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Forget about the ‘big data’ buzzword and start using your own data

data-image-graphBuzzwords are rampant at the moment. All over the world, ‘tiger teams’ are working on ‘north star’ solutions, and irritating the rest of the normal population at the same time. Usually I have minimal time for people who speak in ‘innovation speak’ and not plain English, however there is one buzzword that has caught my attention: ‘Big data’. As an analyst who’s idea of fun is watching Youtube clips on how to build Microsoft Access databases, the arrival of the buzzword ‘big data’ has heralded a renaissance period for all analysts everywhere.

Isn’t it just data? Why is it big?

During the 1990s, creation of data was spurred by the first super-computer being built and more devices being connected to the internet. However it’s only in recent years that extracting and storing data has become so accessible that almost anyone is able to find free solutions to keep data related to their business and draw insight. According to IBM Research, 90 per cent of the data in the world today was created in the last two years alone. There literally is so much more data now that we have leapt into the buzzword world of ‘big data’.

And as accessibility to data has increased, more people are realising that data is not just numbers used for monthly reporting.

We have data, but what do we do now? Record, segment and personalise!

Take an example of a shop that sells widgets – in today’s world, this shop does not need to be a large corporate to use it’s own data to generate more sales. For this shop sales are generated from customers and they can record that a:

  • Customer purchases a specific:
    • Product; at a specific
    • Price; with a specific
    • Volume; at a specific
    • Time during the day; at a specific
    • Time during the year.

Further, the shop assistant might be able to keep notes on the customer in a structured format. The shop assistance has defined descriptions to choose from for each customer (eg. Old, Middle-Aged, Young). The shop assistant will have biases, however you are going from a position of having ‘gut feels about your customer to specific knowledge. Also note that the data is not necessarily numbers. The key thing is that it’s ‘structured’ – each bit of data can fit into a pile without any thought. You are not stranded wondering if the shop assistant meant old, middle-aged or young, when describing someone as ‘new-age’. The important thing is to give the shop assistant defined criteria to classify customers into.

There is no doubt that as a shop owner you would know your customers best, but operating in specific terms can open up unknown opportunities. The first step is to segment your data – that is to group data by certain characteristics. For example:




All of these outcomes are expressed with defined criteria. This criteria can then assist in personalising how you communicate with this customer. If a Middle-Aged customer walks in, you can make the following decisions based on the above knowledge to increase conversion and sales:

  • Spend more time with customer as they are likely to spend more
  • Direct them towards Widget 2
  • Extend shop hours to allow for more of them to come at 8am
  • Market reminders about Widget 2 being available especially around July

The first step of artificial intelligence

Major retailers are still learning how to use data to improve sales. An email from Woolworths or Coles nowadays aggregates spending data (after swiping a loyalty card) and has reminders to purchase things that I often purchase, but also include olive oil – an item I had almost run out of. But this is not some fancy artificical intelligence, rather it’s just a log of simple logic questions being asked about your spending behaviour versus the average consumption of that product. Coles knew when I last purchased olive oil, how much I purchased, and the average consumption of olive oil – from there they could predict (assuming I wasn’t an outlier, olive-oil guzzling fiend) when I would need a new bottle.

The same principles can be applied across any sector to personalise a customer’s experience and increase their likelihood of spending.

Big data can help achieve small incremental growth

Data doesn’t need to be a dirty word. It doesn’t need to be restricted to use only by ‘analysts’ or those in finance. Data should be on everyone’s agenda. Marketing research firm, Adlucent asked 2,000 consumers to name the greatest benefits to receiving personalised information – consumers listed as helping reduce irrelevant ads (46 per cent), a way to discover new products (25 per cent) and making online shopping easier (19 per cent) as the top responses. The fact that data is so accessible gives no business an excuse to ignore using their data to personalise product development, inventory management and marketing to their different customer segments to maximise return.

Arani Satgunaseelan is a principal consultant at ADP & Co, a management consultancy specialising in strategy and analytics for the retail sector. Arani can be contacted at

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