Globally, "Retailing" has been always on a high-octane mode ‑ from the inception of Walmart in the ‘60s to the launch Amazon in the ‘90s. But, as we ushered into the digital landscape with new knowledge on customer data, pertaining to its behaviour - the timeline of decision makings and executions has been drastically shortened. The arrival of "Unified Commerce", armed with "Predictive Analytics" as a support system is acting as a catalyst for the further growth of retail in multiple channels and formats. But, how it is going to work?
Till 2014, everyone was talking about omnichannel retail at any given NRF (National Retail Federation) events. In omnichannel, one has multiple channels and multiple formats, but don’t have one trans-platform software and supporting network. But, in the unified commerce, all the channels of retail operations are connected in real time - both physical and digital, constantly churning out raw data for the authoritative analysis purpose.
When it comes to generating customer intelligence in the unified commerce business model, a structurally stable triangle methodology has to be deployed. Each of the three angles is critical to the triangle for retaining its structure and fulfilling its intended purpose. The three "angles" of the triangle are ‘retail domain experts’, ‘technology experts’, and ‘data scientists’.
Predictive Analytics per se tends to bring in crystal clear clarity with respect to KPIs in retail and with time, the overall system will evolve with the advancements in technologies like in the field of Internet of Things. In real-time, targets can be set at different nodes of supply chain channels through the insights generated via analytics. These targets may form a part of the management-by-objectives in an organisation, along with pre-defined tolerance factors. Now, when an actual value exceeds one of these tolerances, a corrective trigger will get fired seamlessly across the platform.
However, the ‘accuracy’ of such predictive insights will always be a hard-hitting question and it completely depends on what kind of data one is sourcing and harvesting through these inter-connected systems at the first place itself. With time, the quantity of data will grow, so will be the discrepancies. In such scenario, a well-integrated predictive analytics module will act as a system of ‘checks and balances’, providing real-time impetus to managers to deploy corrective actions wherever and whenever it's required.
Predictive data scientists are then needed to use the stored data to build models that achieve those business objectives originally set by the retail expert derived from in-store planogram execution, average sales value, gross merchandize value, average order value, inventory and supply chain costs, etc.
Predictive models find relationships between historic data, subsequent outcomes, consequent measures, and counter measures - so that the near-term and long-term customer behaviour can be predicted. This ‘angle’ of a triangle is an answer to problems such as the probability of when a shopper will make their next purchase and what the value of that purchase will be. Gradually, these relationships will tend to become more complex problems that only machine learning techniques will be able to solve.
How do data scientists determine which derived data outputs are relevant? Usually, data scientists lack the deep domain expertise needed to clarify and prioritise their interpretations. Therefore, a collaboration with retail domain experts is essential. And, to make that data available from the customer's end through well-optimised systems, calls for the presence of technology experts in the equation of collaboration.
It's indeed a high time for retailers to rethink their technology approach with regard to an ever-evolving crop of connected customers who are already accustomed to unified banking and payment system for the past 25 years.
The bottom line is, wherever money gets transacted, a retailer should have its point of sale (POS) deployed at that place, it can be either physical or digital. Yes, there will be the challenges to fight, acquire and retain the customer's short-span attention.
But, according to the Pareto principle of 80:20 - the 20 percent of customers always provide the 80 percent of sales. That 20 percent is changing constantly, and to be able to identify that 20 percent of the top line customers in real time ‑ and manage through extraordinary service ‑ is really the "holy grail" of successful and sustainable "unified commerce".
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)