Sales

Cloudspace Analytics empowers retailers to better understand shopper behavior and develop insights that drive company sales and measurably improve the customer experience. The result is an optimized sales and distribution approach to align with shifts in our markets, customers, and competitors.

Within Sales, we address a range of topics:

  • Stores & Website Insights
  • Fraud & Return Analysis
  • Offering Optimization

Stores & Website Insights

We enable organizations with our Coverage Models to improve the number prospects reached in order to drive and improve traffic and conversions.  We also support retailers to move beyond coverage and develop integrated repositories of their store assets, integrated with geographic information systems (GIS) data and all data on costs, store performance through its lifecycle (opening to modernization to closure) operations and maintenance, and format changes and modernization projects. The data can be used for rapid analysis of what-if scenarios and analysis of financial performance drivers.

  • Optimize Advertising Coverage
  • Locate Stores in optimal locations
  • Allocate Investments between stores
  • Localized Offers based on customer structure and demand
  • Micro Market Planning and Geo-Spatial Analysis
  • Loyalty Participation
  • Customer Retention
  • Productivity Reporting
  • Web Commerce Analysis
  • Visitor Analytics (demographics/visits/loyalty)
  • HR Reporting

Fraud & Return Analysis

Fraud Analysis

We help retailers identify anomalies and patterns by putting in place continuous monitoring tactics that look for unusual patterns in product and inventory movement. We create alerts if anything looks different from the norm.  Our solutions provide a flexible framework to define rules and application of those rules to a variety of data sources. We can build connectors that pull data from log files and then analyze them in real-time.  This automation will lead to reduction in fraud, fewer calls to customer service, and improved profitability.

Product Return Analysis

A product return is the second most common customer-service interaction. A return could occur for several reasons, such as a damaged product, the wrong item was shipped, an item that did not fit, the customer found the same product cheaper elsewhere, or even the customer did not like the item after it arrived and decided to make use of the free returns policy. Once the retailer has all this information, it can optimize the product assortment, determine which vendors are working better, and assess a customer’s history. This will result in reducing the returns rate and lowering the number of tickets opened by the customer service team.

  • Predict orders with a high return probability for triggered service call or email follow ups
  • Identify misleading product descriptions or where sizing hints may be necessary
  • Eliminate troublesome products
  • Investigate how delivery times affect product returns
  • Identify customers that have unusually high return rates

Offering Optimization

Localization & Clustering of Products and Offers

The era of standardization in retail is ending. Retailers are customizing, for localization purposes, a wide variety of value proposition elements, including assortments, pricing, store formats, promotions, and staffing.

We offer analytics that support localization:

  • Identify demand differences at categories and product level
  • Support necessary changes in pricing and assortments
  • Determine variations in promotion responsiveness based on local competitive forces
  • Segment and classify stores by geography and the demographic and behavioral attributes of customers

In-Store Tracking to support Layouts and Planograms

Using In-Store Analytics, we can account for diverse factors like the physical location of shoppers and employees in the store: the layout, fixtures, and planogram of the store; staffing schedules; complete detail on actual sales, and even the weather. Input sources can include video cameras, Wi-Fi tracking tags, RFID, and other in-store systems like those for Point-of-Sale (POS), staffing, and task management.

  • Kinetic Heat Maps – Allow retailers to understand shopper movement in their stores, be it high-traffic areas, bottlenecks, or neglected areas that need attention
  • Department Path Analysis Maps: Shows specific information about the departments that shoppers visit and in which order based on mobile detection
  • Full Path Analysis Maps: Provides the most detail, indicating paths that shoppers take during their movement across departments, through aisles, and towards fixtures based on video analysis