Single Brand

Brand: Coach

An example of an analysis of a brand that sells products in both their own stores and at retailers.


When analyzing a single brand, often it becomes evident that the brand does not have sufficient data to understand itself in the context of the entirety of retail. Whilst the brand has the ability to look at it’s own data from it’s own stores (given sufficient data, the A.I. required as well as assuming that the retailer is capturing and storing the data efficiently), it rarely receives good data from the retailers who stock their brands.

In this case study with Coach, the Retail Intelligence Data Engine tm (R.I.D.E.) scours multiple retailers such as Macy’s, Nordstorm and others to reveal not only how Coach products are sold, but also what they are statistically most purchased with from other brands. As the view emerges, Coach is able to see the customer behind the purchase stylistically, spot emerging trends and even know which products they should not remove from their catalog, lest they harm the sales of associated products. The influencer products emerge based on qunatitative purchase data insights, rather than guesswork or sentiment alone.


Brand: PVH (Calvin Klein, Tommy Hilfiger)

An example of an analysis of a brand that sells multiple brand products in both their own stores and at retailers.


Powerhouses like PVH are a breeding ground for some of the most successful retail companies in the world. Household names like Tommy Hilfiger, Calvin Klein, Izod, to name a few… yet none of these companies can share data amongst one another in efficient ways since they sometimes compete for the same company.

ATo assist portfolio-style multi-brand retailers, R.I.D.E. offers a bi-partisan global view of retail, unvelining how each of these brands affect one another and the shopper across the entire portfolio… not to mention other competitors.

Shopin opens here-before closed doors of knowledge to reveal these interactions as well as which products or brands are the influencers that cause the action. Shopin introduces “the Archcustomer” – a view of the same shopper, aggregated across brands, products and retailers to drive a deeper understanding of what your Shoppers are buying (as opposed to looking at), what they buy it with and measuring the strength of influence of each retailer, brand or product on one another so that your team can see the truth behind the data.

Multi-Brand Nationwide
Retailer Analysis

Brand: Macy’s

An example of an analysis of a retailer that sells products from multiple brands both online and in brick-and-mortar stores.


Retailers like Macy’s are innundated with a very narrow data set that only exposes them to their own shoppers, albiet a very sophisticated view of online and offline sales activity.

Shopin’s Retail Intelligence Data Engine tm digs deep into that data set to go beyond merely providing analytics, but actually giving suggestions on what actions to take and the predictive outcome.

It doesn’t stop there. Shopin’s global view of retail purchase data can help retailers like Macy’s expand their view, to see if the brand catalogues they stock are over or under-represented by comparing what sells well into their demographic at other retailers like Nordstrom or Amazon.

Macy’s could also measure the effect of adding new brands and products, how they each will interact or even the cannibalization effects of removing a brand they think is underperforming, to prevent unexpected losses.


Companies like Macy’s are just innundataed with data... but a lot of times it’s not about having access to data, it’s “how do you use the insights from that data to actually make recommendations and implement... The product that Shopin had, which was looking at your portfolio and not only providing insights and analytics, but then taking it one step further and saying “here are some recommentations, and how we would look at corrolation of inventory at the SKU-level...” - I thought that was different. It’s unique...

Parinda Muley
VP of Innovatiion and Business Development, Macy’s

Multi-Brand Online
Retailer Analysis

Brand: ASOS

An example of an analysis of a brand that sells multiple brand products in both their own stores and at retailers.


As a massive online mult-brand retailer, access to data in context is critical.

The market is intensely competitive and you’re sophisticated from the ground up. Yet no matter how much visual AI you throw at the problem or consultants you hire, you can’t get around the simple problem: access to sufficient purchase data to understand the shopper in context of a wild worldwide web. With all the choices they have, do you really know what your shoppers bought or where?

Without sufficient data, can you tell who the shopper is based on a series of views or just one or two purchases?

Discover how a global view of purchase data is the oracle all your business decisions, suply chain and personalization engine have been missing. It’s all waiting for you at R.I.D.E.