There are very few processes in an organization that are as human as Sales Ops - without them, it's lights out. These teams manage your customer base, communicate your value propositions and thrive on human connection.
At the same time, these teams often find themselves thinly staffed and under pressure to drive results - and consistently. Automation can help, you're probably thinking.
And you're not wrong.
Artificial Intelligence (AI) encompasses a wide range of technologies, including Machine Learning, Computer Vision, Natural Language Processing, Deep Learning, and more. All of these technologies, at their most basic level, assist machines to accomplish specific cognitive activities, sometimes even better than humans.
So how can AI be used in sales? This article aims to help you understand just that. We will take you through everything you need to know about how Artificial Intelligence can be used by companies of all sizes - to elevate your sales teams.
A few words on data going forth...
While AI for sales teams is nearly always focused on automating structured data (think sales reports outlining revenue by region, by product, by month), there is a ton of untapped potential for unstructured data to be found.
There's a simple reason why the focus is unbalanced - automating and analyzing structured data is much easier. There are great tools out there that can help you accomplish some basic forecasting and analytics (some of which we'll mention in this post).
We have decided to take a different approach and focus on the human in the human-centric process and look at automation in the context of a day in the shoes of your sales team. It's unlikely that all your team does is look at numbers all day - and we hope to bring some balance into the field.
So, let’s dive in.
What is AI in sales?
Artificial intelligence in sales involves using AI-powered technologies to improve the sales process from start to finish. It does this by automating many aspects of the sales process, enabling your sales team to focus on closing deals.
This could involve automatically categorizing incoming leads by how promising they are, analyzing previous successful sales communications to identify patterns, evaluating cross-selling opportunities, and much more.
Put simply, AI in sales improves everyday operations in your sales team.
What are the benefits of using Artificial Intelligence in Sales?
AI has been shown to have many benefits in growing revenue – from improved forecasting and customer engagement to enabling better time management, to name a few.
- Increased Revenue: AI lowers the risk of making a mistake by setting up pre-approved discount guardrails. By speeding up the sales process, the time it takes new salespeople to get up and running is also reduced. AI can also assist vendors in providing upsell and cross-sell suggestions to guarantee that consumers get the most value for their money (and that you get the most of your leads).
- Time-Saving: By recording all of their communication, appointments, and other basic sales activities automatically, AI relieves salespeople of time-consuming administrative duties. Sellers may now focus on selling and nurturing customer relationships rather than filling out forms manually.
- Improved Customer Engagement: Intelligent technologies allow sellers to gain a better understanding of their customer’s requirements in order to foster trust and loyalty. Salespeople have access to a 360 view of the client when using AI, allowing them to provide personalized interactions that appeal to customers.
- Improved Forecasting: AI is used in sales forecasting to boost deal forecast accuracy by providing a real-time picture of each transaction in the pipeline and assisting sales executives in determining when to advance or park deals. Deal scores can now be generated thanks to AI-driven insights, allowing salespeople to fine-tune their forecasting method with past data to generate highly precise predictions.
Artificial intelligence is improving efficiency and creating better workflows in a wide variety of industries—sales are no exception. Let’s take a look at how.
Text analysis techniques in AI
So, how exactly does AI improve your sales process?
With text analysis, that’s how.
There are many methods AI uses to perform text analysis, let’s take a look at them one by one.
Text classification
Text classification organizes, structures, and categorizes unstructured text in a way that facilitates the AI model to deliver valuable insights. It’s considered one of the most effective Natural Language Processing (NLP) techniques.
Sentiment analysis, topic analysis, and intent detection are all examples of text classification in AI. Text classification can help the sales process by identifying the intent behind sales inquiries, enabling you to answer accordingly and highlight any key features that could help secure the sale.
Text extraction
Text extraction consists of extracting data that is already present in the text, such as dates, timings, prices, keywords, and more. This helps identify key themes and important data in a body of text, without needing to read it from start to finish.
Text extraction can be useful for rapidly assessing large amounts of data that could help identify potential leads.
Word frequency
Word frequency text classification involves identifying the most frequent words in a text using the numerical statistic TF-ID (term frequency-inverse document frequency). Word frequency analysis can help identify keywords or expressions customers use during successful sales pitches.
Collocation
Collocation helps identify words that commonly appear together in the text. These are most commonly collections of two or three words, such as ‘project management’ or ‘out of office’.
This can be useful for identifying—and subsequently replicating—key phrases and semantic structures used in inquiries that end with a successful sale.
Concordance
A concordance helps identify the context and instances of words or a set of words by looking at the preceding context and the following context. This helps identify how customers use words in different contexts and can help provide greater insights into complex pieces of text.
Clustering
Text clustering consists of understanding and grouping vast amounts of unstructured data. It’s similar to text classification, but clustering is much faster to implement due to not having to tag and label training data—it’s unsupervised Machine Learning.
Text clustering can help sales teams by developing quick insights into vast amounts of text, such as long email chains and transcribed phone calls.
Word sense disambiguation
Word sense disambiguation analyzes words that have multiple meanings and determines which meaning is relevant to the context. It requires you to train the model but can help in clarifying the true meaning of large amounts of data.
Why is text analysis important?
Text analysis helps organizations create valuable insights from huge amounts of data. Manually reading and interpreting data is time-consuming and subjective—text analysis with Artificial Intelligence removes all the legwork and mitigates issues.
It enables organizations to develop insight-driven actions for improving the sales funnel and process. For example, analyzing previous communications with a client can create insights into what language is frequently used in the run-up to a sale.
Insights from AI-powered solutions help businesses make better decisions faster and eliminate any unnecessary guesswork. Let’s take a look at how exactly AI can help you improve your sales techniques and processes.
AI as your sales automation tool
According to a study by Harvard Business Review, companies using AI for sales can increase their leads by more than 50%, reduce call time by 60-70%, and have cost reductions of 40-60%. Given these numbers, it’s clear as day that leaders seeking to improve their bottom line should look into Artificial Intelligence.
There are a ton of granular processes that are waiting to be automated. Let's group them into three core Sales functions:
Managing your marketing, product and sales Qualified Leads
Lead generation
AI-powered lead generation sounds like a pretty wild dream for most salespeople. Imagine knowing your ICP and automatically filtering through LinkedIn or your lead list based on that. Or do the same for company profiles.
Sales automation tools can help you find the people that fit your structured criteria, such as location, employee role, and the number of employees in the organization - but that will leave you probably still with thousands of people to manually qualify.
This is where Machine Learning comes in.
AI tools can categorize the customer profile based on unstructured data and take that into consideration. This could mean including their interests, what they post about, or what the sentiment of your previous communications has been.
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Let's take an example: Say you're looking to filter through company profiles on Crunchbase, as you want to design a cold outbound campaign to target deep tech companies. You'll likely filter this list by location, money raised, and year of founding. You'll then have two options: either categorize these companies manually based on their descriptions - or train a Machine Learning model to help you filter out noise.
Lead scoring and prioritization
It's also crucial to determine the lead fit and priority of the leads that you have now generated. AI-powered platforms can evaluate consumers' chances of converting based on third-party and company data, allowing your sales staff to focus their efforts.
A common example is the lead scoring systems that you come across in call centers. They're used to pre-qualify and score leads to increase the potential of a sale during a call. But - keep in mind that most of these data are unstructured notes or transcripts - and unless you have a very consistent process (or a Machine Learning model) in place, mistakes will likely happen.
Your company's traffic is also a great source of data for lead prioritization. Using website tracking tools, or your website chat widget data, you can utilize how potential consumers engage with your company's digital properties.
By defining what a high-quality lead is for your business, you can set up a workflow to send "trigger reports" directly into your sales staffs’ inboxes.
Upselling and cross-selling
Salespeople utilizing AI technologies have a better chance of finding prospects who are most likely to buy from them. Sales representatives also employ AI to forecast when they are most likely to make sales. The AI algorithms scan for individuals that are highly likely to buy into your firm or business's solutions based on not only behavior, but also sentiment, and the topic of all previous communication.
Day-to-day sales automation
AI might also automate or enhance your workload to free you up for higher-value activities.
Artificial intelligence has the potential to help you with everything from scheduling appointments to assessing a sales team's pipeline by automatically performing these tasks for you. It can even make these processes far easier by leveraging your past usage data to make judgments when providing the outputs.
Chances are if you're doing something daily, there's a way to automate it.
- Sales workflow management - this could include anything from moving your leads to your CRM from multiple sources and routing them to the right person - to scheduling automatic meetings
- Managing your mailbox - sort through your mailbox with ease using email classification tools
- Sort through documents - make sure everything is signed and in order
- Categorize incoming messages or survey responses from your clients
- Performance and productivity enhancement
Forecasting & sales analytics
Even though we have focused on use cases mainly in the space of unstructured data - structured data deserves a mention as well.
Predictive (demand) forecasting
There are Artificial Intelligence technologies that may forecast or anticipate outcomes by looking at past data and using it to predict what may happen in the future. Sales AI systems can make the following common forecasts:
- Deals and prospective deals that are most likely to result in a sale.
- Figure out which deals or prospective deals to focus on next.
- New leads or clients who may be interested in your product or service.
Word of caution, however - the accuracy of Artificial Intelligence in sales forecasting relies on the quality of the data. The more data you have to feed the model, the more accurately it can make decisions.
Using similar techniques to data analytics, Artificial Intelligence can also assist sales executives in forecasting their teams' performance for the quarter ahead, allowing them to take proactive action based on the data.
AI helps make better decisions by giving your team leaders a real-time view of each deal in the pipeline and assisting them in deciding when to advance or park deals.
Deal scores, for example, could be generated automatically using AI with the help of Machine Learning. Sales teams may fine-tune their forecasting procedure with historical data to give highly precise estimates thanks to AI-driven insights.
Price optimization
AI takes all of a customer's sales data, including location, size, and prior successful sales, to determine a suggested price. As a result, salespeople don't have to guess what pricing will assist them in closing a deal.
Studies show that AI can help companies improve their leads by more than 50%, cut call time by 60-70%, and save 40-60% with cost reductions.
Pricing may even be customized in real-time for specific clients, which increases the likelihood of closing the deal. Not only does this help to speed up the sales process. By lowering the amount of time it takes for new salespeople to get up and running, then the time from when they are hired until they can start making profits is reduced, too.
This is because they wouldn't have to worry about overpricing, or worse, underpricing, the product or service. By lowering the number of back-and-forth discussions that slow down the sales process and make it unpleasant, optimal pricing enhances the customer experience.
How to get started using AI in sales
You’re probably now thinking: where do I even start?
Simple—you start where any great plan starts: step one.
Step one: understand what you’re looking to achieve with AI
The first step to implementing AI in sales is to understand how you want it to help you. We’ve outlined some of the ways that you can use AI in sales above, but your use case is unique to your business.
What processes do you want AI to improve? Do you have data to teach the model? Is it organized and ready to be uploaded to the AI model?
Answering these questions will put you in better stead for the following steps.
Step two: find an AI solution that you can use
This may seem obvious, but it can easily be overlooked by sales managers and decision-makers. You need a tool that your team can use—ideally, without weeks of onboarding and training.
You want to find a tool that requires no coding knowledge—this ensures you can use the tool and avoids the need for expensive development support.
There are ample tools to facilitate no-code AI and empower non-technical people to build AI workflows. When choosing yours consider:
- Ease of implementation: how simple is the implementation process? How long does it take?
- Ease of use: does the solution require training? Can the whole team use it with little experience or knowledge?
- Prediction accuracy: how effective is the AI model? Does it effectively decrease your workload?
- Support options: does the AI tool have a support team to help with issues? Are they easy to get hold of?
- Ongoing development: is the AI tool being developed as the AI landscape evolves? Does the AI model learn from the data it processes?
If possible, join a demo before going all-in on your chosen tool to ensure it’s got the capabilities you need.
Step three: train and implement your AI model
Once you’ve identified your needs and chosen your AI tool, it’s time to get to work. You need to train your model to analyze your data and produce the desired results. Here’s a brief rundown on what that entails:
- Upload learning data: you need to train your AI model using clean, labeled data. This is what teaches your AI model to create data-informed predictions.
- Train your model: using the data you’ve uploaded, you train your AI model to identify what you’re looking for.
- Test your model: it’s a good idea to test your model before using it. This gives insights into how accurate your AI model is, and can help when considering whether or not to add human review.
- Human review: consider how much of the machine's output you want your team to review. The more AI predictions that are reviewed by humans, the better the model gets at creating accurate predictions.
- Create an AI flow: finally, it’s time to implement your model. An AI flow puts your AI model into practice by detailing input—how the model receives new data—and output—what it does with its conclusions and predictions.
For a comprehensive breakdown of these steps, check out how we use no-code AI at Levity.
Closing notes
AI-based sales tools provide much more than just automating simple and low-value activities - but it does have their limitations. That said, if you leave this article with remembering only three things, let them be:
- Trust the technology to do its job - There is significant evidence of the benefits of using AI tooling in your work processes. That doesn't mean that AI will take the human out of your customer-centric organization
- Most sales processes require you to understand both how your data looks and how your customers behave. Your choice of tooling should reflect what you struggle with most.
- While most sales tools focus on structured data and predictive modeling, there are countless (and mundane!) day-to-day sales tasks that could - and should - be automated. Once you get these out of the way, you can move towards more complex processes.
If you're ready to get started - give us a shout and let's see if we're a good fit for your processes!
