If you thought a deep-dive on supply chain couldn't be stimulating, think again. Hugh Holman, CEO at Observa, gives us a primer on what it takes to implement retail planning and forecasting. Observa is building retail compliance systems powered by AI, on-demand workforces, machine learning and machine vision - not just to enforce compliance, but to arm retailers with data to plan orders, manufacturing, demand planning.
Brian is on location at Grocery Shop 2019 and is joined by Hugh Holman, the CEO and Co-Founder of Observa.
Observa is breaking open the black box of real-time data that shows what is going on in a physical retail location.
What are the further applications of real-time retail data?
How can brands and retailers work together to create experiential shopping experiences for different groups of consumers?
Hugh has spent most of his career as a technology manager; starting at a startup and soon became the CIO of Aqua Star.
At Aqua Star, Hugh was placed in a role where he was in control of driving technology and technology strategy.
After obtaining his MBA, Hugh moved into a different role focusing on strategy at Aqua Star that let him modernize the company and approach the market differently.
Eventually he took over retail sales and was running a $400 million dollar business in the retail sector which provided the foundational experience he needed to found Observa.
When Hugh was at Aqua Star, he noticed that there was a black hole of data in retail that did not show real-time data of what has happening in the store.
You could subscribe to data sources that provide data in four week chunks, but we do not think in four week chunks in today's retail climate.
At Aqua Star, Hugh was developing trade programs with large corporations and wanted a way to see if these trade deals were driving sales and boosting customer acquisition.
Observa was created to give brands a way to see what the consumer sees in store, in real time.
Observa in actuality is a retail audit service company so they are measuring retail store execution.
Observa has created a network of over 90,000 field workers called Observers that go into stores and use Observa's mobile apps to collect information on what is happening on the shelf where products are placed as well as promotions across the store.
Observa processes the data and images sent in from the Observers using AI and image recognition to generate a thorough snapshot of what is happening in stores.
Observa is agnostic to the data capture mechanism, so it doesn't matter where the images or data is coming from, and they are able to turn this into usable information.
Brian asks Hugh to talk a little bit more about what happens with the images once they are received by Observa.
Observa is using computer vision by using artificial intelligence to do image recognition.
The intelligence and neural network within Observa's system is being trained to recognize products so that when it receives photos, it can instantaneously recognized them and categorize what is in the photos.
This enables Observa to measure what is happening in stores and compare that to expectations and recognized deviations for actionable insights.
Brands can also use the data captured by Observa identify inventory problems and plan for future stock orders.
Inventory data in most computers is not accurate and phantom inventory cannot be removed until you identify the problem: which Observa does.
Retailers and brands always end up erring on the side of caution and will be able to do so with accurate insights if they have a true omnichannel strategy.
The consumer experience today is a combination of brick-and-mortar and online experiences, and the more data you can collect on both experiences will help you make the most informed decisions.
A planogram (or modular) is a design that indicates the placement of retail products on shelves in order to maximize sales.
Observa's technology is giving brands the capability to measure performance directly against planograms.
Planograms are used by the retailers themselves to create the ideal layout to generate the largest profit for individual stores.
By getting the expectation of a store via a planogram, Observa is able to identify the problems within the store and direct change.
Traditionally, the ability to capture data on product placement was reserved for eCommerce, but Observa is bringing those actionable insights to brick-and-mortar.
Brian asks if these new data points are creating new roles in retail strategy thanks to previously unobtainable data.
Observa is also taking a consultative role with retail brands because of the large amounts of data they are capturing across their entire portfolio.
Traffic heat maps have been used in grocery stores for a long time, but newer mechanisms are being used to track customer segmentation.
Retailers have started to adopt checkout-less payment solutions and Observa's data will begin to become even more valuable as purchases happen closer to the items themselves.
Image recognition is an important part of checkout-less payments, and the reliance on barcodes can lead to customers making mistakes when checking out.
The ability to identify the package and not just the barcode on a product provides a huge value for retailers exploring this technology.
Is adding a checkout-less option a lucrative move for most retailers?
Brian asks Hugh who he thinks has more power when it comes to retail: the retailers themselves or consumer brands.
Hugh clarifies that instead of a competition, the relationship between brands and retailers should be a partnership.
There is a lot of conversation about segmentation of the market and presenting curated experiences in retail.
We might see different sections in stores tailored to different people within various categories.
Brick-and-mortar retail is growing and the consumer experience is changing along with this growth.
Going forward, the retailers that pay attention to the wants and needs of the consumer and then try to implement changes that fit these wants and needs are the retailers that are going to be successful.
Warby Parker and Allbirds are testing store locations and seeing what the impact of having tangible product is upon their customer base.
The focus should be on understanding the consumer using data, and applying those actionable insights to both eCommerce and retail presence.
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