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How To Use AI For A Lead Scoring System

It’s no secret that AI and machine learning are being used in a lot of different industries. Advancements in AI technology are changing the way businesses operate. But what you may not know is that AI can also be used to beef up your lead scoring system.

As businesses strive to become more data-driven, AI-powered lead scoring has become an essential part of the marketing mix. By automating the process of sorting and prioritizing leads, a lead scoring system can help sales teams focus on the most promising opportunities.

Before adding an AI to your lead scoring system, you’ll need to know exactly what lead scoring is in sales. In this article, we will discuss how AI can be used for lead scoring and some of the advantages it has over traditional methods. We’ll also give you some tips on how to boost your AI lead scoring results!

How AI for a Lead Scoring System Helps Businesses

AI lead scoring is a process that uses machine learning algorithms to sort and prioritize leads. By analyzing data points about each lead, such as their behavior on your website or the type of content they engage with, AI can provide valuable insights about which leads are most likely to convert.

This information can be used to create a score for each lead, which can then be used to prioritize follow-up actions. For example, a sales team may choose to focus their efforts on leads with a high score, while leads with a low score may be contacted less frequently or not at all.

By scoring leads, businesses can ensure that their marketing and sales efforts are being focused in the right areas. A lot of money can be wasted chasing leads that aren’t going to convert. Predictive lead scoring helps the sales team by providing them with qualified prospects that are more likely to do business with them.

In addition to saving time by automating tasks within the lead scoring process, AI can also provide more accurate results than traditional methods. This is because AI takes into account a more significant number of data points and can identify patterns that humans are likely to miss.

AI Uses Machine Learning to Get Better Results

What is machine learning?

Machine learning is a process whereby a computer system “learns” how to do something without being explicitly programmed. This can be done by exposing the system to a large amount of data and allowing it to identify patterns.

There are a variety of purposes that machine learning can be used for, such as making predictions or classifying objects. In the context of lead scoring, machine learning can be used to improve the accuracy of the scores generated by AI.

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How does machine learning help with lead scoring?

Machine learning can help with lead scoring in two ways: first, it can be used to improve the accuracy of the scores generated by AI. Second, it can “teach” AI how to score leads.

To improve the accuracy of AI-generated scores, machine learning can be used to identify patterns in data that are not easily detected by humans.

For example, a lead who regularly visits your website during work hours and reads articles about your product is likely to be further along in the buying process than a lead who only visited your site once. AI can take into account these types of behaviors to provide a more accurate score.

Machine learning is how an AI for a lead scoring system learns and gets better over time. AI is constantly being exposed to new data. It uses this data to improve the accuracy of its predictions.

At the start, the AI system has a large amount of data that has been manually scored by humans. The AI system will then use this data to learn how to generate scores that are similar to those created by humans.

As more data is fed into the system, the AI will continue to learn and improve its ability to generate accurate scores.

How AI Lead Scoring Differs From Traditional Lead Scoring

Now that we’ve discussed how AI for a lead scoring system works and some of its advantages, let’s take a look at the two main types of lead scoring: traditional and predictive.

Traditional lead scoring is a type of lead scoring that’s been used in marketing and sales teams around the world for a long time. In traditional lead scoring, all leads will start at zero points. They’ll only be assigned points based on the actions they take when engaging with your business.

Points may be earned for things like signing up for your company’s newsletter, filling out a form, engaging with you on social media, or downloading an ebook. A member of the sales team will then add up the points and determine if the lead is strong or not. Those deemed good enough will be contacted.

AI for a lead scoring system is a newer type of lead scoring that uses AI to analyze data points and predict which leads are most likely to convert. This method draws from a much more comprehensive range of data points than traditional lead scoring, such as website behavior, social media engagement, demographic information, and more.

Due to the lower risk of human error, AI lead scoring provides more accurate and reliable results. It’s also a lot faster than traditional lead scoring, as it doesn’t require manual input from sales teams.

Advantages of AI Lead Scoring

Businesses are better off using AI lead scoring because having adequate data is critical in today’s business world. Using AI for a lead scoring system is more accurate than traditional lead scoring, and it also saves time. By using AI and automation, sales teams don’t have to waste time qualifying leads manually.

In a matter of seconds, an AI system can scour multiple different data points such as device type, time spent on the page, number of clicks, social media interactions, and more to give you a complete picture of each lead.

This information can then be used to create an AI-powered score for each lead. The AI system will also be able to automatically update the score as new information is gathered, ensuring that your sales team always has the most up-to-date information.

Machine learning is also constantly improving the AI lead scoring process. As more data is gathered, the AI system will be able to identify new patterns and refine its predictions.

A constantly improving lead scoring model is beneficial for capturing time-sensitive strong leads. For example, if you’re selling a new product that’s in high demand, AI lead scoring can help you prioritize the hottest leads so that your sales team can follow up with them immediately.

AI helps with sales decisions

Your salespeople will always have a number of key sales decision moments — points where they can make a decision that will either move them closer to a sale or disqualify a lead. Knowing what these moments are is essential to a successful sales process. AI can help you find them.

Thanks to AI, you can now track the behavior of your leads in detail and identify the decision moments that your sales team needs to focus on. AI can provide valuable insights into the conduct of leads, including:

  • What activities are most likely to lead to a sale
  • What offers are most likely to result in a conversion
  • When is the best time to contact a lead
  • What’s the best way to follow up with a lead

By understanding the decision moments that your sales team needs to focus on, you can use AI to help you improve your lead scoring system and close more deals.

Disadvantages of AI Lead Scoring

There genuinely aren’t many downsides to AI lead scoring. However, one potential drawback is that AI systems require a lot of data to function correctly. If you don’t have enough data, the AI system won’t be able to make accurate predictions.

Machine learning algorithms haven’t been in use for that long in lead scoring. The more data an AI can learn from, the more effective it will be in predicting solid leads. To get the maximum benefit of AI lead scoring, they need to be able to pull data from thousands of data points.

Another potential drawback of AI lead scoring is that it can be challenging to set up and manage. AI systems are complex, and they require some level of expertise to be programmed correctly. Therefore, you can expect to spend some time and money on AI lead scoring setup and management.

Overall, AI lead scoring is a powerful tool that can help businesses close more deals and improve their sales pipeline. While there are some potential drawbacks, the advantages far outweigh them.

What Kind of Data Does AI Lead Scoring Use?

AI lead scoring models are trained using data about your past customers. This data can come from a variety of sources, such as your website analytics, CRM system, or marketing automation platform.

The more data you have about your leads, the better AI will be able to score them. However, AI lead scoring can still be effective even with limited data.

One of the benefits of AI is that it can learn and improve over time. As more data is collected, the AI model will become more accurate at predicting which leads are most likely to convert.

Implicit data

Implicit data is data that is not proactively shared by the customer. It can be inferred from a person’s actions or from the data they provide. AI lead scoring can use implicit data to get a more accurate understanding of a lead’s buying potential.

Here are some examples of implicit data that AI can use for predictive lead scoring:

  • Social media activity: A lead’s social media activity can provide insights into their interests and what they care about. AI can use this information to determine if they are likely to be interested in your product.
  • Engagement with content: AI can analyze a lead’s engagement with your content to determine their level of interest.
  • Online behavior: AI can track a lead’s online behavior to understand their interests.
  • Lead source: The source of a lead can also be used to score them. AI may give a higher score to leads that come from your website than leads that come from a third-party site.

Explicit data

Explicit data is explicitly provided by the customer, such as their name, contact information, or company size. AI can use explicit data to score leads, but it is often more effective when combined with implicit data.

Here are some examples of explicit data that is willingly given by a potential customer:

  • Contact information: A lead’s contact information can be used to score them. AI may provide a higher score to leads with complete contact information than leads with partial information.
  • Company size: AI may give a higher score to large companies than leads from small companies. This is because large companies are usually more likely to have a budget for your product.
  • Location: AI may give a higher score to leads from specific locations. For example, if you sell products that are only available in the United States, AI will assign a higher score to leads from the United States than leads from other countries.

Setting Up A Lead Scoring Model

There’s no standard lead scoring model because every business is unique and will value different data points. For example, a luxury real estate agent will be targeting wealthy individuals, while a non-profit organization will be looking for people with an interest in their cause.

The best way to set up a lead scoring model is to start with the data you have available. AI can use any type of data to score leads, so you should include as much data as possible in your lead scoring model.

Target customer profile

The first step is to create a target customer profile. This will help you identify the characteristics of your ideal customer. You may already have an idea of who your target customer is, but it’s important to create a profile so you can score leads objectively.

Some of the information you’ll want to include in your target customer profile is:

  • Demographic information: age, gender, location
  • Psychographic information: interests, lifestyle, values
  • Firmographic information: company size, industry, job title
  • Behavioral information: buying history, online behavior

Data sources

The next step is to identify the data sources you’ll use to score leads. AI can use any type of data, so you should include as many data sources as possible. Some common data sources for lead scoring are:

  • Website activity: AI can track a lead’s activity on your website, such as the pages they’ve visited and the forms they’ve filled out.
  • Engagement with content: AI can track a lead’s engagement with your content, such as whether they’ve opened your emails or downloaded your white papers.
  • Social media activity: AI can track a lead’s social media activity, such as their tweets, retweets, and likes.
  • Lead source: AI can identify where a lead came from, such as your website, a third-party site, or a trade show.

Determine the score values

Once you’ve identified the data sources you’ll use to score leads, you need to determine the score values. AI uses a numeric system to score leads, with the highest scores indicating the most qualified leads.

The best way to determine score values is to start with a scale of 0-100. Then, assign points based on how important each data point is. For example, if website activity is very important, you may give leads a score of 20 points for every page they visit.

If you’re not sure how to assign points, you can always experiment and adjust the values as needed. The most important thing is to be consistent in how you score leads.

Monitor your results

Once you’ve set up your lead scoring model, it’s essential to monitor your results. AI is constantly learning and evolving, so your lead scoring model will need to be updated on a regular basis.

You should also continue to monitor the data for changing trends. For example, if you notice that leads from a certain location are no longer converting at the same rate, you may need to adjust your lead scoring model.

The best way to monitor your lead scoring model is to set up a dashboard in your CRM that displays your results. This will help you quickly identify any changes that need to be made.

Create a score threshold

Once you’ve set up your lead scoring model, you need to create a score threshold. This is the minimum score that a lead needs to reach in order to be considered qualified.

The best way to determine a score threshold is to experiment and see what works best for your business. You can start with a low threshold and then increase it as needed. It’s better to avoid placing a high threshold earlier on as you may inadvertently miss good leads.

Train the AI model

After you’ve set up your lead scoring model, the next step is to train the AI. This involves feeding the AI with data so that it can learn how to score leads.

There are a few different ways to train the AI, but the most common is to use a training dataset. This is a dataset that contains both lead information and conversion data.

The AI will use this data to learn how to score leads. Follow this process to train your AI model:

  • Collect data: The first step is to collect data about your past customers. You can pull this data from a number of sources, including the analytics for your website or the platforms you use for customer management and marketing.
  • Clean and prepare the data: Once you have collected the data, you need to clean it and prepare it for use in the AI model. This includes removing any invalid or duplicate entries.
  • Split the data into training and testing sets: Next, you need to split the data into two sets: a training set and a testing set. The training set will be used to train the AI model, while the testing set will be used to validate the accuracy of the AI model.
  • Train the AI model: The next step is to train the AI model using the training data. This step will vary depending on which AI platform you’re using. Refer to the instructions for the proper process.
  • Evaluate the AI model: After the AI model has been trained, you need to evaluate its performance using the testing data. This will give you an idea of how accurate the AI model is at predicting which leads are most likely to convert.

Tips to Boost Your AI Lead Scoring

Lead scoring will never stop being necessary to the sales process. As your business grows, you’ll need to find new and innovative ways to score leads. AI can help you to do just that.

Thanks to machine learning, your AI lead scoring system will continue to be honed and refined. This will give you the best possible predictive score for every lead. However, that doesn’t mean you should be complacent. There are always ways to further improve your lead scoring system.

Here are a few tips to help you boost your AI lead scoring:

Capture as much sales activity as possible

It’s worth underlining how important having a comprehensive sales dataset is for an AI lead scoring system. The AI needs data to learn from, and the more data you have, the better.

Make sure you’re capturing as much sales activity as possible — as close to 100% of the available data as possible, even if it racks up the man-hours to compile. In the long run, doing this will save you a ton of time because it will help the AI hand you more qualified leads.

Compare sales performance against the average results

The intent is for an AI lead scoring system to filter out bad leads so that your sales team can focus only on those more likely to convert. If the system is working the way it should, the number of conversions should start to increase.

To help you understand how well your AI is performing, compare your sales performance against the average results. If you’re finding that your close rate is significantly lower than the average, it’s a good indication that your AI needs some fine-tuning.

Continue to monitor the data for changing trends

Even if your AI lead scoring system is working well, it’s important to continue to monitor the data for changing trends. The sales process is constantly evolving, and what works today may not work tomorrow.

By monitoring the data, you can make sure that your AI lead scoring system is always up-to-date and accurate.

Let the sales team access the AI

The sales team is the ultimate user of the AI lead scoring system, so it’s important to let them have access to it. This way, they can see how leads are being scored and provide feedback to help improve the accuracy of the AI model.

The AI system can also help the sales team in their decision-making while they’re pursuing customers. AI can provide valuable next-step suggestions that the sales team can use to their advantage.

By giving the sales team access to the AI, you’re sure to get the most out of your AI lead scoring system.

Routinely clean your data

Cleaning your data is an ongoing task, but it’s one that’s well worth the effort. Leads change over time, and so do their scores. By keeping your data clean, you can be sure that your AI is always working with the most up-to-date information.

This will help ensure that your AI lead scoring system is as accurate as possible. If your data isn’t cleaned every quarter or more, the data will start to become stale, and the AI will be basing its predictions on outdated information.

Conclusion

AI lead scoring is a powerful tool that can help businesses improve their sales process. By using AI, companies can score leads more accurately and save time in the sales process.

If you’re not using AI for your lead scoring system, now is the time to start. Thanks to machine learning, AI systems are only getting better and more accurate. Investing in an AI lead scoring system is an investment that will pay off for years to come.

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