In retail, the factor that has the highest impact on business is how the changing demographics, lifestyle and consumer needs is affecting the shopping behaviour of customers.
Over the past few years, the way traditional retailers engaged their customers has undergone a paradigm shift. The shoppers now are flooded with offers, they conduct online research, compare products, and even seek bargains. They have a higher control over their buying behaviour – how, where and when they should buy.
Retailers must invest in understanding their customer behaviours and align everything else to attract new customers, retain existing ones, and improve value they can offer to their customers.
Most retailers already have a vast amount of data available to them that could help them analyse and find insights in their customer behaviours. It’s also not enough to understand these insights. The analytics solution that the retailer uses must also help them take actions on these insights.
One of the most common uses of customer analytics is to segment customers based on their buying behaviour and accordingly design offers for each segment at the right price point for the right products and services. This way retailers can significantly boost customer loyalty and create a personalized shopping experience for each customer. This is an ongoing process – as the customers’ needs, preferences and buying behaviour keeps changing, the retailers will have to keep innovating and finding ways to keep their customers attracted.
Apart from this, retailers can also look in to the basket data of customers and apply analytics to identify those customers that have a higher probability to spend more but are currently being underserved. With this information, for example, retailers can design strategies to up-sell and cross-sell products. While thinking about this, it may also sometimes demand changes in the way data is captured at POS. For example, along with what the customers purchased, can we also get information about what they asked for but was not available on the shelf?
Similarly, analytics can also help retailers identify their most profitable and most unprofitable customers. Companies do this by using analytical approaches and tools to calculate the lifetime-value scores and other metrics for customers. Typically 30% of your customers generate 70% of your profits. Your most unprofitable customers fall under the discount-seeker segment. They form the bottom 20% of your customer base and shop with you only for discounts. Once you have identified these customer segments, you can develop appropriate strategies to manage them. The question you need to answer is – How important are these loyal unprofitable customers for your business?
Apart from this, advanced analysis of data can also reveal which customers are most likely to stop shopping from you, and why? With this knowledge, the retailers can take pre-emptive actions to identify these customers and prevent these customers from leaving.
To conclude, retailers can use their data to start understanding customer behaviour, and make decisions based on this knowledge to increase sales as well as margins. They can start making adjustments to their stores layouts, assortments, pricing, and even the products they offer with focus on becoming customer centric and improving their ROI.