Project Scope

I wanted to create a one-time ad-hoc report using Google Data Studio. Every hypothetical situation is for the sake of the project as I do not work for Adidas, therefore I cannot provide the perfect context. So I just assumed I was working there. I hope that this will help to add to your analytical thinking. Quality assurance and data cleaning were done using Python.

Situation

I've been hired as a Bi Analyst for Adidas Group, a company that designs, manufactures, and markets athletic and sports lifestyle products. The company's product portfolio includes footwear, apparel, and accessories such as bags, sunglasses, fitness equipment, and balls.

The Assignment

I am interested in helping the company identify problems and trends when it comes to revenue. To identify which products are driving the greatest ROI and which stores are leading to an increase or decrease in revenue. My task is to quality assure the raw data to identify any data quality issues that could potentially skew my analysis.

The Objective

1. Review the data to identify trends and patterns
2. Suggest a potential solution for my findings

The business aspect

After identifying what would be the high-level goals for this dataset, I narrowed it down to revenue and profit. This is basically the overall goal of the business.

I tried answering business questions like; What products have the highest ROI in California? What are the customers shopping most? what sales method and which retailer has the highest ROI?

This would help the stakeholders identify the problems and trends, and expand the shops that have the highest ROI. The report would also help the marketing team to target their market for the products with the highest ROI.

Who is my audience

I wanted my audience to be the CEO or COO of the company, hence why I focused more on revenue. However, a marketing head would also find the dashboard useful though not entirely. They could use the top products, and best sales method to generate marketing campaigns.

I figured a CEO would mostly be interested in the numbers.

KPIs

My main KPIs were revenue, profit margin, region with the highest ROI, and profits going up or down.

THE AD-HOC REPORT

Recommendations and next steps

As I mentioned I do not have the proper context but I would recommend reviewing the marketing strategy they used in August vs. September. (This is where Google Analytics would come in handy)

This would help to pinpoint the specific reason for the sales drop in the month of September. Examining recent marketing campaigns, advertising channels, and promotional activities. Assessing whether there were any changes that might have impacted customer engagement negatively online.

I would also look further into other aspects like seasonality, economic conditions, or product-related issues. If it is a seasonality issue we can plan better for the next.

I would also look at staff change, and absenteeism at the physical stores to establish whether there have been changes that could be affecting the sales. Surveys for customer feedback would also be a great place to check. This would call for a lot of collaboration with the different teams.

For now, I have hit a dead end as I lack a proper context, but I hope that sheds light on how you can identify the root cause of a sale drop and then give data-driven recommendations.