Picture the scene : a great brand, selling everyday products to Mr and Mrs Anybody. A few years ago, most of its business came from Amazon and simple organic traffic was enough to bring high levels ofperformance. Then came the retail media wave, led in great part by the ecommerce all weight champion, Amazon. And finally, as always, the market follows its leaders. Now this brand is stretching its budget across Amazon and a range of various different retailers, all using different technologies to support their inventory monetization. And I won’t even mention SEO and Google shopping investment to drive traffic to its direct to consumer website ! If I were this brand, I’d be drowning in both the sheer amount of available information and how difficult it is to actually read it and extract information out of it.
Fragmentation is a pain
The problem is that there is no standard on the market. Sure, most technologies try to follow Amazon, but realistically what advantage would have any provider to simply copy what Amazon does ? Where would be the innovation, what would be the USP for those technologies ? So they innovate, and it is a great thing as it carries the market upward, opening new opportunities for advertisers and retailers every day and everywhere. But this leads to a phenomenon that we call “market fragmentation”. And because everybody brings something great to the table that others don’t, it is almost impossible for an advertiser to build a proper, harmonized, global view of their data across all their investment.
This situation leads to various problems that we try to help with : media investments are managed in silos and stretched thin. not out of desire but out of necessity, because it is near impossible to properly compare channels one to another. So we do with what we have and at the end, Amazon wins all, simply because of its size, the same way Google won the SEO and search ads game by enforcing the attribution that made it look good. It is also extremely complex to generalize use cases across the different channels. Usually it is hard enough to deploy a new ingenious strategy on Amazon so that we don’t make the effort to generalize. Amazon is big enough right ? But in the end this simply leads to a more centralized budget while the consumers have a tendency to go in the other direction, expanding their portfolio of preferred marketplaces and ecommerce websites. Brands are putting their eggs in the same basket out of necessity, but consumers are leaving the basket faster than the eggs.
SQUARE: focus on what matters most
At mimbi, we believe that this is a major pain point for any advertiser. This is why we are happy to introduce SQUARE. You can see SQUARE in multiple angles : you could see it as an harmonized model that takes what is common from all the retailers and technology you work with and brings it together seamlessly, letting you concentrate on your business value rather than time consuming data prep and transformation. You could also see SQUARE as the foundation of aggregated data you need to monitor and optimize your investment across all the various platforms you invest on, building on those foundations various use cases navigating between all the strengths of all the platforms and compensating their weaknesses by harnessing an agnostic and global view of your data.
The down to earth way to see SQUARE is this : all the data you will collect in mimbi from any supported platform will be harmonized in a single data model. The column names will be the same everywhere (old is the time where you should map imps, impression, imp_count etc … from all your different sources), metrics will be harmonized (all attribution windows will be the same) and all this common data will be available in a single table. Well, actually five to be sure we cover all the proper dimensions of your data :
- SQUARE Media : will contain all information regarding your media investment, from impression number to attributed sales and budget. While you can turn this data in every direction thanks to our transformation tool (SPOILER ALERT), we have prepared 3 dimensions that we think you will be comfortable with to start with : Campaign, Groups and Keywords.
- SQUARE Product : is your centralized data catalog. You will be able to import your unique categorisation view to mimbi and ensure that this product line vision can be applied to all the insights you build. Old is the time when your Xbox Controller was simply put in the “Video Game” category …
- SQUARE Sales : will centralize all transactions happening in all the sales points you connect to mimbi, being a retailer, a marketplace or your own D2C website. We believe that it doesn’t make sense anymore to look at sales and transactions in a channel centric way, we all know that this ad you saw on facebook or google may be the one that led you to buy on Amazon. So why shouldn’t we harmonize the sales the same way we harmonize the media ?
How SQUARE helps you
Well here are some ideas of how to leverage harmonized data set :
- Unified analytics : before SQUARE, you would either have a tremendous amount of time consuming and tedious work of data preparation and harmonization before you could even start putting your data in your dashboard or you simply didn’t do the work and keep a siloed view.
- Cross-channel optimisation : All your data sitting in the same place and able to almost instantly connect to other sources will help you imagine new ways to optimize your investments. Why not adapt your bidding strategy on one platform when there is an out of stock on another marketplace, just to make sure you keep your sales high while you figure out the issue on the other side ?
- Streamline actionable insights : SQUARE can become your one source of truth synchronizing all your sources together in a comprehensive and actionable environment and then you can feed this enrichment back into your platforms, using your learnings on one side to support the other. And why not feed your generative AI analytics layer with it (Spoiler alert, that is something you will be able to do directly in mimbi).
What's next?
Of course, SQUARE is only the beginning. In mimbi, this is our foundational layer, but you will be able to use all your data raw if you wish to or mix and match your raw custom data with our data model as much as you want. SQUARE is a forever evolving model that will receive multiple new features and data point in the close future. We are already working on a brand new type that will help you sort and categorize your keywords across your sponsored product campaigns and on new tools to consolidate mismatched campaign names to help you with your taxonomy.
We will also be providing you with tools to work on your data as well as various presets so that you always have an idea about where to start. More on this later , but you can already check out our use cases list, just to have some idea of how we can help you.
Our obsession is to ensure you can access the right data in the right place and the right dimension to action your use cases. And if this data, this place or this dimension doesn’t exist, we will just create it for you !
We are committed to accompany our customer along this muddy path that this market is, paving the way for streamlined data led Retail Media investments.
If you want to know more, feel free to contact us!
.webp)