From Data to Dollars: Exploring the Role of AI and ML in Sales

Generative AI tools like ChatGPT and Midjourney AI are making disruptive changes in the ways people use technology. AI can help code, create content, plan a marketing strategy, create abstract designs, and even write jokes and poetry. From students to artists to professionals – AI impacts all.

However, such AI-driven results are far from perfect.

Though it may take years for AI to reach a considerable amount of efficiency to carry out nuanced, human tasks -- leading businesses are leveraging the power of AI and ML to optimize their business processes, mainly through automation and data analysis. 

With AI and ML-backed processes a business can enhance its products, services, and customer support to overall increase customer satisfaction. And happy customers mean more sales and better ROI.

Here are some stats to support that: 

• Around 9 in 10 organizations back AI to give them a competitive edge over rivals (MIT Sloan Management)
• AI service revenue will increase by over 6x in the space of five years (IDC, Omdia)
• AI tech can increase revenue by over $15 trillion in the next decade (PwC)

Additionally, business leaders are increasingly using artificial intelligence and machine learning tools to empower their sales teams. 

Between 2015 and 2019, the number of businesses utilizing AI services grew by 270% (Gartner) 

This blog will discuss what role artificial intelligence and machine learning play in sales. But before that, let's start with the basics.

What is AI?

Since AI is not just one thing, its definition varies. Artificial intelligence can be defined as a confluence of numerous technologies that work together to enable machines to comprehend, act, identify patterns, and learn with human-like intelligence.

Technologies like machine learning, neural networks, natural language processing, and more are all part of the AI landscape. Each one of these subfields of AI is evolving along its path. However, all these subfields use data, analytics, and automation to help businesses achieve objectives across verticals.

What is ML?

Machine learning, as the name suggests, is the ability of a software/program to learn from external data inputs and improve on its own over time. Since ML makes a system smarter with time it reduces the need for human intervention to carry out tasks. For instance, ML algorithms use past behavior and market data to interact with humans and predict their next moves. They can learn what products an online shopper would want to purchase, which shows they will love to watch, and what's the correct answer to their question. Simply put, machine learning gathers data, analyzes them, and makes predictions.

Netflix's recommendations technology is worth $1 billion in revenue annually (Business Insider)

What's the difference between AI and ML?

Many do not know that machine learning is a subset of artificial intelligence. Artificial intelligence has many other subsets like the Internet of Things, Neural Networks, Deep Learning, and more. In other words, all machine learning is AI, but not all AI is machine learning.

The part of AI technology that enables programs to not require human inputs or requires less of it over time is known as Machine Learning. 

Transforming Sales with AI and ML: A Closer Look at their Powerful Applications

Here are some of the key applications of AI and ML in sales: 

Conversational AI 

Conversational AI for sales means that your customers are not left marooned when your teams are not in the office. You can offer customer support and service, round the clock, across locations, and most importantly, without hiring more staff. Let us understand how conversational AI makes this possible: 

Chatbots

Chatbots help businesses to allow website visitors and customers to self-help. They can offer answers to frequently asked questions without needing any human assistance. This saves sales reps from answering the same questions over and over again. Chatbots use AI to detect questions and crawl through the online knowledge base library to display relevant results. 

Chatbots can offer a personalized experience by factoring in information like the visitor's name and past behavior. For instance, this is how a fashion retail chatbot would greet a returning customer who recently bought heels from the website.

Welcome back, Natalie! We've got a new collection of stiletto heels. Click here to check it out -> LINK 

Voice assistants

Voice-assisted shopping is the newest trend among online shoppers, and research suggests voice assistants are very effective. A study by MSI working paper found that consumers who use voice searches happen to browse 13.6% more and spend almost 20% more than those who don't. This 20% corresponds to an extra $493 million in annual sales revenue. These eye-popping numbers reveal the potential and promise this emerging technology offers.

There are over 4 billion voice assistants currently in use (BusinessWire)

Voice searching is therefore getting popular, and businesses are ensuring that they optimize their online footprint for voice searches for more sales. For instance, if you optimize your website for voice search, AI can ensure that searches made on voice assistants like Alexa (Amazon), Siri (Apple), and Google rank better on SERPs. These assistants can bring leads directly to you when they pick up on spoken keywords relevant to your product or service. 

Sentiment Analysis

Thanks to advancements in Natural Language Processing (NLP) -- a subfield of machine learning, it is now possible to rank the sentiments of people and what they mean to a business. This is done using Sentiment Analysis. You can measure online reviews and social media posts by favorability. You can also predict feelings like happiness, anger, fear, or excitement that a customer is expressing online. Businesses can understand their reputation and the feelings people have for them and then take proactive measures to improve, if necessary.

Sentiment analysis can help your sales representatives with a deeper understanding of the customers' feelings. It helps move beyond the traditional metrics like 'booked meetings,' 'the number of outreaches,' 'click,' 'open,' and 'reply rates.' It offers a whole new level of insight. It helps you gauge whether responses from people are positive, negative, or ambiguous.

Smart Recommendations 

AI and ML help create propensity models. These models in turn create predictive modeling to forecast trends of the target audience based on past behavior. With this insight, businesses can offer tailored experiences, and more precisely, tailored recommendations. 

Recommendations driven by propensity models boost sales by upselling and cross-selling to customers as they shop online. According to McKinsey, 35% of all the purchases made on Amazon and 75% of all content watched on Netflix are a result of ML-driven product recommendations.

Lead Scoring and Predicting Sales

Author and business speaker Victor Antonio in a 2018 Harvard Business Review stated that the approach of salespeople towards lead scoring and lead prioritization is rather unscientific. He says that such decisions are often based on gut instinct and without complete information. 

AI algorithms increase leads by as much as 50% (Harvard Business Review)

The machine learning algorithm based on historical customer information about a prospect as mentioned above, along with social media posts and interaction with the sales team (e.g., emails sent, voicemails left, text messages sent, etc.) can rank leads in the pipeline according to their chances of closing.   

Artificial intelligence can look dispassionately at large datasets from several sources and tell you which leads you should prioritize, based on the scores Al has designated.

When it comes to lead scoring and predicting sales, Al can bring a level of logic and standardization to the process that humans just can't match.

Optimizing the Sales Team's Performance

AI-driven predictive analysis helps optimize the sales team's performance. For instance, using data analysis artificial intelligence can help sales managers predict numbers for the quarter in advance. This is done by considering the data on demand and supply, an agent's or a unit's past performance, and other real-time data based on weather and socio-political situations. This can enable them to take proactive measures to further optimize the results, and efficiently meet sales goals. AI combined with a sales CRM can allow the sales teams to predict, prepare and deliver value at every stage of the sales process.

Will AI for Sales, Replace Salespeople?

With tools like ChatGPT disrupting the world right now - every employed person is wondering if they will be replaced by AI in future. And, salespeople have been haunted for a while now, thanks to conversational AI applications like chatbots.

However, research in general, states that artificial intelligence and machine learning are not going to replace salespeople. It is only going to make significant changes in how sales teams work (and for good).

AI-and-ML-driven tools will enable salespeople rather than replace them. In fact, AI tools are taking over those repetitive and laborious tasks, allowing salespeople to invest their time and energy into more complex and demanding tasks. 

Even AI says so…

Here’s what ChatGPT wrote when asked “Will AI replace Salespeople?”

Artificial intelligence (AI) is already being used to augment and support the work of salespeople, but it is unlikely to replace them entirely. While AI can automate certain tasks and provide valuable insights, it still lacks the human touch and emotional intelligence that are essential to building and maintaining relationships with customers.

AI can help salespeople by providing them with real-time data and insights on customers, enabling them to personalize their approach and improve their chances of success. However, it's important to remember that AI can only do what it's been programmed to do and may not always be able to understand the nuances of human interaction.

In the end, AI will likely continue to play a supporting role in sales, rather than taking over the role entirely. The most successful sales teams will be those that are able to effectively integrate AI into their work, while also maintaining the human touch that is so important in sales.

It can be said that instead of automating you out of existence, most AI sales tools will give salespeople superpowers.