The customer insights used by Mallzee Insights are generated from our award-winning shopping app, Mallzee..
The customer insights used by Mallzee Insights are generated from our award-winning app, Mallzee. Mallzee has generated a large collection of user ratings on individual fashion items. This data is utilised to generate insights for our retail partners, as well as aiding discoverability of products and building recommendations for users of the Mallzee app. We employ cutting-edge data science techniques and methodologies to analyse our large sets of data and turn them into valuable information
The app is based on a Like/Dislike browsing experience, which means consumers rate each product they interact with. Shoppers have access to products from over 150 retailers and thousands of brands, with their products purchasable directly through the app.
Consumers benefit from this experience as any products they like are saved to their wish list for later and they can access all of their favourite stores and brands in one app.
This approach has allowed Mallzee Insights to build a truly unique and shopper led view of the UK fashion market for our partners to harness when testing new products.
App users interact with retail products in the context of a collection of products (a feed) or by searching. A feed is a collection of items hand-picked for a targeted audiences or generated based on customer preferences. The comprehensive search functionality allows for querying via free text, suggestions and the use of filters. For data purposes, the context of where a product interaction appears enables us to interpret the user intention when we analyse the data.
With the 1.5 million downloads our apps have received, Mallzee owns a large dataset of more than 500 million interactions users have created on 3 million product, which come from more than 150 different brands. This means that our dataset is very voluminous and varied, allowing us to reliably generate a multiplicity of different insights for our customers.
For our Product Future solution, we present customers with a feed of pre-release products from our retail partners. As a natural part of the shopping experience in the app, these products are interacted with, guaranteeing us large volumes of data with statistical significance in a very short time. We compute a metric to assess how products perform, the Mallzee Performance Score (MPS), which aggregates together all the data relative to raw opinions users have expressed on the product. Immediately afterwards, the product’s MPS is attributed meaning and context by comparing the product with similar ones we have in our full catalogue. This benchmarking is another powerful feature from the comprehensive Mallzee data set, which adds a valuable dimension to the interpretation of the Mallzee MPS score itself.
With the UKs largest data set of product interactions and customer preference data, Mallzee Insights provides a powerful platform for retailers to test their products before committing to stock decisions. By gathering authentic, live opinions from shoppers in a shopping situation, we can give retailers and brands unique insights into product preference and predicted performance, even before the sampling stage. This live data, combined with millions of historic product ratings and incredible competitor benchmarking data, means that Mallzee data provides the most cutting-edge technology on product testing and customer feedback on the market. How we gather and analayse our data and the speed at which we are able to produce results empowers us to help our partners improve gross margin and maximise full-price sell-through.
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