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OFweekrobotHawking once said: “The development of full artificial intelligence may destroy human beings.” However, before that, the emergence of AI-driven sales technology will almost bring the current sales method to an end, and may be in the next wave of technology. The wave has a huge impact on companies that don’t understand the technology.

Imagine a digital sales assistant responding to voice commands. These commands can be as simple as “call the most qualified person in the queue” or as complex as “provide an analysis of the most effective sales promotions for those of my prospects.”such assistants when they are associated with big data sales and marketingautomationWhen connected to data, it will allow salespeople to work faster.

The consumer market is already seeing products like Amazon Echo, Cortana, Siri. According to Strategy Analytics, roughly 88 million voice-activated devices will ship this year. By 2020, this number is expected to reach 347 million, when there will be 970 million voice Robots in use worldwide.

With the exception of a few pioneering companies, the revolution will begin with individual salespeople. This is how smartphones, social media, CRM, etc. became part of the sales environment: salespeople experienced the power of the technology at home, and then began to notice that these technology-enabled experiences were lagging behind their experience at work, which created the “Experience Gap”.

As an interface for the rapid transfer of certain types of data from a Display to the spoken world, it’s not hard to imagine what this means for a salesperson: a spoken-directed sales system or a guided function configuration that solves a configuration price quotation (CPQ), in a car The ability to interact with the sales management system in the middle, as well as quickly generate new types of reports based on the criteria experienced to the assistant, and then view it on the screen.

Next-level zombies act as partners’ assistants – suggesting sales strategies, driving salespeople to understand what a prospect deserves, or prioritizing sales calls based on factors such as deal stage, deal value, or market data suggesting purchase likelihood.

This can be achieved through machine learning, which automatically finds patterns associated with outcomes. The next stage is to change the schema based on the context; when the framework’s data exists in the transfer, the schema must also change due to new competing products, customer behavior, economic conditions, etc.

All of these require access to data, not just a dataset. Applying machine learning to a set of data related to the sales and marketing process is somewhat useful, but its impact is limited. Machine learning must be associated with aggregate data collection covering all aspects of the buying and selling process. In order to provide accurate and timely data, AI must obtain customer data from marketing automation, performance data from the sales department (including data on commissions, quote generation, sales content performance, and others), and data on the market and instant detection. Data (such as sales process or product mix changes and expected effects).

When successfully completed, the results can be very gratifying. It’s like every salesperson has an assistant to help them make decisions, advise on planning, etc.

But the interface will be the most obvious, most contradictory, and relatively least important aspect of an assisted sales solution. The data interface becomes a powerful tool. Success depends on having data that is complete, up-to-date, and most important and accurate, in a variety of discrete repositories, in a format that makes it compatible with the format that any machine learning-initiating system is selling Will take away the ability to assist intelligent solutions to make optimal decisions.

We could see an “arms race” in new sales technologies as machine learning is combined with sales data. But it would be unwise to put assistive technology at the forefront. This new interface, with all the benefits of ease of use, speed, flexibility, depends on a good foundation of data. For those companies that are about to start or are already preparing sales data sources for the future of AI, success will come faster!