Changes in the market can hit businesses like a bolt of lightning, whether from a sudden pricing shock, a policy change or a shift in customer sentiment that reshapes an industry. While leaders all agree that the cost of such ‘surprises’ is far too steep, many teams still find themselves reacting to these changes instead of preparing for them.
The reality of anticipating market trends is complicated. Leaders know they need innovation to stay competitive – particularly artificial intelligence (AI), whose potential remains to be realized. At the same time, they also know that innovation must be informed by reliable data, proactive risk management and expertise to make a real impact. This is a familiar tension across all industries, from retail to airlines, hospitality to grocery.
Recent data confirms this sentiment: in a Mastercard-commissioned study by Forrester of 324 global senior leaders, 41% reported losing competitive ground, and 38% said they missed market opportunities because they struggled to balance innovation with risk management. Nearly one third called their approach to handling market trends “reactive,” and only a small fraction felt their strategy was truly “proactive” or “innovation led.”
And even with today’s advancements in AI, many leaders acknowledge that the technology is only as effective as the data and expertise powering its impact.
These needs are exactly where Joshua Feng and the Mastercard Economic Intelligence team come in. His group partners with economists and data scientists from the Mastercard Economic Institute to interpret proprietary Mastercard data assets and proven external data sources to understand how and where people spend. They then turn those signals into clear readouts, scenarios and guidance that leadership teams often say they would not land on otherwise. Their work gives CFOs, financial planners, marketers and operations a sharper view of what is happening in the market, and what to do about it.
For instance, a national drugstore chain might seek clarity on how broader economic conditions are influencing its performance. In working one-on-one with Mastercard’s advisors, the team would be able to walk away with tailored indicators to adjust how the company planned for the months ahead. Or a large multi-channel brand could leverage a series of over 400 forecasting models built by Mastercard’s insights to inform sales targets with significantly greater accuracy.
In this Q&A, Josh explains how to read shocks and trends, anticipate shifts and act more intentionally.
Why is anticipating market trends so crucial in today’s world
Many CFOs tell me they wish they had a crystal ball. Their financial planning models are often based only on their own company data. Those assumptions work until something unpredictable or unprecedented happens, like supply chain problems or new tariffs. That’s when historical data alone stops being useful. What our team does is help them interpret what’s happening in the wider economy so they can make better predictions.
One of the most effective methods is scenario planning, or modeling what would happen under different economic conditions — a spike in inflation or new tariffs, for example — so companies are not caught off guard. Even when no one knows the exact outcome, running those “what if” exercises help leaders see a range of possibilities and plan accordingly. It builds muscle memory for future shocks and reduces decision lag when things change fast.
In what ways does Mastercard help businesses anticipate and respond to market trends?
At Mastercard, we’re focused on helping businesses see what’s coming next — before it happens. Our teams turn near-real time consumer spend data and economic signals into practical guidance that leaders can use right away. With platforms like SpendingPulse™, organizations can dig into what’s happening in their industry or region and spot shifts as they unfold. Our economic advisory solutions, including Macro360, help executives make sense of the bigger picture and translate those insights into smarter decisions. And with tools like Forecasting & Scenario Planning, powered by AI and statistical modeling, companies can plan with more confidence and accuracy. The results speak for themselves — businesses using these solutions have seen stronger promotions and a positive return on investment over three years.
What are misconceptions about using AI and analytics for market forecasting?
One misconception is that AI can sort everything out on its own. AI is an incredibly useful tool, but good forecasting needs structure. It’s important to start with signals that make economic sense. For consumer demand, we use measures like personal consumption expenditures. AI can speed the work, but economists and data scientists still have to choose the right inputs and guard against spurious patterns.
How are organizations using consumer spend insights to pivot their strategies in response to changing consumer behavior?
Most clients want to see beyond their four walls. A restaurant chain knows its own stores. It does not know how the broader category is doing in that trade area. We use aggregated and anonymized spending data to give that context. They can see if traffic in the area is up or down, which cuisines are gaining traction and how price changes differ by location.
From there, the actions are concrete: They can decide where to open or close, tune pricing by region or shift marketing toward targeted audiences. In short, they can use category and regional spend to benchmark performance and make decisions that line up with what customers are actually doing right now.
How should organizations be structured to better anticipate and act on market trends?
At Mastercard, we advise that businesses start with finding reliable data sources and collect on a repeatable schedule. Second, create an analytical layer that cleans and combines those numbers into models — those are the math-based tools that explain or predict outcomes. Third, apply concrete use cases. These are specific business questions, such as “where to open next?” that the models can help answer.
Most organizations follow a similar path, but they do not put enough thought into how the team drives efficiency, moving directly from data collection to decision making. To avoid this oversight, teams can use AI to short-circuit what is otherwise an arduous chain and reduce the manual steps between data and a decision. If you are investing in data and analytics, it’s important to ask whether that cost consistently leads to better choices. In many cases there is still more juice to squeeze, more value to capture from the same inputs.
Also, connect the disparate pieces. Centralize your data strategy and your analytics strategy. Make sure your team knows what you collect and why, along with how you analyze it and who uses it. Ensure the left hand talks to the right hand and make the handoff from analysis to action clear and fast.
As companies today look towards their new phase of growth, the smartest advantage may very well be knowing what is coming next.
