Marketing Mix Modelling vs SegmentStream: What’s the difference?
Let’s continue our data-driven tool comparison series. This time we’ll focus on two modelling solutions: Marketing Mix Modelling (MMM) and SegmentStream.
Dissimilar to Multi-Touch Attribution, MMM is designed to solve a different challenge altogether. Let’s figure out what these approaches actually do.
Marketing Mix Modelling is a complex statistical analysis, most often regression-based, that looks back at sales over a long period of time (from several months to multiple years) and determines what caused those sales. It looks over significant budget allocations to digital and offline channels at macro level (think of TV and Radio ads, Digital Marketing, Events Sponsorship, Billboards, Printed Ads and other categories) and takes into account market conditions, competitors, special promotions, inventory levels, product prices, seasonality and even weather. Based on this information MMM would further look for spikes in sales, determine whether they are related to any of these factors and calculate the impact of any future activities within these macro channels.
Ex.: A large multinational CPG company needs a fundamental solution to revise all their digital and offline marketing activities, their CMO runs MMM once per year to make data-driven decisions upon budget allocation across brands, markets and channels.
SegmentStream combines AI with Marketing Mix Modeling techniques to analyze the relationship between advertising spend, clicks, website visits, and sales in specific regions. This approach helps in accurately assessing the impact of digital marketing efforts, particularly in scenarios where traditional data tracking faces privacy and cookie limitations.
Additionally, SegmentStream includes an Optimization Suite. This feature provides targeted budget recommendations to enhance return on investment and streamline marketing strategies. Discover more about the Optimization Suite.
For example, consider a large global retailer whose marketing team is struggling with attribution data loss due to privacy restrictions. By implementing SegmentStream, they can make informed decisions on reallocating budgets in real time at both the channel and campaign levels, effectively optimizing ad performance. This practical application showcases SegmentStream’s capability to offer actionable insights in today’s challenging digital marketing environment.
Not quite so, MMM and SegmentStream do overlap when it comes to High-level use cases. Both can be used to make better decisions on channel level budget allocation for digital marketing activities. However, if you need to assess the holistic marketing strategy across online and offline for a long period of time, MMM would be better suited to do so.
On the other hand, due to its macro nature MMM doesn’t allow for tactical media planning, ads optimisation and smart bidding strategies. SegmentStream is perfectly designed for granular hands-on tactical optimisations.
Both solutions have an element of modelling and prediction, and as a result you do need historical data to run them.
SegmentStream, however, only requires a few weeks of data and once the ML model is built you can analyse even freshly launched campaigns. MMM is very different – it would require from several months up to multiple years of data as this approach would assess various correlations across the marketing channel spend and conversion contributions as well as seasonality and other external factors.
MMM is sophisticated and takes around 3-6 months before you can start the analysis. This is why it is usually only performed once per quarter or so.
As for SegmentStream, Once the model is up and running (3-4 weeks of historical data) you can get the results in close to real-time and start acting upon that in around 1 day.
MMM’s primary goal is the high level budget allocation, so it won’t be able to get you lower than the macro channel level (like “Digital Marketing”, “TV Ads”, “Football match sponsorship”, “Billboards”, etc.). Technically it is possible to run MMM on digital activities only, but still in this case you wouldn’t be able to get more granular than channel level (like “paid-social”, “paid-search”, “direct”, “organic”, etc.). SegmentStream on the other hand allows for lots of flexibility and supports a very detailed analysis down to campaign, ad content and ad term.
Since MMM is analysing data on a channel level, and not necessarily tied to cookies, it’s fair to say that MMM mitigates for ITP and cross-device/cross-browser. As for SegmentStream, it is a real master in battling ITP and cookie limitations even on a user level since it delves into various data points including clicks, impressions, and user behavior, compensating for the gaps left by cookie limitations.
Marketing Mix Modeling (MMM) is ideal for companies with a wide range of channels, including TV, radio, and digital. It suits long-term strategic planning and broad budget decisions. SegmentStream, in contrast, is tailored for real-time, tactical actions in digital marketing, offering detailed insights for immediate campaign adjustments, especially valuable in environments with cookie limitations.
If you’re keen to see how it works, request a demo.
You might also be interested in
A guide to incrementality measurement: approaches, benefits and challengesLearn more
Introducing Geo Incrementality TestsLearn more
Google just started phasing out third-party cookies. What does it mean for advertisers?Learn more
Achieve the most optimal marketing mix with SegmentStreamRequest a demo