How Analysts Can Create Sales Stories with BI
Situation: there is a report on your desk consisting of many tables and charts. You need to make an important decision based on it, but there is a catastrophic lack of time. Or another scenario: you have conducted a large-scale study, collected data, analyzed it, and now you need to effectively convey the results to the customer or decision maker. How to do this without turning the presentation into a dull set of numbers and charts?
And how?
My name is Sergey Zayakin, I am a senior marketing analyst at Optimakros, a developer of the eponymous software for integrated planning, budgeting and business analytics. I have been doing analytics for 10 years, and in this article I will tell you how to convey complex data in the form of fascinating and understandable stories for everyone. The solution is analytical storytelling.
Analytical storytelling is a tool that helps an analyst convey complex information to an audience in an accessible language, visualizing data and thereby influencing decision making. Using this technique increases the value of data and your findings, helps to adapt complex information to the audience and highlight key points. After all, more human and “friendly” data will always be perceived better, and a set of dry numbers without effective visualization increases the risk that the audience will get lost in this data.
I do not overestimate the importance of visualization and storytelling. According to the results of a survey conducted by the company Accenture34% of CFOs noted storytelling as one of the skills that is being actively implemented in the financial sector. This indicates a growing demand for specialists who can not only analyze data, but also effectively present the results of the analysis.
How Jon Snow Stopped a Cholera Epidemic with Visualization
Let's start with the origins. Information visualization has an ancient history, dating back to prehistoric times. The first examples are cave paintings, with the help of which people tried to record the world around them.
More familiar historical examples are geographical maps and anatomical atlases.
One of the earliest examples of modern visualization is Joseph Priestley's 1765 timeline, where he marked the years of life of biblical characters. This graph visually resembles the Gantt chart we are familiar with.
My favorite example is the cholera map drawn up by John Snow (not the one you're thinking of) in 1854 during the London epidemic.
Snow came up with something brilliant: he mapped the houses where people were sick with cholera and marked the water pumps that the residents of these houses used. A little analysis, and Snow concluded that the source of the disease was not dirt or air, but water. With this map, he came to a meeting of the board of trustees and demanded that the infected pumps be turned off. Thanks to this visualization, the authorities decided to close the pumps, which led to a decline in the epidemic.
Another great historical example is a map of Napoleon's invasion of Russia in 1812. It shows the flow of troops as they advance and retreat, and a temperature graph. This visualization clearly shows the relationship between geography, army size, and weather conditions.
The first line charts and pie or bar charts also appeared a long time ago. In 1786, William Playfair created the Commercial and Political Atlas with the types of visualizations we use today.
For a long time, creating visualizations was an art that required manual labor. But in the 20th century, there was a transition from ruler and pencil to computer technology. This made it possible to create visualizations quickly and easily.
Let's move on to visualization
Fast forward to today. Analytical storytelling begins after the data analysis is complete and the main conclusions have been formulated. To effectively present them, visualizations are needed, based on which the analyst prepares a story, supporting his theses with data.
All visualizations can be divided into two types:
Research – are used by the analyst to find answers to questions during the analysis process. Here are some possible questions:
How does sales revenue change over time?
When did the change in revenue occur?
Why did revenue change during this period?
What do these results mean for the company?
Informative (explanatory) – used to illustrate facts, points and convey a story to the audience.
Example: When analyzing the effectiveness of a marketing campaign, an analyst might use exploratory visualizations: scatter plots or heat maps that help identify correlations between different factors. And when presenting results to management, informative visualizations are suitable – simple bar charts showing conversion growth in different audience segments, or a line graph demonstrating sales dynamics before and after the campaign.
Making history
When choosing visual elements, the analyst must consider the context: Who is the audience? What message needs to be conveyed? What actions do you want the audience to take?
Creating a story begins with preparing the main part. In it, you describe your hypotheses, the data used and their sources, the course of work and the insights obtained. Then, based on the data obtained, you form conclusions and recommendations, the basis for making a decision – they complete the story. The introductory part is prepared last: it outlines the topic, purpose and objectives of the analysis, its relevance to the audience, and brief conclusions.
Data Visualization Tools
The choice of visualization tool depends on:
Analyst tasks and customer requests
Available resources and time
Analyst skills
Main types of tools:
1. Spreadsheet editors (Excel, Numbers, Google Sheets)
Pros: publicly available, low entry threshold, wide possibilities for data processing
Cons: platform dependent, limited number of available visualizations
2. BI tools (Tableau, Power BI, Yandex DataLens, etc.)
Pros: extensive capabilities for working with data and visualization, interactivity
Cons: medium entry threshold, may require subscription, installation and setup required
3. Programming languages and libraries (Python, R, JavaScript)
Pros: extensive capabilities for working with data and visualization
Cons: high entry threshold, installation and configuration required
4. Online Diagramming Tools (Datawrapper, Flourish)
Pros: publicly available, low entry threshold, wide visualization capabilities
Cons: registration/subscription required, limitations of free versions
Visualization helps not only to present data, but also to breathe life into it, making it understandable and relevant to the audience. Analytical storytelling is a bridge between dry numbers and real business decisions. The ability to visualize data, tell stories based on them, turning them from dead weight into information that allows you to make decisions is one of the key skills of a modern analyst.
