How Real-Time Market Pulse is Driving Retail Evolution

Recent developments in the entire spectrum of eCommerce, from FMCG to luxury retail, vividly illustrate the critical importance of real-time market pulse and data analytics in navigating today's rapidly changing retail landscape. 

The transformation from an "infinite digital shelf" to personalized shopping experiences underscores the shift towards an intelligent real-time dynamic, AI-driven, adaptive digital shelf. Similarly, the departure from indiscriminate discounting to dynamic, data-led pricing that focuses on profitability and margin growth over conversions is a testament to the maturing retail landscape.   

Let's unpack these topics; we'll start by examining the dynamics of real-time content. This will lay the groundwork for our next discussion, where we'll shift our focus to understanding the intricacies and insights of real-time pricing.

Real-time adaptive digital shelf

As eCommerce evolves, so does the consumer journey and how they interact with the digital shelf. Gone are the days of the "infinite digital shelf," where the emphasis was on the sheer quantity of products. Today, technology is helping brands move towards a more targeted, shopper-centric and situation based digital shelf rather than the endless digital aisles that once flooded the internet. 

There are 3 key themes driving this retail evolution. While there are no winners yet, you as a company have to experiment and ensure you are not left behind:

Precision in Context

A context-based digital shelf is the next "big thing" as the online shelf becomes narrower and more precise to meet consumer preferences. Walmart's latest initiative is an example of this shift, as shared by Walmart's CEO Doug McMillon at CES 2024. Walmart introduced a generative AI search feature for iOS, enabling customers to discover products based on the context of the consumer (e.g. shopping for a birthday party vs shopping for balloons) rather than just searching for specific products or brands. This aligns perfectly with the idea of creating a shopping experience that's tailored to individual consumer needs and preferences, is dynamic, and responds to the consumer in real-time.

Behavioral Discovery

Behavioral intent is taking precedence over traditional product searches with the aid of intelligent automation and the digital shelf completely disappears.  

For instance, innovations like the Instacart OpenAI plugin, which automates order placements based on user "behavioral input" (You would like to cook spaghetti bolognese vs simply buying the ingredients), are advancing this concept. This technology not only keeps the digital shelf hidden from the customer but also (in the context of Instacart) allows it to be completely automated by algorithms and be retailer agnostic in terms of order routing. 

Engaging with Intent

Retailers are “extending their shelves” through seamless, and targeted content experiences. The difference compared to “Precision in Context” is that the retailer provides the context, but it is still relevant considering that the consumer is choosing to engage with a particular video. 

An example from Amazon that departs from traditional retail is the introduction of Prime Video Ads and, in particular, Amazon Sponsored TV, giving brands an opportunity to engage with streaming audiences in a more meaningful and targeted manner with “off-platform digital shelf.” This service will focus on shopper preferences, offering brands a platform to serve tailored messages.

This shift is significant because it changes how we approach traditional share of shelf tracking and traditional retail media spending. The focus is now on understanding and predicting customer behaviors, offering an intuitive and personalized shopping journey. It's not just about product discovery anymore; it's about creating a connection with each customer's unique preferences and needs.

The pressing question here is, how do you create these connections with shoppers? How do you decide what the right path forward is for your brand and your customers? The answer lies in data. Leveraging real-time, intuitive analytics that track, compare, and analyze vast amounts of data and give brands insights and tools to build personalized journeys is the answer to winning the new, personalized digital shelf. 

CASE STUDY - Engaging Content Experiences 

Realizing the importance of exceptional customer experience, Kroger Marketplace wanted to ensure that all content across millions of product listings on its platform was consistent, optimized, and accurate. Kroger decided to partner with Intelligence Node’s AI-driven content optimization platform to empower thousands of 3P sellers on its platform to offer optimized, unified content across all their product listings. This partnership highlights the growing importance of optimized product content not only for customer experience but also for online visibility and conversions. 

Real-time pricing decision for survival

Even if you do it all correctly, pricing still matters! The current retail environment demands adaptive, dynamic pricing capabilities to stay competitive and maintain margins. It is imperative to implement systems for real-time monitoring of market and competitor activities. This allows for swift and “intelligent” responses to competitive pricing strategies, promotional campaigns, or new product launches.

Unintelligent pricing

During Farfetch's 86% stock market decline last year resulting in a distressed sale to Coupang, our data consistently indicated that the decline was not a mere “buy the dip” opportunity, but a sustained downward trend. Farfetch's competitive edge dwindled alarmingly. In January 2023, it boasted a 64% rate of best-priced items vs a set of key competitors, this figure plummeted to a mere 29% on the eve of Coupang's acquisition move.

So how and how quickly is competition reacting to your price changes and what is the impact? A term we have coined for instant market response to price changes is the ‘Intelligent Domino Effect’.

CASE STUDY: Intelligent Domino Effect in Pricing  

Intelligent Domino Effect describes smart, swift, cascading price movements in a competitive retail environment, in response to pricing changes made by a competitor. For Intelligent Domino Effect to work, retailers need access to accurate, near real-time pricing data of their competitors. 

For the past five years, IntelligenceNode has been monitoring the performance of more than 20 global luxury retailers and brands across ten global markets. In one specific test case we studied two competitors and how they react to price changes. The study was completed on 10,275 overlapping SKUs. if Competitor A (to maintain confidentiality, we will refer to the competitors simply as "Competitor A/B) decreased the prices for a particular set of SKUs, competitor B decreased the prices of the same SKUs within exactly one week with exactly 1% (obviously they were tracking them) ... but by the time Competitor B changed prices many of the Competitor A SKUs were already sold out, and Competitor B was in a situation where they were lowering prices in a market where competitive pricing was not relevant anymore. Even worse Competitor B was lowering pricing of SKUs that at that point of time were unique to them, hence in theory they were able to exercise pricing power.

Indiscriminate discounting

The absence of quick price and market response can lead to many lost opportunities and loss of margins and sales. Frasers Group's acquisition of Matches Fashion is yet another example of a failed pricing exercise. The acquisition reflects the luxury fashion industry's need to navigate a rapidly changing economic landscape. It underscores the importance of adaptability and strategic foresight in today's market—or, to put it directly, indiscriminate discounting to gain market share is not enough to survive.

MatchesFashion, fueled by private equity funds (a very different strategy compared to Farfetch, but ultimately the same outcome), intensified its efforts to gain market share over the course of last year. In France, they were outpricing competitors in 62.5% of instances, and in the UK, they achieved a 57.5% rate, up from 32% at the year's start. This approach had put them firmly ahead of giants like YOOX, Net-a-Porter, but at a cost, ultimately leading to its distressed sale. Furthermore, just within two months of its acquisition by Frasers Group, it had to shut down MatchesFashion due to soured relationships with brands, a further blow for the luxury industry.  

This example and many similar stories in recent years tell us that this strategy can result in two outcomes:

1. The newcomer/startup heavily burns money, generating considerable competitive pressure. These dynamic forces incumbent businesses to purchase these companies at elevated prices to safeguard their earnings or to challenge a rival. For instance, in the FMCG sector, Unilever's acquisition of Dollar Shave Club is a notable example that had the same fiasco outcome (Unilever is selling Dollar Shave Club after seven years) as MatchesFashion.
2. The retailer/startup heavily burns money, leading to a distressed sale eg Matches Fashion or bankruptcy. 

Are you ready to roll the dice and wait for either of the above outcomes or would you rather price more intelligently based on real-time data analytics and position yourself for success?

So, what can you do about it? 

In the classic "buy vs build" dilemma, companies must decide whether to acquire pre-made software or construct bespoke solutions, a decision that fundamentally impacts resource allocation and strategic direction.

On the one hand, building an advanced real-time price monitoring and digital shelf solution demands a significant investment in IT and technological resources. Our estimates suggest that companies allocate approximately 3-5% of their revenue towards IT and technology endeavors. Notably, tech-savvy giants like Amazon and Walmart, with their expansive revenue bases, tend to invest at the upper spectrum of this range, channeling tens of billions of dollars into cutting-edge initiatives such as Generative AI, Augmented and Virtual Reality, and social commerce platforms, to maintain a competitive edge.

On the other hand, partnering with a specialized third-party technology provider presents an alternative route. This approach enables immediate access to the latest technology, expert insights, and product specialists, ensuring swift implementation and accelerated return on investment. Furthermore, should challenges arise, this route allows for a rapid and cost-effective resolution, minimizing financial and operational risks.

Additionally, it's crucial to underscore the particular challenges faced by traditional retailers lacking in digital expertise. For these entities, even the endeavor to launch a basic digital experiment can transform into a year-long undertaking, potentially incurring millions of dollars in costs. This daunting reality highlights the steep learning curve and resource intensity required for digital innovation, underscoring the significance of the "buy versus build" decision for companies at various stages of digital maturity.

Conclusion

The focus on harnessing real-time data and analytics underscores the retail sector's transformation toward a more nuanced, intelligent, and customer-focused approach. This evolution emphasizes personalized shopping experiences and strategic movements within the entire spectrum of eCommerce categories, illustrating the paramount importance of grasping the market's immediate dynamics and employing data analytics. Such strategies are essential for sustaining a competitive edge, refining product assortments, and securing success within the swiftly changing landscape of retail.