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AI and ML roles in Lead Generation business

Author

Shishir Sarkar
Enterprise Architect
https://www.linkedin.com/in/shishirsarkar1/

There have been large investments made in several nations over the previous year, including America, which has invested between $5 and $8 billion, China, which has invested between $1 and 2.5 billion, and Europe, which has invested 1.7 billion. India is currently playing a highly important role in adopting, developing, and investing in AI-based technologies, with a projected market size of over 5.2 billion by 2023.

Artificial intelligence plays a significant role in the lead and demand generation industry, where we need to increase lead generation accuracy. One of the main problems we are trying to solve is that there are a lot of sources from which we can obtain the data—this is one of the main methods we utilize to obtain client digital footprints.

Capturing the digital footprint, however, is not an easy task. As we all know, a lot of data is generated these days, and there are a lot of augmented digital platforms where users visit and share their interests.

To handle this large volume of data, we need an infrastructure that is capable of handling it. Because there are numerous datasets that our AI system and machine learning model need to filter out, we also require a suitable process engine that can handle and analyze this volume of data.

As is well known, a quality dataset is required for model training, and this can be obtained with an appropriate data processing engine. We require clean, noise-free data to be processed using AI to generate leads and demand with a higher degree of accuracy. We won’t obtain the intended outcome unless and until we can supply accurate and clean data.

As we gather information from various sources, it is further divided into first-party and third-party data categories. The majority of this data has been gathered from many sources, and frequently, it is noisy, contains missing footprints, and is not well-organized.

To attain the best level of data correctness, I will discuss here how we might process the data using an appropriate ETL architecture. After that, these data will be further processed by AI and machine learning models to produce intent, firmographic, and demographic data that are properly categorized.

AI can tailor brand experiences, which encourages user loyalty and engagement. Language-based artificial intelligence is evolving swiftly. Contact them for better leads to turn prospects into target customers, it can assist marketers and businesses in obtaining more accurate leads pertaining to customer demographics and firmographic data.

By using this method, we can provide organizations with data-driven consumer profiling that will enable them to increase sales performance and fortify client connections. Using the previously mentioned data engine method and artificial intelligence approach, the vast array of customer demands, behaviors, and preferences displayed in online business platforms facilitates the raw data and collects the various taxonomies of customer data.

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