How a Data Strategy Could Unveil More Insights

The most successful retailers invest much in measurement and analysis tools across all shops and internet levels to comprehend how they are able to continuously improve.

The most common metrics include:

  • Traffic, and in which the clients came from.
  • Proportion of new vs. returning clients.
  • The amount of searches, products hunted, the amount of bought vs. abandoned.
  • Which shop areas/site pages were visited, time spent, which have been seen before a purchase, which resulted in abandonment.
  • Quantity of purchases made, average basket quantity and value.
  • Geo-Demographic, psychographic and behavioural characteristics of visitors and clients.

Whilst these places are easy to track online with tools like google Analytics, they’re more challenging to monitor in a shop environment. Traffic counters, RFID tags, beacons, apps, CCTV and POS, CRM and loyalty information must be aggregated to glean critical insights.-These insights have to be analysed using an omni channel approach. A customer’s’ travel is constructed from multiple touch points, and with a clearer perspective of behavioural patterns may result in differentiated and more compact activity.

Mastering a Data Plan

Successful retailers have a clear definition of data types in their business across:

  1. Merchandise
  2. Clients
  3. Sales
  4. Finance
  5. Inventory
  6. Purchasing
  7. Supply Chain
  8. Shop
  9. Online

For each of the nine areas above, they have clearly defined hierarchies with many levels of categorisation and tags, in order to analyse high-income tendencies or drill down into granular detail.

The real power of a master information plan comes from being able to drill-down on multiple kinds of operational data (e.g. Products + Clients + Inventory), to answer probing company questions.

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By way of instance, it would be enlightening to not just understand what the best performing brands are from the womenswear class but also which things are within those best-performing brands? What are the common characteristics and buying patterns of consumers who have bought these items? What stations and media touch-points made up their travel? What locations have the greatest sales rates of the product types, classes, and characteristics?

Recognizing operational and sales data can let you optimally plan assortments and stock allocation for each shop, lowering freight costs and increasing sales.


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