HomeData EngineeringData NewsMonetizing Data using Data Analytics

Monetizing Data using Data Analytics

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The economic value of data for companies is challenging to conceptualize and measure directly. Many executives have the wrong perception of data monetization.

To them, the only way to derive economic value from data is to sell it to other companies. As a result, they overlook the immense untapped value that it represents. Companies can monetize by improving customer experiences, reducing costs, finding new customers, and so much more from the data that is produced directly or indirectly using big data analytics and AI.

Of course, this isn’t news to everyone. Many B2B businesses understand that data monetization using AI and data analytics can create higher returns on investment and streamlined operations. However, despite the will and the knowledge, they are unable to maximize results.

The reason for this is simple: They’re still treating data as the tech component of their larger strategy. What they should be doing is putting data in the driver’s seat.

Let’s examine how data analysis using AI and Big Data Analytics can assist in monetizing data.

1) Upselling

While upselling may have originally been viewed as a way to sell more products, it’s now a way to sell more relevant products. With data analytics driving the decision-making, businesses can suggest products that are complementary to their customers’ purchases and that bring value to customers. Greater value for the customer means their satisfaction increases, which helps with customer retention.

In addition, the original goal of making more sales is achieved as well. When the customer sees that their needs are being predicted and addressed, they will likely appreciate the service more. This new way of sale shows that businesses can make more sales and additional revenue by optimizing their operations with a data-driven approach, without selling the data to a third party.

2) Improving Customer Experience

It’s no surprise that customers return to businesses that are easier to deal with. Delivering high-quality support is a growing pain point for many companies. Chatbots based on machine learning algorithms can help relieve some of this pain. These chatbots can handle the most common use cases, and a representative can step in for more unique demands. It can reduce query response times and maximize customer satisfaction.

Chatbots play a crucial and helpful role in solving minor problems for customers, which frees up precious time for customer reps to focus on the more complex issues. Consumers prefer to interact with companies that can respond in real time while making a purchase, much like interacting with a sales associate at a brick-and-mortar store.

Thus an AI-driven chatbot can help your customers find answers to their questions when they place an order. It gives the impression that your brand is always there to serve their needs, even during those late-night shopping binges (when all your sales reps are probably asleep!) Furthermore, AI can integrate fragmented data sources to collect all the information regarding customer experience, to create a customer-centric approach.

3) Optimizing the Time of Your Sales Representatives

Anyone with experience in sales knows that it’s a war zone. Having the highest quality data can optimize the entire process. Salespeople can benefit significantly through an AI-based data-driven business model. They can have all the key facts and figures about each product, vendor, volume, and sales at their fingertips.

Not only that, but they can also have insights into competitors’ products. Salespeople can use that knowledge to track the products they’re responsible for and make fact-based decisions. They can also optimize their time by knowing when and whom to visit, or call a vendor. This management can increase efficiency, reduce waste, and save time.

4) Streamlining Supply Chain and Logistics

Managing the supply chain, especially for large businesses, requires careful planning. Any issues in the chain can create a cascade of problems further down the chain. Even reducing lead times and procurement cycles marginally can have immense benefits in the competitive world of business.

Having data on your side can provide such an edge. AI and data analytics is a great way to analyze the chain to look for improvements. This will significantly impact how buyers conduct business with their vendors.

In practical terms, AI can alert vendors to disruption in the supply chain, recognize suppliers for compliance issues, and quickly identify fraud cases. This can enable more innovative procurement to help better decision-making and offer a real competitive advantage to businesses.

Enable Data Democratization Strategy 

One big obstacle to creating the data-driven business model is the restriction of access to data. This somewhat awkward situation arises because of rigorous information control. How can data analysts do their job if they don’t have access to the information? Without data democratization, it would be impossible for a data-driven business model to flourish.

Data democratization enables data ownership from the IT-centric to business teams, which helps businesses own data and use the information in a timely manner. This also eliminates the data silos and enables the teams to view 360 degrees of the business data in building AI models and data visualizations.

Optimal Data Governance Strategy

With the objective of providing access to data for better decision-making as part of data democratization, organizations can’t ignore the data privacy, regulations, and ethical risks of data sharing.

Businesses need to define a sound data governance strategy for accessing the data without compromising the ROI on data-driven business and security risks. The data governance process should include built-in checks and balances. Decision-makers need to make ongoing changes to facilitate new changes in the market and regulations. This isn’t a one-off thing.

Executive Team Support

It’s time for executives to give top priority to the implementation of data-based business models. At the same time, executives should be aware that adopting AI is a continuous, iterative process that requires course correction over time. Machine learning is known for having a distinct cyclical nature that demands constant fine-tuning and improvement on an ongoing basis.

For many businesses, the foremost challenge is to get buy-in from all stakeholders. Technology executives, such as CTOs, have to provide a holistic view of AI implementation to all stakeholders.

In the age of digitalization, rapidly changing operating environments, and customer behavior, businesses need AI-based analytic approaches to improve ROI. Technology leaders must recognize the importance of a data-driven business model using AI – and raise awareness so that C-suite leaders are more eager to implement appropriate change management strategies. Adopting AI will require everyone involved in running the business to recognize the groundbreaking benefits.

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