Using Data Analysis to Choose the Right Advertising Strategy

This case study demonstrates how the six phases of the data analysis process—Ask, Prepare, Process, Analyze, Share, and Act—are applied to solve a real-world business problem.

The business in this case is Anywhere Gaming Repair, a small company that provides on-site repair services for video game systems and accessories. The owner wanted to grow the business and knew that advertising could help attract more customers. However, he faced a common challenge: there were many possible advertising options (print ads, billboards, TV commercials, public transportation ads, podcasts, radio, etc.), and he did not know which one would be the most effective. In addition, the company had a limited advertising budget.

To solve this problem, the owner asked a data analyst named Maria to make a data-driven recommendation.

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1. Ask Phase: Defining the Real Problem

Maria began with the Ask phase by clearly defining the problem. Instead of focusing only on the symptom (“we need to advertise”), she stepped back and examined the broader context.

Through discussions with key stakeholders—including the business owner, the vice president of communications, and the director of marketing and finance—Maria identified the true problem:

The company did not know which type of advertising its target audience preferred.

This step highlights the importance of collaborating with stakeholders and making sure the analysis focuses on the correct business question rather than superficial symptoms.

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2. Prepare Phase: Collecting the Right Data

In the Prepare phase, Maria gathered data needed for the analysis. Before collecting data, she first clarified who the company’s target audience was: people who own video game systems.

She then collected:

• Data related to video game ownership and purchasing behavior
• Data on different advertising methods and how popular they are with various demographic groups

The goal of this phase was to ensure that the data collected would directly support the business decision that needed to be made.

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3. Process Phase: Cleaning and Organizing the Data

Next, Maria moved to the Process phase, where she cleaned and prepared the data for analysis. This included:

• Removing errors and inaccuracies
• Transforming the data into a usable format
• Ensuring completeness of the information
• Handling outliers that could distort results

Cleaning the data was essential to ensure that the conclusions drawn later would be reliable and accurate.

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4. Analyze Phase: Answering Key Business Questions

In the Analyze phase, Maria focused on answering two key questions:

Question 1: Who is most likely to own a video gaming system?

Her analysis showed that people between the ages of 18 and 34 are the most likely to make video game–related purchases. This confirmed that the company’s target audience should be individuals aged 18–34.

Question 2: Where is this group most likely to see advertising?

Maria then analyzed advertising preferences for this age group and found that TV commercials and podcasts were especially popular among people aged 18–34.

This step transformed raw data into actionable insights that could guide decision-making.

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5. Share Phase: Communicating Insights Clearly

In the Share phase, Maria presented her findings to the stakeholders. She summarized the results using clear, easy-to-understand visuals and explanations.

The goal was not just to show the analysis, but to help stakeholders clearly understand:

• Who the target audience is
• Which advertising channels are most effective
• Why one option makes more sense than others given budget constraints

Effective communication ensured that stakeholders could confidently make a data-driven decision.

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6. Act Phase: Taking Action and Measuring Results

Based on the analysis, Maria made a final recommendation. Although TV commercials were popular with the target audience, they were expensive. Because Anywhere Gaming Repair had a limited budget, Maria recommended podcast advertising as a more cost-effective option.

The company acted on this recommendation by working with a local podcast production agency to create a 30-second advertisement. The ad ran on podcasts for one month.

The results were measurable and positive:

• The company saw an increase in customers within the first week
• By the end of four weeks, the business had gained 85 new customers

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Key Takeaways

This case study shows that the data analysis process is not just theoretical—it is a practical framework for solving real business problems. Each phase plays a critical role:

• Ask ensures the right problem is being solved
• Prepare ensures the right data is collected
• Process ensures data quality
• Analyze turns data into insights
• Share enables informed decision-making
• Act turns insights into measurable results

Overall, this example demonstrates how effective problem-solving using data can lead to clear decisions and real-world business impact.