If you are eager to measure your progress in activities that increase customer loyalty, or evaluate how promotions affect your margins or customer behavior, then this template is for you.
ROIVENUE's Orders Analysis Template is for clients who have integrated their Order Management System with Roivenue. If you're interested in doing this, then you can find more information here.
Note: If you are connecting data to Business Intelligence tools for the first time, please refer to our tutorial on Data Studio or Power BI first. Also, this report is created primarily for data exported by ROIVENUE. If you try to import your own data, the whole report may fail, and you will have to make every single component compatible.
This report focuses on analysing your CRM data, specifically the orders of your customers.
In this article, you'll learn how this can help you with the management of your business. The KPIs we are working within these reports are related to:
- Comparing orders from new customers vs returning customers
- Margins and Costs on orders
- Identifying trends and how KPIs change over time
While some marketers may overlook Average Order Value, you shouldn't make the same mistake. This area can contain lots of growth potential. Just imagine: if your AOV is €50 and you decide to focus on increasing AOV and manage to grow the measurement by 10%, that's 10% extra revenue without acquiring a single new customer.
While this report cannot grow your business for you, it can help you track your progress. For many of the metrics we have included a sequence analysis - you can compare if the metrics are behaving differently for newly acquired customers (Sequence = 1) vs returning customers who knew you from the past (Sequence > 1). Maybe you will find out that the new customers are testing you out with a smaller order and once you earn their trust, they are willing to order more from you.
The analysis focuses on retention by visualizing an answer to the all-important question: "How many of our orders or how much of our revenue is actually generated by orders made by previous customers?"
It's a fact that retaining customers is almost always cheaper than acquiring new ones, but it can be tough to measure improvements in retention. By segmenting marketing costs into acquisition and retention, you can see precisely how much cheaper it is to retain a customer and how much you can afford to spend on each category.
The last part of the analysis focuses on the relationships of your KPIs. It's useful when examining periods like Black Friday, New Year, or Valentine's Day. During these times, many marketers chose to sacrifice some margin in exchange for new acquisitions or growth in revenue. Multiple visualizations on this page show the effects of such maneuvers.
For example, to return to the aforementioned example about AOV: maybe you have run a promo and achieved a 10% growth in AOV as customers became interested in more products for the discounted price.
For example, to return to the aforementioned example about AOV: maybe you have run a promo and achieved a 10% growth in AOV as customers became interested in more products for the discounted price. The question is, under what circumstances can one consider the promotion to be a success? Maybe it cost you 15% of your margin. Did the new customers you attracted and the old connections you reignited make up for this? This report allows you to answer this question and know what to expect next time.
Or, browse the report in Power BI by downloading the file below.
To use the report, set up an export like this:
1) Select the Orders dataset on the Data Source tab.
2) Select Source Data. The Export Periodicity depends on your needs: if this is a one-time analysis or if it needs to be refreshed daily, choose accordingly.
3) Select the same metrics as below. You can also add more if needed.
4) The next steps: attribution model, filters, and date range depend on your needs. If you are not sure what each of these tabs mean, they are described in more detail in this tutorial.
5) Destination - the last step of export - depends on if you want to use Power BI or Data Studio.