How can you measure the impact of your data story on different audiences?

Data stories are powerful tools to communicate insights, persuade stakeholders, and inspire action. But how do you know if your data story is effective and resonates with your audience? In this article, you will learn some methods and metrics to measure the impact of your data story on different audiences, and how to adapt your storytelling approach accordingly.

1 Know your audience

Before you craft your data story, you need to understand who your audience is, what their goals and challenges are, and what level of data literacy they have. This will help you tailor your message, tone, and visuals to suit their needs and expectations. You can use surveys, interviews, or personas to gather information about your audience and segment them into different groups based on their characteristics and interests.

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Dr Andrea Isoni

PhD,Chief AI Officer, AI speaker
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Even if it the same problem , different audiences care about different things. For example I worked a lot in AI for cybersecurity. All the techies wanted to hear is how the latest Generative Adversarial Network (GAN) algorithm architecture performed into a special anomaly detection case and how to improve its performances, tweak the parameters etc. All the stakeholders wanted to hear is the number of alarms per week, the number of solved/unresolved investigations performed of such alarms (false true positive ratios) and if the procedures were followed and registered correctly. The underline tech (GAN) may be the same but the wording is completely different depending on the audience.

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2 Define your objectives

Next, you need to define what you want to achieve with your data story, and how you will measure your success. Depending on your purpose, you may have different objectives, such as informing, educating, persuading, or engaging your audience. You can use SMART criteria (specific, measurable, achievable, relevant, and time-bound) to set clear and realistic goals for your data story, and align them with your audience's needs and expectations.

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3 Choose your metrics

Once you have your objectives, you need to choose the metrics that will help you track and evaluate the impact of your data story on your audience. Depending on the channel and format of your data story, there are different types of metrics you can use, such as web analytics, social media analytics, email analytics, or feedback forms. Reach is a common metric used to measure how many people saw your data story, while engagement looks at how people interacted with it. Conversion measures how many people took the desired action after seeing your data story, and satisfaction evaluates how satisfied or dissatisfied your audience was with it.

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Kaushik B.

Co-Founder, CTO & Chief Data Scientist | AI Solutions at Global Scale | US Patent | Ex-Intel | ML & AI Advisor
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It is perfectly fine to focus on metrics such as F1-score, Precision, Recall, AUC, Youden’s Index, Penalty Costs etc. while developing the model. However, your job as a data scientist does not end as soon as you have validated your model with amazing results. Model accuracy scores don’t excite the critical decision-makers in the business unless you translate those into metrics that make more business sense to them. One of the key reasons in cases of consumer analytics and marketing campaign effectiveness improvement solutions is the usage of incorrect metrics. This is where Gain Charts and Lift Charts act as very effective communication tools.

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4 Collect and analyze your data

After you have chosen your metrics, you need to collect and analyze the data that will show you the impact of your data story on your audience. You can use various tools and platforms to gather and visualize your data, such as Google Analytics, Facebook Insights, Mailchimp, or SurveyMonkey. You can also use descriptive and inferential statistics to summarize and interpret your data, such as mean, median, mode, standard deviation, correlation, or regression.

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5 Compare and contrast your results

Finally, you need to compare and contrast your results across different segments of your audience, and see how they differ in terms of their response and behavior. You can use charts, tables, or dashboards to display and compare your results, and look for patterns, trends, or outliers. You can also use hypothesis testing or ANOVA to determine if the differences are statistically significant or not.

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6 Adjust and improve your storytelling

Based on your analysis, you can identify the strengths and weaknesses of your data story, and see what worked well and what didn't for different audiences. You can use this feedback to adjust and improve your storytelling approach, and make it more relevant, engaging, and persuasive for your audience. You can also use A/B testing or experiments to try out different versions of your data story, and see which one performs better.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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LakshmiKanth Kanugovi

Founder
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Any data story must be created based on your target audience. Answering "What's in it for me?" to each target audience is crucial for creating an impact. The story's narration could be the same but the focus or emphasis must be changed for the respective target audience.

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How can you measure the impact of your data story on different audiences?

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3

4

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1 Know your audience

2 Define your objectives

3 Choose your metrics

4 Collect and analyze your data

5 Compare and contrast your results

6 Adjust and improve your storytelling

7 Here’s what else to consider

Data Science

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How can you measure the impact of your data story on different audiences?

1

2

3

4

5

6

7

1 Know your audience

2 Define your objectives

3 Choose your metrics

4 Collect and analyze your data

5 Compare and contrast your results

6 Adjust and improve your storytelling

7 Here’s what else to consider

Data Science

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