Brent Brown is the Sr. Vice President & General Manager of North America for Sensormatic Solutions . Data is the backbone of digital retail enterprises, but not all data is created equally. “Source,” or “original,” data is the gold standard.
It’s raw, unprocessed data that has not yet been analyzed or integrated. Robust source data feeds multiple systems of record and is used to inform a wide range of business decisions.
For retailers, source data is an organic account of activities and actions within a store. The data is unique and customized to the business collecting it, reflecting their specific circumstances and customer base. And because source data isn’t dependent on other information, retailers know they can trust it. What makes source data particularly valuable is that it doesn’t just tell retailers about the decisions shoppers made — it helps them understand why they made those decisions during their time in the store and enables them to better predict shopper behavior in the future. Using source data, enterprises can optimize their retail locations at all levels.
Who has this data, and who uses it?
Every retail enterprise has at least one stream of source data: the transaction log (T-log). It’s the classic example of retail source data — a simple list of the store’s transaction history. Although valuable, T-logs don’t tell the whole story. They’re post-transactional, only showing retailers what the customer ultimately bought, when and where they bought it and how they paid for it.
T-logs don’t tell retailers about the decisions that led to that purchase. They don’t reveal where the customer traveled in the store or what they looked at. They can’t provide information about the products the customer decided not to buy, when they chose not to buy them or how they found the items they did buy. More sophisticated enterprises have robust source data analytics programs that fill in the blanks left by T-logs. These programs utilize existing technology like Wi-Fi networks, cameras and other sensors to track data that goes far beyond customer purchases.
By monitoring multiple levels of consumer behavior in a retail environment, enterprises can collect source data that helps them understand why, when and how shoppers decided to buy a product — or didn’t. It can also help identify sources of shrinkage, traffic trends, problem areas and staffing inconsistencies. This information can help stores rethink their floorplans, staffing, ordering, inventory and more.
Source data is valuable at every level in a retail enterprise. From executives to associates on the floor, everyone can benefit from robust source data programs. Item-level inventory data can help associates track what’s on the floor, what’s in the back and how to serve customers best both in-store and online. It can help managers lay out the store, decide what promotions to run, optimize ordering and staffing and track theft events and perpetrators. It can help executives understand the habits of their consumers to guide high-level decisions about the enterprise.
How does source data impact business?
When used in the right way, detailed source data — including information on shrinkage, inventory, shopper traffic and more — can be a potent tool for improving a business’ top and bottom lines. Understanding the customers’ behavior and motivations doesn’t just improve sales — it can also lower operations costs. Retailers should also look into tools or partner organizations capable of integrating and analyzing the data streams. This can help ensure that data is used effectively and potentially lower costs even further by generating additional actionable insights.
Consider these scenarios:
• A prominent men’s clothing retailer implements an integrated analytics and data collection platform. The store and its analytics partner review the available data sources — in this case, point-of-sale logs, cameras, traffic mapping and RFID tracking — to monitor shopper movements and decisions throughout the store. They notice that suit sales in what should be a prime location are down. They find that shoppers pick up suits then decide not to try them on, and they see that the dressing rooms are far from the suit displays in this location. The store hypothesizes that this is the issue: Shoppers look at suits but don’t try them on and, therefore, don’t purchase them. It moves the display to a more convenient location relative to the dressing rooms to encourage shoppers to try on the clothing. This change leads to more shoppers trying on and buying suits at that location.
• A retailer implements an item-level inventory program that tracks in-store and online sales, various causes of shrinkage and more. The enterprise integrates its website with its internal inventory system to show real-time inventory at multiple locations. When customers search the website for in-store pickups near them, the website reflects each store’s actual inventory, and the customer knows the item they need is in stock. The store can do this because it trusts that its data is accurate — which helps its customers trust the retailer.
These are just two examples, but it’s easy to see the various ways source data and practical analysis can help businesses improve business outcomes, enhance efficiency and optimize shopper experiences.
Thinking of source data as a new currency.
When most people think about “currency,” they think of dollars or euros. Data is currency. It’s material, and both consumers and enterprises carry it. Today, retailers can harness that data to gain a more comprehensive view of their enterprises, helping them better understand what their customers respond to both in-person and online.
An integrated source data collection program can tell a retailer more about its business than any individual source of data. It can fuel intelligent solutions that analyze the data to provide actionable insights. From rethinking floorplans to ordering, inventory, restocking, staffing and more, source data is the information that guides all facets of a business. Retailers who invest in utilizing effective source data collection and analysis will have a significant leg up when it comes to understanding their customers’ needs — and delivering on them.