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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

 
 

ASSORTMENT OPTIMISATION READINESS QUIZ

STORE CLUSTERING READINESS QUIZ

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Would you like machine learning to tell you how much sales leakage you have in each store through outdated category management systems and processes?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

Instantly move from a basic to a cutting-edge ‘Space Range & Offer’ process. Leapfrog your competitors.

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.

Would you like to experience a customized demo, get answers to your specific questions and find out why RETAILIGENCE is the right choice for your organization?

REQUEST DEMO

You can also call us on +44 (0) 2035 732737, drop us a line at info@retailigence.co.uk

ASSORTMENT OPTIMISATION READINESS QUIZ

STORE CLUSTERING READINESS QUIZ

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

Competing with JDA and RELEX, they showed a satisfactory & fast approach.

Large European Retailer

Hardlines, including Electronics & Other goods

I haven’t seen a more user-friendly interface in any other Assortment Optimization tool, this is a very important aspect for us since the 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.

Karla Chinchilla

Head of Category Management (Electronics Retailer)

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

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

Do you use a ‘concentric’ ranging approach with space the only differentiator determining the assortment ?

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

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

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

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

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

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

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

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

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

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

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

Do you use data science to analyse 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 by store size?

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

Do you cluster Stores at a company wide level?
(rather than for each category or department)

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

Do you use a solution to cluster stores?
(rather than manual or spreadsheets)

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) 

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

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

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

Do you cluster by product attributes?
(e.g. brand, price position, material)

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

Do you use data science to analyse patterns in detailed store/ sku level sales history to cluster like stores?

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

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