The big retail analytics conundrum

Knowing your customer is a motto as relevant today as it was in the days of the neighbourhood retailer. With unlimited shopping options, securing customer insights to build enduring one-to-one relationships is more critical now than ever before.
Teradata India

Knowing your customer is a motto as relevant today as it was in the days of the neighbourhood retailer. With unlimited shopping options, securing customer insights to build enduring one-to-one relationships is more critical now than ever before.
India’s retail market has emerged as one of the most dynamic industries in the country and is expected to nearly double to US$ 1 trillion by 2020 from US$ 600 billion in 2015, driven by income growth, urbanisation and attitudinal shifts. This year, the retail market has seen predictive analytics help in obtain significant market gains, both with regard to online and offline models.
It is critical to perfect the art of listening to customers, and with it, the science of targetting the right customer with the right information, at the right time through the right channel. Now, with Dussehra and Diwali festive seasons around the corner, retailers are ramping up their readiness to meet high customer demand. However, smart retailers should also be focused on sales and inventory data flowing in from previous festive/ high-demand seasons to better analyse demand trends to ensure they’re adequately equipped to meet sudden changes or spurts in demand. Incorporating sophisticated analysis of historical sales, inventory and promotional data to help better plan merchandise needs and predict customer behavior using predictive analytics technology can help meet this requirement.
Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behaviour patterns. Apart from enabling businesses to derive foresights and take forward-looking decisions, predictive models unveil and measure patterns to identify risks and opportunities using transactional, demographic, web-based, historical, text, sensor, and unstructured data. Analysts foresee that, by 2016 nearly seventy per cent of high-performing companies will manage their business processes using real-time predictive analytics.
For example, a retail organisation can determine actual product demand and better plan and customise their assortment mix to meet the needs of each store in a cluster by evaluating sales performance by category. This capability may include determining proper service allocations for existing items to introducing new items. It also helps identify which products to delete from specific or all stores in a cluster.

Some of the areas that it helps both online and offline retailers include the following.

i. Segmentation based on interaction and behaviour data – move from the traditional segmentation techniques to classify customers based on behaviour. Segments such as value hunters, frequent abandoners, those that follow latest trends, those that wait for sale periods and more;
ii. Personalised custom product recommendations basis past and present browsing history or purchase patterns;
iii.Shorten the order cycle between purchases and  increase order velocity;
iv. Identification of the channels in which they need to invest to acquire customers and retain older ones. It is important to follow your customer journey across sessions, across channels and time. There are customers who browse on the mobile, put in the basket on the PC and eventually buy offline at retail stores or those that browse at stores and buy online, it’s becoming increasingly important to understand these patterns;
v. Decoding customer conversations - Grouping conversations based on key words/ phrases allows identification of topics. Sentiment analysis detects words/ phrases that hold a positive or negative connotation;
vi. Predictive search to intelligently predict possible purchases basis behaviour preference;  
vii. Pricing Management which works in correlation with sales information to determine the right prices at the right time to maximise revenue and profit;
viii. Returns analysis – Identify customers as “serial” returners with reason for return, identify products with high returns and reason for return, predict high returns and include in demand planning, identifying fraudulent returns;
ix. Supply Chain Management by understanding consumer demand, to effectively manage the overall supply chain process which includes planning and forecasting, sourcing, fulfillment, delivery, and returns. This alone can open up a huge route to cost optimisation and savings.

It is important to understand the power of communication to achieve customer loyalty. Retailers need to use the right analytics technologies so that they can gain a single view of their customers with the ability to manage communications across multiple channels to ensure the ultimate shopping experience.
In conclusion, it can be said that predictive analytics helps retailers to understand trends and relationships, predict propensity, identify new audience clusters and communicate more effectively and personally – all that will allow retail organisations – both online and offline, to stay ahead of competition.

Authored by: Sunil Jose, Managing Director, Teradata India, a big data, analytics and integrated marketing applications company

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