Case study · Meta
One record of every login journey
6 weeks → 3 hoursinvestigation time
At a glance
- Client
- Meta
- Sector
- Social media
- When
- 2025 to 2026
- Type of work
- Data from many systems, in one place
- Tools
- Data modelling
- Alerting
The challenge
Login data for Facebook and Instagram sat in more than 19 different sources. Nobody could calculate a true end-to-end login success rate, and each investigation needed its own joins.
What we did
We traced every source and chose the most resilient and reliable ones. Then we led the work to make login outcome data and its retention standard across both apps, in one canonical dataset of each login journey, with alerts against service level objectives.
The result
The first true end-to-end login success rates across surfaces. Investigation time fell from 6 weeks to 3 hours, and faster responses to incidents protect more than £1.5 million of revenue each year.
We did this work before we started Mithril.
Related case studies
A single view of every booking
96%of bookings with no attribution now have one
Read the story
- The challenge
- JustPark had no campaign attribution for its bookings. The team could not see which campaigns and platforms performed best, how each campaign ran against its budget, or the exact effect of its marketing.
- What we did
- We led the discovery, design, build and rollout of a single view of each booking. It joins media delivery, spend and campaign data with first-party session and user data, through new ways to resolve identity. We worked with engineering, marketing and revenue operations.
- The result
- Attribution for 96% of the bookings that had none. One source of truth for booking attribution, for campaign reports and customer segments.
- BigQuery
- dbt
- Looker
- Google Cloud
- Python
- SQL
- Semantic modelling
One source of truth for every product
Read the story
- The challenge
- The website pulled its product information together from many different sources, including Commercetools and an internal database.
- What we did
- We joined the sources into one source of truth for all product information: one BigQuery table model, built with dbt Cloud, that the website reads through an API.
- The result
- The website presents every product from one table model.
- BigQuery
- dbt Cloud
- Python
- Airflow
Moving customers from Commercetools to Shopify
50M+orders moved, with prices and history
Read the story
- The challenge
- World of Books moved to Shopify for version 2 of its website. Its customer and order data had to move from Commercetools first.
- What we did
- We built a producer–consumer pipeline in Python and Google Cloud Storage. It moved customer profiles, multi-currency order history, pricing logic and event history.
- The result
- More than 10 million customer profiles and more than 50 million orders moved. A/B tests across the business and its regions on Shopify became possible, and version 2 of the website is live on Shopify.
- Python
- Airflow
- Google Cloud Storage
- SFTP
- Shopify API
- Data structures and algorithms
Talk to us
Do you have a data problem like this one? Email kel@mithrilcorp.co.uk.