Category: Ecommerce | Solution: Datama Compare | Type: Recurring | Client: TheFork
Tags: #Controlling #Comex #Business #Mixeffect
Context: Weekly business review for Comex
TheFork is a prominent player in the restaurant reservation industry and part of the TripAdvisor family. The key contributor to TheFork’s success story is its robust online platform, where diners can effortlessly discover, book, and review restaurants worldwide.
Amidst this thriving landscape, TheFork’s Digital Data Insights & Analytics team has assumed the mantle of ensuring the platform’s continued growth and success based on data analysis. One of their missions is to closely monitor and suggest recommendations to enhance the performance of TheFork across its different channels, with a special focus on revenue performance against budget projections. This results in a weekly business meeting with the executive committee where the team presents the status of Revenues vs. Budget, explains reasons for the gap and suggests actions. That gap has always been presented as a waterfall chart in a PowerPoint slide. Previously, the process to generate that waterfall was a bit heavy: extracting manually data from Tableau, using an Excel model that often need to be updated and then paste the result into PPT. But not only that process was time consuming, but the results were also visually and mathematically improvable.
Therefore, the team leveraged Datama Compare license to automate that analysis.
Approach: A simple but efficient Volume Price analysis
Market equation
The “market equation” is basically the mathematical and business link between the Primary KPI (aka “North Star Metric”) of your performance and the sub-indicators.
The most basic version of that market equation for almost any business and that people learn at school is known as “Volume Price analysis”.
At The Fork, the volume will be number of bookings and the price is the “average booking value” which ends up in a simple market equation as follow:
Dataset
A dataset for Datama is a structured table aggregating selected metrics on a set of appropriate dimensions.
In terms of metrics, the dataset to be plugged into Datama basically just needs to have a column with the number of bookings and another column with the Revenues. (Datama will create the ABV ratio calculation in the tool)
In terms of dimensions, the goal is to compare budget vs. actual, so a “Type” of data column is essential: we need the data in a single dataset with a column that basically says what is “Actual” and what is the “Budget”. That might not be trivial, since the data often come from different systems (Actual from a transactional database, and Budget from a FP&A modeling tool, or simply Excel) but fortunately enough, the job of bringing those two sources into one had already been done by the team.
Additionally, within dimensions, the executive committee is used to break down results by country since the whole organization is split at regional level and each one is responsible for its own P&L. So “Country” will obviously be another dimension in the dataset.
Note that for once, the team was not willing to add any other dimension to the analysis, in order to simplify the message to the executive committee by always breaking down the results by the same country dimension, so Datama’s feature of scoring dimensions interest was not leveraged.
We end up with the following dataset (obviously, the data has been randomized and anonymized).
The data comes from Tableau where it is easy to extract and connect it to Datama
Takeaways
Once TheFork connected to Datama, the team was able to immediately view the waterfall and related comments.
Below is a (fake) example of actual numbers being +34% vs. budget, thanks to a massive over performance of Country B bookings, which also boosts the ABV since booking values in Country B are above the average (aka “mix effect”).
For clarity’s sake, the difference to budget is displayed in percentage points rather than in euros. This makes understanding how the +34% vs budget breaks down easier, and streamlines the visuals. To that end, the “display in pts” feature was co-developed with Datama developers allowing the use of the waterfall in a presentation without any reworking.
A simple screenshot of the waterfall (or using the “download as PPT” feature in Datama) then creates in seconds a slide for the exec team that used to take hours of work before Datama.
Outcomes
With Datama Compare, the Fork’s analytics & insight team was able to level up the accuracy and readability of a key report in the executive committee routine, ultimately driving better decision making to improve overall performance.
The tool also helped to speed up the time to generate this document, which gave more time to analysts for human thinking on the meaning of variations, and what decision they should make.
Overall, this came as an additional successful use case within multiple use cases on which Datama is used across the data organization, and which they plan to double down over the next months.
