How Artificial Intelligence Will Revolutionize the Sales Industry

Artificial intelligence (AI) is becoming a major player in the sales scenario, with major applications before, during and after the sale is done. From scavenging through big data that no human could ever analyze, to fully automating a process through intelligent, machine-learning bots, AI is already critical to bolstering a brand's marketing efforts.

Often called the “AI revolution,” the introduction of computer-based solutions to automate the sales process is still taking its first steps. However, we’re not so far from a world where self-managing scripted systems are going to be a substitute human intelligence altogether. Just take a look at how well Google Translate is now able to understand human languages, or how targeted ads keep haunting our searches like there’s a hidden “someone” out there who really knows our tastes.

Artificial intelligence is definitely bound to change the sales industry in the future, but it is already impacting it in very significant ways. (Read also: How Should I Start Learning About AI?)

Smarter Sales Forecasting

Artificial neural networks (ANNs) are the synthetic reproduction of a mammal brain: a large network of interconnected processors that operate in parallel. Just like a simplified version of human neurons, these computing units process information, learn from experience and identify patterns. Although they lack the flexibility and ability to adapt like biological interfaces, ANNs may take previously solved examples to build a system which is able to make new decisions.

One of the traditional uses of ANNs is to analyze historical data collected in spreadsheets to make rather accurate predictions and sales forecasts. Now that data-driven business management has become the norm, AI is taking revenue projections to new heights. Today, collating signals and turning them into actionable insights hinges not only on accurate reporting of performance, but also on the ability to predict future sales trends.

Correctly estimating future revenue requires a comprehensive understanding of current trends, reliable historical sales data, and the latest performance of the current sales pipeline. Because it’s in such a sensitive part of the business, many companies have preferred to run forecasting by hand in the past rather than "farm it out" to AI.

Today though, businesses are relying on artificial intelligence to take over every aspect of forecasting the sales pipeline. Using some of the newest AI-powered tools like DataRails, sales managers and C-suite execs can connect their data streams from customer relationship management (CRM), invoicing platforms and accounting systems to the Excel sheets they’re used to working with. Then the machine does the rest, refreshing metrics as needed, thereby turning spreadsheets into viable reports that include business intelligence (BI) visualizations and AI-enhanced projections.

AI has been exceptionally successful in streamlining sales forecasting because so much of it is predicated on historical data and the current pipeline. These numbers are often difficult for humans to input consistently without error. Formatting or magnitude mistakes abound simply due to the high volume of data. This can have serious ramifications for the business’s revenue.

By automating a lot of the collection and number crunching, AI frees up human employees to incorporate their knowledge of current trends to make an accurate sales forecast.

Deep Learning Algorithms

Shortly after we search online for any one of our interests, tons of ads for closely related products start appearing across seemingly every online platform. Deep learning algorithms have already started scanning through the big data to forever change the world of automated ads. Google’s search engine always included a certain degree of machine automation in the form of algorithms, but since 2016 it also introduced deep learning ones.

Driven by highly advanced neural nets, they constantly analyze information ranging from spoken smartphone commands to social network photos and statuses, and, obviously, search engine queries. They possess their own “intelligence,” and since they’re much faster and can act on a much larger scale than humans, they are already able to outperform us in this task. Their training process never ends, but in these last few years they've been able to learn so much about our behaviors that they can now predict almost every step of the average user.

Machine-Learning Bots and Sales Automation Platforms

All bots are programmed to find the quickest, most effective way to achieve a goal – in this case, automate the sales process. Machine-learning bots go beyond that, and, in time, learn to optimize their process by gathering data and information from customers. But the biggest challenge that every AI faces is collecting the data required to train the algorithms. While for giants that deal with practically endless amounts of user data, like Google and Facebook, this might not be an issue, but for smaller companies it definitely is.

AI-controlled bots can easily reach millions of customers, find the right ones to contact, write follow-up emails and automate the entire sales sequence. By minimizing their marketing expenses with these smart solutions, even small and medium-sized businesses (SMBs) can now compete with the big players and their enormous budgets. Salesforce integration and smart deduplication functions allow less-than-huge companies to reduce their workload by up to 90 percent, and save precious resources as well as employees' time. (Read also: WIll Robots Take Your Job? That Depends.)

Nurture Leads with Automated Engagement

AI is slowly growing in use all over the sales industry. The overarching trend is that AI is not replacing employees, but rather helping them make better use of their time. Automated lead engagement is no different. Just as automated emails changed the game for marketers, automated messaging with website visitors using AI is helping the sales team streamline the conversion funnel.

AI assists with messaging over a series of touchpoints. First, an AI team member is available to interact with potential leads 24/7, and can help build rapport and nurture a relationship with a potential lead. Machine learning can allow AI to identify trigger words and respond to leads with relevant information, all the while utilizing data from the CRM.

Finally, AI can ensure sales teams only engage with qualified leads, or “clean” the CRM of leads that can’t be qualified, thus saving sales reps time and effort. For example, Exceed.ai, which offers AI-assistants to work alongside their human counterparts, reports that engagement campaigns were taking 20%-25% of their sales team’s time. By implementing automated engagement, productivity was boosted and more leads were nurtured and ultimately converted.

AI can help nurture leads with timely, relevant interactions that help the leads receive useful information at the right stage of the funnel, and for the sales team to gain a better picture of who they’re talking to before they personally conduct a human-to-human sales call with the lead. (Read also: The Top Ways AI is Improving Business Productivity.)

Assisting Humans with Customer Experience

User engagement and customer experience are critical aspects of the post-sale process. Existing clients are more valuable than new ones because of their loyalty and referrals. However, when assisting customers or securing new prospects, many salespeople may not be able to understand the customers' pain and problems. They may lack the confidence to uncover their issues, leading to fumbles and misunderstandings that ultimately cause them to spoil the relationship with the client.

Machine learning engines might help human customer service agents by determining who would serve that customer best. In addition, AI-assisted speech recognition may help spot keywords that trigger vital service enhancements, such as alerting a manager to assist the call when the word “supervisor” is mentioned.

AI and ML automated customer interactions accounted for less than two percent in 2017 according to Gartner. Servion Global Services predicts that by 2025, 95% of customer interactions will be automated, a mind-boggling increase in a relatively short amount of time.

Conclusion

Improved marketing automation is leading to greater scaling, better outcomes and reduced costs. Impractical tasks are already being handled by self-sufficient machines, and newer AIs support the human workforce every day by facilitating their operations.

Although a few employees are bound to lose their jobs to robots in the future, an AI-augmented sales process might help our society become a little more fair and equal. In fact, Even SMBs that cannot afford to hire hundreds of employees could then compete with larger corporations.

However, the ultimate beneficiaries of this alleged revolution are undoubtedly going to be customers, who will enjoy a much smoother and more finely tailored buying experience.