How Data Analytics is changing the future of retail industry

Managing and channelizing data to work towards customer delight as well as generate healthy profits is crucial to survive prosperously.
Retail Technology

As retail market becomes extensively competitive, the ability to optimize on serving business processes while satisfying customer expectations has never been more important. Therefore, managing and channelizing data to work towards customer delight as well as generate healthy profits is crucial to survive prosperously.

In the case of big retail players internationally as well as in India, data or rather big data analytics is now being applied at every stage of the retail process –tracking emerging popular products, forecasting sales and future demand through predictive simulation, optimising product placements and offers via customer heat-mapping and many more. Alongside this, identifying the customers likely to be interested in particular product SKUs based on their past purchase behaviour, working out the best way to approach them through targeted marketing efforts and finally working out what to sell them next is what makes the crux of data analytics.

There are a few key impact areas where retail players see a ready use as far as data analytics is concerned.

Impact Areas:

Forecasting Trends: Today retailers have advanced tools available to them in order to know the trends in the industry. Trend forecasting algorithms sift through the buying data to analyse what marketing departments need to promote and what not.

Predicting Demand: Once the retailers get a deep insight into buying trends of the customers, they focus on the sectors where there will be high demand. This involves gathering demographic, seasonal, occasions led data and economic indicators to build a picture of purchase behaviour across the targeted market. This really helps in inventory management in a better way.

Identify the Highest ROI Opportunities: After having a better understanding of their current and potential customer base, retailers use predictive risk filters and data-driven intelligence to model expected responses in marketing campaigns, as measured by propensity to buy / likely to buy.

Predicting Future Performance: Each customer interaction can have a large impact on existing or potential relationships. Rolling out a new idea to the whole sales force is a risky endeavour, as a wrong decision could lead to immediate as well as prolonged loss of profit. Instead, leading business organizations have found that the best way to isolate the cause-and-effect relationship between any strategic shift and key performance indicators through use of  a test-and-learn approach – trying an idea with some reps or customers and comparing the performance of the ‘test’ group to the performance of a well-matched ‘control’ group

Price Optimisation: Data Analytics plays a vital role in determining the pricing. Algorithms track demand, inventory levels, and competitor activity and automatically respond to market changes in real time, allowing action to be taken based on insights in a time saving manner. It helps in determining when prices should be dropped – commonly known as ‘mark down optimisation’. Prior to the age of analytics most retailers would just reduce prices at the end of a buying season for a particular product line, when demand has almost gone.

Creation of Client Profiles: Big data management segment buyer’s data to create personality points, demarcating faceless mass into slots, through studying their purchases. Transaction reports and loyalty plans are combed through, to bring out relevant data and action on it.

Accommodating the Small-Scale Retailers: Big Data is also at the helm of small-scale retailers, who can take assistance from platforms playing providing the services. Also, there organisations, mostly start-ups, which offer social analytics to induct the products onto social media networks. Thus, small-scale businesses can bask in the merits of big data without stretching their budget into bankruptcy

But, while this may sound really impressive, big data management and analysis comes with its own set of challenges.

Challenges:
Several issues will have to be kept in mind to optimize the full capabilities of big data. Privacy, security, intellectual property, and even liability policies need to be stringent in terms of big data. Since big data encapsulates high end analytics, specially trained professionals need to be added to the team to utilize and functionalize the big data. Companies need to integrate information from multiple data sources, often from third parties, as well as deploy an efficient data to aid such an environment. Lastly, many times, companies fall in short-sightedness, failing to implement insights from analytics. This can be fixed by continuous alterations of retail styles where a certain team is allotted for task of arrangement of insights and their implementation.

Conclusion:
Retailing is at the platform for more data-driven disruption because the quality of data available from internet purchases, social-network conversations, and recently, location-specific smart phone interactions have emerged into a new entity for digital based transactions. Improved performance, better risk management, and the ability to unearth insights that would otherwise remain hidden, are the benefits organisations reap through utilization of big data management. 

Authored By: Atul Soni, Co-Founder, Cuberoot Technologies

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