In today’s Omni-channel environment a ‘knowledge deficit’, not only hinders an organisation’s understanding of the status quo, but also impedes its ability to engage on a personal level with its customers. Plus, with the volume of data only increasing, time is of the essence when it comes to harnessing this valuable asset in the world of Omni-Channel retailing.
One thing is clear: retailers are transforming to thrive in the Omni-Channel economy.
Real-Time decision making is now achievable and central to driving sales and reducing costs. Big data technologies and mobile data visualisation have become key enablers to rapid response decision making.
With omni-channel retailing, customers make purchases using various methods working together cohesively. For example, a store that allows customers to browse and purchase merchandise online, and then pick the item up in-store. Or the ability to buy goods from a mail-order catalogue, but return them to a brick-and-mortar store. Or log into an online account, print a return label and mail it back, without leaving home. A fully comprehensive omni-channel buying experience allows customers to search online locating products in their local area, buy, return, and/or exchange products through all channels. Creating an exceptional omni-channel experience requires the support of a highly scalable data analytics solution to ensure accuracy, visibility, and consistency.
Reports summarising average behaviour don’t provide the useful insights needed to determine how individual customers are likely to behave, general behaviour tendencies are simply too broad. In order for retailers to create a meaningful dialogue with customers that honours the shoppers preferred level and mode of engagement, deep customer intelligence and predictive analytics need to be deployed
It is well recognised that high quality in-store sales personnel will increase a stores revenue by 20%, Amazon applied this ideology online and delivered a 29% increase in revenue. Web site product recommendations have traditionally been displayed to customers based on rudimentary rules such as – Does the customer currently have the product or not?
So much more is now possible, the ultimate goal being a dynamic and personalised experience for retailer’s customers driving up cross sales and reducing churn
To advance retailers profitability Elastacloud enables a more forensic approach to customer data using advanced analytics and real-time decisioning to determine the best possible recommendations for each individual customer
By applying advanced analytic techniques to detailed viewing behaviour and propensity scores, then tying digital and offline data together, retailers are able to predict the outcome of any recommendation that was made to each customer.
Transforming Bricks and Mortar
Customer traffic analytics are now being used before and after store entry. Inside stores, analytics on customer movement give great insight to retailers on display layout and product placement, and further quantify where to place featured products. These tools transform brick and mortar operations into an online clickstream, the ability is there to segment shoppers into groups based on an individual’s total number of visits and average shop time. This data can of course be anonymised and used for store planning. Before the interior is even constructed, architects apply the traffic data to massively improved virtual reality technology to go on pre-construction walkthrough using Microsoft HoloLens. It’s technically possible, then, to collect similar movement data from a focus group of shoppers in advance of a building out new interiors