Sales AI is here: terms you need to know
Nearly half (46 percent) of company executives are looking to invest in artificially intelligent tools for their sales and marketing teams. Sales AI is going to change the way people sell and make sales team more efficient, productive and ultimately, drive some serious revenue growth.
Sales leaders are eager to learn more and start implementing. Gartner predicts that by 2020, 30 percent of all B2B companies will employ AI to augment at least one of their primary sales processes.
What this means for you (the savvy sales leader you are) is making sure you’re ahead instead of behind the curve and really understand the terms being thrown around. Sales AI is still very new, but hundreds of tools are already flooding the market. And with each tool comes new technical buzzwords, often leaving sales leaders dazed and confused.
We’ve searched through the best case studies, ebooks and guides to bring you the most crucial need-to-know terminology to get you started on your sales AI implementation journey.
1. Artificial intelligence (AI): Machines that learn from data and can perform tasks that normally require human intelligence. These include tasks like visual perception, speech recognition, decision-making and language translations.
2. Sales AI: A tool that utilized artificial intelligence to improve the sales process. This can be in the form of automation in which a simple sales task is completed autonomously or through augmentation which assists in making predictions.
3. Augmented intelligence: Tools and technology designed to elevate human workers and aid them in working smarter. This is seen as a compliment to humans rather than a replacement. Often referred to as intelligence augmentation (IA).
4. Automation: Having a machine or tool that can perform a function with minimal human involvement.
5. Sales automation: Using technology to automate sales processes through static roles. For example, converting leads into the next stage in the CRM based on triggers that occur elsewhere like sending out certain documents through email.
6. Business Process Automation (BPA): Automation of business processes and workflows as a whole rather than one step or process with the goal of making the organization as efficient and productive as possible.
7. Robotic process automation (RPA): Software that automates tasks and processes usually done by humans. This can be tasks like processing, manipulating data, and triggering responses. Essentially this is software automating the existing tools in your tech stack.
8. Autonomous business processes: When a series of business tasks can all be fully automated with little human interaction or interference.
9. Algorithm: In math and computer sciences an algorithm is the process or equation that a machine goes through to solve a problem, complete a task or perform a certain computation.
10. Machine learning: A sector of AI when a machine uses a specific algorithm to solve a certain problem or do a certain task. These tools learn by finding patterns in data sets that they can then use to create an outcome. This is also called data mining.
11. Neural network: Networks in an ML algorithm that simulates how the human brain works, where a network of firing neurons are connected in a to make decisions based on the input.
12. Deep learning: A sector of machine learning that stacks neural networks on top of each other to achieve much higher accuracy than any other ML algorithm has before.
13. Chatbot: A software designed to replicate human conversations.
14. Knowledge-based AI: Humans assemble a handcrafted set of rules that are used to make decision graphs. These graphs often take a very long time to manual create by subject matter experts.
15. Unsupervised learning: Machine learning models that are trained without receiving the correct “answer” to the problem their solving, meaning they learn through a process of trial and error.
16. Supervised learning: Machine learning models that learn by comparing its own output to the “correct” output. If the system is incorrect it adjusts the algorithm accordingly.
17. Reinforcement learning: Systems that learn based on a reward. They create outcomes are then are rewarded or punished based on those. It is only told whether the outcome is correct or not. Once the correct output is achieved, it will optimize for maximum reward.
18. Natural language processing (NLP): The ability for a computer to understand, interpret and manipulate human language. This is also called text mining.
19. Predictive analytics: When a machine can make predictions about the future using current and historical data.
20. Intent detection: When a system uses NLP to predict the intention of a human message. This can be used to assist in getting the message to the right department or helping respond to the message.
21. Crowdsourcing: A mechanism to motivate people to do something, in the context of AI it’s used to create data sets that are then used to train AI.
22. Information extraction: When a machine mines for interesting pieces of data found in natural language text (for instance names, companies, telephone numbers, etc.).