskill. The CMSWire State of the CMO study indicated that creative thinking and problem-solving skills as essential professional skills.
Editor’s note: You can learn more about the State of the CMO study in this latest episode of The CMO Circle.
If you are a CX leader, you face a flood of martech choices for data aggregation, advanced analysis tech and more solutions to support creative problem-solving for a campaign. All of this means you should examine your analysis processes before turning to specific solutions. Doing so ensures an accurate understanding of how data, and the subsequent story crafted around the data insights, is applied within those processes.
Related Article: How to (Actually) Build a Customer Data Strategy
Your Customer Data Story Begins
So, where should CX leaders begin with crafting data stores? A solid starting point for compelling data stories can begin with the segments below. Each segment reveals progressive steps that demonstrate how to highlight processes and information that support customer experience opportunities.
Data Stories Workflows and Objectives
Here are five concepts for planning better accurate insights about your data.
| Workflow Steps | Objectives |
|---|---|
| Ask Questions That Connect Presented Data to Outcomes | Identify the key questions your data story needs to answer. |
| Identify Quantitative and Qualitative Data Based on Those Questions | Plan effective data stories that consider context and integrate both numbers and narrative. |
| Focus on Customer Journey Touchpoints | Spotlight data as it relates to customer touchpoints. |
| Set Up Takeaway Messages in the Visualization | Prioritize how your audience may receive a message when selecting visuals. |
| Make The Recommended Action Steps Clear | Ensure your data story leads to clear, actionable outcomes. |
Ask Questions That Connect Presented Data to Outcomes
Begin by identifying the key questions your data story needs to answer. The need stems from the outcomes being sought. What customer pain points need addressing? Where are the gaps in your current experience? What patterns suggest untapped opportunities?
The answer to many questions will not be immediate. In fact most answers may require steps to process a powerful answer.
However, all questions and associated steps are organized, creating a data store framework. The best data stories demonstrate clear connections between customer experience metrics and business performance. Show how addressing the answers to these questions also addresses specific experience gaps that could impact revenue, customer retention or brand loyalty.
By framing your investigation around specific experiences, you ensure your data story delivers actionable insights rather than just interesting statistics.
Identify Quantitative and Qualitative Data Based on Those Questions
Effective data stories integrate both numbers and narrative. That integration makes perfect sense when the data consists of quantitative metrics, such as NPS scores, conversion rates and product sales figures. These are expressed as numeric data, so importing the data into a model requires just highlighting the data in the venue selected, be it Google Looker Studio or in a data analysis model created with Python.
On the other hand, qualitative insights from customer feedback, surveys and support interactions are not straightforward. These often consist of text, a format that is interpretable in advanced statistical models used for machine learning and AI.
How to Prepare Data for Deeper Analysis
To address the interpretation bottleneck, qualitative data is usually transformed into a computational format. This is done through encoding methods, where a number is assigned to represent qualitative categories of data.
CX leaders can help data analysts establish what categories need to be encoded into an analysis. This is especially true for complicated methodologies like survey data. Many responses for a survey, for example, must be changed into numeric values if a machine learning model is planned in an analysis. This often occurs with sentiment analysis of customer responses to a given experience being surveyed. The transformation maps out survey categories against the analysis data used in a planned model.
No matter if you have quantitative or qualitative data, context for observations will be crucial in setting the conditions for the data. CX leaders should be able to describe details that influenced the observations in the dataset, such as when the data was collected, how outliers are interpreted, and response rate metrics that indicate the sample size relative to a given survey population.
Complementary Data Storytelling Skills
Key skills and tools that enhance your ability to craft impactful data stories from both qualitative and quantitative insights.
| Skill or Tool | What It Supports | Example Usage |
|---|---|---|
| Data Framing | Crafting questions that align with business objectives | Reframing NPS drops as retention signals |
| Contextual Enrichment | Adding situational awareness to raw data | Explaining sales dip due to seasonality or promotions |
| Sentiment Analysis | Extracting emotion and tone from customer feedback | Analyzing support tickets for frustration trends |
| Visualization Literacy | Translating data into stakeholder-friendly formats | Choosing bar charts vs. treemaps for comparisons |
| AI-Assisted Exploration | Scaling analysis with speed and pattern recognition | Using ChatGPT or ADA to surface anomalies in journeys |
Focus on Customer Journey Touchpoints
A discussion on dataset influences will spotlight data as it relates to customer touchpoints — what is the data measuring relative to customer engagement? Answering this question will reveal experience gaps and opportunities to figure out how to best map metrics to specific stages in the customer journey. Even if an answer is not immediate, you will at least pinpoint the kinds of analysis results that have the greatest potential for impact.
This journey-based approach transforms isolated data points into a coherent narrative about how customers interact with your brand over time.
Set Up Takeaway Messages in the Visualization
The toughest aspect of storytelling is to craft your data story within the presentation format of slides, reports and stand-alone apps. You need to prioritize how your audience may receive a message when selecting a visual.
Webinar
Oct
22
Beyond Storage: Smarter Content, Bigger Impact with DAM + AI
Discover how the DAM + AI duo makes content smarter, stronger and more accessible.
Register
Webinar
Nov
6
Fix the Content Bottleneck: Build a Better WebOps Strategy
Content stalled? Dev overloaded? You’re not the only one. Learn how streamlined WebOps bridges the publishing gap.
Register
Webinar
On demand
Agentic AI Playbook: Real-World Customer Service Use Cases You Can Deploy Now
Boost self-service by 30% and slash call volume by 63% with agentic AI.
Watch Now
Webinar
On demand
Call Spoofing Trends Financial Institutions Can't Afford to Ignore
Don't let robocalls sabotage your customer relationships. Learn how to secure your voice channel.
Watch Now
Webinar
On demand
CMS Briefing: A Live Look at What’s Next in AI-Driven Platforms
Learn how leading organizations are using AI‑driven tools to publish faster, personalize smarter and stay secure.
Watch Now
Webinar
On demand
Ready or Not: How Data-First Organizations Are Unlocking Agentforce Potential
Learn how to cut through the noise, activate Agentforce and build a Salesforce AI strategy that actually delivers.
Watch Now
You must craft thoughtful data visualizations that complement the message accessibility that your audience has. Choose visualization formats that highlight patterns, comparisons and trends most relevant to your overall data story. The stakeholders who see the visual must instantly recognize comparisons of data, like the delta between bars in a bar chart, or what categories of data make up a dataset, like the tiles in a treemap.
Remember that simple visualizations often communicate more effectively than complex ones: prioritize message clarity over deep terminology and detail sophistication. By focusing on what matters most to your audience, you increase the likelihood that your data story will drive meaningful change.
Make the Recommended Action Steps Clear for Your Data Story
Your data story should end with specific, actionable recommendations. What experience improvements should be prioritized? What metrics should be tracked to measure success?
These concrete next steps transform your data story from an informational presentation into a catalyst for customer experience innovation.
By following these data story principles, you can craft analyses that go beyond reporting metrics and guide your marketing colleagues to meaningful customer experience opportunities. The result is better communication of your data and insights, as well as better customer experiences and stronger business results.
Learn how you can join our contributor community.
