# Use analytical dimensions in your analyses
## Why use analytical axes?
Why use analytical axes?
The **analytical axes** allow you to enrich your billing data in order to analyze your performance from different angles.\
Thanks to them, you can tailor your analyses to the reality of your business — by customer, product, region, acquisition channel or any other business dimension.
By using analytical axes in Fincome, you can:
* **Filter and break down your KPIs** (MRR, churn, NRR, ARPA, etc.) according to custom dimensions,
* **Compare your performance** between different customer, product or market segments,
* **Save your favorite filters** to reuse them in your future analyses.
💡 *Before going further, make sure you have configured your axes. If you haven't done so yet, consult the article* [*Add analytical axes*](https://help.fincome.co/en/conducting-in-depth-analyses/segment-your-data-with-analytical-dimensions/broken-reference)*.*
## 1- Filter your metrics and save custom segments
Filters allow you to restrict the analysis to a precise subset of your data (for example, your European customers or your monthly subscriptions).
### ➤ Apply a filter
1. Go to the **Analytics** from Fincome then choose the metric you want to analyze.
2. Click on **Filter above the chart** (funnel-shaped icon).
3. Select the desired analytical axis (e.g.: *Industry*, *Customer size*).
4. Choose one or more values to include or exclude.
5. Your charts and tables will automatically update according to the applied filter.
### ➤ Combine or compare multiple filters of the same type
Fincome lets you play with two filtering logics:
* **Combine multiple values within the same filter** :
* Example: if you add a filter `Country = France + Belgium`,
* You will get **a single curve** grouping the data from both countries (France **and** Belgium),
* It's as if you had selected multiple values in the same field.
* **Create multiple separate filters on the same axis** :
* Example: if you create a filter `Country = France` and another filter `Country = Belgium`,
* You will get **two distinct curves** on the chart: one for France, the other for Belgium,
* This allows you to **visually compare** two subgroups on the same metric.
💡 *In summary: multiple values in the same filter → a single aggregated curve; multiple filters of the same type → differentiated curves.*
### ➤ Save your filters as a custom segment
Once your filters are configured, you can save this combination to reuse it at any time:
1. Apply the desired filters.
2. Click on **Save the segment**.
3. Give your segment a clear name (e.g.: *Enterprise Clients – Europe*).
4. You can then find it in one click in the list of your saved segments.
## 2- Break down your KPIs by analytical axis
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The **breakdown** (or *breakdown*) allows you to split a metric according to the values of an analytical axis, to visualize its distribution.
1. Click on **Break down** next to the filter.
2. Select an axis (e.g.: *Product*, *Acquisition channel*, *Region*).
3. Fincome then displays the KPI distribution according to that axis (for example: MRR by product).
💡 *Unlike the filter, the breakdown does not exclude any data: it compares the subcategories within the same set.*
### ➤ Difference between filtering and breaking down
| Action | Purpose | Effect on the data |
| -------------- | ----------------------------------------- | -------------------------------------------------------- |
| **Filter** | Restrict the analysis to a specific scope | Excludes all other data |
| **Break down** | Visualize the distribution of a metric | Displays all the data, segmented by the value of an axis |
💡 *Good practice: filter first to define your analysis scope, then break down to compare within that scope.*
### ➤ Limits of the breakdown
* Only one breakdown can be applied at a time.
* You cannot break down data that is already aggregated (for example MRR movements already broken down).
* If you have already broken down data, you must **reset** the breakdown before choosing a new one.
## 3- Combine filters and breakdown
The functions **Filter** and **Break down** are not mutually exclusive: you can combine them to deepen your analyses.\
The best practice is to:
1. **Filter** first your dataset to define the scope,
2. Then **break down** a metric within that scope.
> Example: Filter your dashboard on the cohort of customers acquired in **Q1 2024**, then break down the **churn** of that cohort by **product type**.\
> You will thus obtain the distribution of churn product by product within that cohort.
💡 *It is not possible to nest several successive breakdowns, but you can chain several filters before breaking down.*
## Concrete use cases
Some examples of analyses possible with your analytical axes:
* **Break down MRR growth by geography** to identify the most dynamic areas.
* **Analyze churn by product** to spot underperforming offerings.
* **Compare ARPA by customer category** to understand purchasing behaviors.
* **Measure retention by acquisition channel** to adjust your marketing strategy.
* The HR company **Octime** made full use of filters and cohort breakdown in Fincome to structure its SaaS growth.\
By analyzing its key metrics by time cohorts, the team was able to track the evolution of MRR, retention and customer expansion according to acquisition date.
> Result: Octime identified the best-performing cohorts, optimized its pricing and improved collaboration between Finance and Product teams.
🔗 *Read the full case study:* [*Structuring the growth of an HR SaaS – Octime x Fincome*](https://www.fincome.co/fr/customers-stories/structurer-croissance-saas-rh-octime-fincome)
