Have you ever considered how weather might influence a company’s performance? To what extent can it make an impact, and how? Perhaps you’re aware that weather affects KPIs, but not in enough detail to explain its influence. This is often seen as too general or obvious to be actionable in the commercial sector and may be overlooked by decision-makers unless a thorough analysis uncovers valuable insights.
This is where Datama stands out as a powerful data analytics tool, capable of measuring the impact of one or more seemingly “unrelated” dimensions on key KPIs—insights that may go unnoticed by human intuition alone. Below is a case study showcasing how Datama helped reveal the mix effect of temperature on sales KPIs, demonstrating the potential depth of understanding it can deliver.
How much do you know about the mix effect?
To understand the mix effect, we should start with the performance effect.
The performance effect refers to changes in total revenue, profit, or other key performance indicators (KPIs) due to changes in individual performance factors, like selling more units (higher volume), increasing prices or improving efficiency (cost reductions). If one of these factors evolves, the performance would change. This is often easily observed since increasing or decreasing independent factors are visible enough. Here’s an example to illustrate the performance effect:

On the same basis of sales units, the increasing prices of products lead to increasing sales revenue.
Different from performance effect, the mix effect refers to the impact of changes in the composition (or mix) of products, customers, sales channels, or other factors that contribute to total performance. This effect often hides behind the fast evolution of composition of factors, difficult to be discovered. For example:

The total revenue increased from €20,000 to €20,500, but the mix (composition of sales units) changed—more sales came from the lower-priced Product B. Thus, even though total revenue rose slightly, profitability (average price) decreased from €67 to €62 because of selling more low-price products. This is a mix effect. These indirect effects play an important role in business decisions while not everyone can detect them with any digital tools.
You can read more about performance and mix effect by reading our previous article .
Let’s deep dive into our real case study below.
Context : A client influenced by weather
Our client, a company specializing in the manufacturing and sale of winter clothes, operates an e-commerce platform that is significantly affected by weather conditions. In September 2024, the client observed a notable overperformance in revenue compared to the same period in 2023. The reasons for this exceptional growth remained unclear and required further investigation.
To uncover the factors behind this remarkable overperformance in September 2024, the client partnered with Datama’s data analysis solution which examines key performance drivers such as sessions and revenue by using data from September 2023 and 2024 while incorporating a critical external factor: daily maximum temperatures.
First step: Data structure and trend analysis
Before going further, the general co-relation between the temperature and sales revenue has been examined. With a sample extracted dataset including Date, max temperature per day, number of sessions and Revenue, a negative proportional trend has clearly displayed as below:


According to the chart, the higher the max daily temperature is, the lower sessions and sales are during the September 2023, which indicates that the fresh days contribute more to the business performance.
Second step : Analyse the mix effect of temperature in Datama Compare
Always within the same sample structure dataset, Datama is able to automatically show the mix effect of the evolution of temperature on average visits of clients on this e-commerce platform.
More precisely, in Datama Prep, the data was uploaded, and a light processing step was performed using the Clean block to bucket the temperatures. This step grouped various temperature values into intervals, making them more representative than individual temperature figures.

Next, the Datama Compare block automated the waterfall visualization by automatically splitting September 2023 and September 2024 as the comparison periods. The difference in total revenue between these two months was immediately displayed as follows:

With the help of this immediately shown waterfall, we can easily see that 100% of the increase of Sessions per day is driven by a mix effect on Temperature, because we had more “cold” days ([14-20°]) on which the traffic is generally higher and less “hot” days ([28-34]).
By comparing data from 2023 and 2024, Datama identified not only a general trend, but also a mix effect, to see how exactly the lower temperature influences an increase in activity on the client’s per day, which temperature range contributes the best and the weight of contribution.

Here’s another chart available in Datama showing how different temperature ranges influence the increase in sessions per day. The wider the arrow spreads horizontally, the greater its mix effect on the final results. The purple one, which spreads the widest horizontally, specifically represents the range from 14°C to 20°C showing that in 2024, 60% of the days was on this bucket of temperature compare to around 13% in 2023. This increase of cold days was made especially at the expense of hot days (orange arrow)
Results: Insights for better decisions
Datama’s Compare platform enabled rapid data visualization and clear explanations of the weather’s effect through intuitive graphs. Leveraging these insights, the Client was able to:
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- Identify the direct and indirect impact of weather conditions on its business performance.
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- Understand how temperatures influence purchasing behavior and the average number of sessions gained by degree of temperature.
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- Assess the importance of incorporating external data, such as weather, into their strategic analyses such as optimizing marketing campaigns, based on weather forecasts.
Conclusion: Go beyond initial insights with Datama solution
This use case underscores the importance of going beyond initial analysis and demonstrates the power of Datama’s solution in uncovering mix effects and indirect impacts on key performance metrics. It highlights the value of integrating external factors, such as weather, into performance analysis for businesses where these elements play a crucial role.
By quantifying how each temperature variation affects revenue, companies can gain deeper insights into external influences on their KPIs. Understanding not just what drives performance but also to what extent allows businesses to better anticipate and adapt to market fluctuations. With Compare, Datama continues to prove that data analysis is essential for generating actionable insights, enhancing strategic decision-making, and making external factors more tangible in business performance evaluation.