Many professionals often refer to data as the “new oil.” Yes, data shares some essential characteristics with oil, but it also has some distinct values that are not inherent in oil. For example, unlike oil, data does not decrease over time. You use it and still keep it. In addition, unlike oil, it is non-rivalrous, which means that multiple people can use the same data at the same time.

In a nutshell, data is a valuable resource that many organizations may use to generate revenue, which is one of the reasons for the recent emergence of infonomics and the desire of many firms to monetize their data assets.

How Does Data Monetization Work?

Before we get into how to monetize your data, let’s go over the most important ways you can use data to enhance your profits.

Here are three common usage scenarios:

1. New revenue models

What insights can you provide that no one else can? That might be to your customers, to your customers’ customers, or even to organizations that are currently partners or peers. 

Note that the real value in your data probably won’t come from simply selling raw datasets but by using that data to answer pressing questions for parties that can’t access this information on their own.

2. Boost retention

One of the most expensive and resource-intensive activities for any company is attracting and onboarding new customers. 

It’s far more cost-effective and will lead to greater ROI per customer, to keep hold of the ones you have and upsell to them when you can. Close analysis of data from your CRM or ERP, combined with external data from sources like social media platforms and review sites, will help you create more accurate predictive models, pre-empting churn and enticing customers back into the fold. 

3. Prevent revenue leakage

Do you ever discover late in the day that you have an unpaid invoice? Or that you’ve forgotten to bill for a particular service? 

It’s easy to let things slip through the net, but over time these small costs add up to very big losses. Using the right analytics approach, you can uncover common patterns in types of unpaid charges or late-paying customers and nip the issue in the bud. This should soon lead to stronger cash flow.

10 Ways Businesses Can Monetize Their Data

In this post, we will discuss ten distinct strategies, both direct and indirect, that businesses can use to monetize data. While the ideas presented here are not exhaustive or comprehensive, they might serve as a starting point for developing a data-monetizing strategy.

1. Improving Customer Acquisition and Retention

Data, being transformational, can be used to create a simulation of consumer profiles from existing data using machine learning. This faux data can then be compared to the genuine data to identify potential clients for lead creation. Businesses can uncover patterns in users’ shopping actions for other similar products/services by merging social media data with current data, and then utilize that knowledge to predict their behavior to optimize search results and marketing efforts. Data may help firms stay proactive by providing insight into future market patterns, allowing them to plan ahead of time.

2. Enabling Competitive Advantage

Data allows firms to better understand consumer threshold pricing and the price-quality tradeoff that customers from various geographies and demographics are ready to make. Another strategy to gain a competitive edge is to enhance offers by creating a solution that is deeply ingrained in complicated manufacturing processes and cannot be simply replicated. Cross-learning from and cross-selling to customers is another approach for businesses to improve things rather than simply proposing the same items and services that a client has previously purchased.

3. Creating New Business Line

Sentiment research not only helps organizations understand their customers’ perceptions of their products and services, but it also provides insights into new areas that could be explored. A/B testing is another inexpensive way to experiment with data and discover new opportunities. Customer data can also be used to build a bridge that connects consumers to the services they require from other businesses through a platform business model.

4. Breaking into New Markets

Organizations with data can enter new markets by providing analytics-powered applications as a service to other organizations, regardless of industry, that do not use data. Digital data streams can be combined with existing datasets to help comprehend, assess, and adapt what other firms are doing. Data can also be utilized to do detailed market research in order to identify unknown territories that could be broken into.

5. Bartering for Goods and Services

Businesses can trade their data assets for products and services from partners and suppliers. That way, companies don’t have to deplete their earnings or perhaps lose the data transferred. Even outdated datasets might be important resources for other organizations and so traded for monetary worth. Regulatory limits on money can also make data a more viable currency.

6. Bartering for Favorable Terms and Conditions

Bartering for favorable terms and conditions is an indirect way of data monetization with a high potential for revenue generation. For example, bartering entails being transparent, which encourages other businesses to do business with you. There’s also the benefit of avoiding lawsuits and expenses because both parties involved in bartering are always completely aware of what they’re getting into. Bartering also provides businesses with access to critical information that can help them generate income while also ensuring their survival in the market.

7. Reducing Cost Overruns

Another option for businesses to indirectly monetize their data is to improve operational efficiency in both internal and external procedures. Data may be used to automate numerous routine processes that might otherwise result in cost overruns. Building early warning systems can also help detect problems in a timely manner, reducing the financial risk associated with unexpected business problems. Mining project documentation and communications for early warning signs of project difficulties is another effective technique for firms to avoid excess costs.

8. Offsetting the Cost of Data Management

Data mining is a key topic here. It can be used to improve data quality by simplifying and downsizing data warehouses, reducing the cost of administering the data environment. Data licensing can also be used to share data management costs with partners. Digital transformation technology can also be used to convert outdated data, notably image and video data, into more useable formats. Charge-back as a service and funding projects for future data values are another way to reduce the cost of information management and thus save money.

9. Creating Additional Streams of Revenue

Data can be monetized directly by selling it to other businesses through brokers or by providing insights to third parties who would otherwise not have access to the data. User data on products can also be used to entice marketers to websites. Suppliers rarely interact directly with customers, but they must be aware of how their product is performing through the partner firm with which they collaborate. Creating an information bridge here yields extra cash.

10. Improving the Lives of Citizens

Businesses may cut physical labor by leveraging data effectively, and less labor equates to a healthier workforce. In turn, a healthier workforce reduces the cost of health insurance claims and expenses. Delaying gratification in order to help communities solve pressing problems using available data resources can help businesses establish brand loyalty.

Read Also: How do You Manage an Online Business?

Meanwhile, assisting the government in catalyzing the use of its data to make effective public decisions can yield benefits such as lower tax rates and contracts. In general, leveraging data resources leads to growth, allowing firms to hire more people and, as a result, improve residents’ lives.

7 Ways to Monetize Data in Retail and Supply Chain

Many retail and supply chain organizations that do not use the potential of data enrichment are passing up opportunities to grow, optimize, or manage risk. From geographic information to purchasing history, data offers retailers and supply chain suppliers a great chance to get insights into business activity, customer behavior, and other patterns. And, because different types of data can be useful in a wide range of applications, even information that appears to be of no immediate use to one company can benefit other organizations that require it.

Using data to evaluate current business operations, find new opportunities, and enhance efficiency can result in significant savings for any shop or supply chain provider.

Here are seven efficient methods for monetizing data created throughout the retail business and supply chain:

1. Retail analytics

Retail analytics include all of the critical analysis and data that enables retailers to make better decisions on inventory, demand, customer behavior, sales, marketing, the supply chain, and much more. Driving insights through these measures enables better-educated decisions regarding future trends and how best to capitalize on them.

Companies may run leaner operations, offer better product selections, and forecast demand throughout the year by leveraging information that is already available to any retailer, fulfillment facility, or manufacturer. All of this contributes significantly to monetization, and thus the bottom line.

2. Improved marketing

Advertising is no longer a game dominated by influential Madison Avenue figures. Digital marketing technologies have enabled firms and organizations of all sizes to reach customers across a wide range of platforms, reducing the need for expensive middlemen and speaking directly to an intended audience.

These digital marketing technologies have advanced rapidly in recent years, with a focus on integrating extensive analytics and AI to provide highly tailored communications and experiences. Personalization has had such an impact that it is now considered a standard expectation in retail rather than a trend-setting differentiation. Retailers who fail to deliver individualized purchase experiences will lose customers to competitors who do.

3. AI and MLOps

Artificial intelligence and machine learning applications are rapidly expanding. For example, according to Gartner, 55% of firms had Generative AI in production or in pilot mode as of October 2023, up from 19% six months earlier.

On top of the previously mentioned options for customization, AI has the potential to make a significant difference in the retail business on a variety of levels. AI also allows for more flexible logistics and supply chains, making it easier and faster to match inventory to demand and ensure that the appropriate products are available to the right people at the right time. It also allows for more proactive and market-leading offerings and campaigns based on predictive analytics results.

4. Fraud detection

Monetizing data entails not just increasing earnings, but also lowering losses. One approach to accomplish this is to limit the risk of fraud, which can be a significant threat to any retailer, both internally and outside. Every data point represents an opportunity to examine behavior and patterns, perhaps detecting fraudulent activities.

This could include purchasing and accounting data, employee shifts and warehouse movements, or video and audio recordings from the store. All of this data, paired with analytical skills, enables merchants to undertake much more extensive and educated investigations, detecting fraud before it causes significant financial damage.

5. Geolocation targeting

Contacting a consumer when they are ready to make a purchase can help you close the deal. Geolocation data allows merchants to incorporate moments of connection into in-store shopping experiences by transmitting product information directly to a customer’s mobile device. Location data can generate new chances to sell products and improve retail experiences by adopting features that can send personalized discounts or direct customers to the exact location of an item in a shop.

6. Improved business cooperation

Because retail companies and supply chain providers work in concert with each other to fulfill customer needs, it’s important for partner companies to speak a shared language. Boosting the level of cooperation between manufacturers, stores, and vendors can turn data into cost savings throughout the entire supply chain.

7. Higher customer satisfaction

Keeping customers happy keeps them coming back. Identifying new ways of boosting customer satisfaction is a smart long-term strategy to ensure that customers are able to find, purchase, and receive products without pain or friction. Because data makes it possible to better understand customer needs, retail and supply chain companies can greatly improve the customer journey by extracting valuable insights from how customers interact with a company. Data can also be taken advantage of by a comprehensive CRM system, which allows brands to build long-term relationships with new and existing customers.

Finally

In the coming months and years, data will solidify its position as the lifeblood of modern retail, as well as one of the most powerful tools for generating improved customer experiences, differentiation, and, ultimately, competitive advantage. However, the rapid advancement of data and analytics technologies, as well as the increasing demand for specialist knowledge, imply that many shops will want the assistance of an expert partner to keep up.