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Big Data Analytics for the Retail Industry

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Big Data Analytics for the Retail Industry

How big data analytics have changed the brick-and-mortar and online retail industries for the better.

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“Forrester predicts that cross-channel retail sales in the U.S. will reach about $1.8 trillion by 2017.”

It seems like the retail industry is where the most action takes place — almost always. Because that segment is, in one way, an expression — as indeed a representation — of the output of so many other industries. In that sense, if you have got a good hold of the nitty-gritty of running a retail business and, you are doing it sustainably, then you have just mastered the art of business (in general). Admittedly, that is a huge statement to make. Do we have enough ground to substantiate the statement? Probably yes.

Let us put into perspective as to what all used to happen in the retail sector before the IT revolution, what is happening currently, and what can potentially unfold in the near future. This will help us co-relate the same with other industries too. Further, the approach for implementing big-data & analytics for the retail segment will likely lead the way for other industrial verticals for it to be recognized as a business imperative.

Some Indicators of What Used to Be

  • Retail buying or selling used to be physically undertaken
  • In many countries, things (products, offerings, etc.,) used to be available in different outlets, and some only at certain outlets (places)
  • Mode of transaction used to be physical (cash)
  • Limited channels for easy dissemination of product or service information
  • One had to actually  use (buy) contemporary products to be able to compare and decide the best fit (suitable)

Then, with the advent of information technology, things started to transform. Although it may not have had a direct bearing, automation of many processes brought about a paradigm shift in how the retail industry functioned. There sprang up malls, shopping complexes, and super-markets with multiple payment options (credit /debit cards) for customers, and greater spread of items to explore, review, compare, and choose from: all of which helped to greatly improve the buying experience. The millions of POS (Point-of-Sale) applications, supply chain automation and, increased eye-share and mind-share through proliferation of information via the electronic media (Televisions, Internet, etc.), helped businesses in better delivering their offerings to customers.

Present Scenario

A study found that 80% of retail consumers are now using a computer, smartphone, tablet, or in-store technology while shopping.

The internet helped bring about the phenomenon (yes, it is!) called e-commerce. Powered by the internet, millions of businesses went online to give themselves another sales avenue (front). Subsequently, with telephony getting affordable and internet becoming more easily accessible and, the influx of more and more gadgets to be used in day-to-day life, it was time for online shopping or e-commerce to go platform and form-factor agnostic: to be available on the web or phone or tablet - regardless of the operating system or technology or the brand.

Thus, in an increasingly connected world, user interaction and engagement with a brand through any medium continues to result in huge volumes of data that has been put to effective business use by applying analytics.

“A study by McKinsey found that the uses of IoT in retail could have an economic impact of $410 billion to $1.2 trillion per year in 2025.”

Analytics run on business data – both historic and real time, are helping retail businesses to do

  • Customer profiling: Based on a customer’s activity with the brand/company offerings, a great deal of information about the customer is captured. Business owners can now know their customers’ preferences, interests, requirements, spending capacity, etc. better, offer products or services accordingly, and promote relevant offerings to build loyalty.
  • Recommendations: The power of  real time big-data analytics is such that smart businesses (e-commerce portals) no more wait for the customers (visitor) to ask for an option. Instead, they have a recommendation engine (propelled by analytics) that finds out what the customer is likely to check out and buy (based on the current/past search or buy). Thus, cutting down on the product discovery time (otherwise wasted by not knowing and, hence, not finding the right or desired product), which in turn leads to increased sales, revenues and, profitability even (although many recent e-commerce portals, especially the ones based on the market-place model offering aggressive discounts, are yet to break-even).
  • Social media sentiment analysis: Online social networking has assumed the role of a great influencer in recent times, what with most deciding to express their happiness or dissatisfaction (as the case may be) with a brand on the various social media platforms that they are active on. While the good reviews, likes, and shares may mean greatly increased brand affinity and potential revenues, the flip side is that it could act as a negative influencer, fanning and flaring sentiments against a brand or company or service. Thus, businesses would do well to have a well thought out and thoroughly researched analytics solutions that will analyze the social media sentiments (regarding the respective brand) in real-time, and facilitate damage control or course-correction, if required.
    “43% percent of social media users have purchased a product after sharing or favoriting it on Pinterest, Facebook, or Twitter.”
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    There could yet be many more scenarios where analytics can be better employed to run retail businesses effectively and profitably.

    Extending customer profiling

    “96% retail executives surveyed agreed that Big Data initiatives are important in helping retailers stay competitive.”

    Customer retention by all (lawful) means is one of the biggest challenges faced by all industries, particularly the retail industry. We have seen how customer profiling, recommendations, and social media sentiment analysis can help improve business efficiency. There are other aspects of customer profiling (through analytics) that are equally potent of helping businesses retain a happy and loyal customer base.

    Customer retention can be facilitated by undertaking a study of customers based on their spending capacity, indulgence, interests, preferences, etc., so as to understand the dynamics involved as also to spot the pain points, which can then be addressed.

    Listed here are some areas through which retail businesses can benefit by understanding their customer base properly and, accordingly, aligning their strategies to achieve optimal business efficiency and customer retention.

    • Customer Segmentation
      • Spending Capacity
      • Spending Behavior
    • Customer Loyalty
      • Duration of association
      • Numbers of transactions
      • Transaction Amount
    • Customer Feedback / Rating
      • Loyal
        • Satisfied
        • Not Satisfied
      • Non-regular customer
        • Satisfied
        • Not Satisfied

    An analytics case-study conducted recently by MSRCOSMOS LLC on (online) sample data found some interesting patterns, which show that businesses need to be wary and cognizant of the fact that with customer’s it isn’t always what meets the eye. The most interesting finding, noticed in the loyalty-rating combination for a particular merchant, was that a few of the most loyal customers gave adverse feedback and a very low rating for the merchant!

    Image title

    In another iteration, customer loyalty towards a specific merchant (brand/company) was depicted based on the longevity of association (represented in terms of year of customer and last transaction date/year) and the numbers of transactions through that period and the corresponding transaction amount. The report shows the number of transactions and the average transaction amount for a specific customer during his / her association with the merchant. It also depicts the customer feedback based on the rating received by the merchant from each of customer. More insights could be gained by further analysis and criteria definition which can then be consumed by decision makers through easy-to-use (and understand) dashboards.

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    The reports here stand testimony to the fact that any business, especially in the retail realm, cannot afford to ignore the noise made by its operational data as, along with opportunities, there could potentially be some threats to the business, that are better nipped in the bud. As in the case below, even the most loyal of customers reach a tipping point where their affinity (liking) towards a brand or company begins to wane. It is such types of transgressions that can be spotted and addressed by big data analytics.

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    All the above references to results are only indicative, and intended to make a case for the possibilities of using big-data and analytics for operational excellence and optimum business performance.

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    Topics:
    big data analytics ,retail industry ,real time analytics ,analytics ,big data ,bigdata ,real time insights ,real-time analytics ,ecommerce

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