How a major retail chain partnered with MASS Analytics on automated marketing mix modeling, cutting data preparation from one week to half a day with an automated data pipeline.
- At a glanceSector
- Retail
- Scope
- One major retail chain, data from many sources prepared in one automated run
- Engagement
- Completed project
Always-ON measurement, delivered on the MassTer platform.
The challenge
A major retail chain wanted to understand the levers behind its sales. Its marketing mix model depended on data from many sources, and preparing that data took a full week before any modeling could begin. The chain wanted to cut the time spent on data processing for marketing mix modeling, move the saved time to higher value work, and automate the process.
A week of preparation, and a lot to untangle
The retail chain’s data covered different products and several regions, with multiple metrics in each file. Files for different periods overlapped, some values were missing, and everything arrived as separate Excel files. Preparing it all took a full week.
“A full week went into preparing the data before any modeling could start.”
A member of the retail chain’s analytics team
Four costs of the old way of preparing data
- A full week of processing. Modeling could not start until the data was ready.
- Many sources and structures. Separate files, overlapping periods, several regions and missing values.
- Hidden errors. Other ways of preparing the data allowed mistakes that nobody could detect.
- Data left unused. Without a practical way to prepare it, some data stayed out of the analysis.
Six data problems sat behind the week of preparation.
| Data problem | What it meant for the team |
|---|---|
| Different products | Each product’s data had to be lined up. |
| Date overlaps | The same dates appeared in more than one file. |
| Multiple regions | Each region had to fit one common structure. |
| Multiple metrics | Measures had to stay consistent across files. |
| Missing values | Gaps had to be found and handled. |
| Separate files | Many separate Excel files, in different formats. |
Table 1: The six data problems in the retail chain’s project. All six were prepared in a single automated run.
The solution
MASS Analytics replaced the manual work with an automated data pipeline. The pipeline takes data from many sources and structures, standardizes it, and delivers it ready to load into the modeling platform. It works with retail data structures and with the formats typical of consumer goods data. For this project, that meant the retail chain could prepare all of its data in one connected workflow.
One automated run replaced the manual steps
Setting up the pipeline was light. The team installed the required software, placed the program and its configuration files in the same folder, and started the process with one simple command. That single run prepared the data across all six problems: different products, overlapping dates, several regions, multiple metrics, missing values and many separate files. The result was a standard dataset, ready for the model.
Fast, automated ingestion is the base of Always-ON Analytics
Data ingestion is where modern marketing mix modeling and the traditional approach part ways. When preparation takes a week, every model refresh competes with the data work. When it takes half a day, the data side no longer slows the cycle, and models can be refreshed on a regular rhythm. That is the foundation of Always-ON Analytics, where an integrated platform and automated data pipelines keep data ready for decisions. Good marketing mix modeling data analytics start with data that is ready on time.
Results and Impact
With an automated data pipeline, the retail chain cut data processing from one week to half a day. That gave back about 40% of the time a marketing mix modeling project usually takes, and kept hidden preparation errors out of the model.
½ day
to process the data, down from 1 week
~40%
of usual project time gained
6
data problems handled in one run
1
command to run the whole process
Data processing fell from one week to half a day
Processing time for the retail chain’s data dropped from one week to half a day. Because data preparation takes up so much of a marketing mix modeling project, that change gave back about 40% of the usual project time. The retail chain could move that time to other parts of the project that bring higher value.
Hidden errors and unused data stayed out of the way
The automated pipeline avoided the undetectable preparation mistakes that came with other ways of preparing the data. It also removed the barrier that had kept some data unused, since there was now a practical way to prepare it. The retail chain now had one automated process to prepare its data. A second retail case shows what comes next: automated data preparation that supports regular model refreshes.
Want to see automated data pipelines at work?
MASS Analytics connects automated data pipelines to its integrated platform, so your data is ready when your model needs it. Talk to our team.
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