It has been interesting watching the birth and growth of the phrase BIG DATA. Some see it as a big scary unwieldy ‘thing’ and other see it as the holy grail – it will deliver magical results but is illusive.

A couple of definitions to help understand it:

Big Data: noun

Extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.


Big data – Wikipedia

A broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy.


What is Big Data: IBM

Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is big data.


So let’s accept:

  • it’s big
  • it’s out there
  • it’s not only for large corporates
  • we need to use it in our businesses – big & small
  • most retailers in Australia aren’t collecting any data at all let alone tapping into big data but there are baby steps to the path to ‘big’


Let’s investigate what it means for retailers and how they can use it.

A few basics retailers need to get started:

  • Point of Sale (POS) System – preferably including Inventory Management
  • e-commerce website
  • social media activities
  • customer loyalty program


Many of the above tools have build in analytics and business building tips that are great to get started. 

Retailers need to collect customer details with these tools:

  • POS
  • Online
  • In-store


Collecting data, very local and specific data is the best way to get started.

Data is useless unless it is ANALYSED and USED! The data we want to analyse includes:

    • Campaigns and programs they respond to
    • When they are active
    • Their preferences and buying signals
    • Email subjects they like, click and open
    • Their activity online


    • Average sales value
    • Average sales value at different times of the day and different days
    • Tweaking store opening hours to match sales / customer behaviour
    • Sales person’s performance


    • How often the customer purchases online & instore
    • Average sales per customer
    • Customer preferences – shopping basket assortment


    • What is selling
    • What is in stock
    • What needs to be ordered
    • Historical seasonal data for following year / season


    • Sales per customer
    • Landing page
    • Pages the visit
    • How long they spend per page / on site overall
    • Where the customer was before they landed on the site
    • Customer behaviour – do they respond to pictures or text etc 


    • Overheads expenditure
    • Value of Expenses
    • Sales overlaid on expenses
    • Where savings can be made
    • Supplier analysis – make yourself known to suppliers who are bought from the most and negotiate special buys, collaborative activities etc


    • Do customers respond to sales incentives
    • Customer conversation
    • Customer reactions – which posts have the most engagement
    • What sites do your customers like and spend time on


    • Train staff to use the tools and analyse the data to do their job
    • Provide tablets in-store to enable staff to use the tools and access information on the customer they are serving
    • The most proficient staff should then train and supervise other staff to be more productive


It is important to further analyse the results of above comparatively per day, per month, per year.

The business will be better and more profitable by analyzing the above and making business decisions based on the information and data.

More importantly, it will enable the business to interact and communicate with the customer with personalized information, based on actual behaviour, which in turn will create a more loyal customer.

Once the business is seamlessly using and integrating the data into the business then it can look outside to additional data and take it to the next level!


This article was commissioned by Klugo and appeared on their blog, click here to view.

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