Using AI and data analytics to monetize data: 4 techniques

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The economic value of data to businesses is difficult to conceptualize and measure directly. Many executives have a poor perception of data monetization.

The only way they can get economic value from the data is to sell it to other companies. As a result, they overlook the immense untapped value it represents. Businesses can monetize by improving the customer experience, reducing costs, finding new customers and more from data produced directly or indirectly using big data analytics and AI.

Of course, this is not new to everyone. Many B2B companies understand that monetizing data using AI and data analytics can lead to higher ROIs and streamlined operations. However, despite the will and knowledge, they are unable to maximize results.

The reason is simple: they still treat data as the technology component of their larger strategy. What they should do is put the data in the driver’s seat.

Let’s take a look at how data analytics using AI and Big Data Analytics can help monetize data.

1) Upselling

While upselling was originally seen as a way to sell more products, it’s now a way to sell more relevant products. With data analytics at the heart of decision making, companies can suggest products that complement their customers’ purchases and deliver value to customers. Greater value for the customer means that their satisfaction increases, which contributes to customer retention.

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

2) Improve the customer experience

It’s no surprise that customers are returning to more manageable businesses. Providing high quality support is a growing problem for many businesses. Chatbots based on machine learning algorithms can help alleviate some of that pain. These chatbots can handle the most common use cases and a representative can step in for more unique requests. It can reduce query response times and maximize customer satisfaction.

Chatbots play a crucial and useful role in solving minor issues for customers, freeing up valuable time for customer representatives to focus on the more complex issues. Consumers prefer to interact with businesses that can respond in real time to a purchase, much like interacting with a salesperson in a physical store.

So, an AI-driven chatbot can help your customers find answers to their questions when they place an order. It makes it seem like your brand is always there to meet their needs, even during those late night shopping spree (when all of your sales reps are probably asleep!). Additionally, AI can integrate fragmented data sources to collect all information regarding the customer experience. , to create a customer-centric approach.

3) Optimize the time of your salespeople

Anyone with sales experience knows this is a war zone. Having the highest quality data can optimize the whole process. Salespeople can benefit greatly from an AI-powered, data-driven business model. They can have all the key facts and figures about every product, supplier, volume and sales at their fingertips.

Not only that, but they can also gain insight into competitor’s products. Salespeople can use this knowledge to track the products for which they are responsible and make evidence-based decisions. They can also optimize their time by knowing when and who to visit, or call a salesperson. This management can increase efficiency, reduce waste and save time.

4) Streamlining the supply chain and logistics

Supply chain management, especially for large companies, requires careful planning. Any problem in the chain can create a cascade of problems further down the chain. Even a marginal reduction in lead times and supply cycles can have immense benefits in the competitive world of business.

Having data on your side can provide such an advantage. AI and data analytics is a great way to analyze the chain to look for improvements. This will have a significant impact on the way buyers do business with their suppliers.

Concretely, AI can alert suppliers to disruptions in the supply chain, recognize suppliers for compliance issues, and quickly identify cases of fraud. This can enable more innovative purchasing to help better decision-making and provide a real competitive advantage for businesses.

Activate the data democratization strategy

One of the main obstacles to the creation of the data-driven business model is the restriction of access to data. This somewhat delicate situation is the result of rigorous control of information. How can data analysts do their job if they don’t have access to the information? Without the democratization of data, it would be impossible for a data-driven business model to flourish.

Data democratization enables data ownership of enterprise-centric IT teams, which helps organizations own data and use information in a timely manner. It also eliminates data silos and allows teams to see business data 360 degrees when building AI models and data visualizations.

Optimal data governance strategy

In an effort to provide access to data for better decision making in the context of data democratization, organizations cannot ignore data privacy, regulations and the ethical risks of data sharing.

Organizations need to define a strong data governance strategy to access data without compromising the ROI of data security and business risks. The data governance process must include built-in checks and balances. Policymakers need to make continuous changes to facilitate further changes in the market and regulations. It is not a unique thing.

Support to the management team

It’s time for executives to make the implementation of data-driven business models a top priority. At the same time, leaders need to be aware that AI adoption is an ongoing, iterative process that requires course correction over time. Machine learning is known to have a distinct cyclical nature that requires constant adjustments and improvements on an ongoing basis.

For many companies, the main challenge is getting buy-in from all stakeholders. Technology leaders, such as CTOs, need to provide a holistic view of AI implementation to all stakeholders.

In the age of digitization, rapidly changing operating environments and customer behavior, businesses need AI-powered analytical approaches to improve ROI. Tech leaders need to recognize the importance of a data-driven business model using AI – and raise awareness so C-suite leaders are more keen to implement appropriate change management strategies. Adopting AI will require everyone involved in running the business to recognize the revolutionary benefits.

ABOUT THE AUTHOR:

Veera Nallam is Founder and CEO of Xtendlabs

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