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Sales departments rely on a combination of human ingenuity and precise data analytics and predictions.
While nothing can replicate the human aspect of a successful sales team, machine learning can improve many automatic processes that support the team. Here are some ways machine learning is changing sales.
One of the key ways artificial intelligence (AI) and machine learning have had an impact across industries is their ability to automate repetitive tasks and increase the efficiency of employees and systems. The impact on sales teams has been similar. For example, chat bots are able to help people browsing your company's website with simple requests. As machine learning improves, it will be able to take over more complicated menial tasks that are currently performed manually, such as confirming or sending reminders about meetings and scheduled sales calls. While it's easy for sales associates to send these messages, automating it will free those employees up to work on sales pitches, analyze data and network. Sales associates should be prepared to take on larger quotas or more complex sales assignments by the time much of the traditional busy work is in the hands of machine learning programs.
There are some consequences of increased machine learning reliance you might not consider at first. One of these is the need to improve sales training in educational institutions and on the job. Sales associates will need to be knowledgable about the types of machine learning programs being implemented at their companies, as well as learn how to use or work with these programs. For example, if meeting reminders become fully automated, employees may need to be reminded simply to check that their meetings have been scheduled rather than trying to set things up themselves. In schools and supplemental trainings at companies, there should be an increased focus on complex sales and driving customer engagement.
A useful feature of AI and machine learning is collecting, monitoring and analyzing network traffic. Organizations can use machine learning applications such as NetFlow to automatically analyze the flow and volume of user traffic on the organization's website or network. These programs improve on network analysis in several ways. They automate the process, increase the storage and analysis capabilities of the software and provide more detailed insights and predictions.
Sales force and function capabilities will inevitably evolve as machine learning programs become better able to support them. Software like CRMs will be able to process data faster than ever before, as well as capable of performing predictive analytics and providing informed insights and statistical analysis. Additionally, many machine learning functionalities will be able to absorb low-level jobs in sales and provide increased support for higher levels. These include chatbots and voicebots.
As AI and machine learning become more integrated, it's likely that customer service and marketing departments will converge with the sales department to create hybrid teams. Driving revenue, facilitating customer experiences and providing information and support will be a collaborative effort from one department, all of it supported by the increased automation and processing power afforded by machine learning.
While departments may blend together, roles within those departments will need to become more specialized. Automation makes the processes behind sales more efficient, but at the same time the customer-facing sales representative can't be expected to perform upkeep on the AI tools supporting him or her. While low-level sales positions will likely be reduced, other skills such as research, data analytics, coding, computer engineering and accounts management, among others, will become important skills to add to a sales department. This means new, better defined roles will likely be available within the sales team.
Because so much of it is based on predictions and insights, sales is one of the areas of business that can benefit the most from machine learning advancements.
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