As inflation looms, brands have to work harder for loyalty
Amid economic pressures and tariff uncertainty, consumers are reassessing their shopping choices and showing less loyalty to brands, according to the EY Future Consumer Index released Wednesday. EY surveyed 20,000 consumers across 26 countries.
Price sensitivity was the No. 1 purchase consideration for 4 in 5 consumers globally. More than half of consumers are very concerned about rising living costs.
'The survey findings tell us it's simply not enough to be just good enough,' said Rob Holston, EY Global and Americas consumer products sector leader, 'Brand loyalty is in the balance and consumers aren't just buying names anymore — they're buying value, quality, purpose and performance.
Consumers are concerned about the economy, and as prices rise, they want more from brands to earn their loyalty.
Anxiety about the economy is widespread. In addition to the EY survey, the Conference Board found that consumer expectations hit a 12-year low in March as expectations for employment, personal income and business environment over the next six months dropped.
More than three-quarters of consumers are changing their purchase behavior in response to price increases, the EY survey found.
In many cases, consumers are moving away from brand names. About one-third of consumers say they no longer consider brands when making purchase decisions. About two-thirds say private label items are just as good as branded products, and just over half only buy branded products when they are on sale.
On an earnings call Wednesday, Dollar Tree said the inflationary environment had led to increased traffic at the discounter's stores.
'What's been most interesting is this time around, this inflationary environment, all shoppers across all income cohorts, including the higher income, is finding Dollar Tree as part of their solution,' Dollar Tree CEO Michael Creedon said. 'We believe it doesn't matter how much money you make, everybody is hurting right now.'
Though consumers are widely prioritizing prices over brand loyalty, all is not lost for loyalty: Two-thirds of respondents say they still value brands.
But consumers have higher expectations for value, trust and relevance.
Among customers who have left a brand for another, about half say they would return to a premium branded product if it offers better quality or performance, and one-third say they would return for better value.
As prices rise, consumers want more value for what they're paying. Customer experience can be a differentiator, Mario Matulich, president of Customer Management Practice, told CX Dive earlier this month.
'Those that are prioritizing CX as a primary offering, as a major piece and a foundational piece of their value proposition, those organizations are a position to gain market share in the in the next interim period where [inflation] may be the case, where prices are going up,' Matulich said.

Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles


Fast Company
2 days ago
- Fast Company
How to understand the new work expectations of Gen Z
Each generation of employees is shaped by its times. In today's era of 'perma-change,' Generation Z is exhibiting distinct professional traits. Having come of age during a period of economic instability and a global crisis, they're less likely to hang their hats on a single career identity. They're less focused on salary and more drawn to balance, but they're also highly pragmatic. The latest Gen Z workplace trend, adopting a standard work uniform, is just one example of that pragmatism. It also shows that while they value work-life balance, they're also open to clever ways for achieving it. Leaders stand to gain—or lose—a lot by making the effort to understand Gen Z. Because if they're not satisfied, they'll move on. According to the a 2024 workplace survey by EY, 38% of respondents said they were likely to quit their jobs in the next year—a rise largely driven by Gen Z. Understanding their work habits and expectations is essential to retaining top talent. As the CEO of a company with over 750 employees and a growing percentage of Gen Zers, I've had the opportunity to observe generational differences firsthand. Here are the strategies I use to address the habits and expectations of our youngest cohort. Promote an automation-first mindset I've written before about the virtues of lazy employees. While that adjective is usually pejorative, I use it to describe something powerful: a professional who looks for the easiest, most efficient way to get something done. In my experience, Gen Z tends to share this superpower. These days, that often entails using the latest tools and apps on the market. Indeed, Gen Z expects tech tools at work to match the ease of use of social media apps they use in their personal lives. If there's a new project management platform that matches the intuitiveness of TikTok, chances are they'll be proficient in hours. Promoting an automation-first approach in your organization empowers Gen Z employees to tap into their digital fluency and find the most efficient ways to complete tasks. At my company, for example, we encourage employees to set aside time to stay informed about the latest tech releases relevant to their job functions (with the help of sites like G2) and share their favorites with the team. Crucially, leaders should emphasize that tools like AI are meant to enhance, not replace, human work. This approach naturally fosters multigenerational collaboration. While older generations might impart important lessons in leadership and management, younger employees can bring their innate tech literacy to the table. This not only breaks down unnecessarily rigid hierarchies, but it also helps to engage Gen Zers and boosts their feelings of investment in the company. Offer personalized training and development In the past, employee training was fairly linear. For professionals in a given role, the progression from entry-level skills to management typically followed a similar path. Today's requisite skill sets look different on a conceptual level. Deloitte has called it the return of the Renaissance figure —someone with multidimensional talents, interests, and knowledge. That means building skills in tools and technology, data and analytics, as well as in management, creativity, and people leadership. The onus is on leaders to ensure employees receive the training they need. Don't assume they already have the necessary skills, especially since younger employees may sometimes overestimate their abilities. In addition to traditional (and irreplaceable) person-to-person training and mentoring, I'm a big proponent of AI platforms to offer employees personalized, scalable training, including both 'hard' and 'soft' skills. Companies like BetterUp, for example, offer employees actionable professional development skills, like how to handle a sensitive work conversation. What's more, as your company grows, AI tools are a cost-efficient solution for continuing to offer employees at all levels the training they need. To bring it all together, create a training pipeline that gives younger employees hands-on opportunities to apply the skills they're learning and build the ones they aspire to develop. Present flexibility on your terms It's no secret that Gen Z is more accustomed to flexibility than any other generation. Many of them entered the workforce when working from home was the norm. For younger professionals, a flexible workplace is a priority. According to ZipRecruiter 's 2025 Annual Grad Report, 82% of college students hope to work remotely at least one day a week. However, just 33% (of the class of 2023) want fully remote workplaces. Some companies are already on board with offering hybrid work arrangements. I happen to believe that working in the office is important for collaboration, training, and doing our best work. Striking a balance can be tricky. To address the needs of Gen Z without overthrowing your organization's goals and values, leaders can offer flexibility in intentional ways. For example, a structured hybrid schedule—like a few days of their choice each month to work remotely—can give young professionals the breathing room they need. You could also offer work-from-anywhere weeks once a quarter, allowing employees to work where they feel best able to focus in that moment. Even if the norm is to work within the office, leaders should make it explicit that employees can request time away if a personal need arises—whether it's a mental health day, a family obligation, or just space to recharge. You can also reinforce the idea that your organization values its employees' rich, full lives outside of work. Gen Z employees who feel free to share their full selves, including their unique interests and hobbies, are more likely to feel engaged and committed to their organization.
Yahoo
3 days ago
- Yahoo
Will AI ‘completely rewire' loyalty programs?
This story was originally published on CX Dive. To receive daily news and insights, subscribe to our free daily CX Dive newsletter. A growing number of brands are using AI to improve customer experience with loyalty programs and streamlined operations. These brands are using the technology to offer more personalized experiences and automate highly repetitive, manual processes. The technology is a game-changer, particularly for customer analytics. Loyalty program leaders have been able to segment customer data for years, allowing them to target offers to specific groups, said Patricia Camden, EY Americas loyalty leader. 'But with those segments, you're still just talking to a broad, generalized group that you've put into a bucket.' AI, on the other hand, takes it a step further. The technology enables brands to target offers to specific individuals by helping them understand 'what each human values' instead of 'pushing the same reward to everyone' or those within a particular segment, Camden said. AI also allows loyalty programs to actively shape consumer behavior and habits while deepening a brand's relationship with a customer, Camden said. 'It really allows the brand to tailor the rewards, messaging, offers and experiences to individual preferences and behaviors in real time,' Camden said. 'That's probably the most powerful thing about AI and how it can improve loyalty.' A fast casual restaurant, for example, could send a customer unique offers for new items to help 'unlock a secret reward' designed for that customer or provide them additional points for their loyalty, Camden said. 'It's really loyalty gamified but in a way that feels personal, not gimmicky,' Camden said. Those changes have the potential to disrupt existing customer relationships and establish new ones. 'Loyalty will become less about what a brand wants to push and more about how consumers want to engage,' Camden said. 'AI is going to completely rewire the role loyalty plays in the customer experience.' AI can help loyalty program leaders stretch their limited resources by helping create content for hyperpersonalized offers and optimizing campaign spend. 'It really saves marketing teams time and budget,' Camden said. Instead of assigning staff such tasks as exchanging and reconciling transactions between program partners or providing individual customer preferences to hotels and retailers, AI can manage such tedious tasks, said Brendan Boerbaitz, senior manager at Deloitte Consulting. AI can also improve predictive analytics. One EY client, for example, uses its loyalty program to ensure that customers renew their relationship with the brand each year and now uses AI to identify and target offers to customers who are likely to churn, Camden said. More and more loyalty programs are using AI for fraud detection, too. Unlike humans, AI can quickly 'connect dots at scale' to ensure points and benefits are issued correctly, said John Pedini, principal analyst at Forrester. 'It can help flag unusual patterns before they become expensive problems,' Camden said. However, before integrating AI into their customer loyalty programs, brands must 'develop use cases that provide clear and measurable value,' including personalization, segmentation, variant testing and low- or no-code campaign development, Pedini said. It's best to focus on applications of AI that take an existing process and make it better, more efficient or cheaper, Pedini said. Starbucks, for instance, uses its proprietary AI platform dubbed Deep Brew 'to drive automation, operational efficiency and loyalty engagement by identifying and incentivizing specific members with personalized offers and rewards,' Pedini said. But not all use cases need AI. Forcing AI on business problems that could be solved via more conventional, lower-cost solutions is a 'big pitfall,' Boerbaitz said. When deciding whether to implement AI, Boerbaitz urges brands to consider the following questions: If AI were stripped from the document, would it be clear what problem is being solved? Do I truly understand the specifics of the problem we're solving down to the level of the user? Is this problem underserved by other tools and techniques? AI adoption is a 'team sport' that requires cooperation to avoid redundant work and conflicting initiatives, and build data sets, tools and models for multiple applications, Boerbaitz said. 'It takes engineering, architecture, strategy, change management, data and loyalty teams all coming together to make AI programs in loyalty successful,' Boerbaitz said. It's also important not to rush the process. Brands should avoid launching an AI model too soon because the technology depends on high-quality data to deliver on its promises. Launching a model with outdated or incomplete data could lower accuracy and create 'more issues than it solves,' Pedini said. 'The worst thing you can do is have incomplete data sets,' Camden said. 'If the AI makes assumptions based on what it knows, you can end up sending something that is not appropriate or not what the client expects to see.' That can take away the 'emotional element' of loyalty programs, Camden said. 'If a brand lets AI take the wheel without real human guardrails, the customer experience could start to feel impersonal, off base and overcurated,' Camden said. Loyalty program managers should ensure their data is comprehensive, including all channels and touch points, and properly labeled, Pedini said. Sound data governance of policies, standards and procedures is also vital to ensuring privacy, preventing bias and complying with regulations, Pedini said. It's also essential for humans to be involved in loyalty programs because first-party data can help businesses improve their product strategy, brand positioning and service design. However, that won't happen 'if the machines take over,' Camden said. 'AI should not be used to replace our thinking.'
Yahoo
5 days ago
- Yahoo
How EY's finance transformation team is approaching AI strategy
This story was originally published on To receive daily news and insights, subscribe to our free daily newsletter. As finance leaders face pressure to modernize and deliver ROI on their spending on technology and consulting, EY's finance transformation team is focused on putting emerging technologies to work not just for clients, but inside the firm itself. The team, led by Deirdre Ryan, global finance transformation leader, is playing a dual role: Helping CFOs navigate AI adoption while also piloting those same tools internally across EY's finance and consulting functions. In a recent interview inspired by her session at the Gartner Finance Executive Conference last month, Ryan explains how EY is using agentic AI to reshape FP&A workflows and why being 'client zero' is critical to building credibility in the market. She also discusses how CFOs can avoid repeating past mistakes from automation efforts, what it takes to lead a finance organization through transformation and how to do so with clarity, purpose and psychological safety. Global Finance Transformation Leader, EY Notable previous employers: Deloitte Dontech Dun & Bradstreet This interview has been edited for brevity and clarity. DEIRDRE RYAN: We feel very strongly that we have to be client zero. If we're going to advise clients on new technologies, we need to understand them ourselves and use them in real scenarios. We created a platform called EYQ. It's essentially a private environment where our people can interact with large language models securely. We made it accessible on laptops and mobile devices, and it's helped our consultants build hands-on understanding of the tools we're asking clients to adopt. As of recently, we've had over 150,000 consultants using it globally. It's one of the largest private LLMs in the world that EY developed in-house. In finance specifically, we've been building and piloting an agentic AI solution for FP&A. It looks like a normal dashboard, but what makes it powerful is that as actuals come in, it generates AI-driven insights automatically. That's helpful, but the real impact comes from scenario planning. It's built on driver-based forecasting, so we've identified the variables most correlated with forecast accuracy. You can adjust those and instantly see how the outlook changes. It goes even further. We've modeled it so that there are three AI agents working like analysts, with a manager agent that synthesizes and returns the best answer. You can ask something like, 'What would a one percent drop in GDP do to our forecast?' and it does the work. It's not removing human oversight — someone still has to take action — but it's changing the way FP&A work gets done. One client saw it and said, 'I have an army of silent FP&A analysts now.' That stuck with me because that is where the function is headed. That brings up another important point. Psychological safety is something we talk about a lot. When tools like this are introduced, it's natural for teams to wonder what it means for them. They may worry their work is being replaced or question what their future looks like in the function. This is where leadership matters. People entering the workforce today don't want to spend three days in Excel. They want to work with tools that help them think and act strategically. If you're in FP&A and you're given the choice between spending days building a model in spreadsheets or using agentic AI to get that answer instantly and then focusing your time on what to do about it, people are going to choose the latter. That's how you retain talent. If finance doesn't evolve, it risks losing its best people. So yes, we're advising clients on these tools, but we're also living it internally. We're applying it ourselves, and we're navigating the same leadership, talent and change management conversations that our clients are. That's what being client zero means. It's difficult for CFOs today because they still play a very traditional role. They must protect and preserve the assets of the organization and mitigate risk, but now they're being asked in a meaningful way to drive innovation within finance and across the enterprise. They need to understand disruptive technology well enough to make smart capital allocation decisions and guide the business forward. So, CFOs have to start getting their hands dirty. A lot of people I meet have seen demos or presentations, but haven't used the tools themselves. You have to understand the capabilities. Start small — maybe it's a proof of concept to help your team come up the learning curve — but that gives you insight into what these technologies can do. And from there, you can ask the bigger question: How do we apply this in a meaningful way to our finance organization? That's why our team tells CFOs to not just look at the tech, but think about the end game. What do you want your finance function to look like once you've integrated these tools? You have to do some things in parallel, which is tough because CFOs are already being asked to do so much. But this is one of those areas where you can't afford to take a one-track approach. You don't want to repeat what happened with robot process automation. Very few companies realized the value they expected. It became very democratized — people used it to automate a few hours of work here or there — but it didn't lead to large-scale transformation. That's the risk with AI and generative AI. The technology is unbelievably powerful, but without a strategy, you end up with fragmented efforts. You have to ask: Where is the puck going to be, and how do we get there? That means setting a clear end state, helping your finance team come up the learning curve, and avoiding what I call death by a thousand cuts — a little pilot here, a tool there, but no cohesive vision. So, yes, you want experimentation, and maybe that's informal — sharing a cool use case in a meeting. But it also needs to be backed by a very intentional strategy tied to how your finance function delivers value. For this, there are two big buckets I talk about with CFOs. One is productivity, and yes, you can absolutely drive productivity using these tools. We have great examples of that. And honestly, any of my competitors could give you the same 200 use cases for technology within finance. So I'm not saying that's a bad thing, but many of those use cases have been around for a long time. So if you're going to pursue productivity, you need to ask where you're going to get the biggest ROI. What's going to move the needle? The second bucket, and this is where I think the real value is, is decision insight. That's about using these tools to provide better analysis that helps your peers in the C-suite make smarter, faster decisions. And while that's much harder to quantify, I think it's equally important. Sometimes I ask CFOs to imagine a scenario. Let's pretend your data is perfect, it comes in on time and everything is consistently defined. Of course, that never happens, but let's just pretend. What is the kind of analysis you'd want to do on demand that could give you a competitive advantage? And it's interesting, because many CFOs haven't even had the [capacity] to think that way. They're so tied to traditional metrics like revenue and profitability that they haven't had the chance to ask, 'If I had access to better data and AI tools that let me explore it faster, what decisions could I make differently?' That kind of thinking is where AI can really change the game for finance. I think it depends on what you mean by 'a single source of truth'. We all know CFOs need to ensure the financial statements are accurate. And with technology of all types, there has to be a level of trust that the data is producing results that fairly represent the performance of the organization. Do I know any company whose data is 100% perfect all the time? No. Especially not large, acquisitive organizations. But what I always tell clients is, you have to prioritize. Not every data point needs to be perfect, but the ones that drive the most value do need to be consistently defined and captured across the enterprise. You could spend the next 10 or 20 years cleaning data, and it still wouldn't be perfect. The better approach is to identify the data that will drive meaningful analysis and ensure that it's reliable. That way, when you present insights to the executive team, you have confidence in the underlying information and the decisions it supports. It's about being intentional. Know what value you're trying to unlock, and focus on the data that supports that value. Recommended Reading How PwC's tax team is using agentic AI Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data