Introduction
- Explain what data storytelling is and why it is important for businesses of all sizes.
- Provide some statistics or examples to show how data storytelling can increase revenue, engagement, and decision-making.
- Preview the main points of the article: how to identify your audience’s knowledge gaps, how to choose the right data and visuals, how to craft a compelling narrative, and how to measure the impact of your data stories.
Data storytelling is the art and science of using data to communicate a message that informs, persuades, or inspires your audience. Data storytelling can help you achieve various business goals, such as increasing revenue, engagement, and decision-making.
According to a study by McKinsey, data-driven organizations are 23 times more likely to acquire customers, six times more likely to retain customers, and 19 times more likely to be profitable. Another study by Forbes Insights found that 74% of executives say that data storytelling is very important for their business.
In this article, you will learn how to use data to improve your brand’s storytelling. You will discover how to identify your audience’s knowledge gaps, how to choose the right data and visuals, how to craft a compelling narrative, and how to measure the impact of your data stories.
Identify Your Audience’s Knowledge Gaps
- Define what knowledge gaps are and how they affect your communication with your audience or organization.
- Explain how to close the knowledge gap by finding out what your audience wants to know and sharing it with them.
- Provide some tips or examples on how to avoid common mistakes, such as talking about what something is rather than why, or using irrelevant or misleading data and metrics.
A knowledge gap is the difference between what your audience knows and what they need to know to understand your message. If you do not close the knowledge gap, you risk losing your audience’s attention, trust, or action.
To close the knowledge gap, you need to find out what your audience wants to know and share it with them. You can do this by conducting surveys, interviews, or focus groups, or by using data analytics tools, such as Google Analytics, to understand your audience’s behavior, preferences, and needs.
Some common mistakes to avoid when closing the knowledge gap are:
- Talking about what something is rather than why it matters. For example, instead of saying “Our product has 10 features”, say “Our product can help you save time, money, and hassle”.
- Using irrelevant or misleading data and metrics. For example, instead of saying “We have 1 million followers on social media”, say “We have 1 million loyal fans who engage with our content and recommend us to their friends”.
- Assuming that your audience has the same level of expertise or interest as you. For example, instead of using technical jargon or acronyms, use simple and clear language that your audience can understand.
Choose the Right Data and Visuals
- Explain how to select the most relevant and accurate data for your story, based on your audience, your goal, and your message.
- Explain how to use data visualizations to communicate your data in an easy-to-understand and engaging way.
- Provide some tips or examples on how to choose the best type of chart, graph, or infographic for your data, and how to design them effectively.
Once you have identified your audience’s knowledge gaps, you need to select the most relevant and accurate data for your story. The data you choose should be based on your audience, your goal, and your message.
Your audience is the people who will consume your data story. You need to consider their demographics, psychographics, and expectations. For example, if your audience is young and tech-savvy, you can use more interactive and dynamic data visualizations, such as animations or dashboards.
Your goal is the action or outcome you want your audience to take or achieve after consuming your data story. You need to consider the type and level of your goal, such as awareness, education, persuasion, or inspiration. For example, if your goal is to persuade your audience to buy your product, you can use data that shows the benefits, features, and testimonials of your product.
Your message is the main point or takeaway you want your audience to remember from your data story. You need to consider the clarity and relevance of your message, and how it aligns with your audience’s needs and values. For example, if your message is to show how your product can improve your audience’s lives, you can use data that shows the before and after scenarios of using your product.
After selecting the right data for your story, you need to use data visualizations to communicate your data in an easy-to-understand and engaging way. Data visualizations are graphical representations of data, such as charts, graphs, or infographics.
Some tips to choose the best type of data visualization for your data are:
- Use bar charts to compare categorical data, such as sales by region or product.
- Use line charts to show trends or changes over time, such as revenue growth or customer satisfaction.
- Use pie charts to show proportions or percentages, such as market share or customer segments.
- Use scatter plots to show correlations or relationships between two variables, such as price and demand or age and income.
- Use maps to show geographic data, such as population density or climate change.
- Use icons, images, or symbols to show qualitative data, such as ratings, emotions, or opinions.
Some tips to design effective data visualizations are:
- Use colors, shapes, and sizes to highlight the most important or interesting data points, such as outliers, anomalies, or patterns.
- Use labels, legends, and titles to explain the data and provide context, such as units, sources, or definitions.
- Use axes, scales, and grids to show the range and distribution of the data, such as minimum, maximum, average, or median.
- Use white space, alignment, and contrast to create a clear and balanced layout, and to avoid clutter or confusion.
Craft a Compelling Narrative
- Explain how to create a narrative that gives more context and meaning to your data, and that resonates with your audience’s emotions and values.
- Explain how to use storytelling techniques, such as hook, conflict, resolution, and call to action, to structure your data story and keep your audience hooked until the end.
- Provide some tips or examples on how to use language, tone, and style to enhance your data story and make it memorable.
After choosing the right data and visuals for your story, you need to create a narrative that gives more context and meaning to your data, and that resonates with your audience’s emotions and values. A narrative is a sequence of events or actions that form a coherent and logical story.
Some tips to create a narrative for your data story are:
- Use storytelling techniques, such as hook, conflict, resolution, and call to action, to structure your data story and keep your audience hooked until the end.
- A hook is an opening statement or question that captures your audience’s attention and curiosity, such as a surprising fact, a provocative quote, or a personal anecdote.
- A conflict is a problem or challenge that your audience faces or can relate to, such as a pain point, a gap, or a dilemma.
- A resolution is a solution or outcome that your data provides or supports, such as a benefit, a feature, or a testimonial.
- A call to action is a request or suggestion that your audience should do or think about after consuming your data story, such as a purchase, a subscription, or feedback.
- Use language, tone, and style to enhance your data story and make it memorable.
- Language is the words and phrases you use to convey your data and message, such as nouns, verbs, adjectives, or adverbs.
- Tone is the attitude or emotion you express through your language, such as formal, informal, positive, negative, or humorous.
- Style is the way you arrange and present your language, such as short or long sentences, active or passive voice, or rhetorical devices, such as metaphors, similes, or analogies.
Measure the Impact of Your Data Stories
- Explain how to evaluate the effectiveness and impact of your data stories, based on your goal and your audience’s feedback.
- Explain how to use data analytics tools, such as Google Analytics, to track and measure the performance of your data stories, such as views, shares, comments, conversions, etc.
- Provide some tips or examples on how to use the insights from your data stories to improve your future data storytelling efforts and achieve your desired outcomes.
After crafting a compelling narrative for your data story, you need to evaluate the effectiveness and impact of your data story, based on your goal and your audience’s feedback. You need to measure how well your data story achieved your desired outcomes, such as views, shares, comments, conversions, etc.
Some tips to measure the impact of your data stories are:
- Use data analytics tools, such as Google Analytics, to track and measure the performance of your data stories, such as views, shares, comments, conversions, etc.
- Views are the number of times your data story was seen by your audience, such as page views, impressions, or reach.
- Shares are the number of times your data story was distributed by your audience, such as likes, tweets, or emails.
- Comments are the number of times your data story was discussed by your audience, such as reviews, ratings, or feedback.
- Conversions are the number of times your data story led to a desired action or outcome by your audience, such as purchases, subscriptions, or downloads.
- Use the insights from your data stories to improve your future data storytelling efforts and achieve your desired outcomes, such as revenue, engagement, or decision-making.
- Revenue is the amount of money your data story generated or contributed to your business, such as sales, profits, or ROI.
- Engagement is the degree of involvement or interaction your data story created or maintained with your audience, such as loyalty, retention, or advocacy.
- Decision-making is the quality or speed of the decisions your data story influenced or supported for your audience, such as awareness, education, persuasion, or inspiration.
Conclusion
- Summarize the main points of the article and restate the benefits of data storytelling for your business.
- Provide some resources or recommendations for further learning or action on data storytelling.
- Thank your audience for reading and invite them to share their thoughts or questions on your data stories.
Data storytelling is a powerful way to use data to improve your brand’s storytelling. By following the steps in this article, you can identify your audience’s knowledge gaps, choose the right data and visuals, craft a compelling narrative, and measure the impact of your data stories.
If you want to learn more about data storytelling, you can check out these resources:
- Data Storytelling: The Essential Data Science Skill Everyone Needs
- The Power of Data Storytelling
- Data Storytelling: What It Is, Why It Matters, and How to Do It Right
- How to Tell Great Stories with Data
- Data Storytelling: A Practical Guide
