Lawmakers Eye Dynamic Ecommerce Pricing

Several American lawmakers have expressed an interest in limiting or prohibiting data-driven price changes. The recent activity dates back to at least 2021 and may stem from inflation concerns and increased AI usage.

For example, in December 2025, Instacart drew strong criticism from Democratic Senator Charles Schumer of New York after it permitted grocery stores to test AI-powered, dynamic pricing.

The experiment showed an average variation of about 7% between the lowest and highest prices for specific grocery items. But there were standouts, according to Consumer Reports, with Wheat Thins ranging from $3.99 per box to $4.89, 23% higher.

Schumer likened the price differences to gouging and asked for an investigation by the Federal Trade Commission.

Personalized dynamic pricing leads to profitable merchants and satisfied shoppers.

Tennessee Bill

Meanwhile, a proposal from Tennessee state representative John Ray Clemmons, a Democrat, illustrates how the dynamic pricing debate could shift from headlines to law.

Clemmons’ House Bill 1468 would prohibit “personalized algorithmic pricing,” which it defines as “dynamic pricing set by an algorithm that uses personal data.”

That definition targets any system that adjusts prices based on information tied to an individual shopper, including purchase history, browsing behavior, loyalty status, location signals, and other attributes. Conceivably, it could include aggregate data applied to individuals.

Tennessee HB 1468’s enforcement mechanism is also notable. It makes personalized algorithmic pricing an “unfair or deceptive act or practice” under the state’s consumer protection statute. That approach gives the state’s attorney general broad enforcement power and exposes retailers to legal liability, even if no consumer can point to a false claim or deception.

For ecommerce merchants, the risk is clear. If bills such as Tennessee’s spread, dynamic pricing could become legally hazardous not because prices are changing, but because the systems doing the changing rely on customer behavioral data — the same data that powers modern online merchandising, email marketing, loyalty programs, and conversion optimization.

Unfair?

The criticism of Instacart’s AI-pricing and the political momentum behind bills such as Tennessee’s HB 1468 incorrectly assumes that prices determined by data and software are somehow less legitimate than those set by a manager with a clipboard.

Put another way, to some lawmakers, dynamic pricing feels unfair.

But not every shopper cares to pay the same price. Consider coupons, which manufacturers and grocery stores routinely issue. Every shopper knows coupons exist. But not all use them, nor do they care that they are paying a different price.

Optimization

And that is the point. Optimization drives ecommerce price changes.

Vaidotas Juknys is chief commercial officer at Decodo, a web data infrastructure provider. He told me, “Dynamic pricing is widely used across modern commerce to help businesses align prices with demand, manage inventory more efficiently, and remain competitive in fast-moving markets.

“Broad restrictions risk limiting those benefits and may ultimately lead to higher average prices if companies lose the ability to adapt in real time.”

To be sure, dynamic optimization results in different prices across shoppers, who can accept or reject offers.

Algorithm-based pricing is likely a key component of ecommerce in the emerging AI world, presenting many opportunities for merchants:

• Relevant discounts. Customer-level pricing enables merchants to offer discounts to shoppers who would not convert otherwise.

• Conversion rate optimization. Algorithms can detect purchase intent signals (repeat visits, cart additions, time on site) and trigger pricing to close the sale.

• No wasted discount. Blanket promotions reduce margins companywide. Personalized pricing can limit discounts to specific segments, preserving profit while still driving growth.

• Customer retention. Pricing tied to loyalty status or purchasing history can reward and encourage repeat customers.

• Inventory efficiency. Merchants can use shopper behavior to promote overstock items to likely buyers.

• Smart acquisition offers. Personalized pricing can support first-time buyer promotions, helping brands compete with marketplaces without permanently lowering prices.

• Boost marketing ROI. Personalized incentives can link to traffic sources, campaigns, and shopper cohorts, helping merchants measure the profitability of paid acquisition at the order level.

Yet shoppers benefit, too. Dynamic systems can reduce prices when supply is abundant and demand is weak. The result is more discounts, better availability, and fewer shortages than a rigid one-price approach.

Will AI Solve Ecommerce Personalization?

A nascent firm armed with a fresh $12.3 million investment aims to deliver on the promise of ecommerce personalization.

A personalization engine shows the right product to the right shopper at the right time.

In theory, it makes everyone happy. Shoppers see relevant and engaging products. Merchants sell more.

It sounds simple enough. Think of an ecommerce website with products for sale. What item(s) does the site show to a particular user to entice a sale? How does it know what to show?

Data Right Now

This question of “what to show” is how Matteo Ruffini, chief science officer of the Swiss start-up Albatross AI, described the problem his company solves during a February 2025 interview.

Many ecommerce personalization and recommendation solutions rely on historical shopper behavior. The systems look backward over months or years, at purchases and browses, for instance.

The folks at Albatross also use past behavioral data, but they’ve added a real-time, right-now predictive element.

The Albatross product, according to a Forbes contributor, “captures every user action in a session and passes it into [an AI] transformer model that behaves like a language model for intent. The inputs are event triplets — user, action, item — instead of words. The model analyzes not just the action but the sequence of actions and the context that connects them. It updates continuously and responds in milliseconds without retraining.”

Essentially, the company claims to have the first AI infrastructure for training models on sequential, live events.

Albatross claims to have the first AI infrastructure for training models on sequential, live events.

3 Challenges

Albatross AI addresses at least three long-standing problems with predictive ecommerce recommendations:

• Long training periods.
• Categorizing new shoppers.
• Cold starts for products.

Training

Personalized and segment-based recommendations depend on machine learning models that need time and data to mature. It can take weeks or months to gather enough data for meaningful recommendations. Moreover, the model must retrain often.

Some recommendation solutions train in cycles, such as daily or weekly, and they require reams of historical shopping activity. The result is recommendations that can lag behind rapidly changing demand signals, seasonal trends, influencer surges, or unpredictable cultural moments (such as the pandemic).

A shopper’s intent can change today, but if not in the next training cycle, the system cannot react.

Emerging platforms such as Albatross explore continuous or incremental learning, reducing reliance on scheduled retraining and moving toward models that reflect active sessions.

New shoppers

A second long-standing challenge is how recommendation systems treat new shoppers. Historically, these systems relied on popularity-driven rankings or generic best-sellers while they waited to gather enough signals to personalize.

Cookie-less personalization or probable identity matching offers only limited relief.

The industry is now shifting toward what could be described as “first-minute personalization,” meaning that intent signals within a single session — scroll depth, dwell time, bounce patterns, micro-hovers, theme switches — become the primary inferences.

The goal is to reduce the number of interactions required to understand a shopper’s interests and intents.

Cold start

The third obstacle is the cold start product problem.

An ecommerce catalog is rarely static. New SKUs arrive every day; marketplaces can add thousands per hour.

Current recommendation algorithms need interaction data before they can confidently suggest an item. Hence new products may remain buried.

Marketers can mark them as new and provide preferential treatment in search and on category pages. But those actions can defeat the purpose of personalized recommendations.

AI approaches are beginning to leverage content embedding, multimodal representation, and sequential modeling to infer probable relevance before engagement data is available. Essentially, AI understands much better which shoppers will like the new product.

Research continues to uncover ways to combine item metadata, textual or image-based descriptions, and user-sequence context so that new items are visible on day one.

AI and Commerce

The three challenges apply to other trends in ecommerce and the ongoing AI transformation.

LLMs such as ChatGPT, Perplexity, and Gemini are attempting to rank products for individuals through agentic commerce. Yet none of these will deliver unless they can interpret shopping intent.

In short, recommendation engines and AI shopping agents are becoming blurred. Product discovery and purchase decisions are merging.

AI Makes Loyalty Programs Smarter

Artificial intelligence is transforming ecommerce customer loyalty programs.

A May 2025 analysis found a “strong positive correlation” between AI use and customer loyalty, suggesting that AI can significantly enhance customer retention, engagement, and satisfaction.

The analysis, “Artificial intelligence’s effect on customer loyalty in the context of electronic commerce,” comes from Hasan Beyari of Umm Al-Qura University, Makkah, Saudi Arabia. It reviewed and consolidated findings from several studies, academic papers, and articles.

3 Ps

Beyari’s report focused on three AI capabilities that could change how ecommerce loyalty programs and customer retention marketing operate:

• Personalization. AI enables relevance.
• Prediction. AI enables anticipation.
• Performance. AI enables reliability and service quality.

Personalization

AI-driven personalization has several advantages over other technologies or manual recommendation systems. These advantages stem from AI’s ability to process massive amounts of data in real time and adapt dynamically to individual shopper behavior.

Beyari’s analysis identified a “strong” relationship between ecommerce merchants employing AI recommendations and repeat purchases.

When a recommendation feels useful and relevant rather than somewhat random, shoppers interpret that utility as care rather than mere promotion.

AI personalization is already affecting product detail pages. It can change layouts,  descriptions, and even images.

AI can even learn from conversion rates, getting progressively better as it adapts and personalizes.

The good news for ecommerce merchants is that just about every software provider is trying to add AI.

Prediction

AI enables merchants to move from reactive to proactive loyalty.

AI can monitor declining open rates, cart activity, and average order value. Predictive AI can re-engage a customer at the most opportune time, such as when she’s at risk of churning or ready to reorder.

AI anticipates needs. If a subscription customer typically orders protein powder every 30 days, an AI engine can predict the next purchase window and send a reminder or loyalty bonus at day 27. That small gesture confirms expectations of convenience and care.

The key here, according to Beyari’s analysis, is identifying micro patterns of engagement or disengagement and acting on them.

Performance

Performance in the context of ecommerce loyalty describes the program’s speed, accuracy, and trustworthiness. Here, the report finds that AI enhances each of these.

AI chatbots and virtual assistants can instantly resolve routine inquiries — “How do I redeem a loyalty coupon?” or “Where is my order?” — without involving customer service representatives.

Beyari’s review cited studies showing that AI-powered customer service improved satisfaction and perceived efficiency; both are strong predictors of loyalty.

AI adds integrity, too. Fraudulent redemptions and fake accounts can erode trust in loyalty programs, especially points-based ones.

Machine-learning models can flag unusual behavior — such as bulk redemptions or location mismatches — before damage occurs. AI also forecasts redemption rates, helping merchants manage liabilities and inventory.

Ecommerce Loyalty

Loyalty programs are often a later-stage marketing tactic. An online shop typically evolves from paid acquisition and search engine optimization to lifecycle marketing, and then to loyalty and retention marketing.

Artificial intelligence is not replacing loyalty programs. It is refining them.

The evidence shows that AI creates a feedback loop between behavior and reward, turning every purchase into a learning event. It is an active and dynamic system that personalizes in real time, predicts what customers need, and performs well behind the scenes.

The result is what Beyari describes as a “moderate to strong positive association” between AI use and loyalty. It’s an academic way of saying the technology works.

Prime Day’s Mobile AOV Challenge

Amazon’s Prime Day 2025 event set a new benchmark outside of the popular marketplace.

Amazon was humming during the July 8-11 Prime Day sale. The company reported record revenue, and according to Adobe Analytics, Prime Day is now an ecommerce industry-wide sales initiative akin to Black Friday and Cyber Monday.

Not Just Amazon

U.S. online retailers generated at least $24.1 billion in sales during this year’s Prime Day period, up 30% from 2024, again according to Adobe, which tracked more than 1 trillion visits to merchant websites and 100 million SKUs — all outside of Amazon.

Adobe also reported that, for the first time, revenue from mobile devices surpassed desktops during a Prime Day event.

Smartphone shoppers spent at least $12.8 billion, or 53.2% of the total.

That percentage suggests that mobile is the primary driver of ecommerce sales, with broad implications for how merchants design shopping experiences, promote products, and manage operations.

Hence the most important Prime Day takeaway may not be total revenue but rather the device.

Small Orders

For merchants, mobile dominance could mean relatively higher per-order costs and thus thinner margins unless sellers take steps to increase average order value.

“Adobe Analytics data shows that consumers have embraced mobile shopping for purchases that are more frequent and lower in price, said Adobe Digital Insights analyst Vivek Pandya, in a separate July 2024 report.

“Adobe’s data also shows that basket sizes on mobile are 32% smaller than on desktop, which presents both a challenge and opportunity for brands to refine mobile experiences and close the gap to drive revenue, said Pandya.”

Mobile AOV Gap

Fortunately, merchants can deploy several tactics to boost mobile order values.

Merchandising

Retailers have long depended on up-selling, cross-selling, and product bundling to increase AOV. Implementing those tactics on mobile merchandising requires deliberate user experience and offer design.

For example, apparel shops could offer “complete the look” product bundles near the mobile checkout button or even in the cart itself.

Similarly, stores could introduce progressive discounts and implement a progress bar or text notifications — “Spend $10 more and get 15% off” — to show mobile shoppers how close they are to the next deal or discount.

Retention

More frequent, smaller purchases could create additional opportunities for follow-up engagement and lifecycle marketing.

Repeat customers have always been crucial to ecommerce profitability. On mobile, sellers could send shoppers post-sale reminders and follow-ups via SMS or the newer RCS, driving incremental revenue.

Fulfillment

Lower AOVs from mobile transactions result in a higher fulfillment cost percentage.

It’s more efficient to ship multiple items together than separately, as smaller and more frequent purchases lead to more packaging, more labor, and higher per-order carrier costs.

Reduced packaging is not necessarily viable, as lightweight or thin materials may save on shipping costs but also increase the risk of damage, returns, and customer dissatisfaction.

A better approach is strategies that encourage larger shipments, such as the merchandising tactics above, perhaps combined with the sustainability benefits of shipping items together.

AOV Challenge

Adobe’s Prime Day reports from the past three years show a trend toward mobile commerce and lower AOVs.

Facing an AOV challenge, merchants should encourage shoppers toward larger, more profitable transactions through thoughtful design, messaging, and fulfillment.

Why Your Loyalty Program Isn’t Working

Loyalty programs are more than the usual rewards of 10% off, free shipping, and birthday emails. Done well, loyalty incentives focus on psychological and behavioral science to deepen retention.

Smarter Segmentation

Seasoned marketers segment for campaigns, but what about loyalty impact? Try building segments based on motivational context, not just purchase history.

• Redemption behavior. Who hoards rewards, and who redeems quickly? Target accordingly.

• Dormancy within loyalty tiers. Users with no activity for 60 days may need a different prod than recent converts.

• High browse, low buy customers. Use loyalty nudges to bridge the gap with non-monetary perks or risk-free trials.

Build loyalty throughout the shopping journey:

• Acquisition. Display loyalty perks on product pages and modals, and on ad copy (Meta, Google) that speaks to exclusive benefits.

• Onboarding. Pre-enroll customers or ask for their birth dates and unique interests early to tailor benefits faster.

Sephora’s Beauty Insider program offers tiered perks, birthday gifts, and exclusive experiences that encourage purchase frequency and aspiration.

Sephora’s Beauty Insider program encourages purchases and aspiration via tiered perks, birthday gifts, and exclusive experiences. Click image to enlarge.

More Than Discounts

Discounts offer short-term gratification, but they don’t build lasting loyalty. Instead, think about what motivates long-term engagement:

• Progress effect. People are more likely to complete a task when they feel they’ve already started. Pre-load new customers with points or status and visually highlight their progress.

• Variable rewards. Unpredictable perks (e.g., surprise freebies, mystery discounts) can spur action and boost engagement.

• Goal-gradient hypothesis. The closer people are to a goal (e.g., a gift at 100 points), the more effort they exert to reach it. Use dynamic emails or texts to show progress bars and remaining required actions.

For your high-value customers, consider layered benefits based on lifecycle and psychology:

• Exclusive access. Think status and belonging, such as early drops, members-only content, and personalized products.

• Identity-based rewards. Customers want recognition. Use first-party data (e.g., style quiz responses, dietary preferences) to personalize loyalty perks that align with their values.

• Mission-aligned incentives. Offer donation matching, carbon offset rewards, or “choose your perk” flexibility for cause-conscious customers.

Beyond Email

Experienced teams know this, but it’s worth reiterating: An email-only loyalty program is limited and often ineffective. A little integration goes a long way in making the program feel alive, not automated.

Connect loyalty data to:

• SMS platforms for real-time nudges (“You’re 10 points from your next reward!”).

• Ad platforms.

• Customer service platforms so agents can surprise and delight based on tier or behavior.

In short, customers remember the shopping experience and interaction with your brand, not points alone. Design rewards to tap into progress, surprise, exclusivity, and identity. Move from boring and predictable to habit-forming and sticky.

How Lifecycle Marketing Powers Ecom SMBs

Small and midsized ecommerce businesses can benefit from lifecycle marketing and a strategic approach to engaging shoppers.

What follows is a shopper lifecycle marketing framework for an online store selling niche print-on-demand t-shirts. Each step includes the framework and implementation details.

• Attraction
• Purchase
• Fulfillment
• Retention
• Reorder

Attraction

The “attraction” step aims to introduce the store, products, or unique value proposition to shoppers amid a crowded digital marketplace.

An ecommerce marketer might use several channels and features to achieve this end, such as:

• Content marketing. Shoppable videos and engaging blog posts powered by search engine optimization and structured data markup for rich snippets.

• Advertising. Programmatic ads with AI-driven targeting on Google Ads, Meta Ads, and similar.

• Social media. Emerging platforms, influencer partnerships (Shopify Colabs), and social commerce features on X, TikTok, Instagram, and Reddit.

• Marketplace. Presence on Amazon and Etsy. Use social commerce on Facebook Shops, Instagram Shopping, and products on X.

Here are the t-shirt shop’s implementation steps:

• The shop used Semrush to identify 4,200 target keyword phrases. These keywords are transformed into articles using an automation built with Zapier, ChatGPT, and Midjourney. A marketer identifies key points, AI drafts the blog article, and a human edits it.

• The store changed content management systems to improve technical SEO.

• Ads on Meta, Google, X, and various email newsletters.

• The store used Shopify Collabs to connect with influencers.

• The business is launching on X and Pinterest. Using another Zapier automation, the shop repurposes a single article into 15 social media posts and schedules them weeks in advance.

The site includes email capture on all its blog pages, so subscriptions are the top performance indicator, but site traffic and ad engagement are also measured.

An example of one of the ads the t-shirt shop uses to attract shoppers. Click image to enlarge.

Purchase

The “purchase” step aims to convert shoppers into customers through a seamless and secure purchasing experience.

In this step, the business collects a customer’s email address, physical address, and, if possible, permission to send text messages.

Ecommerce marketers typically excel at this step via the following tactics.

• An optimized online store. User-friendly product pages, streamlined checkout, and mobile optimization.
• Marketplace optimization. Using Amazon or similar to close the deal.
• Email. Cart abandonment reminders, limited-time offers, and similar to get every order.
• SMS. Offer text message order confirmation, delivery tracking, and special offers.

At this step, the t-shirt shop relies heavily on Shopify, its ecommerce platform. The shop can offer a variety of payment and delivery options and email and SMS for all transactional messages. It uses an “opt-out” approach to email marketing.

Fulfillment

It sounds simple, but it can be difficult. An ecommerce marketer must focus on fulfilling orders and the overall purchase experience. This typically involves communication.

• Set up order confirmations and shipping updates via email or SMS.
• Offer customer support via AI-powered chatbots, email, and telephone.
• Use social media to address customer service inquiries.

The t-shirt shop faced challenges at this stage because it uses print-on-demand services to fulfill orders. The company took a few steps to help.

• Set delivery expectations on every product page and the post-purchase email sequence.
• Send incoming messages immediately to the customer support person.
• Implemented an AI chatbot in 2025.

The t-shirt shop lets customers know it might take 10 days to fulfill the order. Click image to enlarge.

Retention

The “retention” phase in this shopper lifecycle marketing framework engages customers post-purchase to encourage reorders and referrals. Tactics include:

• Email and SMS to offer loyalty program updates and exclusive offers.
• Email newsletter to keep shoppers interested in the store’s content.
• Referral programs to incent shoppers to share products or content with friends.
• Review requests, encouraging testimonials and ratings.
• Social media to develop community.

This store is launching a program where customers earn points for purchases, reviews, and social shares. Shoppers can use the points to make additional purchases. The shop also has a weekly email newsletter.

Reorder

When she returns for a repeat purchase, the goal should be to recognize the shopper and make the order even better. But it takes work to get that subsequent purchase.

With this in mind, ecommerce marketers might try:

• Retargeting ads aimed at previous customers.
• Personalized product-focused email marketing.
• Targeted product placement in an email newsletter.
• SMS deals and discounts.
• Bonus loyal points for each order.

Here, the t-shirt shop uses its newsletter, ad retargeting, and email promotions to help garner a second sale. When a shopper completes an additional purchase, the shop sends an email sequence to recognize the loyalty.

Lifecycle Advantage

An ecommerce lifecycle marketing framework provides a roadmap for shopper engagement, which should drive both initial and repeat sales.

Wallet Passes Are a Mobile Marketing Channel

The technology powering mobile airline boarding passes also enables push notifications for retail marketers.

Mobile wallet passes are digital representations of tickets, boarding passes, and cards for auto insurance, loyalty programs, coupons, and memberships.

Stored within mobile wallet applications such as Apple Wallet, Google Pay, or Samsung Pay, these passes allow consumers to access and use them directly from their smartphones, streamlining transactions, improving security, and avoiding physical alternatives.

Marketing Opportunity

Wallet passes also work for brick-and-mortar and ecommerce shops, with three key features: persistent storage, updates, and push notifications.

Consider an airline boarding pass.

• A traveler checks in remotely for a flight and adds the boarding pass to her wallet. The pass will remain in the wallet until she deletes it manually. Passes stay for years and, often, move from an old phone to a new one.

• If the flight’s departure gate or time changes, the digital boarding pass updates with the most recent information.

• Finally, the boarding pass can send notifications on a smartphone’s lock screen, informing the traveler about the new gate or departure time.

Expired boarding passes from the author’s digital wallet.

Imagine similar steps applied to a retailer.

• A shopper adds a store loyalty card to his wallet to earn discounts.

• During a week-long promotion leading up to Black Friday or Cyber Monday, the store offers daily updates with special offers and discounts. Each day, the digital loyalty card (wallet pass) shows the sale item and offer.

• The retailer can also send push notifications directly to the lock screen on the shopper’s smartphone, announcing each day’s deal.

The store could include external links in either the loyalty card or the notification (depending on the mobile operating system), sending the shopper directly to a sales landing page to make a purchase.

In some cases, it could be a single-click checkout, where tapping the message link leads directly to the order confirmation page. Few other channels offer this level of conversion potential.

Notably, the mobile wallet pass reaches a shopper directly on her phone. It’s a potent channel during the holiday shopping season amid saturated email and advertising campaigns.

Digital coupon and loyalty cards can link directly to the checkout. These hypothetical examples are from the Apple Wallet documentation.

A Good Fit?

A do-it-yourself approach to wallet pass marketing is entirely possible. All major mobile operating systems have well-documented software development kits (SDKs) or application programming interfaces (APIs).

Yet one hardly needs to make the effort. Prominent mobile pass providers include Vibes, PassKit, and Airship. The Shopify app store has more than 70 options for wallet passes. Some providers offer 1,000 persistent loyalty card passes for less than $100 per month.

Still, shoppers need a reason to add a pass. The motivation might be a loyalty card with its promise of points and prizes or a recurring monthly coupon. Both likely appeal to shoppers more than downloading an app.

Clearly, however, wallet passes don’t make sense for every retailer. They likely fit merchants with:

• Physical and online stores,
• A wide range of products,
• An existing loyalty program,
• Frequent sales or promotions,
• Diminishing returns from other promotional channels.

Nonetheless, mobile wallet passes are a potential marketing channel for sellers online and off. The technology’s ability to persist on shoppers’ smartphones, provide real-time updates, and send push notifications directly to locked screens makes the passes especially valuable during high-competition periods such as the holidays.

Is Personalized Shopping Private?

Personalization at scale seems contradictory. It’s like having a party with several million friends. Yet at its best, ecommerce personalization drives conversions anonymously.

How can merchants balance customer personalization and privacy? I asked that question to payment and security pros.

Personalizing ecommerce shopping drives sales. Maintaining privacy is the challenge.

Balancing Act

Robin Anderson, vice president of acquiring products at Tribe Payments, an open banking facilitator, has seen ecommerce personalization evolve from simple tracking and recommendation systems to sophisticated, artificial-intelligence-driven experiences. He believes all commerce channels, from online to in-person, will become more personalized.

“Hyper-personalization is on trend in payments and the flipside, which is privacy,” he said. “It’s not only about the data you capture and leverage to drive engagement; it’s also about the mechanisms to allow consumers to call back that data later. It’s a real balancing act, and I don’t think anyone has quite cracked it yet, but certainly there has been a lot of rapid innovation.”

Compliance

Keeping up with privacy regulations, which vary by region, is critical for ecommerce merchants, stated Sandra Tobler, co-founder and chief customer officer of Futurae, an authentication platform.

“Privacy guidelines such as Europe’s GDPR and PSD2 have a profound impact on ecommerce merchants, requiring them to handle customer data with greater care and transparency,” she said. “Compliance with these regulations is crucial to avoid hefty fines and to build customer trust.”

Tobler recommended using advanced authentication to verify legitimate customers. Multifactor authentication, biometrics, and behavioral analytics can help protect customers’ accounts, build trust, and decrease churn rates. Advanced solutions use data collected during authentication to tailor security measures for each user. A key aspect of this approach, continuous authentication, assesses a user’s behavior and context throughout the shopping journey.

“If users are shopping from a familiar location and device, the system can allow them to proceed with minimal friction. However, if the system detects an unusual location or device, it might prompt for an additional authentication step to ensure security. Recognizing returning customers and allowing them to move through the shopping journey without repeated prompts contributes to a smoother experience, increasing customer satisfaction and loyalty.”

It is also important to separate nonsensitive data, such as behavior patterns, geolocation, and devices, from sensitive, such as credit card numbers and other personally identifiable information.

“Decoupling sensitive data aligns with privacy regulations by minimizing the amount of personal information processed during authentication,” she said. “The end-to-end encryption of sensitive data, such as credit card numbers and personal identification information, protects the original, even if intercepted.”

Sensitive Data

Jason Howard, CEO at Caf, an identity authentication provider, agreed that collecting only required information for specific transactions is foundational to regulatory compliance.

“Many jurisdictions around the globe have created consumer data privacy laws, and running afoul of these regulatory statutes can be costly. That’s why we recommend incrementally collecting information from users only as needed. Such an approach creates a better customer experience, thus leading to less abandonment and quicker time to revenue.”

Howard additionally noted that decentralized identity solutions enable secure and transparent transactions without relying on intermediaries or data storage. These solutions also simplify the authentication process and eliminate the need for repeated verifications when customers access different platforms.

“With robust biometrics, merchants can be assured that users are who they claim. Biometrics help protect against stolen identities, impersonation, and account takeover attacks.”

Embedded commerce — selling products on external channels — has created new revenue channels and opportunities for attackers, Howard added. Fraudsters exploit the refund process within embedded payment systems in various ways, such as requesting refunds for products or services they never purchased or falsely claiming that the goods they received were defective.

Ecommerce companies need technology to detect that behavior. Behavioral analytics can identify suspicious patterns and fraud. AI models can uncover patterns in large datasets that may previously have gone undetected. AI can also detect manipulated images or documents.

Checkouts

Peter Karpas, CEO of Bold Commerce, a customized checkout provider, observed that personalization has thus far stopped short of the checkout experience.

“Personalization in ecommerce is less about who one specific customer is and more about the experience,” he said. “For example, a shopper that lives 20 miles from a store should be offered a checkout with options for pickup and delivery, whereas a shopper farther away should just see shipping.”

Rather than creating millions of unique customer experiences, Karpas suggested that brands tailor shopper journeys and segments. Checkout, for example, could be two or three versions, depending on the segment.

“Retailers realize personalizing checkout isn’t the same as everything else,” he said. “They’re finding it disproportionately impacts conversions, average order value, and customer lifetime value.”