The customer is always right. No longer just part of Marketing 101, the axiom holds more sinister implications for retailers. Especially today, where being “always-on” isn’t just a phrase-it is a business norm!
Permit me to elaborate-today’s customers are an increasingly sceptical lot, a fact that isn’t helped by disruptive forces like mobile devices and social media. Today, the bottom-line, no, necessity is shifting from passive to active brand engagement. And why not? The number of customer touch points is increasingly exponentially. It is thus only logical that brands ought to be where their customers are.
So, coming to the crux of this piece-big data analytics can help retailers achieve this. And lots more. Why? Well, retailers typically have access to a significant volume of data. Generated across the supply chain and at diverse customer touch-points, this data is further multiplied via digital customers and social media channels.
However, retailers will do well to remember that merely aggregating a vast amount of structured and unstructured data isn’t likely to translate into healthy bottom-lines. The idea is to extract actionable insights from the data pile. Of course, this is just the tip of the iceberg. Let’s break down the argument.
How Big Data Analytics is the Ticket
Uncovering Actionable Customer Insights
Big data analytics enables retailers to uncover a mine of information pertaining to a customer’s behaviour and usage patterns. This further enables them to push contextual, relevant and personalized offerings in a timely manner and at any point in the customer’s journey.
This process is usually based on where that customer stands from a behavioral point-of-view. By examining certain factors, such as the amount of time the customer has spent on the network, usage patterns, etc, the retailer can reach out to the customer via an SMS (or other ways) that highlights the latest offerings they can avail of. Moreover, by deploying big data, all of the retailer’s data is turned into actionable and behavioral insights. These are further used to ensure that the appropriate treatment (in terms of marketing) is applied to each customer at the right time. Essentially, big data helps the retailer to “plot” events on a timeline for each customer, which are then analysed and familiar patterns are highlighted, in order to predict the customer’s behaviour.
Ensuring Retailer Loyalty
Big data analytics can be leveraged to examine a retailer’s behaviour. Thereafter, personalized offers and incentivization schemes can be developed, to ensure enhanced and improved retailer loyalty.
Enhancing Channel Productivity
A “top-down” approach is usually adopted, with regard to the sales channel. This implies that targets are identified at the operator’s level and are expected to be absorbed by the channel’s various elements across the organization. However, region-wise analysis of past performance and trends can improve the accuracy of predicting future sales potential and help the operator set channel targets accordingly. The bottom-line is an optimally utilized sales channel, with relevant targets and adequate incentives.
Before one gets carried away, though, it is important to remember that big data analytics isn’t the panacea to all retail-related evils. It will, however, play a significant role in helping brands keep pace with their customers. After all, the customer is always right. It is just a question of pushing the right buttons to ensure the customer stays. Isn’t that the whole point, after all?