To compete in this new world, it’s more important than ever that retailers adopt a data-first, analytics-centric approach across the board, from supply chain to store to online.
The retail landscape is undergoing a dramatic transformation, signaling the start of the analytics arms race. The conventional weapons of the past, which included syndicated data, targeted loyalty mailings etc., are simply not going to be as effective in the current environment where stores have to compete with the likes of Amazon and non-conventional brick-and-mortar formats. For convenience, grocery and discount, fresh food offerings and in-store experiences are becoming a core differentiator as the center store moves online.
On one side is Amazon, which is everywhere now with a combination of online and physical stores. They have led the rise of e-commerce, creating a disruptive and highly convenient shopping empire by engaging shoppers with personalized experiences like no other retailer. AmazonFresh was launched to reshape the US$800 billion food industry and was designed to compete with grocers and mass merchants such as Walmart and Target. With their massive loyal Prime customer base, the Alexa, drones and now an employee-free concept known as the AmazonGo, it’s nearly impossible to say what the industry will see next.
Adding to this, Amazon also bought Whole Foods recently, delivering the most significant threat to the food industry. Amazon will deliver marketing relevancy and personalization integrated with sophisticated pricing and personalization algorithms.
On the other front, the emergence of dollar stores and hard discounters which rely on aggressive pricing strategies has created loyalty through value. These retailers are asking brands to deliver absolute net lowest, which is passed to consumers alongside a no-frills retail experience.
Bringing the power of retail analytics
To compete in this new world, it’s more important than ever that retailers adopt a data-first, analytics-centric approach across the board, from supply chain to store to online. Until retailers offer a seamless, convenient and compelling shopping experience, they will be left behind.
This is where companies like SwiftIQ come in, providing retailers, brands and distributors with leading, on-demand insights and decision-making platform from the most highly granular, real-time store, customer and supply chain data to create compelling shopping experience and maximize profitability. The platform incorporates a suite of applications powered by high-scale data processing and artificial intelligence (AI) to convert billions of records of shipment, customer and transaction data into prescriptive and predictive analytics on-demand. We provide granular insights on promotion measurement, category management, shopping behavior analysis, merchandising actions and store-level forecasting.
“Historically, information was power in the retail industry: the more you knew about your customer, the more you could generate growth,” said Lea El Hage, Product Marketing Manager at SwiftIQ. “However, in today’s hyper-connected world and with the rise of e-commerce pure-players, information became a necessity. Those who are not able to identify purchase patterns and offer a compelling shopping experience to their customers have been thrown out into a survival mode: it’s the survival of the fittest. Analytics is transforming the way businesses operate and are helping companies unlock their data’s full potential to shift from a product-centric approach to a customer-centric one. The golden ticket in this new retail landscape is connecting data to insights and insights to decisions. Analytics is changing the retail game by providing visibility across all engagement channels to better understand customer buying patterns and behaviors.”
Functions that make the solution unique
Given the competition and demand for retail solutions, it is no surprise that there are more than a handful of companies attempting to offer analytics in this vertical. What makes SwiftIQ stand out, according to Hage, are four major factors.
- Deep data granularity: Due to their lack of granularity and accuracy, legacy syndicated market share insights have become insufficient to activate growth solutions with localized and personalized strategies. Full-store basket-level transaction data, enabled by SwiftIQ, allows for complete store coverage and basket-level analyses like cross purchase correlations, basket size, seasonality and dayparts to know when to run or target offers and they analyze non-UPC items like foodservice which are critical to profitability.
- Speed to insights: SwiftIQ can process basket-level data on-demand and can also handle real time data answers whereas syndicated data is limited to measuring the previous 15 days’ transactions. Notably processing basket-data for a grocer with large baskets takes 8-15x the computer power vs. even high volume, low basket size retailers like convenience stores and quick service restaurants.
- Type of insights: The key to growth lies in the ability to generate insights that are not simply descriptive or explanatory, which is what syndicated data provides, but predictive and prescriptive. SwiftIQ does not only identify the problem but enables recommendation and action plans to overcome it and moreover, anticipate it. With basket-level insights, retailers and brands can build profitable bundles and measure the ROI on promotions, displays and media based on full-store impact, not just sales of the item that was promoted.
- Profitability: The company’s platform, powered by basket-level data enables profitable bundle and promotion analyses versus what is possible with just sales data as opposed to syndicated data that measure estimated ROI
The technology behind the solution
According to Hage, the company’s infrastructure is Amazon-like and transformative to provide the on-demand insight and agility necessary for retailers to gain a competitive edge. It ranges from an end-to-end ecosystem that provides full use of the power of retail analytics including shipment, transactions, shoppers and web data to an API infrastructure that allows you to interact with any data record, write queries and embed insights into third party systems.
And as technologies like AI develop further, the company expects more effective results. “Development in technologies will help us optimize decision automation and develop even more innovative applications with improved machine learning to better identify, target and engage customers and enhance their shopping experience,” Hage said.
The company now has multiple power users who connect to the platform daily and run multiple queries. SwiftIQ builds its applications in partnership with its customers to optimize their experience while using the platform, tailoring them to their specific business needs.