Intro
To recognize the patterns of successful changes to your product performance, consider the topic in the context of data querying. Data querying is like asking questions about sets of data. You use specific commands to retrieve, filter, sort, and group information stored in your database. This allows you to extract valuable insights and patterns from your data.
From this perspective, defining the right questions to find the useful answers is what leads to progress:
Segments Dashboard: Historical data from the past to evaluate already applied strategies, by compiling an assessment of your segment performance.
Product Insights: Real time data to plan future action, by allowing investigation of possible patterns or potential sources of cost-saving or sales-boosting changes.
Evaluating with Segments Dashboard
An important part of evaluating your product performance includes understanding two factors:
The power of combining different product metrics into the same query
The role of time in your data presentation
Combining Metrics
It can be simple to compare two different sets of data according to a single metric, but this simplicity may be deceiving. Consider the following example of Profit On Ad Spend (POAS):
First, let's look at ROAS:
Product A: ROAS 5 at spend $1000
Product B: ROAS 2 at spend $200
The fast conclusion is that Product A is better. But let's next look at Gross profit:
Product A: $100
Product B: $500
Now it's apparent that Product B may be more profitable but isn't getting the marketing support it needs to excel.
The picture becomes clear when we use POAS rather than either ROAS or Revenue:
Product A: 0.1 POAS
Product B: 2.5 POAS!
To learn more about calculated metrics, read here: Calculated Metrics.
To learn more about custom metrics and attributes, read here: Integration: Product Attributes.
Data Over Time
Besides the many influences of how a product's value changes over its lifecycle, it can be vital to investigate the constantly changing details of product performance over time.
For example, a product that may qualify in your Bestsellers segment criteria during January may not apply towards the same criteria in February. So by only executing data analysis with a date range limited to February, you could miss the stellar performance of this product back when it was successful.
Practical Examples
Now, examine these data querying principles in a familiar context of campaign evaluation. Consider the following data analysis premise:
Data query: "I excluded Poor Performers from my campaign at the beginning of October. Did my spend go to good products instead? Have I reached better results overall?"
To assess your product performance in this case, we'll analyze three topics:
Spend distribution
Segment performance
Any possible opportunities for future use
1. Analyze Spend Distribution Shifts
Data query: "After excluding my bad products, what's the best way to examine my spend distribution?"
The ideal chart for this is the Segments Comparison chart, to analyze any changes of one metric over time for two segments together.
For this data query, your analysis criteria can include:
1st segment: Poor Performers, limiting bad products
2nd segment: Bestsellers, good products boosted with the budget saved from Poor Performers
Metric: Amount Spent, the area of interest
According to the chart, we can observe diminishing Spend over time on your Poor Performers segment (purple line), and increasing Spend on your Bestseller segment (blue line). Therefore, the exclusion was successful and you did indeed redirect spend from Poor Performers to Bestsellers.
2. Analyze Influences on Performance
Data query: "So the spend was redirected, but how did this influence my product performance?"
Since we already know that this spend was focused on the Bestsellers segment, we can examine it more closely to learn more about its performance.
The ideal chart for this is the Trend in Time chart, to analyze one segment according to two metrics.
For this data query, your analysis criteria can include:
Segment: Bestsellers, to determine its performance
Metrics: ROAS and Revenue, to measure relevant impacts
Time frame: October, the same time period that spend was shifted from Poor Performers to Bestsellers
According to the chart, we can see your Bestseller segment grew significantly not only in Revenue (purple line) but also ROAS (blue line), so in the same time period we redirected spend from Poor Performers to Bestsellers.
3. Analyze Further Improvements
Data query: "How my Bestsellers did compared to other segments of products?"
At this stage of data analysis, there are usually even more practical questions to ask such as whether your Bestsellers segment was the segment with the highest growth in that time period. If there are any other product segments that were performing well even without additional spend, then it would be useful to identify them.
The ideal chart for this is the Segments Summary chart to get an idea of how all your other segments performed in this same time period.
For this data query, your analysis criteria can include:
Segments: Any segments you have saved (even all of them)
Metrics: The relevant metrics you are interested in, such as Amount Spent or Product ROAS
According to the chart, we can see most of your Spend (blue bars) went into the Bestseller segment as desired in the time period of the campaign. But through the discovery of other segments, we found one promising group of products! New Arrivals are heavily underpromoted, but showing a very high ROAS of 5. This could be your next area of interest for future campaigns and improvement.
Next up, learn more about crafting product segmentation in Product Insights.
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