How analytics help retail stores maintain, increase sales

With increased competition, analytics allows retailers to make a variety of intelligent decisions that help delight customers and increase sales.
How analytics help retail stores maintain, increase sales

Today, retail chains are expanding at an unmatched rate to span multiple geographies, markets, demographics and communities. Also with the increased competition leaving little or no room for mistake, analytics allows retailers to make a variety of intelligent decisions that help delight customers and increase sales.

These decisions range from assessing the market, targeting the right segment, forecasting demand to product planning and localising promotions. This has also created a need for understanding the customer behaviour, promotion planning, optimising the supply chain and benchmarking the store as well as the online performance.

Benefits

Starting from the in-store footfall of customers to inventory level optimisation, the benefits of analytics can be utilised in various facets of the retail journey. Advanced Analytics solutions such as Inventory Analysis, Price Point Optimization, Market Basket Analysis, Cross-sell/ Up-sell Analytics, Real-time sales analytics etc. can be achieved using techniques like Clustering, Segmentation and Forecasting.

These techniques help in deriving insights or attributes in the form of seasonal demand patterns, sales trends, customer segments, market sentiment etc. Further, sections help in understanding the role of analytics in some of the key areas of the retail value chain.

One of the major challenges faced by the retail industry today is ‘customer retention’. But customer analytics solutions like dwell time analysis, shelf space optimization, Zone Analysis etc. have made the ability to understand customers’ shopping patterns and behaviour easier.

Further, these insights can be effectively used to improve a customer’s shopping experience in turn helping to increase the footfall by acquiring new customers as well as ensuring repeat purchase from the existing customers.

Maximising profits

Another key area for retailers is targeting the right customer segments, which share similar characteristics and maximising the profitability from them. Techniques such as Segmentation and Clustering using tools like SPSS, SQL, JMP etc. can be easily applied to such scenarios. They help in predicting the behaviour of existing and new customers by simply categorizing them into a particular cluster and then running targeted marketing and services activities for increasing the ROI significantly.

For maximising profits using up-selling and cross-selling techniques, analytical models and frameworks help in identifying what products or services a customer is in the market for. This data can then be used for predicting what specific products they might be interested in or determining whether and how much of an incremental incentive is required to entice the customer to convert.

Campaign management

Analytics also plays a crucial role in developing robust Loyalty Programmes for efficient campaign management. It starts with designing the campaign calendar over a sales cycle period followed by executing and establishing the right customers for each campaign.

An interactive dashboard representing various KPI metrics can be used for quick and easy review to measure and evaluate the performance of the Loyalty Programme. Also, analytical solutions such as client lifetime value optimisation can be for designing tier based Reward Programmes. The benefits of using such a structured approach are two-fold.

Firstly, the marketing department can systematically review the discreet metrics associated with each campaign and adjust or discontinue campaigns as needed based on understanding of the consumer behaviour. Secondly, a disciplined regimen of marketing analytics can be used for justifying the dollars spent on specific programs by demonstrating the success of those campaigns.

Gaining competitive edge

Another highly advanced and futuristic application for analytics in retail is measuring the impact on online channels by assessing the in-store experience. This uses techniques of Web Analytics coupled with use of devices – such as sensors, Wi-Fi and GPS geo-fencing for reading the facial expressions. This is a very powerful tool for cross-channel retail businesses.

In the period of cut throat competition and overwhelming change, retailers are constantly looking out for techniques to improve profitability. Analytics with its statistical models and scientific techniques acts as a stable all-round partner for gaining the required competitive edge.

The writer of this article is Durjoy Patranabish, Senior Vice President – Analytics, Blueocean Market Intelligence. The views expressed here are personal.

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