Salespeople have one glaring issue with data qualifying companies, buyers, timing, and what to say in the outreach message.
For a long time, companies have tried to address this issue by adopting manual approaches researching companies, mining for relevant data from LinkedIn, and using broad criteria to narrow down the list of prospects.
Artificial intelligence is changing things.
Instead of replacing salespeople, it is used to organize, identify patterns, and recommend the best course of action.
From Contact Lists to Valuable Insights
Most B2B sales teams build their prospecting efforts around databases.
They may scan through thousands of contacts, narrowing down the list by employee count, location, industry, and other criteria, until they identify a few hundred companies that fit the required criteria. The issue is that any of these organizations can be a disqualified lead simply because they fit the criteria.
Two similar companies can be at different stages in the sales cycle, with one showing active interest in buying solutions while another not considering suppliers for the next two years.
This is when artificial intelligence and more advanced data processing is required.
Instead of scanning tags, machine learning algorithms can process hundreds of signals to recommend the hottest lead.
What Information Can Be Used For Lead Scoring?
The more you know about the prospect, the more likely you are to build a successful sales campaign. When it comes to B2B prospecting, there is plenty of data to be processed.
Some of it is quantitative and can be sorted using simple sorting algorithms company size, industry, revenue, and other metrics.
Other data requires natural language processing algorithms to extract it from text and associate it with the sales team’s needs: recent news about the company, vacancies, website optimization, and similar signals. This information can be used to identify patterns and recognize quality leads.
For instance, if there is no sign of expansion, hiring, and technological advancements, it is probably not the best time to call this company to sell your product or service.
Lead Scoring Made More Accurate and Dynamic
In many ways, lead scoring resembles the traditional marketing mix modeling. Both approaches rely on historical data to identify patterns and recognize qualities of successful deals. The difference is that dynamic lead scoring does not rely on a static set of criteria. While traditional mix modeling will use the same set of indications for years, lead scoring algorithms will update the parameters regularly.
For instance, a marketing mix model will use a static set of qualities to recommend the hottest leads. A company should have more than 200 employees to receive 10 points, be in the technology industry to receive another 10, and have a director-level position at the request to get 5 more.
In other words, a marketing mix model is a great tool, but it is static and will only deliver the best results if the sales team knows what qualities to look for.
Artificial intelligence can learn what qualities a closed deal typically has and use this information to score and sort leads that are currently under consideration.
The main benefit of using artificial intelligence in sales prospecting is that it learns and adapts, rather than relying on a fixed set of criteria. With that said, a lead score should rarely be used as a qualification. Instead of a hard threshold, think of it as a list of the hottest prospects.
A salesperson should have the ability to prioritize their time and effort. A machine can do that more efficiently than a human and it is much faster than trying to sort through 500 contacts. If the AI identifies the top ten prospects, the salesperson will be able to scan through them and research each in detail.
The Value of Quality Data
No matter how advanced the artificial intelligence is, it will not be able to make better recommendations than a human, if the data is wrong. Duplicates, incorrectly sorted contacts, and irrelevant information will hurt the accuracy of any recommendation. This is why it is so important to dedicate time and effort to data validation, even as more and more processes are automated.
Modern AI b2b lead generation can help sales teams identify patterns and sort through thousands of bits of information in search of relevant insights. However, these tools are only as good as the data they receive.
As the practice of AI-assisted prospecting becomes more widespread, it will be necessary to dedicate more time and effort to understanding the quality and relevance of data before it is used in any capacity.
Assisted Personalization in Sales Outreach
One of the more interesting applications of artificial intelligence in sales is research-assisted personalization. The sales outreach software recommends relevant companies to target and identify key players, but the process typically stops there. The salesperson still has to research the prospect and personalize the outreach message, which is both time-consuming and ineffective.
No matter how much effort is dedicated to creating a personalized sales message, it will not be worth it, if a generic email template is used to send an outreach message, simply because the recipient’s name was added at the beginning of the subject line.
In reality, personalization should be based on signals and relevant data and artificial intelligence can identify both. A salesperson may want to use the following information to build a case for a sale:
- Prospect’s industry
- Company’s main markets
- Signs of expansion
- Leadership changes
- Industry-specific challenges
- Buying process
All of this information has to be gathered manually, if no AI is used. With an AI assistant, it can be extracted from databases and websites with the help of natural language processing algorithms. The salesperson will receive the information needed to make a case for a sale, without spending much time on the research.
At the same time, it is important to remember that a machine can identify ten signals, but only a person can decide which one to use as a basis for a sales pitch.
Why Salespeople Are Still Indispensable
Artificial intelligence can identify patterns, but it does not understand context. An ML algorithm can recommend that a particular company should be contacted, simply because it is similar to a closed deal. It does not consider that the company may be in the process of bankruptcy.
A machine learning model can recommend that a certain job title should be targeted, but it does not understand that this person is not authorized to make purchases.
This is why, in many ways, the value of AI in sales prospecting is defined by its ability to prioritize. A machine can sort through thousands of signals and identify the ones that point to a qualified lead. It can help a salesperson understand the deal landscape and build a case for a sale. However, only a person can build a relationship and use the information provided by the AI to close a deal.
Another advantage of keeping people at the center of the sales process is experience. Salespeople know how to navigate ambiguous situations and identify opportunities that do not follow any pattern.
They can understand the nuances of the decision-making process, internal hierarchy, and other subtleties that artificial intelligence is not capable of understanding.
The Bottom Line on Sales AI
In theory, artificial intelligence can revolutionize nearly any process by identifying patterns and making recommendations. This is especially true for B2B lead generation and sales prospecting. There is one fundamental difference: artificial intelligence can assist salespeople in their daily operations, but it is unlikely to replace them anytime soon.
At the end of the day, researchers and salespeople are human, and humans are not good at repetitive tasks.
It makes perfect sense to let an algorithm organize and sort through thousands of data points, identify patterns, recommend the hottest leads and build research notes. Salespeople still have to interpret this information and reach out to prospects.
This is when the value of AI in sales comes into play.
By automating the tedious and repetitive elements of the sales process, companies can give their employees more breathing room and make better use of their time and effort.
The more mundane aspects of prospecting data collection, organization, pattern identification, and qualification can be automated with the help of AI sales assistants.
At the same time, human qualities, empathy, experience, and intuition will remain vital in defining the best course of action.
Artificial Intelligence: Prospects and Pitfalls
One of the most important aspects of responsible sales prospecting is knowing what information can be used and where it came from. As artificial intelligence and machine learning revolutionize the way businesses develop and nurture relationships with clients, it is important to make sure that companies comply with privacy standards, do not abuse customer data, and consider the ethics of AI-assisted sales.
The same goes for personalization and automated outreach. Just because it is possible to use a customer’s name in an outreach message, does not mean that it is a good idea. That is why it is so important to have internal restrictions and ethical guidelines.
Businesses that employ AI-assisted prospecting should have specific policies regarding data, automation, and personalization.
The Future of Sales and B2B Lead Generation Lies in Assisted Prospecting
Artificial intelligence is unlikely to replace salespeople, but it will change the nature of their work. Manually sorting through thousands of data points is no longer a viable option, if an algorithm can do it in a matter of seconds. However, simply handing prospects to an AI model and letting it do its work is not a sustainable strategy. Instead of waiting for instructions, salespeople should use artificial intelligence to organize data, identify patterns, sort through countless prospects, and provide relevant insights.
Assisted prospecting ensures that companies dedicate the right amount of time and effort to the process. It maximizes the use of data and analytics, while making room for human qualities that are so important in the B2B sales space.
