Can a consumer electronics store do MMM in Excel?
Usually only a rough one. If you sell consumer electronics, launches, price drops and stock-outs can move weekly sales as much as ads do. An Excel model needs a column for each, or it hands their effect to whichever channel spent that week.
By Joris van Huët, Founder & CEOUpdated 5 min read
Run the numbers for your store: the free safety stock calculator.
Usually only a rough one. If you sell consumer electronics, launches, price drops and stock-outs can move weekly sales as much as ads do. An Excel model needs a column for each, or it hands their effect to whichever channel spent that week. Use it to choose what to test, not to set the budget.
If you sell consumer electronics
If you sell consumer electronics, your sales calendar is lumpy. A new model, a price cut on last year's version or a restock can each lift a week with no change in ads. Black Friday week stacks a discount, a demand spike and your biggest budget into a single row.
Your buyers usually take their time, too. Headphones, laptops and cameras get compared, reviewed and left in a basket before anyone pays. If that sounds like your store, part of an ad's effect lands in later weeks, and the decay you set for each channel matters more.
What one store's data shows
One store's anonymised GA4 export, 1 January 2024 to 21 August 2026. It holds shares of revenue only: no ad spend, no order counts. Nothing in it says the store sells electronics, so read it as one store and not a benchmark for yours.
| What the export shows | Share of revenue | Source cell |
|---|---|---|
| Journeys with 1 touch: 14 distinct paths, 0.5 days to buy | 79.5% | Journeys sheet, 1 touch row |
| Journeys with 4 to 9 touches: 1,568 distinct paths, 16.9 days to buy | 5.4% | Journeys sheet, 4 to 9 touches row |
| Journeys with 10 or more touches: 1,792 distinct paths, 16.0 days to buy | 3.0% | Journeys sheet, 10 or more touches row |
In this export, quick buyers carry the revenue. On the Journeys sheet, journeys with 1 touch hold 79.5% of revenue at 0.5 days to buy. The long journeys hold little and take longest. On the same Journeys sheet, 4 to 9 touches take 16.9 days and 10 or more take 16.0 days.
If you sell electronics, read your own rows before you set a decay. If your long journeys hold a bigger share than these, a week's spend keeps paying for two weeks or more. A decay of zero would then hand that later revenue to whatever ran in the later weeks.
The export cannot show the weeks that matter most to an electronics model: launches, price changes and stock-outs. Those live in your own calendar and your stock history.
What changes for an electronics store?
Launch weeks need their own column. Put a 1 in the weeks a new product went live and a 0 elsewhere. Robyn's guide lists price and promotional activity among its context variables. Meridian's guide says the choice of control variables matters for estimating the causal effect.
Stock-outs need one too. If your best seller sold out for three weeks, sales fell while the ads ran on. Without an in-stock column, Excel reads that as ads that stopped working.
Price is a column, not a channel. Add the price or the discount depth of your main products. Otherwise a price cut's lift gets split among the channels that happened to spend that week.
Sales off Shopify count too. If buyers research on your site and buy from a marketplace or a shop, your sales column misses part of what the ads did. Model total sales across every place you sell, or say plainly that the model ignores that demand.
What to do this week
- Mark launch, sale and stock-out weeks. In Shopify, go to Analytics > Reports, filter the Category to Sales and open Total sales by product. In the Dimensions menu, click + and add week as the time unit. Pass: you can name a cause for every spike and dip. Fail: some spikes have no cause, so add flag columns before you fit anything.
- Check how long your buyers take. In Google Analytics, click Advertising, then Key event attribution paths under Key events, and read Days to key events. Pass: most purchases arrive within a week, so a low decay is a fair start. Fail: they spread over weeks, so test decays above zero for every channel.
- Find your biggest spend week. In Google Ads, segment the Campaigns table by Time, then Week, and find the highest weekly cost. Pass: the budget you plan for your next launch sits inside the range you have already spent. Fail: it sits above it, so treat the model's answer for that budget as a guess and test it.
Check the homework. Your GA4 Attribution paths export already holds the evidence. Causality Engine reads that one file and shows what each channel caused next to what last-click gave it, in 1 to 2 minutes, for €99 once (excluding VAT), refundable within 30 days. Check the homework
Sources, 1 October 2026: An Analyst's Guide to MMM (Meta); Collect and organize your data (Google); Sales reports (Shopify); Setting and comparing time ranges for your reports (Shopify); Key event attribution paths (Google); Use segments in your tables (Google)
Related answers
Frequently asked questions
How do I model a product launch in an Excel MMM?
Give it its own column: 1 in launch weeks and 0 otherwise. If the lift fades over a few weeks, use a column that counts down instead. Without it, the regression hands the launch's sales to whatever channel spent that week.Should marketplace sales go into my electronics MMM?
If your ads send buyers to marketplaces, yes, or the model undercounts the ads. Use total sales across every place you sell as the target column. If you can only get Shopify sales, say plainly that the model ignores marketplace demand.Do stock-outs break a marketing mix model?
They distort it. Sales drop while the ads keep running, so a model without an in-stock column reads the gap as ads that stopped working. Add a column with the share of each week your top products were in stock.
Go deeper: Causal attribution, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
Keep reading
Terms in this article
- AnalyticsAnalytics is the systematic computational analysis of data. It reveals customer behavior and measures campaign performance.
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Black FridayBlack Friday is the day after Thanksgiving in the United States. It marks the start of the Christmas shopping season and is a major sales event for retailers.
- Google AdsGoogle Ads is an online advertising platform where advertisers bid to display ads, service offerings, and product listings.
- Google AnalyticsGoogle Analytics is a web analytics service that tracks and reports website traffic.
- IncrementalityIncrementality measures the true causal impact of a marketing campaign. It quantifies the additional conversions or revenue directly from that activity.
- Incrementality TestingIncrementality Testing measures the additional impact of a marketing campaign. It compares exposed and control groups to determine causal effect.
- InfluencerAn Influencer affects purchase decisions due to their authority, knowledge, or relationship with their audience. They drive consumer behavior.