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Maximise Customers’ Demand in Store

AI Suite to: Cluster Stores | Curate Assortments | Allocate space to categories | Track and remove in-store operational issues

Webinar: Evolution and Continued Journey of Category Management (CATMAN) in Retail

Having worked globally at executive and board levels at retailers like Tesco, Colgate-Palmolive, bp Pick n Pay, Woolworths and Pepkor Holdings Limited, there are few people better placed than Paula D. to speak about the continuous evolution of Category Management and how retailers can be even more customer centric in their offer. Spaces are limited. Please book your spot today.

Your company is missing 3-5% of potential sales because your customers cannot get what they want in your stores

Would you like machine learning to tell you how much sales leakage you have in each store through outdated category management systems and processes?

Sub-optimal category management means poorly assorted stores eroding customer trust as well as tying up your working capital with long tails of slow sellers. Also, there is no effective means to monitor and fix operational issues in stores.

RETAILIGENCE offers a rapid POC which will prove to you in just 4 weeks how you can pinpoint the exact products and stores missing potential and provide a machine learning driven solution to cluster, assort and monitor your stores.

Easy to deploy and intuitive to use, we are certain you will find a presentation and demo useful.

Harness the power of Retailigence’s adaptive ML Solutions

RETAILIGENCE is intuitive, user friendly and visual. It uses data without prejudice, unsupervised and without hindsight bias.

Instead of historical, RETAILIGENCE is future facing and uses retailer data to drive itself and create a credible and optimal customer offer. The intelligent control tower monitors the store clusters and assortments and flags issues and suggests corrective measures.

POC with RETAILIGENCE took 2 weeks because it uses a machine learning algorithm which facilitates data analysis however it is structured.

I haven’t seen a more user-friendly interface in any other Assortment Optimization tool, this is a very important aspect for us since user engagement is the factor for success. The clustering adds value because it’s flexible enough to allow us keeping our basic S-M-L-like groupings wherever convenient, and we can apply ML algorithms to those categories with more complex structures and broader variety of shoppers or performance.

IS YOUR STORE ASSORTMENT SET UP TO BRING CUSTOMERS BACK INTO STORE POST CoVID?

Do you take account of a product's true sales potential when considering whether to range it in a given store? (rather than just historical sales)

Do you cluster your stores to help define the correct range in each store?

Do you use a manual/spreadsheet approach to ranging your stores?

Do your assortment tools allow you to adjust the weighting given to sales value versus volume or margin for each category?

Do you believe your assortments are tailored to meet the different customer needs across your store estate?

Are you able to ensure a complete customer offer across all key options in a category? (e.g. through CDT)

Do you use data science to analyze detailed store /SKU historical sales baskets to identify correlations and sales patterns when ranging stores?

CLUSTERING – THE KEY FIRST STEP TO CURATED CUSTOMER ASSORTMENTS HOW GOOD IS YOURS?

Do you cluster considering 2 or more attributes together? (e.g. store revenue, demographic profile, store grade, store size, competition, climate)

Do you cluster using a single measure? (typically either sales value, sales units or margin)

Does your clustering provide unique insight into how your customers shop differently across your estate?