Automating retail data preparation.

The challenge

Data preparation is the part of Marketing Mix Modeling that analysts dread most. Every refresh means pulling media spend, sales, promotional, and competitor data from sources that were never built to talk to each other, then cleaning, aligning, and reformatting all of it before a single model can run. Industry experience puts data preparation at up to 60 percent of a typical MMM project’s timeline. For a major U.S. furniture retailer that needed to run Marketing Mix Modeling at least three times a year to keep its planning cycles on schedule, that bottleneck was not a one time cost. It repeated every refresh, and it was getting worse as the pace of planning increased.

The manual process could not keep pace

Each of the retailer’s three annual refreshes started from zero. Media spend arrived in different formats across digital and offline channels, sales data came from divisions that used their own regional and store level structures, and promotional calendars needed manual reconciliation against actual store activity. A single mislabeled channel or an inconsistent date range could quietly distort a model’s coefficients, so every dataset had to be checked as well as cleaned. None of the work from one refresh carried over to the next. The team re-solved the same formatting and validation problems every time, leaving less time for the modeling and scenario work the business actually needed.

What manual preparation could not deliver

• Inconsistent formats. Media, sales, and promotional data arrived in different formats, time granularities, and even currency conventions from one source to the next, so every feed needed its own manual translation before it could sit in the same dataset.
• Error risk. Manual reformatting introduced the errors that are hardest to catch: a mistyped channel name, a missing week, a duplicated row, any one of which could quietly distort a model’s coefficients.
• No reuse. Each refresh solved the same formatting and validation problems from scratch. Nothing about one cycle’s cleanup carried forward to speed up the next.
• A shrinking window. With data preparation absorbing most of the project calendar, the time left for exploring the data and interpreting results kept shrinking, even as the business asked for faster answers.

The table below breaks down where MASS Analytics focused the automation effort, and what changed as a result.

The solution

MASS Analytics addressed the bottleneck in two phases: first understanding exactly what the retailer’s Marketing Mix Modeling project needed from its data, then deploying an automated data cleaning and integration pipeline to deliver that data on every future refresh without manual rework. This turned data preparation from a recurring cost into a one time investment with a permanent payoff.

Scoping before automating

Before any automation began, MASS Analytics mapped every input the model required: media spend across digital and offline channels, impression data, sales revenue by region and store, promotional activity, competitor actions, and external factors like holidays and broader economic trends. The team then defined the exact output specification the model needed, down to schema, time series granularity, and column naming, so the automated pipeline would produce a consistent result on every run rather than a plausible looking one.

A reusable, not a one-off, pipeline

For each data source, MASS Analytics built a processing sequence: a defined chain of steps that formats, cleans, transforms, and validates the raw data before it reaches the model. Once a sequence was defined for a given source, it became a permanent asset. The same sequence runs unchanged on every future refresh, reusable and auditable rather than rebuilt from memory each time. The retailer’s three model refreshes a year now draw on the same processing sequence every time, removing the manual rework that used to repeat with each cycle.

Results and Impact

Automating the data preparation phase cut the retailer’s data processing time by 60 percent. What used to consume most of the project calendar now happens in a fraction of the time, and every future refresh inherits the same processing logic automatically.

60 percent less time preparing data, redirected to analysis

With data preparation no longer absorbing most of the project calendar, the retailer’s analysts could put that time into data mining, scenario testing, and interpreting what the model actually found, instead of rushing through those steps to hit a deadline. The same processing sequence also removed a class of error that manual work could never fully avoid: typos, copy paste mistakes, and inconsistent formatting that had previously required a second and third pass to catch. Because the pipeline applies the same defined rules on every run, each of the retailer’s three annual refreshes now starts from a dataset that has already been checked, not one that still needs to be.

A foundation for bringing modeling in-house

The automation effort did more than save time on each refresh. With the technical complexity of data preparation handled automatically, the retailer’s internal team could focus on interpreting results and running what-if scenarios rather than wrestling with source files. Combined with clear documentation of how each processing sequence works, this gave the retailer’s own team the foundation to take on more of the Marketing Mix Modeling process itself, and the autonomy to keep running it at the pace their planning calendar demands.