Lead Qualification Assessment

Lead qualification can be framed as a Bayesian inference problem. The goal is to estimate the probability that a lead is a viable buyer, P(Buyer|Evidence), given the evidence collected.

Bayes’ Theorem: P(Buyer|Evidence) = [P(Evidence|Buyer) * P(Buyer)] / P(Evidence)

Where:
* P(Buyer|Evidence) is the posterior probability of the lead being a buyer given the evidence.
* P(Evidence|Buyer) is the likelihood of observing the evidence if the lead is a buyer.
* P(Buyer) is the prior probability of a lead being a buyer.
* P(Evidence) is the probability of observing the evidence.

Buyer readiness can be characterized by: Financial Capacity (ability to afford), Intent to Purchase (desire and urgency to buy), Decision-Making Authority (power to make purchasing decisions), and Time Horizon (timeframe within which they intend to purchase). Numerical values (e.g., on a scale of 1-10) can be assigned to each dimension.

Pre-approval involves lender verification of income, credit, and assets.

Loan-to-Value (LTV) Ratio: LTV = (Loan Amount / Appraised Property Value) * 100. A lower LTV indicates lower risk for the lender and better financial standing for the buyer.

Debt-to-Income (DTI) Ratio: DTI = (Total Monthly Debt Payments / Gross Monthly Income). A DTI below 43% is generally considered good.

A/B testing can quantify the impact of pre-approval focus. Group A receives immediate emphasis on pre-approval; Group B does not.

Adapt established psychometric scales (e.g., Likert scales) to assess buyer intent. Examples: “I am actively looking to purchase a home in the next [Timeframe].” (Strongly Disagree - Strongly Agree), “I am highly motivated to find the right property.” (Strongly Disagree - Strongly Agree).

Observe behavioral cues indicative of high intent: Frequent inquiries about property details, willingness to provide detailed information, promptness in responding to communications.

Buyers with a stronger preference for immediate gratification (from time preference theory) are likely to have higher purchase intent.

Identify key influencers who may sway the buyer’s decisions through social network analysis and influence mapping.

Conjoint analysis❓❓ can be used to understand the relative importance of different attributes to different decision-makers.

Utilize survival analysis techniques (including Kaplan-Meier Estimator and Cox Proportional Hazards Model) to predict the time until purchase.

Cox Proportional Hazards Model: h(t) = h0(t) * exp(β1X1 + β2X2 + … + βnXn)

Where:
* h(t) is the hazard rate at time t.
* h0(t) is the baseline hazard rate.
* βi are the coefficients for the predictor variables Xi.

Lead Qualification Score = w1 * (Financial Capacity Score) + w2 * (Intent Score) + w3 * (Decision Authority Score) + w4 * (Time Horizon Score)

Where:
* wi are the weights for each dimension.

Normalize the scores for each dimension to a common scale❓ (e.g., 0-1) before calculating the weighted sum.
* Score_Normalized = (Score_Raw - Score_Min) / (Score_Max - Score_Min)

Example: w1 (Financial Capacity) = 0.4, w2 (Intent) = 0.3, w3 (Decision Authority) = 0.15, w4 (Time Horizon) = 0.15

Implement a reinforcement learning algorithm❓❓ to dynamically adjust the weights assigned to each dimension of buyer readiness.

Continuously A/B test different lead qualification models.

Utilize regularization techniques (L1 or L2 regularization) to prevent overfitting.

Be transparent with leads about the data collected and how it is used.

Adhere to all relevant data privacy regulations (e.g., GDPR, CCPA).

Favor simpler, more interpretable models. shapley values❓❓ can be employed to add to model interpretability.