launched its own media network<\/a>, 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\u2019s data about guest travel behavior to place more targeted ads that reach a specific customer segment. Insights garnered from Marriott\u2019s 164 million Marriott Bonvoy loyalty program members power the platform.<\/p>\nCreating media networks isn\u2019t 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.<\/p>\n
Hyper-personalize at scale to connect across the customer journey<\/strong><\/p>\nWe hear a lot about hyper-personalization, but it\u2019s 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.<\/p>\n
Fast forward, and personalization is happening at a greater level\u2014hyper-personalization. Dynamic offers are being 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. The greatest opportunity is in connecting the customer identity graph with the enterprise graph to improve outcomes.<\/p>\n
The customer identity graph stitches together important individual identifiers across devices\u2014from usernames and phone numbers to loyalty card numbers to offer an accurate, up-to-date snapshot of customer attributes and behaviors. These 360\u00b0 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\u2019s data lake or data warehouse to paint a clear and timely picture of what\u2019s 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.<\/p>\n
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.<\/p>\n
Become a data-driven organization with a strong data strategy<\/strong><\/p>\nMonetizing 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.<\/p>\n
Some organizations are quick to adopt leading-edge technologies, like artificial intelligence and machine learning tools, but they haven\u2019t yet solved 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.<\/p>\n
Data strategy requires the people, processes and tools to deliver on it\u2014and 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.<\/p>\n
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. Economic uncertainty shouldn\u2019t lead to business paralysis. In fact, it\u2019s a time to position for a more lucrative future, but that will require laying the foundation to do so.<\/p>\n
![\"Customer](\"https:\/\/solutionsreview.com\/data-management\/files\/2023\/05\/MicrosoftTeams-image-3.jpg\")
<\/p>\n![\"Expert](\"https:\/\/solutionsreview.com\/data-management\/files\/2023\/01\/oie_6194352a4wFeWoG.png\")
In times of economic uncertainty, businesses face several challenges. The impact of the pandemic coupled with trade tensions, and supply chain constraints, has created a haze of uncertainty. Today\u2019s regulatory climate adds to the mixed economic signals. The key tenets of new data privacy policies and regulations are about respecting customers\u2019 individual rights when it comes to personalization.<\/p>\n![\"Download](\"https:\/\/solutionsreview.com\/data-management\/files\/2019\/01\/data-management-speedbump-cta.jpg\")
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