Blogs. Webinars. LinkedIn. Artificial intelligence (AI) for sales has been popping up everywhere (and with good reason). The reasons for using sales AI are numerous and expanding. AI for sales is here to stay, whether it’s for prioritising leads, automating tedious jobs, or forecasting and analytics.
“How does this actually aid my team?” you might wonder.
We’ll respond to that and then talk about what you need to put in place within your company to ensure that your Sales AI project is successful.
In this article, RisePath will guide you through the process of starting and handling a sales AI project.
What can AI do to help salespeople sell more effectively?
Numerous studies suggest that artificial intelligence (AI) increases top-line income and improves conversion rates, but what exactly does it accomplish for your team?
AI-guided selling will contain four components by 2025, according to a survey.
- Correlation models
- Executive sales process steps
- Collect and detect buyer signals
- Measure business outcomes
These, on the other hand, are on a higher level and point to strategic components. Let us now turn our attention to the more practical aspects of the situation.
Coaching based on data
About 43% of respondents claimed AI-assisted managers in increasing seller productivity through data-driven coaching, which involves defining goals and evaluating sales efforts against the team’s best achievers. Over 3/4th of salespeople met or exceeded their goals by using data to coach them on actions with the best chance of paying off.
What Is The Best Next Step?
Another key benefit of AI, according to 36% of large businesses, was determining whether sales agents were talking to the proper people. Based on previous interactions, AI can advise you which profiles are likely to convert and what you need to do this quarter to improve your standing.
Choose the Right Customers And Prospects
According to the survey report, “Artificial Intelligence And The Future Of Sales And Marketing,” nearly half of the organisations polled (49%) employed AI to identify and target prospects and customers. A nearly equal percentage of organisations (48%) stated AI-assisted them in gathering information about prospects and consumers.
How does AI accomplish this? Salespeople can use data such as previous deals and client profiles to find and prioritise opportunities that are most likely to convert. Given the consumer profile, this will direct salespeople’s attention to the “correct” deals, those with a high conversion rate.
Salespeople can use tools like AI-powered sales assistants to analyse how well a deal is progressing and the best course of action based on prior data.
What is the best way to get started with Sales AI?
Before you jump on the AI bandwagon, keep in mind that AI is not a one-size-fits-all answer. It won’t appear out of nowhere and provide you with what you’re looking for. Instead, it’s a process that must be customised to your company and problem, as well as calibrated to your data.
Here are the five most important items to have before starting an AI project.
1. AI’s Raw Material: Training Data
The fuel that powers your AI engine is data. It’s not simply about the amount of data. You’ll need data sources that are free of errors, diversified, and trustworthy. Because AI algorithms take into account previous data, data accuracy is important. To acquire the most accurate recommendations for your industry, size, and style of business, the AI must be trained and customised using your organization’s data.
Let’s look at an example to better grasp the information needed. Your sales over the following four weeks can be predicted by AI. But only if you have a lot of granular, historical data over a long period of time. AI will not help you if all you have is data from the last several weeks. Extrapolation is a basic forecasting technique that can be used instead.
But where can I find sales information?
AI is frequently packaged with products like an all-in-one CRM, which is an advantage of adopting sales technology. This means that the AI is fed by CRM data, and if the CRM’s data quality is strong, your forecasts will be accurate.
A unified CRM can capture data from marketing efforts, which can then be used by AI to answer queries such as, “Which types of customers respond well to campaigns and also end up closing deals?” The answers to these questions aid sales and marketing in fine-tuning their strategies.
2. Selecting the Appropriate Business Use Case
To begin, select a use case that will result in a small and quick win. You can then utilise this to gain stakeholder buy-in for future projects and create the foundation for instilling the data culture required for more complex projects with more stakeholder impact.
From a sales viewpoint, 36% of businesses utilise AI to forecast sales. AI is used by almost one out of every three businesses for lead scoring and prioritisation, as well as automating simple chores.
3. Establishing the Goalposts
What does it mean to be successful? Is it a higher number of leads, conversions, sales, and, finally, revenue? To begin, don’t choose too many measures. What is the most critical metric you want AI to influence?
The best method to assess a sales AI project’s success is to link the outcomes to something you already track. You’ll be able to compare your AI-driven outcomes to previous data and standards this way.
When assessing success, there are three basic categories to consider:
- Finance Revenue and Pipeline goals: You can measure closure rates (if you’re using AI for lead scoring) and quarterly revenue predictions (if you’re using AI for forecasting). The most obvious is tracking revenue growth. Even yet, it’s sometimes difficult to assign it to AI because so much depends on external factors like how the economy is doing, what other strategic initiatives are in the works, and so on. As a result, pick a statistic that you can simply link to your sales AI.
- Customer experience: AI can assist salespeople with the “next-best-action” based on previous successful deals. For example, if a lead is nudged with a follow-up, the lead will not become cold. You may track how closure rates changed before and after AI was implemented using these actions. You can even go beyond typical customer satisfaction scores, such as NPS, and into Natural Language Processing. You may read what your customers have to say about their shopping experience in the comment section.
- People goals: Happier sales teams outperform their less happy counterparts in every statistic imaginable, including quota achievement, revenue growth, and customer satisfaction. According to a study, 81 percent of very happy sales teams saw annual sales growth in the last two years.
You may also use productivity measures such as “How long does it take for a group of workers to achieve a specific result before and after AI implementation?” “How many hours of non-selling time have been converted to selling time as a result of AI-assisted task automation?”
4. Roadmap for Data Science
It’s important to build an executable roadmap for your sales AI project after you’ve set up the essential metrics.
You should plan out two approaches: a business-driven strategy and a data-driven one.
When you compare these two ways, you’ll notice that you might not have enough data to answer certain of your business issues. You can also have data that doesn’t help you solve the issues you do have.
You will, however, find a sweet spot: data to solve a list of issues that you do have. Take a look at these and get started with them.
5. Adoption: Getting Ready to Change
For most salespeople, sales AI is a dark box, and there’s no compelling reason for them to “do what it suggests.” You must clearly explain why it benefits them and why they should be concerned.
It’s typically a good idea to begin your AI implementation from the ground up. Find out what real-world issues your salespeople are dealing with that AI can help with.
Be cautious of the technology you introduce. Sales technology is frequently chosen for sales teams rather than with them. The technology adopted does not allow salespeople to perform their tasks more effectively. Instead, it aids managers in tracking sales operations.
Before you start a sales AI project, ask your team certain questions. Basically, you should be able to respond to the question regarding a concrete outcome that the AI project assisted your team in achieving that they couldn’t do previously. After that, you can move on to things like organising roadshows.
We recently went over the five essential components you’ll need before starting your sales AI journey: data, use case, metrics, plan, and guaranteeing adoption once the project is live.
The use case you choose for execution is one of the most important success elements. You can choose between a use case that eliminates manual operations (such as sending emails) and one that is prone to human mistakes (like forecasting).
You should also make sure that your objectives are attainable. According to research, 30% of all B2B organisations use artificial intelligence to supplement at least one of their major sales processes. Are you going to be one of them?