Announcing Shopability For Profitability, a book which will discover how retailers can hear and react on the voice of shoppers.
In this book we will investigate the causal relationship between shopper behavior and economic results of the retail outlets throughout the world. Be it a flagship sports store on Oxford Street in London, a suburban VW dealership in Deventer, Netherlands, a Samsung pop-up store, or small children’s clothing specialist, shoppers behave according to how retailers succeed in predicting assortments, how staffers serve customers, and how space is laid out to make it easier for shoppers to browse. There is a lot of “evidence” in form of shopper behavior which is left behind, and retailers can use it to radically improve shopping experiences.
Shopping is an important aspect of modern lives. Within the perimeter of stores, humans decide how they will look, what they will eat, which tools will they use for work and many other decisions which have tremendous impacts on their lives. Now, more than ever, it is important to decipher shopping behavior and learn how retailers can influence better shopping experiences, essentially creating more shop-able stores. Think of a store as user interface (shopper interface) which allows users to have a frictionless experience and enhance shopper decision making. Shopper interfaces can be measured, adapted, and enhanced for the sake of creating most enjoyable experiences. This will eventually lead to higher loyalty indexes, and thus drive better profitability. Shopability For Profitability.
Shift from analytics to decision-making support
There is a fundamental disconnect between how decision-makers make decisions and how underlying data to support decisions are being collected and presented. Daily decision-making in retail is fast, impulsive, pressed by a short-term need to deliver financial results, and siloed through many functions. On the other end of the spectrum, all shopper activities, behaviors, and what they actually buy (POS data) is stored in many disparate places. As a result, many retailers currently have hundreds of backward looking Excel reports.
Decision makers read and understand data to support their decisions. At the end of the day it gives clarity and eases the discomfort of relying on one’s “gut feeling”. There are hundreds of reports, charts and spreadsheets used to forward “data analytics” agendas within companies. More advanced companies use “live reports” which come again as charts, graphs, and numbers in management consoles and dashboards. We are not making a claim that these tools are not relevant, however, dashboards and graphs have one thing in common… they cannot communicate. Even if managers have the education and data literacy to understand the logic behind charts, often they do not have the time. This is why we believe that the era of common analytics will soon evolve to the new level of decision-making support. Decision making at the speed of thought.
It is of crucial importance that every single data point tells a story, which can be absorbed quickly and acted upon within the realm of functions who actually have authority to act. Otherwise, data is only a report - a historical look of what happened and loses incremental value to both understand what is happening now and also predict the best possible outcomes. Combined with the capability of fast experimentation times, and a tool which tells you the ROI of every action you are to take, these toolsets will evolve from being piles and reams of data to real-time actionable decision making support. Within these environments, different retail functions will collaborate and understand the impact of their common undertakings.
This shift is inevitable in almost every industry. Today, doctors use IBM Watson to help diagnose an illness far sooner than before. Computer vision algorithms scan X-rays and give much more precise prediction of possible diagnosis. Similarly, the same algorithms which help a self-driving car function, are being used through existing CCTV cameras within retail environments to capture shopper behavior. Those algorithms could diagnose potential issues in the shopper funnel and cast new light on how to remove issues for better shopper experiences.
Online web-shops are responsive down to a single user and adapt to their needs, behaviors in real time resulting in impeccable user experiences, and increased profitability. Stores of the future will use modular design and will adapt in real time to shopper’s needs. For instance, tables which are convenient during week days, may be automatically be removed during Saturday rush hours to allow more space for influx of shoppers.
As more industries are changing and adopting postulates of behavioral economy and decision making support algorithms, we believe that retail is on the verge of the same change.
We aim for you to use our forthcoming book as a guideline on how to manage this shift within your own company, and give you ready-to-go ideas on where can you start even without employing any external resources.
And when you do, it will give you best practice guidance on where you need to put your emphasis when deciding to deploy a modern, machine learning based decision making support platform.