Behavioural Data Science Week
Ganna Pogrebna
Keynote Speaker| Author| Host| Professor, Behavioral Data Science Pioneer| Exec Dir. AI & Cyber Futures Institute| Lead, Behavioral Data Science at Turing| Winner, TechWomen100 & Women in AI APAC | YouTube: Data-driven
Issue 24
December 12, 2024
Editorial Note
Welcome to this week’s edition of Behavioural Data Science Week! As the holiday season approaches, winter sales take center stage in the retail calendar. But have you ever wondered how artificial intelligence reshapes these sales events, both for businesses and consumers?
This week, we explore how AI is transforming the winter sales season by leveraging behavioural data science to anticipate consumer needs, optimise pricing, and create hyper-personalised shopping experiences. We’ll also examine ethical implications and emerging trends, helping you navigate the complex relationship between data, algorithms, and commerce.
This week’s cover image uses a fragment of the artwork created by freestocks.
If you find this edition insightful or simply enjoyable, please leave a “10” or a ?? in the comments—your feedback helps shape future issues!
Yours in discovery,
Ganna
AI’s Role in the Winter Sales Frenzy
Winter sales have always been about understanding consumer psychology: What will make shoppers click “buy” during a hectic season? AI has significantly influenced this process, turning the art of sales into a precise science. Consider a use case from Amazon, where machine learning algorithms analyse historical sales patterns to predict which items will see the highest demand during Black Friday and post-Christmas sales. By doing so, the platform ensures stock availability while offering competitive pricing tailored to customer expectations. This capability isn’t limited to e-commerce giants. Smaller retailers are also using AI tools like Shopify’s AI-driven analytics to identify customer segments that are more likely to respond to promotional emails or limited-time discounts. By analysing vast amounts of behavioural data, AI-powered systems predict customer preferences, suggest optimal pricing strategies, and even anticipate demand surges.
Evidence from multiple sources indicates that AI significantly impacts consumer behavior, particularly during the holiday shopping season. For instance, Adobe's forecast highlights that traffic to retail websites from generative AI tools has doubled, and 40% of consumers plan to use AI tools during the holiday season to enhance their shopping experience. Additionally, AI's ability to provide personalised recommendations and streamline the shopping process is noted as a key factor in influencing consumer decisions. Furthermore, AI is expected to shape nearly 19% of Cyber Week sales, contributing to substantial revenue growth. This aligns with the broader trend of consumers increasingly relying on AI for finding deals and making informed purchasing decisions.
While AI doesn't inherently "force" consumers to spend more, its application in personalised marketing, dynamic pricing, and recommendation systems can significantly increase purchasing behaviour. Studies show that personalized recommendations powered by AI lead to higher conversion rates and larger basket sizes. For instance, recent research highlights how recommendation algorithms create tailored shopping experiences, encouraging consumers to make additional purchases they hadn't planned for. AI-driven email campaigns or targeted ads use customer data to suggest relevant products, tapping into shoppers' preferences and increasing the likelihood of unplanned spending. AI's role in dynamic pricing—adjusting prices based on demand, competition, and consumer behavior—has been shown to maximize retailer profits during peak shopping periods. According to studies in Marketing Science, consumers often perceive these price changes as personalized deals, which can incentivize faster purchasing decisions.
AI tools are increasingly adept at deploying psychological triggers like scarcity ("only 3 left in stock") or urgency ("sale ends in 2 hours"), . These tactics often result in impulse purchases, particularly during high-stakes sales events like Black Friday or post-Christmas sales. Algorithms that highlight trending products or real-time purchases by other customers enhance social proof, a psychological phenomenon where people mirror others' behavior. This strategy, documented in consumer psychology studies, makes shoppers more likely to buy items perceived as popular or in-demand. Literature points to the potential for AI to amplify compulsive shopping tendencies. By continuously nudging consumers with curated offers and time-sensitive deals, AI can exploit vulnerabilities in self-control, particularly for those prone to impulsive buying. AI's role in simplifying the shopping process—through tools like voice assistants (e.g., Alexa, Siri) or one-click checkout systems—has been linked to increased spending. A recent study found that consumers are more likely to complete purchases when friction points like complicated checkouts are removed.
Does AI Make Us Spend More?
The evidence suggests that AI contributes to increased spending, but the degree depends on various factors, including consumer behavior, susceptibility to marketing, and the specific design of AI tools. AI creates an environment where spending feels easier, more relevant, and often justified by personalised offers. While it provides convenience and value to consumers, the psychological and behavioural nudges embedded in AI systems subtly encourage higher expenditure, especially during peak shopping seasons. For consumers, understanding these AI-driven mechanisms can help mitigate impulsive buying during sales. For businesses, leveraging AI to create personalized, ethical shopping experiences can enhance customer satisfaction while driving sales without exploiting consumer vulnerabilities.
What does this mean for the retail landscape? AI has democratised access to powerful sales strategies, enabling businesses of all sizes to compete effectively during the winter sales season.
Hyper-Personalisation: The New Sales Frontier
Personalisation has always been a hallmark of good salesmanship, but AI takes it to unprecedented levels. Machine learning algorithms can segment customers based on behavioural data such as browsing habits, past purchases, and even time spent on specific product pages. For instance, ASOS uses AI to create personalised recommendations during sales events. By analysing customer data in real-time, the platform offers tailored discounts and product bundles that align with individual shopper preferences. This approach not only increases conversion rates but also fosters customer loyalty.
A more advanced example is AI-driven dynamic content personalisation, where websites adapt their layout and messaging based on the user’s profile. If a customer frequently shops for winter gear, the homepage might highlight discounts on coats and boots, accompanied by personalised recommendations for matching accessories.
What does this tell us about consumer behaviour? Shoppers are more likely to engage when they feel understood. Personalisation creates a sense of connection, turning what could be a transactional experience into something more meaningful.
Optimising Pricing Strategies with AI
Winter sales are a balancing act: price too high, and you lose customers; price too low, and you lose profit. AI helps retailers find the sweet spot by using dynamic pricing algorithms that adjust prices based on demand, competitor activity, and inventory levels. Consider Zara’s use of AI for pricing optimisation. The retailer’s algorithms monitor real-time sales data and adjust prices accordingly, ensuring that stock is cleared efficiently while maximising revenue. Similarly, airline companies employ AI to predict demand fluctuations for holiday travel, enabling them to offer competitive yet profitable fares.
What does this mean for consumers? While dynamic pricing ensures better deals for some, it also raises questions about fairness. Customers browsing the same product may see different prices based on their location, device, or browsing history, challenging traditional notions of equity in commerce.
The Psychology of AI-Driven Sales Tactics
AI doesn’t just observe our behavior—it shapes it, crafting experiences that influence our choices in subtle yet powerful ways. Through insights derived from behavioral data science, AI has mastered the art of nudging, using psychological triggers to steer consumers toward specific actions, often without their conscious awareness. These nudges, when strategically deployed, are transforming how we shop, particularly during high-stakes events like winter sales.
Imagine browsing an online store and lingering over an item you’re unsure about. Suddenly, a message pops up: “Only 2 items left in stock!” That sense of urgency isn’t coincidental—it’s a calculated move by AI to leverage scarcity, a psychological trigger that makes the item feel more valuable and prompts quicker decision-making. Similarly, as you scroll further, a notification appears: “5 people are viewing this product right now.” This isn’t just a random update; it’s social proof at work, a tactic designed to tap into our natural inclination to follow the crowd.
But AI’s influence doesn’t end there. For shoppers who leave items abandoned in their carts, algorithms spring into action, offering personalised rewards to bring them back. A discount code or extra loyalty points tailored to the shopper’s preferences becomes the final nudge needed to seal the deal. These seemingly simple interventions can have profound impacts on consumer behavior. The potential of AI-powered nudges is vividly illustrated by a case study from Alibaba’s 2023 Singles’ Day sale, the largest shopping event in the world. By integrating scarcity messages and real-time social proof with personalised discounts, Alibaba turned hesitation into action, achieving a record-breaking $84.5 billion in sales. The platform's use of AI not only heightened urgency but also created a seamless, highly engaging shopping experience that left little room for second thoughts.
These tactics highlight how AI doesn’t merely react to consumer behavior—it actively shapes it, creating a dynamic interplay between human decision-making and machine intelligence. While these strategies can feel like magic to the consumer, they’re grounded in robust psychological principles, demonstrating how data-driven insights can elevate both engagement and profitability. As AI continues to evolve, its ability to influence consumer behavior will only become more sophisticated, reshaping the shopping landscape in ways we’re just beginning to understand.
What does this reveal about human psychology? It highlights the power of subtle cues in decision-making. AI doesn’t force behaviour—it guides it, often in ways we don’t consciously notice.
Ethical Considerations: Balancing Profit and Integrity
While AI offers immense potential, it also raises ethical concerns. The use of behavioural data for sales optimisation can veer into manipulative territory, eroding trust and transparency. For instance, dynamic pricing algorithms have faced backlash for exploiting urgency to inflate prices, particularly in industries like travel and healthcare. Similarly, the overuse of scarcity tactics can lead to consumer fatigue, diminishing the effectiveness of these strategies.
How can businesses strike the right balance? Ethical AI practices, such as transparent pricing policies and opt-in personalisation, are essential. Retailers must prioritize long-term trust over short-term gains, ensuring that AI enhances rather than exploits the shopping experience.
Emerging Trends in AI and Retail
As we look to the future, several trends are reshaping how AI will influence winter sales:
These innovations promise to make winter sales more accessible, engaging, and sustainable, redefining what it means to shop in a digital age.
Takeaways: Redefining Winter Sales with AI
AI is transforming winter sales from a chaotic rush into a finely tuned operation that benefits both businesses and consumers. By leveraging behavioural data, retailers can anticipate needs, personalise experiences, and optimise pricing strategies like never before. However, with great power comes great responsibility. As AI becomes more integrated into retail, businesses must ensure their practices are ethical, transparent, and aligned with consumer trust.
Research Highlights
These are the studies combining behavioural science and data science components, which caught my eye this week. Note that inclusion in this list does not constitute an endorsement or a recommendation. It is just something I found interesting to read.
Voice-activated shopping assistants (voice AI), such as Amazon Alexa or Alibaba Tmall Genie, have been gaining popularity worldwide as a new channel for online shopping. In this paper, we use large-scale archival data of consumer-level purchase records from Alibaba, the world’s largest e-commerce platform, to empirically investigate how consumers’ adoption of Tmall Genie affects their consumption. The results show that the average consumer’s weekly spending on Alibaba increased by 16.6% within the first four months after adopting voice AI. Additionally, we explore specific product features that moderate the effect of Genie adoption by examining the repeat purchase, product substitutability and familiarity, supporting a mechanism that involves reducing information acquisition costs. The positive effects of Genie adoption remain significant on repeat purchase in the long term although they attenuate over time. Furthermore, our analyses reveal that on average, the voice channel has a positive spillover effect on spending on the PC channel but no significant effect on the mobile channel. The channel dynamics are contingent on specific shopping contexts. Our results demonstrate that voice AI devices with shopping capabilities can enhance the growth of the affiliated e-commerce platform. As the first study to empirically examine the impact of voice AI adoption on e-commerce consumption, our paper provides valuable implications for e-commerce platforms and retailers leveraging voice-activated shopping.
AI enhances personalised shopping experiences and operational efficiencies. Retailers like Walmart and Amazon leverage AI for demand forecasting and customer insights. Ethical AI use and transparency are vital for maintaining consumer trust. As consumers navigate the ever-evolving retail landscape, businesses are increasingly harnessing the power of artificial intelligence (AI) to elevate the shopping experience. From personalised product recommendations to streamlined operations, AI is emerging as a game-changer in retail management. The latest research by Bain & Company reveals critical insights into the transformative potential of AI-driven strategies.
The 2024 holiday season marks a pivotal shift as AI-savvy consumers demand hyperpersonalized, value-driven experiences, challenging retailers to adapt with speed, agility and precision. AI is revolutionizing retail operations and customer engagement, unlocking new benchmarks for success. This article delves into four AI-powered trends driving the 2024 holiday shopping season, uncovering strategies for retailers to thrive in a rapidly evolving, consumer-driven landscape.
Events and Opportunities
You may find the following events and opportunities of interest. Note that inclusion in this list does not constitute an endorsement or a recommendation.
You may find the following events and opportunities of interest. Note that inclusion in this list does not constitute an endorsement or a recommendation.
Events:
Vacancies:
JPMorganChase, Chicago, IL, USA
Stockland, Sydney, Australia
HiTechGroup, Sydney, Australia
Resources Group, Sydney, Australia
Progressive Leasing, Remote Location
Your Feedback
This winter sales season, consider how AI is shaping your shopping experience. Is it helping you find what you need—or nudging you toward what it thinks you want? Share your thoughts—or simply drop a ?? if this edition resonated with you.