Artificial Intelligence revolutionizing Sales and Marketing in Virtual Systems

Artificial Intelligence revolutionizing Sales and Marketing

Published by FirstAlign

Machine learning, Artificial intelligence and robotics are revolutionizing sales and marketing in the traditional online and e-commerce-based system in exceptional ways. Online stores such as Amazon and Alibaba are using real-time AI assistance to manage and boost up their sales. Google and Facebook use AI and machine learning methods to target and enhance their Advertisement and product marketing Techniques.

Scientists and statisticians have speculated that Digitization, IT, machine learning, robotics, and artificial intelligence will drive the 4th industrial revolution and shift the decision-making process from human to machine and smart computer systems. Machine learning is an approach to artificial intelligence, and deep learning is a machine learning division that focuses on algorithms inspired by human brain structure and function (Siau, 2017).

AI provides companies with a reliable and effective approach that the companies can use to exponentially increase their sales and do efficacious marketing. This also increases customer satisfaction and maximizes relationship and value.

Today different AI techniques are used in sales and marketing environment. Techniques such as case-based reasoning, knowledge-based systems, fuzzy logic, neural networks, artificial immune system, genetic algorithms and Hybrid approaches are used by companies to device effective and valuable sales and marketing management systems (K. A. H. Kobbacy, 2007).

Do you know how chatbots are changing what we know about ...

Chatbots are one of the imminent applications of AI in the sales and marketing sector. Chatbots are smart AI systems that act as a customer services representative and gives solutions and services according to customer needs. Knowledge management systems are designed through AI that collects and manages data from customer experiences and customer trends. This data is studied and new techniques of sales and marketing are designed by the AI systems for increased and effective sales (Liebowitz, 2001). Today, websites can automatically update using the site, and website pages can be formatted using eye-tracking data automatically.

Also read:How AI impacts Media and Entertainment

Digitalization of sales and marketing through machine learning can help majority firms and businesses to harness large databases and compare it to the data they retrieved at their respective websites and sources (Peterson, 1995). This helps in generating potential targets and opportunities that companies and firms can use to their advantages. Machine learning requires a vast quantity of data (Bigdata) and a high computing power (Malthouse, 2017).

A significant advantage of the use of sales analytics data and machine learning models is that consumer feedback and purchasing data is typically used by the company to bring forward new products and new customers. This leads to the exponential growth of your company in a very low period, saving money and assets.

There are two types of machine learning methods (Gareth, 2013):

Supervised learning, or learning including the construction of a statistical model to forecast or estimate output based on one or more inputs.

Unsupervised learning, or learning is based on Inputs but the results are not tracked and supervised (Gareth, 2013).

In the sales and marketing sector, compared with conventional statistical methods, machine learning is better at predictions, because the interaction between inputs and outputs is strongly nonlinear and dynamic. Both generative and discriminative models are used to identify and rectify the problem faced by management, sales and marketing personnel. These models are used to identify and classify data based on certain attributes such as ratings, comments of product.

Results of artificial intelligence and computing technologies have already begun in the third industrial evolution phase. Personal sales and sales management have several routine tasks (for example, ordering entry, notification of new products). Since the main task of sellers in developing relationships lies in the fact that these tasks take time and energy away, the advent of automation AI and Machine learning in routine tasks increases their productivity (Marr, 2016).

Sales and marketing alignment best practices tips and ...

For repetitive and non-productive processes, AI continues to ease the burden of the companies. AI also Integrates targeting wit advertisement, this way each customer isn’t offended or at unease due to the advertisements he sees during surfing. After the targeting has been done, the demand prediction and revenue forecasts in the target market(s) are relevant. Machine learning and AI can help to meet this demand (Blank, 2013).

Carbonneau, Laframboise and Vahidov in 2008 used SVM and neural networks to forecast demand and prove that the computational techniques such as curve, moving medium and linear regression are superior to conventional predictive methods. More broadly, machine learning by time-series simulation is very effective in sales prediction (Carbonneau, 2008).

The greatest impact on all routine, standard and repeatable activities have been and continue to exist in automation and AI technology on sales. For these situations, hardware helps the reliability of the selling products. The expanded usage of machine intelligence and AI models and algorithms will replicate shopping trends with the maximum scope, and provide companies with knowledge on the data to be used, thereby enabling companies to predict roadblocks and possible risks in their businesses.

Fuzzy logic is commonly used in architecture and planning. For process planning and management, neural networks are used. Furthermore, new and more efficient methods and techniques are researched and made for the sales and marketing sectors. In the coming future, it is speculated that all the companies and industries will integrate with AI and ML for their management and sales tasks.

Reshaping the World with Fuzzy Logic | We Are WorldQuant

One of the most prominent and well-known platforms that provide these solutions to companies that are willing to use AI for sales and other assistance is PEGA. It helps you build decision strategies for your firm or company using high-end artificial intelligence and state of the art ML models. It also provides you with an adaptive and predictive model in which you can customize your customer’s experience of your business.

It provides you with solutions to management, marketing, prediction, and customer engagement. This company is the leader in cloud computing and market innovations. This has resulted in improved consumer loyalty, decreased costs and enhanced customer value. The most prominent aspect of this company is that it provides you with real-time solutions and assistance for your Customer services and management services. All of this is provided to you under one platform. For further details please visit https://www.pega.com/.

REFERENCES

  • Blank, S., 2013. Four steps to the epiphany (2nd edition).. s.l.:K and S Ranch.
  • Carbonneau, R. L. K. &. V. R., 2008. Application of machine learning techniques for supply chain demand forecasting.. European Journal of Operational Research,, Volume 184, p. 1140–1154..
  • Gareth, J. D. W. T. H. R. T., 2013. An Introduction to Statistical Learning: with Applications in R. Spinger, Issue
    (http://dspace.agu.edu.vn:8080/handle/AGU_Library/1332)
  • K. A. H. Kobbacy, S. V. a. M. H. R., 2007. AI and OR in Management of Operations: History and Trends. The Journal of the Operational Research Society, 58
    (http://www.jstor.org/stable/4622664), pp. pp. 10-28.
  • Liebowitz, J., 2001. Knowledge management and its link to artifcial intelligence. Expert Systems with Applications, Volume 20, pp. 1-6.
  • Malthouse, E. &. L. H., 2017. Opportunities for and pitfalls of using big data in advertising research.. Journal of Advertising, 46(2), p. 227–235..
  • Marr, B. (. W. e. m. g. r. f. t. 4. i. r., 2016. Forbes.
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  • Peterson, R. C. M. &. B. S., 1995. An exploratory investigation of voice characteristics and selling effectiveness.. Journal of Personal Selling and Sales Management, 15 – , p. 1–15.
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  • Siau, K. L. a. Y. Y., 2017. Impact of Artificial Intelligence, Robotics, and Machine Learning on Sales and Marketing. MWAIS 2017 Proceedings., 48
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