Lead Scoring

Chapter: Classifying Potential Customers

Introduction
Classifying potential customers is a crucial step in converting them into successful deals. Instead of treating all potential customers the same way, classification allows us to focus our efforts and resources on those with the highest probability of becoming actual customers. This chapter covers the scientific and theoretical principles of classifying potential customers and explores the techniques and strategies used to evaluate and prioritize them.

Importance of Classifying Potential Customers

• Improve Resource Allocation: Allocate time, effort, and financial resources effectively to potential customers who have the highest probability of conversion.
• Increase Conversion Rate: By focusing on high-quality potential customers, the conversion rate from potential customer to actual customer can be improved.
• Reduce Marketing and Sales Costs: Avoid wasting time and resources on potential customers who are unlikely to convert.
• Improve Customer Satisfaction: By better understanding the needs and desires of potential customers, customized solutions can be provided that increase their satisfaction.

Theories and Scientific Principles of Classifying Potential Customers

• Probability Theory: Used to estimate the probability of converting a potential customer based on historical data and current information.
  • Example: If we have historical data showing that potential customers who interact with specific content on the website have a higher conversion rate, we can use this data to estimate the probability of converting a new potential customer who interacts with the same content.
• Cluster Analysis: Used to group potential customers into clusters based on their common characteristics, allowing the development of customized marketing and sales strategies for each cluster.
  • Example: Potential customers can be grouped based on demographics, purchasing behavior, and interests, then targeted marketing messages can be developed for each group.
• Regression Analysis: Used to identify the factors that significantly affect the probability of converting a potential customer, helping to prioritize the most important potential customers.
  • Example: Regression analysis can be used to determine that company size, industry, and buyer role are important factors in determining the probability of converting a potential customer.
• Scoring Model: Points are assigned to potential customers based❓❓ on a set of criteria, such as demographics, behavior, and engagement, then they are ranked based on their scores.
  • Example: Points can be assigned to potential customers who have downloaded an e-book, visited specific pages on the website, or attended a webinar.

Criteria for Classifying Potential Customers

• Fit: The extent to which the potential customer matches the target market for the product or service.
  • Does the potential customer fall within the targeted geographic area?
  • Does the potential customer operate in the targeted industry?
  • Does the potential customer have the targeted company size?
• Interest: The level of interest the potential customer has in the product or service.
  • Has the potential customer interacted with marketing content?
  • Has the potential customer visited the company’s website?
  • Has the potential customer requested additional information?
• Intent: The extent to which the potential customer is ready to make a purchase.
  • Has the potential customer requested a price quote?
  • Has the potential customer requested a free trial?
  • Has the potential customer spoken with a sales representative?
• Budget: Does the potential customer have the necessary budget to purchase the product or service?
  • Has the potential customer mentioned a specific budget?
  • Can the company afford the cost of the product or service?
• Authority: Does the potential customer have the authority to make the purchase decision?
  • Is the potential customer the decision-maker?
  • Can the potential customer influence the purchase decision?
• Timing: Is the potential customer ready to buy now?
  • Does the potential customer have an urgent need?
  • Does the potential customer have a specific timeline for purchasing?

Techniques for Classifying Potential Customers

• Classification Matrix: Used to evaluate potential customers based on multiple criteria and classify them into different categories based on their performance.
  • Example: The classification matrix can be used to evaluate potential customers based on fit, interest, and intent.
• Customer Relationship Management (CRM) Systems: Used to track potential customer information and activities, automate the classification process, and provide insights into the most valuable potential customers.
• Marketing Automation Tools: Used to track the behavior of potential customers on the website and in email messages, assign points❓ to them based on their behavior, and classify them automatically.
• Artificial Intelligence and Machine Learning (AI and Machine Learning): Used to analyze historical data and predict the probability of converting potential customers, automate the classification process, and provide insights into the most valuable potential customers.
  • Machine learning algorithms can be used to train a potential customer classification model using historical data, then use this model to classify new potential customers.

Equations and Mathematical Formulas (as applicable)

• Calculating Potential Customer Scores:
  • Lead Score = (Weight1 * Metric1) + (Weight2 * Metric2) + ... + (WeightN * MetricN)
  • Where:
    • Lead Score: Total score of the potential customer.
    • WeightN: Weight of criterion N (reflects its importance).
    • MetricN: Value of criterion N for the potential customer.

Practical Examples and Related Experiences

• Case Study: A software company implemented a potential customer classification system based on their behavior on the website and in email messages. After implementing the system, the conversion rate increased by 20% and marketing costs decreased by 15%.
• A/B Testing: Different email messages were tested for potential customers in different categories. It was discovered that an email message customized for potential customers in the targeted industry led to a 30% higher response rate compared to a general email message.

Classifying potential customers based on leads❓❓ per source and ratio of leads to closed business

• Analyzing Lead Sources: The sources from which potential customers come should be analyzed to assess their effectiveness. Sources that generate highly qualified potential customers should be prioritized in marketing efforts.
• Measuring Conversion Rate: Track the proportion of potential customers who convert into successful deals for each source. This ratio provides a clear indicator of the quality of potential customers coming from each source.
• Example: If advertising via search engines generates a large number of potential customers but the conversion rate is low, while referrals generate fewer potential customers but the conversion rate is much higher, this indicates that referrals are a more valuable source.

Potential Customers to Avoid

• Unqualified Leads: Customers who do not match the target market or do not have sufficient budget or decision-making authority.
• Negative Leads: Customers who have a negative experience with the company or product or service.
• Competitors: Customers who work for competing companies.
• Information Seekers: Customers who are only looking for information and have no intention of buying.

Next Steps

• After classifying potential customers, a customized sales and marketing strategy should be developed for each category.
• High-quality potential customers should be followed up with immediately and regularly.
• Customized content should be provided to potential customers based on their needs and interests.
• The results of potential customer classification efforts should be measured regularly and adjustments made as needed to improve performance.

Conclusion
Classifying potential customers is a vital process for converting potential customers into successful deals. By understanding the theories and scientific principles of classifying potential customers, and using the appropriate techniques and tools, companies can improve resource allocation, increase the conversion rate, reduce costs, and improve customer satisfaction.