Marketing Mix Modeling
Modelling the omnichannel media plan to identify the optimal investments.
A decision-making tool for a media plan optimised for performance.
- Period
- Since 2022
- Services
- Marketing Mix Modeling
- Churn prediction
- LTV by cohort
- Client
- La Redoute Suisse
- Conferences
- Marketing Vaud
- Perspective 2
- Perspective 4
For 180 years, La Redoute has made the French art of living, in fashion and for the home, accessible to as many people as possible. La Redoute is a timeless company, a story across generations founded on creation and constant innovation. From the wool mill to the legendary catalogue, La Redoute is today one of Switzerland’s leading e-commerce names in fashion and home.
Challenge - For a well-established company such as La Redoute, the main objectives are not only acquiring new customers but also retargeting existing ones.
It is therefore essential to identify the media levers — digital and offline — that pay best, and those that are over- or under-invested, so as to allocate the marketing budget better. That lets the company raise its profitability and reach its commercial objectives more effectively, and more efficiently.
Moving beyond conventional attribution models.
Put a marketing mix modeling strategy in place.
Valuing the customer over the long term.
Calculate LifeTime Value (LTV) and predict customer churn, by segment.
Improving the return on their ICX.
Fit media investment to the performance of each lever.
Close to one transaction in two comes from media
Transactions in French-speaking Switzerland, decomposed by the Marketing Mix Modeling model between baseline, CRM and media, from 15 December 2021 to 30 September 2022. Illustrated with fictitious data. Choose a lever and the unit.
Each bar starts where the previous one stops: together, the three levers make 100% of the period’s transactions.
Source: fictitious data for illustration, Bright Marketing Mix Modeling report; numbers rounded to the nearest ten in the original figure · Chart: bright.swiss
Strategy - Identifying the media channels that perform best, from the data, through marketing mix modeling.
It matters to assess the impact of marketing channels separately by language region, because experience shows the optimal marketing mix can differ between them.
On that, the MMM (Marketing Mix Modeling) model we use is effective at identifying:
The volume of conversions generated by each media channel.
The real cost per acquisition (CPA) for each media channel, online and offline.
The optimal budget per channel, to maximise the volume of transactions.
The media investment that can be reduced if the annual budget falls.
The results of the model also let marketing intuitions be compared with what the data concludes, and set against each other. Where the divergences are large, it is essential to understand why. From there, certain areas for improvement can be identified, which lets the following year’s investment be adjusted accordingly.
For a company based in French-speaking Switzerland, the German-speaking market calls for a different strategy, specific to that market. Several models therefore have to be built, so as to take the cultural differences into account and bring in further, distinguishing variables for each market. That lets us produce a recommendation that is realistic and that takes each market’s particulars into account.
We fit our package to our clients’ business criteria. For La Redoute, we built our framework using, among other things, algorithms from Meta’s open-source package “Robyn”. The tool was developed by analysts and engineers at Meta and by the community — of which we are part. It offers, as things stand, mature algorithms for this kind of analysis.
Implementing the MMM project for La Redoute followed a process close to that of a technical project:
- Framing and design: examine the business model, the mechanisms that contribute to generating revenue, calculate the LTV (which we then broke down across cohorts), and so on. The aim being a design faithful to, and cut for, the client’s business.
- Setting the objectives and what is at stake, so as to deliver a result that is concrete and can be acted on.
- Collecting transactional (anonymous) and budget data (media spend).
- Interpreting and reviewing the data, so as to isolate and understand what can be used.
- Iterative modelling.
- Analysis, scenarios and recommendations
Impact
Following the modelling, we delivered a recommendation setting out the optimal investment for every media lever in La Redoute’s marketing mix, online and offline.
To make sure our recommendations hold, we put the model to the test through a backtesting phase. Since the model is built to reproduce as precisely as possible the impact of spend across channels on sales, we generate data for the period analysed — following the model’s characteristics — and compare it with the real data.
The result is an index called R2, which we can read as the model’s precision, or dependability. Above 75%, we can consider the modelling relatively dependable. For La Redoute, every model built had an R2 above 80%.
We also proposed an optimal investment scenario on the assumption that La Redoute had to, or chose to, reduce its media investment — the aim being to limit any fall in revenue.
La Redoute commissioned us to analyse its media mix so as to map where the transactions come from, revealing which channels matter most and allowing budget to be reallocated towards those that are under-invested. What we delivered — a genuine decision-making tool — confirmed La Redoute’s intuitions on parts of its acquisition strategy, validated the soundness of its media strategy, and also gave useful business insight, notably on the role of CRM in the customer’s path to purchase.
La Redoute puts the MMM results to work by adjusting its media investment, so as to hold a media plan that is coherent and as effective as it can be.
During the Marketing Mix Modeling study, Bright listened closely, developing a fine understanding of our business model and so of what is strategically at stake for us.
We spoke with the team regularly, so that we could interpret the study properly and turn its results into operational objectives. Working together was most pleasant, and instructive. Thank you.
In a landscape where the channels multiply and tracking capability erodes, measuring what campaigns really achieve has become both critical and complex. Marketing mix modelling brings a rational answer to that complexity, drawing on the data to inform decisions — never replacing intuition or human expertise, but completing it intelligently.
For more than five years we have been running active R&D on the most advanced MMM frameworks on the market: Robyn, Lightweight, Simba and others. That groundwork lets us offer bespoke models today, fitted to each client’s business and to the reality of their sector. We do not sell a single solution; we build decision-making tools that are relevant, nimble and effective. Because understanding better is deciding better.
