Localization, Clustering and Real Estate Asset Management

Retail Analytics

Use analytics to boost your brand and sales, better inform business decisions and provide customers with a more seamless shopping experience.

Localization, Clustering and Real Estate Asset Management

Technological advances—from Internet stores to data mining to electronic product tags—are providing retailers with deep insights on local buying habits. Retailers can develop repositories of their real estate assets, integrated with geographic information systems (GIS) data and all data on leases, costs, store performance, operations, and maintenance to optimize their real estate portfolio. 

Our experts can analyze your data to spot clusters and communities with similar buying habits and demographics; customize your offerings to local markets, and roll out different types of stores, products, pricing, marketing, and even customer service strategies. Many different elements of your company can be customized. Localization can be offered around three areas:

  • What is Being Sold (Offerings)
    • Branding (store banner names, product labels)
    • Store Formats (size and layout, store design type)
    • Merchandize Space and Assortment (division, category, department, classification, attributes like size and color, packaging design, etc.)
    • Pricing (everyday low vs high-low policies, ranges, point, matching policies)
    • Promotions (channels, temporary price reduction levels, in-store displays, markdown policies)
    • Vendor Policies (information sharing, expense sharing, product collaboration)
    • Marketing Programs (spending levels, media mix, major messages) 
    • Store Service Policies (store hours, labor quality, delivery policies, checkout stations)
    • Vendor Policies (direct store delivery, replenishment and stocking, customer education) 
    • Operating Policies (inventory level, stocking strategies, shrinkage controls, and information sharing)
  • Where it?'s Being Sold (Location)
    • Consumer Characteristics (demand pattern like store purchase and area purchase)
    • Geodemographics and Attitudes (population density, age, income, marital status, ethnicity, religion, lifestyle segment, psychographic)
    • Special Demand Drivers (school seasons, hunting and fishing seasons, activities and sights, special events, and climate zones)
    • Competitor Characteristics (store saturation levels, market share, store locations, store formats, pricing levels, promotion policies, and marketing programs) 
  • When it's? Being Sold (Time)
    • Hour, week, day, month, and season 

Cutting-Edge Retail Analytics Expertise

Experfy provides the world's most prestigious talent on-demand

5199d2f5 80be 497c afcf 7eeb317462e1
Works at Pricewaterhouse Coopers
Senior Consultant - Retail, Internet, & Operations
44f66e05 91f8 4a87 a9b7 9ceaa54137db
Worked at AutoZone
Senior Merchandising Analyst
Empty user
Works at Walmart Labs
Software Engineer

Request a Free Consultation

Tell us about your business problem and we help you define it further.

The Harvard Innovation Lab

Made in Boston @

The Harvard Innovation Lab