Skip to Main Content

Generative AI: Using artificial intelligence to make human impact.Learn how


3 Ways to Fight Economic Uncertainty with Customer Data

Leading organizations are monetizing customer data and hyper-personalizing experiences to grow in 2023.


The times, they are a-changing. The world has been in a haze of uncertainty for years now. The pandemic, trade tensions, the war in Ukraine, supply chain constraints, workforce challenges and more are among the reasons why The World Uncertainty Index has been on the rise since 2016.[1]

Today’s regulatory climate adds to the mixed economic signals. The key tenets of new data privacy policies and regulations are about respecting customers’ individual rights when it comes to personalization. While there is a tremendous opportunity to use data to personalize interactions, businesses today must work harder to earn consumer trust by being clearer about how data is stored and used. Our recent research found that nearly half (47%) of Americans surveyed are not willing to share their data with any company.[2]

Pie chart indicating 47%

In addition to building trust, businesses must prepare for what the new year has in store: What are the best ways to improve the customer experience using data? Will customers continue to interact in the same ways? How can companies increase revenue while customers’ belts are tightening?

Three big moves to fuel success in 2023

Rather than sitting and waiting to find out answers, we believe businesses can take steps now to invest in the right places to emerge stronger in 2023. These are the big three:

Monetize data to add new revenue streams

It’s time to find new ways to unlock revenue in the face of today’s economic headwinds. The cookieless future is pushing companies to maximize the value of what they already have—first-party data. Businesses can monetize both their onsite digital and physical properties and their offsite experiences using first-party data.

Leading businesses are developing media networks to monetize data. For instance, Marriott launched its own media network, an omnichannel cross-platform advertising solution that enables curated content experiences and tailored offerings to customers throughout their travel journeys. Other brands can tap into Marriott’s data about guest travel behavior to place more targeted ads that reach a specific customer segment. Insights garnered from Marriott’s 164 million Marriott Bonvoy loyalty program members power the platform.[3]

Creating media networks isn’t the only way to monetize data; opportunities include loyalty programs, data marketplace, partner supply chain and other enterprise optimizations.

For example, quick-service restaurants (QSRs) can use loyalty program data to deliver personalized experiences that pique appetites and drive basket size on mobile apps. Hospitality companies can use what they know about customer preferences to tailor travel packages, personalize hotel amenities and share relevant deals and advertisements at the right time via the right channel.

People also like it when companies use their data to deliver better experiences. We found that 42% of U.S. consumers surveyed say they like it when companies provide product recommendations based on their shopping history.[4]

Pie chart indicating 42%

Hyper-personalize at scale to connect across the customer journey

We hear a lot about hyper-personalization, but it’s beginning to take on new meanings. It all started with personalization. Companies like Sephora developed loyalty programs and began to offer rewards and discounts. They used the data collected from the program to send personalized communications and recommendations. 

Fast forward, and personalization is happening at a greater level—hyper-personalization. We are seeing dynamic offers orchestrated across real-time events and product replenishment models that are based on what customers buy online and in-store.

Now, hyper-personalization is growing, too. We think the greatest opportunity is in connecting the customer identity graph with the enterprise graph to improve outcomes.

The customer identity graph stitches together important individual identifiers across devices—from usernames and phone numbers to loyalty card numbers to offer an accurate, up-to-date snapshot of customer attributes and behaviors. These 360° views of customers are critical to tailoring offers, messages, products and services, but they are more valuable when combined with the enterprise graph.

The enterprise graph links data from across the organization’s data lake or data warehouse to paint a clear and timely picture of what’s going on inside the business. Key areas that are built into enterprise graph predictions include supply chain, order management and other enterprise functions. Blend these two depictions, and this is where the magic happens.

Circles represent paid data from online/offline media and direct data from the platform ecosystem, circle in the middle represents the customer

Many businesses stop at dynamic creation or dynamic orchestration based on what they know about customers. The future of customer experience is making sound predictions based on machine learning about individual behavior. That connection will enable dynamic product creation and deliver the right message at the right time.

Organizations that use data to predict with a high degree of accuracy what will help guide a consumer along the buying journey can then take that model and those predictions and apply them to enterprise data at scale.

For instance, we worked with a U.S. grocer to build a closed-loop digital platform to drive exponential value from customer data. Myriad data sources are now linked across the enterprise, enabling a clearer picture of customer behavior which has helped the business improve customer satisfaction, loyalty and share of wallet. The platform scaled and flexed to seamlessly integrate new lines of business with suppliers, third-party vendors, consent forms and mobile apps.

Become a data-driven organization with a strong data strategy

Monetizing data and delivering hyper-personalized experiences at scale will only happen if the organization is built on an underpinning of data and guided by a sound data strategy.

Your enterprise data strategy is a blueprint for moving from data-poor to data-informed.

Some organizations are quick to adopt leading-edge technologies, like artificial intelligence and machine learning tools, but they haven’t yet solved for fundamental issues that hinder data sharing or exploitation. For instance, not having legal consent can get in the way of data sharing. Regulatory compliance measures like customer consent must be built into the data strategy.

Data strategy requires the people, processes and tools to deliver on it—and not all organizations have these basics in place. For instance, the people delivering on the strategy may be in different parts of the organization that are operating at a variety of speeds, leading to a lack of cohesion. Processes may be outdated and not adapted to execute the data strategy. The technology may be fractured or patched together in a Frankenstein way that does not enable seamless sharing and collaboration.

Getting the fundamentals right is essential to modernizing the data strategy and allowing companies to monetize data and engage with customers more effectively.

Out of uncertainty, into a data-driven future

Businesses cannot control what is happening in the world around us today. However, they can decide whether they will do things differently or sit and wait. These are some quick actions that your business can take to weather the storm of uncertainty—and come out ahead:

1. Earn trust with customers. Customers won’t share data if they don’t trust how your business will use it. Our research shows that consumers in the U.S. say they feel more comfortable sharing data if a company clearly explains how data is being used (55%) and if the organization states that it complies with privacy laws and regulations (46%). Now is an important time to build progressive consent management into your infrastructure to align with regulations while also building consumer trust.

Two pie charts side by side: one indicating 55% and one indicating 46%

2. Adopt a test and learn approach. Retailers have been exploring the moves outlined above for years. Others have a lot to learn when it comes to being data-driven organizations that can monetize data and be hyper-personalized. A test and learn strategy enables experimentation and can allow the best ideas to come to fruition more quickly. Start small, build the value proposition, and then test it and learn to unlock value.

3. Put the customer at the center of all you do. We can get technical when talking about maximizing data. But no matter what avenue your business pursues, the customer should always be at the center and then design services and approaches from there. They are what matters most in the equation.


We believe that economic uncertainty shouldn’t lead to business paralysis. In fact, it’s a time to position for a more lucrative future, but that will require laying the foundation to do so. Let’s get started.



  2. Publicis Sapient Customer Data Survey 2022
  4. Ibid.
Raymond Velez
Raymond Velez
Executive Vice President

Related Reading