Artificial Intelligence (AI) is becoming a major player in the sales scenario, before, during and after the sale is done. From scavenging through big data that no human could ever analyze, to fully automating the 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. (Want to learn more about AI? Then check out How Should I Start Learning About AI?)
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 much more 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 biologic 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. After a short “training period” during which the neural network learns using historical problem data in which the outcomes are known, the AI is able to recognize patterns and provide solutions and estimates.
Thanks to this ability, they can be used to efficiently allocate marketing resources and optimize a company's advertising efforts. By interpreting a plethora of parameters such as marketing costs and gross profits, ANNs can be used to predict next period's sales with a relatively narrow margin of error.
Shortly after we search online for any one of our interests, tons of ads for closely related products start appearing everywhere. Deep learning algorithms 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 it is only recently that Deep learning ones have been introduced.
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.
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 info from customers. But the biggest challenge that every AI must face is to collect the data required to train the algorithms.
