‘Retail Analytics’ A Modern Day Revolution

The first industrial revolution started just over 250 years ago. It was followed by a second industrial revolution just under 150 years ago. In my opinion, we are now in the midst of a third industrial revolution. Rather than steam power or electricity being the catalyst, this revolution is driven by big data and datImage 2a analytics. Data is the new currency.

I often hear the terms ‘big data’ and ‘data analytics’ being used interchangeably. I am not particularly precious about terms. What I am completely focused on however, is enabling the realisation of busin
ess value and competitive advantage from the promise of this third industrial revolution.

So, what is retail analytics? The answer is refreshingly simple. It is data analytics in a retail context. In other words, the prevailing use cases are retail focused. Data analytics is essentially defined by its use case. With this in mind, it can be shaped to pretty much any context. For example; service analytics, business analytics, digital marketing analytics, security analytics. This list goes on and on

Ultimately, the basic goals of data analytics and consequently retail analytics are to:

  1. Transform data to information, to knowledge and to wisdom
  2. Drive the creation of actions based on this resulting wisdom (insight)
  3. Anticipate what is likely to happen and prepare for it
  4. Influence what may happen to gain competitive advantage

I have also heard this described by Splunk as ‘making data accessible, usable and valuable to everyone’. What sets data analytics apart from traditional business intelligence is that the focus is on real time insight, allowing today’s decisions to be based on today’s data. The art of the possible in terms of queries do not need to be specified ahead of time. Once you have the data, you can ask whatever you like, however you like. I must admit that I tend to favour the term ‘right time’ over ‘real time’. Use case workshops are very useful in determining how quickly data needs to be collected and reported or acted upon. Real time does not be an incendiary concept. It needs to be a standard option based on business requirements.

As a general rule, the quicker you can put in to the finger tips of the decision makers, those beacons of enlightenment, based on what has actually happened, the more resilient and effective those decisions can be. This is especially true if decisions need to be made in real time and there is an appetite to automate decision making and instigating process and work flows based on those decisions. Automation should of course be for known good processes.

One of the most critical decisions an online retailer can make is when to put up a holding or busy page on their website to protect it from being overwhelmed by sheer load from visitor traffic. This decision has profound implications for key success factors such as customer experience, ability to trade and brand credibility. We have all seen the newspaper headlines around ‘Black Friday’ trading. Optimised application of well-formed data analytics can make the difference between glorious peak trading and painful peak profile.

 

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