Retail Analytics: The Third Industrial Revolution

The first industrial revolution began just over 250 years ago. Another followed just under 150 years ago, and now we are in the midst of a third – rather than using steam power or electricity, this revolution is now being driven by big data and data analytics. It is clear that data is the new currency.

[easy-tweet tweet=”It is clear that data is the new currency.” hashtags=”data, tech, comms,”]

Data analytics is essentially defined by its use, so, what is retail analytics? The answer is refreshingly simple: employing data analytics in a retail context. With this in mind, it can be shaped to fit 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
  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.

[easy-tweet tweet=”What sets data analytics apart from traditional business intelligence is that the focus is on real time insight” hashtags=”cloud, tech”]

For example, 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. Using data analytics for real time insight enables retailers to predict these trends and make well-informed decisions ahead of time, often saving the business from potential trading disasters.

As a general rule, the quicker you can put enlightening information at the fingertips of decision makers, based on what has happened, the more effective the decisions they make can be. This is especially true if decisions need to be made in real time and if there is an appetite to automate decision making and instigating process and workflows based on those decisions. Overall, optimised application of well-formed, outcome-driven data analytics can make the difference between glorious peak trading and painful peak profile.

[easy-tweet tweet=”Using data analytics for real time insight enables retailers to predict trends” hashtags=”cloud, tech”]

However, it’s not just in predicting trading patterns where retail analytics can add value, there are a number of emerging trends that can be identified in every day retail scenarios. Firstly real-time offers i.e. creating targeted offers that can be received at a kiosk or on a receipt as a result of the day’s shopping. Video analytics are also becoming commonplace in order to gather information on the flow of shoppers in-store, measuring how shoppers observe product placement and to gain insight on how best how to lay out displays. Finally, sentiment analysis is becoming a huge tool, examining the language and extracting that data from blogs, social networks, reviews etc to gauge customer feeling towards a product or service.

It may take a little longer for retailers to transform themselves into precision analytical machines and the initial investment may inhibit some retailers from exploring their analytic capabilities. However, some of these investments in analytics can generate income quickly, improve productivity and even lower costs. Not only that, the ability to predict buying trends, customer preferences and trading patterns helps to safeguard business against future disruption.

The overall trends are clear: retail is a data-intensive industry, and taking advantage of all that data to operate and manage the business better requires analytics. Most retailers have only scratched the surface of what is possible, and now it’s up to decision makers and business owners alike to fully realise and embrace the potential of this third industrial revolution.

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