High-Yield Referral Networks

5.1.1 The Science of social❓ Networks: Graph Theory and Network Analysis

• Definition 5.1.1.1: A graph G = (V, E) consists of a set of vertices V (representing individuals or entities) and a set of edges E (representing relationships or connections between vertices).
• Degree Centrality (Cd): The number of connections a node has. Cd(i) = ∑j aij, where aij = 1 if node i is connected to node j, and 0 otherwise.
• Betweenness Centrality (Cb): The number of times a node lies on the shortest path between two other nodes. Cb(i) = ∑j<k g jk(i) / g jk, where gjk is the number of shortest paths between nodes j and k, and gjk(i) is the number of those paths that pass through node i.
• Closeness Centrality (Cc): The average distance from a node to all other nodes in the network. Cc(i) = (|V|-1) / ∑j dij, where dij is the shortest distance between nodes i and j.
• Eigenvector Centrality (Ce): Measures the influence of a node in a network.

Experiment 5.1.1.1: Referral Probability and Network Centrality

• Hypothesis: Individuals with higher centrality measures (degree, betweenness, closeness, and eigenvector) are more likely to generate referrals.

5.1.2 The Psychology of Referrals: Social Influence and Reciprocity

• Definition 5.1.2.1.1: Social influence refers to the process by which an individual’s thoughts, feelings, attitudes, or behaviors are affected by other people.
• Conformity: Individuals tend to adopt behaviors and attitudes that are consistent with social norms and the actions of others in their social group.
• Compliance: Individuals are more likely to agree to a request when it comes from someone they like or trust.
• Authority: People tend to obey authority figures, even if the request conflicts with their personal beliefs.
• Definition 5.1.2.2.1: Reciprocity is a social norm where individuals respond to a positive action with another positive action, rewarding kindness with kindness.
• Mathematical model of reciprocity: R(a, b) = f(Benefit(b, a), Cost(a, b), RelationshipStrength(a, b))
  • Benefit(b, a): Perceived benefit received by individual b from individual a.
  • Cost(a, b): Perceived cost incurred by individual a to provide the benefit to individual b.
  • RelationshipStrength(a, b): Strength of the relationship between individuals a and b.
  • f: a function that maps the inputs to a level of reciprocity.

Experiment 5.1.2.1: Impact of Gift Giving on Referral Rates

• Hypothesis: Providing a small, unexpected gift to clients increases referral rates due to the principle of reciprocity.

5.1.3 The Economics of Referrals: Incentive Structures and Game Theory

• Definition 5.1.3.1.1: Incentive theory posits that individuals are motivated to act based on the perceived rewards or consequences associated with their actions.
• Definition 5.1.3.1.2: Game theory is a mathematical framework for analyzing strategic interactions between rational decision-makers.
• Mathematical model of Incentive effect: U(i) = B(i) – C(i) + I(i)
  • B(i) = Benefit derived by individual i from engaging in an activity.
  • C(i) = Cost incurred by individual i from engaging in the activity.
  • I(i) = Incentive provided to individual i for engaging in the activity.
  • Individuals will engage in the activity if U(i) > 0.

Experiment 5.1.3.1: The Effect of Incentive Magnitude on Referral Volume

• Hypothesis: Increasing the monetary incentive for referrals leads to a higher volume of referrals, but with diminishing returns.

5.2.1 Identifying and Segmenting Referral Sources

• Definition 5.2.1.1.1: Segmentation involves dividing a population into distinct groups based on shared characteristics.
• Definition 5.2.1.2.1: Predictive modeling involves using statistical techniques to predict future outcomes based on past data.

5.2.2 Building and Maintaining Relationships

• Definition 5.2.2.1.1: Relationship marketing focuses on building long-term relationships with customers rather than focusing on individual transactions.
• Definition 5.2.2.2.1: Social exchange theory posits that relationships are formed and maintained based on a cost-benefit analysis.

5.2.3 Systematizing the Referral Process

• Definition 5.2.3.1.1: A system for tracking referrals from initial contact to successful closing.
• Definition 5.2.3.2.1: A systematic approach to requesting referrals from clients.

Experiment 5.2.3.1: The impact of timing and personalization on referral conversion rates

• Hypothesis: Personalizing referral requests and timing them strategically (e.g., one month after closing) increases the referral conversion rate.