Insights

3 Ways to Fight Economic Uncertainty with a Customer Data Strategy

The times, they are a-changing. In the midst of global crises, constraints and transitions across industries, uncertainty has become the new normal.

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. However, consumers still expect optimized and personalized experiences. There is still a tremendous opportunity to use data to personalize interactions, but businesses today must work harder to earn consumer trust by being clearer about how data is stored and used. Publicis Sapient’s 2023 Customer Data Survey revealed that nearly half (47 percent) of Americans surveyed are not willing to share their data with any company.

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 with the help of a first-party data strategy

Rather than sitting and waiting to find out answers, businesses can take steps now to invest in the right places to emerge stronger in the future. 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 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.

Media networks are projected to be worth more than $230 billion by 2030, but  they aren’t the only way to monetize data; opportunities include loyalty programs, data marketplaces, partner supply chain data sharing 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. Publicis Sapient research revealed 42 percent of U.S. consumers surveyed say they like it when companies provide product recommendations based on their shopping history.

Hyper-personalize at scale to connect across the customer journey

Hyper-personalization is beginning to take on new meanings. It all started with traditional personalization. Companies like Sephora developed loyalty programs and began to offer personalized rewards and discounts. They used the data collected from the program to send personalized communications and recommendations. 

Fast forward: phase two personalization is happening on a broader scale with hyper-personalization. Dynamic offers are being orchestrated across real-time events. For example, predicated real-time product replenishment machine learning models that are based on what customers buy online matched with their identity in-store.

Now, phase three hyper-personalization is enabling fully connected enterprise optimization and personalization. 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 data like supply chain and inventory levels.

The enterprise graph links data from across the organization’s different domains within a 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. Businesses should blend these two data domains within their enterprise; this is where the magic happens.

Many businesses stop at dynamic creation or dynamic orchestration based on what they know about customers. The future of customer experience is making 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, Publicis Sapient 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 an organization that supports data-driven decision-making. A company’s 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. Organizations need to increase their data literacy and empower data-driven models to guide personalization and enterprise optimization.

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.

Embrace certainty in the future with a data-driven strategy

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

• Earn trust with customers. Customers won’t share data if they don’t trust how a business will use it. Publicis Sapient’s 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 percent) and if the organization states that it complies with privacy laws and regulations (46 percent). Now is an important time for companies to build progressive consent management into their infrastructure to align with regulations while also building consumer trust.
  

• Monetize data. Creating new revenue streams with consented data not only unlocks new value for the organization but it increases the customer and prospect signal powering all the strategies. Monetization provides the platform to greater collaboration with enterprise partners and accordingly driving better enterprise outcomes.
• 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.
• Put the customer at the center of everything. It is easy to get technical when talking about maximizing data. But no matter what avenue a 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.
   

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.