Author: Krish

  • Impact of Inflation on Grocery: Pricing Insights on Leading US Retailers

    Impact of Inflation on Grocery: Pricing Insights on Leading US Retailers

    Inflation, like an invisible force, silently shapes the dynamics of economies, gradually eroding the purchasing power of consumers and leaving its imprint on various industries. High costs, hiring lags, and stagnating earnings pose severe challenges to businesses. One industry segment that intimately feels the impact of inflation is grocery, where price increases can be extremely concerning for the average consumer.

    Over the last 12-plus months, the US has experienced a notable rise in inflation, stirring up concerns and influencing the way we shop for everyday essentials. Rising costs of raw materials, transportation, and labor have all played a role in driving up prices. Additionally, disruptions in global supply chains and fluctuations in currency exchange rates have further exacerbated the situation, creating a complex web of interdependencies.

    To understand the magnitude of this phenomenon across leading e-retailers, we delved into an in-depth analysis of four major retail giants: Walmart, Amazon, Target, and Kroger.

    Each of these retailers possesses a unique business model and competitive strategy, as well as faces unique challenges. This leads to distinct approaches to managing inflationary pressures. Walmart for instance, expects operating income growth to outpace sales growth in 2023. Given the persistence of high prices and the potential for further macro pressures, the retailer is taking a cautious outlook. In 2022, Amazon’s eCommerce business swung to a net loss of $2.7 billion, compared to a profit of $33.4 billion the previous year.

    Amid these challenging circumstances, understanding the grocery pricing trends and strategies becomes imperative for retailers, both online and in stores to adapt and thrive in the current economic landscape. By examining their pricing trends, we can gain valuable insights into how these companies navigate the turbulent waters of the grocery industry against the backdrop of inflation.

    Our Research Methodology

    The data collected for our analysis encompassed a diverse range of products, from pantry staples like flour and rice to perishable goods like dairy and produce – a basket of around 600 SKUs matched across Amazon, Kroger, Target and Walmart, between January 2022 to February 2023.

    Further, we separately focused on the prices of a smaller subset of 30+ high-volume daily staples that are likely to yield higher sales and margins for these retailers.

    Average Selling Price of a Broad Set of Grocery Items

    Our analysis reveals that Walmart consistently offers the lowest prices, with an average of 8% below its closest competitor, Target, despite an annual price increase of about 5%. Walmart seems to prioritize a “stability and predictability” strategy over margin optimization. The retailer’s 8% growth last quarter indicates that this strategy is bearing fruit. However, it’s important to note that this approach may have its drawbacks as Walmart’s margins come under pressure.

    Average selling price trend across a basket of 500+ SKUs across Target, Walmart, Kroger, Amazon in the grocery category from Jan ’22 to Feb ’23.

    In order to weather inflationary pressures, Walmart may adopt a cautious approach to growth while also focusing on securing margins. Reports suggest that the retailer has been pushing back against consumer packaged goods (CPG) manufacturers following a series of price hikes to counter inflationary cost pressures in early 2023. One of the reasons behind Walmart’s growth and increased sales can be attributed to ‘non-traditional’ higher-income households now seeking deals and discounts at Walmart as their spending power declines.

    Interestingly, Amazon emerges as the highest-priced retailer, followed by Kroger, which increased its prices by 10% throughout the year. Consumer perception commonly associates Amazon with the lowest prices, but the data tells a different story. In fact, Amazon has been charging 12% to 18% higher prices than Walmart for groceries and is still maintaining its success.

    While the company’s online sales declined by 4%, it saw a significant 9% increase in revenue from third-party seller services, such as warehousing, packaging, and delivery, in 2022. Amazon’s strong logistics and same-day delivery services give it a competitive advantage over other retailers, contributing to its revenue growth and margins. Interestingly, this presents an opportunity for Walmart and other retailers to increase prices while maintaining their strong competitive price positions.

    Kroger, on the other hand, seems to be aiming for a premium price perception, consistently raising prices almost every month. Kroger’s pricing strategy appears to be closer to Amazon’s.

    Average Selling Price for High-Volume Daily Staples

    Pricing strategies often change for different categories of products. To better understand this, we focused our analysis further on a small subset of 30+ high-volume staples across retailers. These include baked goods, popular beverages, canned food, frozen meals, dairy, cereals, detergents, and other similar items.

    Average selling price trend of 30+ high-volume daily staples across Target, Walmart, Kroger, Amazon in the grocery category from Jan ’22 to Feb ’23.

    Walmart, possibly overestimating the impact of inflation, has continued to keep its prices the lowest, potentially aiming to increase margins through volume.

    The level of price disparity across retailers is expectedly lower here, with Amazon and Kroger closely tracking Walmart’s average prices.

    Target’s pricing strategy stands out as it consistently emerges as the highest-priced retailer for daily staples, despite being one of the lower-priced retailers for a broader basket of grocery items. This suggests that Target’s underlying technology may not be as optimized to address market dynamics compared to other leading retailers. In our opinion, Target may want to strengthen its efforts to track pricing more intensely for this sub-category.

    A Data-fuelled Approach is the Need of the Hour

    In the challenging economic landscape, retailers and grocery stores are under pressure to maintain their revenues and margins. Adopting a comprehensive and dynamic pricing strategy is crucial. Understanding which product categories are experiencing price increases among competitors can help retailers make informed decisions on pricing at both the category and product level.

    Retailers should consider their balancing margin performance with consumers’ willingness to pay, rather than implementing broad price increases that may harm customer trust. Price increases can be challenging for both customers and merchants. Retailers who employ a data-driven and insight-based approach are more likely to succeed.

    Keep an eye on the DataWeave blog for analysis on pricing, discounting, stock availability, discoverability, and more, across retailers and brands from other industry segments as well.

    For immediate insights, subscribe to our interactive grocery price tracking dashboard. Better still, reach out to us to speak to a DataWeave expert today!

  • Compete Profitably in Retail: Leveraging AI-Powered Competitive Intelligence at Massive Scale

    Compete Profitably in Retail: Leveraging AI-Powered Competitive Intelligence at Massive Scale

    AI is everywhere. Any retailer worth his salt knows that in today’s hyper-competitive environment, you can’t win just by fighting hard – you have to do it by fighting smart. The solution? Retailers are turning to AI in droves.

    The problem is that many organizations regard AI as a black box of sorts – where you can throw all your data (the digital era’s blessing that feels like a curse) in at one end and have miraculously meaningful output appearing out the other. The reality of how AI works, however, is a lot more complex. It takes a lot of work to make AI work for you – and then to derive value out of it.

    Image Source: https://xkcd.com/1838

    Following the advent of the digital era, businesses across industries, particularly retail, were left grappling with massive amounts of internal data. To make things worse, this data was unstructured and siloed, making it difficult to process effectively. Yet, businesses learned to leverage simple analytics to extract relevant data and insights to affect smarter decisions.

    But just as that happened, the e-commerce revolution stirred things up again. As businesses of all shapes, sizes, and types moved online, they suddenly became a whole lot more vulnerable to other players’ movements than they were just about a decade ago, when buyers rarely visited more than one store before they made a purchase. In other words, retailers are now operating in entire ecosystems – with consumers evaluating a number of retailers before making a purchase, and a disproportionate number of players vying for the same consumer mindshare and share of wallet.

    Thus, external data from the web – the largest source of data known to man at present – is becoming critical to business’ ability to compete profitably in the market.

    Competing profitably in the digital era: Can AI help?

    As organizations across industries and geographies increasingly realized that their business decisions were affected by what’s happening around them (such as competitors’ pricing and merchandize decisions), they started shifting away from their excessive obsession with internal data, and began to look for ways to gather external data, integrate it with their internal data, and process it all in entirety to derive wholesome, meaningful insights.

    Simply put, harnessing external data consistently and on a large scale is the only way for businesses to gain a sustainable competitive advantage in the retail market. And the only way to practically accomplish that is with the help of AI. Many global giants are already doing this – they’re analyzing loads of external data every minute to take smarter decisions.

    That said, though, what you need to know is that all this data, while publicly available and therefore accessible, is massive, unstructured, noisy, scattered, dynamic, and incomplete. There’s no algorithm in the world that can start working on it overnight to churn out valuable insights. AI can only be effective if enormous amounts of training data is constantly fed back into it, coaxing it to get better and more astute each time. However, given the scarcity of readily available training datasets, limited and unreliable access to domain-specific data, and the inconsistent nature of the data itself, a majority of AI initiatives have ended up in a “garbage in, garbage out” loop that they can’t break out of.

    What you need is the perfect storm

    At DataWeave, we understand the challenge of blindly dealing with data at such a daunting scale. We get that what you need is a practical way to apply AI to the abundant web data out there and generate specific, relevant, and actionable insights that enable you to make the right decisions at the right time. That’s why we’ve developed a system that runs on a human-aided-machine-intelligence driven virtuous loop, ensuring better, sharper outcomes each time.

    Our technology platform includes four modules:

    1. Data aggregation: Here, we capture public web data at scale – whatever format, size, or shape it’s in – by deploying a variety of techniques.

    2. AI-driven analytics: Since the gathered data is extremely raw, it’s cleaned, curated, and normalized to remove the noise and prepare it for the AI layer, which then analyzes the data and generates insights.

    3. Human-supervised feedback: Though AI is getting smarter with time, we see that it’s still far from human cognitive capabilities – so we’ve introduced a human in the loop to validate the AI-generated insights, and use this as training data that gets fed back to the AI layer. Essentially, we use human intelligence to make AI smarter.

    4. Data-driven decision-making: Once the data has been analyzed and the insights generated, they can either be used as it to drive decision-making, or then integrated with internal data for decision-making at a higher level.

    With intelligent, data-backed decision-making capabilities, you can outperform your competitors

    Understandably, pricing is one of the most popular applications of data analytics in retail. For instance, a leading, US-based online furniture retailer approached us with the mission-critical challenge of pricing products just right to maximize sell-through rates as well as gross margin in a cost-effective and sustainable manner. We matched about 2.5 million SKUs across 75 competitor websites using AI and captured pricing, discounts, and stock status data every day. As a result, we were able to affect an up to 30% average increase in the sales of the products tracked, and up to a 3x increase in their gross margin.

    DataWeave’s powerful AI-driven platform is essentially an engine that can help you aggregate and process external data at scale and in near-real time to manage unavoidably high competition and margin pressures by enabling much sharper business decisions than before. The potential applications for the resulting insights are diverse – ranging from pricing, merchandize optimization, determination of customer perception, brand governance, and business performance analysis.

    If you’d like to learn more about our unique approach to AI-driven competitive intelligence in retail, reach out to us for a demo today!