How Retailers Are Using AI And Emotion To Build Loyalty In 2025
Artificial Intelligence
Customer loyalty isn't something retailers can take for granted in today's crowded marketplace. It's something they have to earn and re-earn through ongoing, meaningful engagement. And changing consumer behavior is only adding to the complexity. Shoppers today no longer think solely in terms of 'online' or 'in-store.' According to VML's Future Shopper 2024 report, 61% of shoppers globally want seamless communications across sales channels, with their journey documented and data following them the whole time – up five percent from just one year prior. They expect what is often called a 'phygital' experience: a seamless journey that spans digital and physical channels, where retailers can adjust to customer preferences, life events, and life stages in real time.
To meet these rising expectations, retailers are reimagining loyalty as not a points system or occasional reward, but an ongoing relationship. A recent report from Braze, the 2025 Global Customer Engagement Review (CER), explores the ways leading brands are stepping up to meet the moment by rethinking how they engage and retain their audiences. According to the report, leading retailers break away from a one-size-fits-all approach by blending together AI-powered personalization, emotional resonance, and experimentation. These retailers are treating loyalty not as a fixed outcome, but as a continuous conversation – one that is shaped at every touch point, treating it both as an art and a science.
AI is powering the next generation of retail engagement
It's no secret that AI is now a key driving force behind the next generation of retail experiences. Retailers already use it to analyze sentiment, predict behavior, and personalize at scale, with leading brands being 39% more likely to use AI to adjust messages based on real-time interactions. As AI becomes more embedded in workflows, it's not just about automating tasks, but also unlocking new levels of impact. Braze Chief Product Officer, Kevin Wang, shared 'the real magic of AI is in helping marketers operate with greater efficiency and creativity. It allows teams to deliver personalized experiences in the moment faster and more seamlessly than ever before, while driving meaningful business results.'
But with great power comes greater responsibility. As AI and data driven strategies advance, so does the need for transparent, consent driven data practices. According to a recent Prosper Insights & Analytics survey, 58.5% of consumers are either extremely or very concerned about AI using their data, signaling a growing public unease that can't be brushed aside. In response, retailers are increasingly cautious about how customer data is used and shared.
Prosper - How Concerned are You About Privacy Being Violated From AI Using Your Data
More notably, Braze's 2025 Retail CER shows that internal data sharing now outweighs legal or regulatory worries among surveyed marketing executives, suggesting that today's retailers are motivated not just by compliance, but by a deeper commitment to doing right by their customers. The future of AI-powered retail engagement depends not only on smarter technology, but also on how thoughtfully it's applied, in addition to how well it sustains consumer trust. As marketers unlock new levels of capability, the next challenge is ensuring those interactions feel authentic, and emotionally resonant.
Leaning on technology to drive emotional connection
Beyond efficiency and seamless journeys, retailers that are doing this well today are leaning into technology to listen to their customers' digital body language, delivering messages that resonate in the moment and create emotional connections, not just transactions. Braze data found that brands that are emotionally connecting with their customers are the ones who are more likely to exceed their revenue goals. Moreover, top leaders show a strong willingness to course-correct when messaging misses the mark.
According to Prosper Insights & Analytics, 73.3% of shoppers prefer to speak with a live person while making online purchases. This highlights that technology should enhance, not replace, the human element of engagement.
Prosper - Prefer To Communicate With Live Person or AI Chat Program for Online Shopping
Technology allows brands to reach customers one-on-one, but without great content, those connections fall flat. If the endgame is enduring relationships with customers, a focus on customer retention and the relative cost of customer acquisition makes perfect sense. But what does that mean in practice? It means using customer data more efficiently, leaning on technology to enhance, not replace human connection, and embracing a culture of experimentation to continuously improve. In fact, Braze data shows that 93% of retailers rely on technology to add emotional resonance to their messaging, whether through personalized channel preferences, milestone triggered messages, or community based campaigns.
A strong example of this can be seen when looking at e.l.f. Cosmetics, a brand under e.l.f. Beauty. The company partnered with Braze and Stitch to revamp its digital engagement strategy and loyalty program. By expanding into channels like SMS and push notifications, the company saw a 125% increase in monthly mobile app usage (over six months) and stronger customer connections. This demonstrates how creative, cross-channel messaging can drive both emotional resonance and measurable results.
Culture of experimentation separates leaders from the less agile
Leading retailers share a common trait: they work collaboratively and prioritize frequent experimentation. There is no doubt that the most successful teams are agile, cross-functional, and committed to refining engagement strategies to uncover valuable customer insights. These efforts go beyond marketing alone. 'The most forward-thinking retailers treat experimentation as a team sport. When marketing, data, and engineering come together around a shared goal, that's when real innovation and customer understanding happens,' shared Wang.
Top brands are bringing in analytics, engineering, and other teams to build a more robust and dynamic understanding of their customers. Still, there's room for growth. While many retailers have embraced experimentation, too few are breaking down internal barriers. This lack of shared ownership creates gaps that could be closed through stronger communication and alignment. Ultimately, retailers that foster a culture of testing and collaboration are far better positioned to meet changing customer expectations and lead with greater agility.
Winning Loyalty is an Ongoing Conversation
Loyalty isn't a one-and-done achievement; it's a continuous dialogue between brands and their customers. To win loyalty, retailers must listen closely and evolve with each interaction. AI and emotional resonance give retailers the tools to do this at scale, but it's the commitment to transparency, experimentation and human connection that sets leaders apart. The retailers who will thrive in 2025 and beyond will be those who lean into their customers' digital body language, and treat engagement as a relationship to be nurtured, not a transaction to be optimized.
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Yahoo
15 hours ago
- Yahoo
Trust in AI is growing in finance, especially behind the scenes
This story was originally published on CX Dive. To receive daily news and insights, subscribe to our free daily CX Dive newsletter. A majority of customers trust the use of AI in behind-the-scenes tasks at financial institutions, according to a TD Bank survey conducted by Ipsos released Tuesday. Among the 2,500 U.S. consumers polled, 70% are comfortable with technology being used for fraud detection, and 64% are comfortable with it being used in credit score calculations. Consumers also believe that AI should offer more ease. Two-thirds believe it can expand access to financial tools, and nearly half expect benefits from AI like 24/7 banking access. As consumers have become more familiar with AI tools, their trust in the technology has slowly grown. Nearly 7 in 10 consumers say they are at least somewhat familiar with AI — a finding seen in other surveys, too. Notably, half of consumers trust that AI will provide reliable, competent information, trusting AI just as much as news stations. But consumers are more comfortable with AI in specific use cases and the more complex or sensitive the matter, the more they want to speak to a human or know that a human will be reviewing AI before making any decisions. Consumers are less inclined to want to only use AI when it comes to tasks that one might typically use a financial adviser for, according Ted Paris, EVP, TD Bank AMCB, and head of analytics, intelligence & AI. When it comes to personal finance, 3 in 5 of consumers were comfortable with the idea of using AI financial tools for budgeting and automating savings goals. But less than half were comfortable with more complex tasks like retirement planning and investing. Banks enjoy high consumer trust — more than 4 in 5 consumers trust banks for accurate information. As they deploy AI, it's important that they maintain that, Paris said. 'What's probably the key piece, is creating and enabling and allowing customers and colleagues to feel that they can trust the outcomes of what this capability then generates,' Paris said. One of the ways TD Bank is approaching this is by always having a human in the loop, meaning that the output of an AI solution will be passed through some internal expert before going to a client. 'We need to make sure that first, anything that we're doing is directed toward a particular need,' Paris said. 'We need to make sure that this is going to meet all hurdles that we would set, legal, regulatory, for security and privacy.' Sign in to access your portfolio
Yahoo
a day ago
- Yahoo
Braze Inc (BRZE) Has Trembled 37% During the Year, Here's What You Need to Know Before Investing
Braze, Inc. (NASDAQ:BRZE) is one of the 11 Best Tech Stocks to Buy On the Dip. The company continues to face challenges from an uneven and noisy macroeconomic environment, noted the management. It has been facing elevated churn in the enterprise segment and prolonged deal cycles due to switching costs. Moreover, the weakness in the South Asian market has also resulted in falling investor sentiment. The stock has fallen more than 37% on a year-to-date basis due to these challenges. However, despite the market condition, Braze, Inc. (NASDAQ:BRZE) continued its momentum from FQ4 2025 and surpassed expectations with its FQ1 2026 results. It grew its revenue by 19.64% year-over-year to $162.06 million, surpassing estimates by $3.46 million. The EPS of $0.07 also exceeded expectations by $0.02. Notably, the company grew its GAAP-operating margins by more than 900 basis points and also marked the fourth consecutive quarter of profitability, with $7 million as net income. A web developer hunched over their laptop coding a customer engagement platform. The growth was driven by a growing customer base of 2,342 customers, which increased by 240 compared to the previous year, with a notable rise in large customers. In addition, Braze, Inc. (NASDAQ:BRZE) has been enhancing its AI capabilities, on March 27, it announced its agreement to acquire OfferFit, an AI decisioning company, for $325 million. The completion of this acquisition was announced in the FQ1 2026 earnings release. Management also highlighted notable business wins during the quarter including Beyond, Inc., Chamberlain Group, Evite, Freshket, Fubo, LUSH Cosmetics, Njuškalo, and ThredUp. Braze, Inc. (NASDAQ:BRZE) anticipates FQ2 2026 revenue to be between $171.0 million and $172.0 million, reflecting confidence in top-line growth. While we acknowledge the potential of BRZE as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock. READ NEXT: The Best and Worst Dow Stocks for the Next 12 Months and 10 Unstoppable Stocks That Could Double Your Money. Disclosure: None. Sign in to access your portfolio
Forbes
a day ago
- Forbes
Four Strategies To Outsmart Data Pitfalls In Product Innovation
Ramalakshmi Murugan leads product strategy and operations for the Google Play Analytics team at Google. In the age of "data is the new oil," product teams are increasingly leveraging vast amounts of data to fuel innovation. However, many product initiatives, irrespective of having enough data, fail. The truth is, the very data meant to illuminate the path can become a labyrinth of pitfalls if not navigated with caution and a clear strategy. In the journey of moving from "data-driven" to "data-informed," it is imperative that product leaders understand the data pitfalls that might impede their efforts. Here, I will provide four decisive strategies to circumvent the most common blunders and ensure that your data is indeed guiding you toward groundbreaking innovation. 1. Beyond The 'North Star Metric': Embracing A Holistic Data Ecosystem The Pitfall: Many teams are obsessed with the "North Star Metric," fixating on a single KPI to the exclusion of all else. While a North Star can provide focus, an overreliance on one metric often leads to a myopic view. Optimizing for a single metric can also lead to "local maxima"—perfecting an existing solution but missing out on truly disruptive opportunities. The Strategy: Instead of solely chasing a single North Star, cultivate a holistic data ecosystem. • Balance quantitative and qualitative data. Quantitative data tells you what is happening (e.g., conversion rates, CTRs), but qualitative data tells you why (e.g., usability studies, user feedback). Combining both provides a richer, more nuanced understanding of user needs and pain points. • Track a balanced scorecard of metrics. Beyond the North Star, define a set of complementary metrics (e.g., customer churn, ARR) that cover different aspects of product health and provide a more comprehensive picture. • Contextualize data with market and competitive insights. Your internal product data is only part of the story. Data should be interpreted within a larger context, such as market trends and competitor movements, to inform strategic innovation. 2. Beware The Bias Beast: Actively Combating Cognitive Traps The Pitfall: Human beings are inherently biased, which can affect data collection, analysis and interpretation, leading to misguided product decisions. Some examples of bias that I frequently see teams struggling with are: confirmation bias (seeking data that confirms existing beliefs), survivorship bias (focusing only on successful outcomes) and selection bias (skewed samples). The Strategy: To tame the "bias beast," implement these active combat strategies: • Formulate clear hypotheses (and be ready to disprove them). Before diving into data, define specific, testable hypotheses. This forces you to consider what data would disprove your assumptions, rather than just confirm them. • Diversify your data sources. Relying on a narrow set of data increases the risk of blind spots. Seek out diverse data sources (e.g., surveys, customer support logs, social listening, A/B tests), and foster diverse teams with different perspectives and backgrounds to challenge assumptions. • Prioritize "why" over "what." When analyzing data, constantly ask "why." Why are users behaving this way? Why did this metric change? Don't just report the numbers; dig into the underlying reasons. • Implement structured experimentation (A/B testing with rigor). A/B testing is a powerful tool, but only if done correctly. Ensure proper randomization, sufficient sample sizes and clear control groups to minimize bias and truly understand the causal impact of changes. 3. Turning Data Overload Into Actionable Product Insights: Prioritizing 'Need To Know' The Pitfall: In the quest for data-driven glory, companies often fall into the trap of data hoarding: collecting data without clear objectives or a structured plan for its potential applications. I think this is the main cause of "analysis paralysis," an overload of information that makes it difficult to retrieve valuable insights. The Strategy: Shift from data hoarding to a "need to know" mindset, prioritizing actionable insights. • Define clear objectives first. Prior to data collection, set clearly defined business goals you would like to achieve, along with the questions that need to be answered to accomplish those goals. This will direct your effort in data collection and analysis. • Prioritize data quality over quantity. Wrong, incomplete and inconsistent data is more dangerous than having no data because it can lead to making very bad decisions. Establish policies around data governance, and perform periodic audits on the data to ensure integrity and trustworthiness. • Democratize access (with guardrails) and foster data literacy. Empower product teams to access and analyze data, but provide the necessary training and tools to do so effectively. This includes understanding data definitions, limitations and ethical considerations. Self-service analytics, when properly governed, can accelerate insight generation. 4. Beyond The Numbers: Cultivating Intuition And Storytelling The Pitfall: Some teams become so fixated on data that they lose sight of the bigger picture, neglecting human intuition, creative vision and the ability to tell a compelling story with their findings. This can stifle truly innovative ideas that don't immediately show up in current data. The Strategy: Remember that data is a tool, not a dictator. Enhance its power with the following strategies. • Harness the power of informed intuition. While data is crucial, it doesn't replace the insights gained from years of experience, empathy for users and a deep understanding of the market. Use data to inform and validate your intuition, not to replace it entirely. • Leverage strategic storytelling. Learn to weave compelling narratives around your data, highlighting the problem, the insight, the proposed solution and the expected impact. Visualizations, clear explanations and a focus on the "so what" are key. • Embrace "no data" scenarios with calculated risk. For truly novel innovations, there might be no existing data to guide you. In these cases, lean on expert judgment, strategic partnerships and carefully designed, low-risk experiments (e.g., MVPs, pilot programs) to gather initial feedback and iterate. By proactively identifying and mitigating potential data pitfalls and by diligently implementing these four key strategies, product strategy teams can transition from having a superficial reliance on data to a deep and comprehensive mastery of it. This profound understanding and skillful application of data will empower them to move beyond conventional approaches and unlock their inherent capacity to fuel the creation of truly exceptional products. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
