Your data holds value. So much value that it’s driving massive revenue increases and growth for some of the world’s largest merchants (think Walmart, Amazon, etc.). How does your data produce cash for retailers? The short answer is: data monetization. Discover how retail data monetization works and how you can start enjoying the rewards of your own data.
What is Data Monetization?
Data monetization refers to the process of leveraging data to achieve measurable economic benefits. With third-party cookies no longer an option for data acquisition, firms have shifted to zero- and first-party data. Zero-party data is information that a customer voluntarily and deliberately provides with a business.
This could be what they enjoy, what they intend to buy, or their personal information. It is particularly accurate because it comes directly from the source, with no guesswork involved. Businesses that understand how to acquire this type of data in a sensible and legal manner can use it to personalize and tailor online shopping experiences to each customer.
First-party data sharing refers to information shared directly between consumers and businesses through purchases, transactions, and other exchanges. To remain nimble and in touch with their customers, organizations must collect first-party data in order to gain important insights into client preferences and behavior. Businesses require first-party data to make crucial business decisions, and data monetization offers the transactional framework that enables this.
The global market for data monetization was expected to be $1.6 billion in 2020, with a projected total value of $4.1 billion by 2026! Despite this, most shops either have a mountain of untapped first-party data or simply do not gather it owing to a lack of awareness and/or technology.
Retailers are missing out on enormous revenue and upsell opportunities due to a failure to fully leverage this data.
Most retail firms have a wealth of client data stored in internal databases or on a third-party data platform. This data is derived from purchases, transactions, exchanges, and loyalty programs. This data, when used wisely and ethically (directly from consumers), can fuel data monetization services that can increase retail income streams, improve supplier and vendor partnerships, and create more tailored customer experiences.
How Does Data Monetization Work?
There are two main categories of data monetization: internal and external data monetization. Both of these categories provide an array of potential monetization opportunities.
Internal data monetization uses data and analytics to inform decisions and strategies that improve how a business operates and performs. By utilizing data science and an advanced analytics ecosystem, organizations can use their data to create new products and services and improve internal operations and processes.
External data monetization is the process of using first-party data as an asset to create new products or services and selling them to external third parties. This could include data analytics, reporting, trends and forecasting, and so on. A business that collects customer data can either provide paid access to this data or sell insights derived from this data to other businesses looking to improve their operations and performance.
For retailers, most of their data sits in extensive customer databases, collected from customer transactions and loyalty programs. They can use this data to drive internal data monetization by enhancing marketing and communication campaigns, improving inventory planning and management, and offering new products and services. They could also use this data to power external data monetization by offering retail media solutions or selling data insights.
The Benefits of Data Monetization in Retail
For both online and brick-and-mortar retailers capitalizing on data monetization, there are numerous benefits. Data monetization as a strategy benefits all involved parties, particularly in a multi-brand store environment. Let’s take a closer look at the multi-fold benefits that leveraging data monetization can provide.
Strengthens partnerships
For multi-brand retailers like Walmart and Amazon, data monetization can drive valuable insights that strengthen partnerships and loyalty with different brands. Retailers that sell insights extracted from first-party customer data help brands and suppliers boost their online and in-store performance. The insights unlocked provide a window into shopper behavior and trends that allow brands to better understand and cater to the needs and preferences of shoppers and adjust their processes accordingly.
Improves operational efficiency
Data insights taken from customer browsing, purchasing, and returns history bring a depth of understanding that surface-level observations just can’t replicate. Retailers providing these data insights (or the data necessary to power them) empower other businesses to make data-driven decisions that improve their operational efficiencies. For instance, a business that can identify and determine which target audiences are most interested in buying a product and where they’re located improves demand planning and inventory management.
Develops new services
Data can also be used to fuel innovation and create new services to enhance the customer experience. This is relevant for both retailers and businesses buying data and/or insights from retailers. Retailers and other businesses can use their in-store data to develop location-based services like an app that provides personalized, real-time offers to customers based on their in-store location, special promotions, and tailored communications that improve shoppers’ experience across different regions. Customer data can also be collected to help analyze and improve the success of new marketing campaigns and partnerships, improving the ROI of these initiatives.
Read Also: Monetizing Social Media Data: Opportunities and Challenges
Retail media is an advertising business that a retailer creates to sell its first-party data and advertising space to other companies. Companies that use a retailer’s advertising space will pay to have their ads (created and targeted using first-party data) appear on the retailer’s on-site channels (including websites, mobile apps, and marketing emails), social media pages such as Google, Apple, and Facebook pages, and in-store signage.
Real-world examples include Walmart’s Walmart Connect and Walgreens’ Walgreens Advertising Group, both of which provide retail advertising to third-party retailers.
Retail media networks
A retail media network is an advertising infrastructure that consists of a collection of digital platforms and channels, including websites, apps, and digital pages, that are available as advertising space to third-party brands. These networks help to deliver contextually relevant adverts to shoppers browsing the channels, based on their first-party data. A key advantage of leveraging retail media networks, aside from more targeted advertising prowess, is the ability for businesses. to measure ad campaign performance that provides clear ROI on ad spend.
Buying advertising space on a retail media network can help businesses of all sizes across industries roll out and expand their digital marketing strategies. This is especially beneficial for smaller businesses that might not have the budget or experience necessary for building a robust and complex digital marketing campaign, as well as measuring it. Most retail media networks offer ad space, first-party data, and analytics as a service as part of their core offerings.
Offering the platform and opportunity to put brands in front of their customers, as well as access to relevant first-party customer data, make retail media networks one of the most popular and lucrative long-term means of data monetization for retail giants around the world.
8 Retail Companies Using Big Data
Big data’s potential and effect are being recognized by businesses, and this technology is being used to boost their operations. Big players, particularly those with additional resources to invest in technology, have achieved excellent outcomes by applying big data analytics.
Let’s look at eight retail organizations that have successfully used big data to improve their performance.
1. Home Depot
Home Depot’s CMO, Kevin Hofmann, stated, “Our data helps us to know our customers on a more individual level. Our targeting capabilities will allow us to reach them at the correct time and place, and we hope to adapt all of our messaging to the audience.”
Home Depot’s big data investment enabled them to accurately target their adverts so that even persons on the same street see different ads when they visit their website. They also experiment with weather-triggered adverts, which are only displayed under specific weather conditions. Additionally, Home Depot develops inventory-driven ads that are moved ahead based on inventory availability in certain stores. As a result, their online shop grew by $1 billion over the past four years, and the overall growth measured 20%.
2. Walmart
Walmart is one of the retailers that uses big data to determine peak hours in their shops and pharmacies and improve worker scheduling to better serve their customers. The company also employs simulations to create efficient routes from the port to the store, reducing delivery times. Walmart, a major store with millions of products, decided to rethink merchandising to include popular things as well as new and reduced items.
Finally, the retail organization leverages customer data to provide a tailored shopping experience and anticipates its consumers’ wants. Walmart has seen a 15% rise in online sales, resulting in an additional $1 billion in revenue.
3. Walgreens
According to Andy Kettlewell, Vice President of Inventory and Analytics, “Data is critical for everything that we do at Walgreens, and with that data, customers are telling us what they buy and what they need.”
Walgreens is a drugstore chain that serves eight million people daily, both online and in-store. They can accurately manage their inventories and deliver a better overall experience by collecting and analyzing massive volumes of client data.
4. L’Oreal
The Head of Experimental Data Intelligence, Philippe Benivay, stated, “Data and artificial intelligence allow us to move faster to create cosmetic products that meet the infinite diversity of beauty needs and desires of consumers around the world.”
L’Oreal is one of several retail companies that use big data to drive R&D and create new formulae that appeal to their target audience and market trends. They use data analytics to develop new goods and track their performance with their customers. The organization has employed technology to improve KPIs and increase sales income.
5. PetCube
PetCube is an innovative pet tech company that develops software that captures and analyzes pets’ behavior and uses it to create an interactive video experience. Their inventions allow pet owners to play and talk to their pets and even give them treats using their platform and evaluate how the pet is acting and feeling to provide helpful insights to their owners. The product has already gained traction and attracted half a million dollars from Kickstarter as well as raised $14 million in seed, venture, and Series A.
6. McDonald’s
McDonald’s is a famous case study that utilized data analytical technologies to enhance customer experience. For example, they measure when large groups of people are likely to show up at the drive-through to accommodate them better. The company has also equipped their drive-throughs and restaurants with digital menus that allow customers to preorder and complete their purchases a lot faster.
The digital menus display special offers and promotions based on the weather, time of the day and season, local events, and purchase history. The company has also implemented big data applications in retail to optimize inventory management, which is exceedingly more important for low-margin businesses like food service.
7. Nike
Nike collects customer data through its app, including additional solutions like Nike Training Club and Nike Run Club apps that analyze its fitness data and offer personalized guides for their workout sessions. The added value facilitates a stronger bond between the company and clients and encourages them to purchase fitness equipment and clothes that are tailored to their goals and experiences.
Nike went even further and invested in a product Nike Fit that scans customers’ feet to identify the perfect shoe style, type, and size. The app then stores this data to make personalized suggestions whenever the user shops online.
8. Netflix
Netflix was at the forefront of using big data to attract new customers and retain the existing ones. The company offers personalized movie and series recommendations based on what the user watched for longer periods. The algorithm can also predict which shows will become the new hit and which will remain fairly unnoticed. They also tailor the thumbnails and trailers depending on the person’s watch history and reviews.
The streaming service company also applies big data insights for its movie production strategies. For example, they can calculate the costs of shooting in one location versus another and identify how to streamline the entire process to minimize expenses.
Big data and retail industry solutions can assist your organization in a variety of ways, including increasing sales and improving the customer experience. However, the adoption of data analytics involves more than just storing and processing data; it is also about how retailers use big data to extract important information and detect insights.
