Customer Engagement Scoring
Customer Engagement Scoring is a method used to quantify and measure the level of interaction and involvement a customer has with a brand or company. It's a crucial metric in modern marketing that helps businesses understand the strength of their customer relationships and predict future behaviors.
1.1 How many ways can we do it and list them out?
There are several approaches to Customer Engagement Scoring:
Rule-based scoring: Assigning points based on predefined actions
Recency, Frequency, Monetary (RFM) analysis
Net Promoter Score (NPS)
Customer Effort Score (CES)
Machine Learning-based scoring:
a. Supervised learning models (e.g., logistic regression, random forests)
b. Unsupervised learning models (e.g., clustering algorithms)
c. Deep learning models (e.g., neural networks)
Time-decay models
Multi-touch attribution models
Sentiment analysis-based scoring
Behavioral segmentation
Engagement mapping
2.0 Pros & Cons
Pros:
Provides quantifiable metrics for customer relationships
Helps in customer segmentation and personalization
Facilitates proactive customer retention strategies
Identifies upsell and cross-sell opportunities
Improves resource allocation in marketing and customer service
Cons:
Can be complex to implement, especially for small businesses
Requires significant data collection and management
May raise privacy concerns if not handled properly
Can become outdated quickly if not regularly updated
Potential for bias if not properly designed and monitored
2.1 In what situations we use this
Customer Engagement Scoring is used in various situations, including:
Customer retention programs
Personalized marketing campaigns
Product development and improvement
Customer support prioritization
Loyalty program management
Sales lead prioritization
Churn prediction and prevention
Customer lifetime value estimation
Marketing ROI assessment
Customer journey optimization
2.2 Key Drivers related to Topic And Business Factors affecting the current topic
Key Drivers:
Customer interaction frequency
Purchase history
Website visits and behavior
Email open and click-through rates
Social media interactions
Customer support interactions
Product usage (for SaaS or digital products)
Referrals and advocacy
Business Factors:
Company size and resources
Industry and competition
Customer base diversity
Product complexity
Sales cycle length
Regulatory environment
Technological infrastructure
Data availability and quality
Organizational culture and data-driven decision-making maturity
3.0 Mathematical Formula:
While there's no one-size-fits-all formula for Customer Engagement Scoring, here's a general approach using a weighted scoring system:
Engagement Score = Σ(wi * vi)
Where: wi = weight of engagement factor i vi = value of engagement factor i
For example, let's consider a simple model with three factors:
Purchase Recency (w1 = 0.4)
Website Visits (w2 = 0.3)
Email Interactions (w3 = 0.3)
Values could be normalized on a scale of 0-100.
Engagement Score = 0.4(Purchase Recency) + 0.3(Website Visits) + 0.3(Email Interactions)
For a more advanced approach using machine learning, you might use logistic regression:
P(Engaged) = 1 / (1 + e^-z)
Where z = b0 + b1x1 + b2x2 + ... + bnxn
b0 is the intercept, bi are the coefficients, and xi are the engagement factors.
Rule-based scoring: Assigning points based on predefined actions
Rule-based scoring is a straightforward method where specific customer actions or behaviors are assigned point values based on their perceived importance to engagement. For example:
Logging into the platform: 5 points
Completing a profile: 10 points
Making a purchase: 20 points
Referring a friend: 15 points
Attending a webinar: 10 points
The total engagement score is the sum of points from all actions. This method is easy to implement and understand but can be less nuanced than more advanced techniques.
Recency, Frequency, Monetary (RFM) analysis
RFM analysis is a method that considers three key aspects of customer behavior:
Recency: How recently did the customer make a purchase or interact with the brand?
Frequency: How often does the customer make purchases or interactions?
Monetary: How much does the customer spend?
Each aspect is typically scored on a scale (e.g., 1-5), and these scores can be combined to create customer segments or an overall engagement score. For example, a customer who purchased recently (5), buys frequently (4), and spends a lot (5) might have an RFM score of 545.
Net Promoter Score (NPS)
NPS measures customer loyalty by asking one key question: "On a scale of 0-10, how likely are you to recommend our product/service to a friend or colleague?" Based on their responses, customers are categorized as:
Promoters (score 9-10): Loyal enthusiasts
Passives (score 7-8): Satisfied but unenthusiastic
Detractors (score 0-6): Unhappy customers
The NPS is calculated by subtracting the percentage of Detractors from the percentage of Promoters. While not a comprehensive engagement measure, it's a widely used indicator of customer satisfaction and loyalty.
Customer Effort Score (CES)
CES measures how much effort a customer has to exert to get an issue resolved, a request fulfilled, or a product purchased. It typically asks customers to rate their agreement with a statement like "The company made it easy for me to handle my issue" on a scale (e.g., 1-7).
A low effort score indicates a smooth, effortless experience, which is associated with higher customer loyalty. CES is particularly useful for evaluating specific interactions or touchpoints in the customer journey.
Machine Learning-based scoring:
a. Supervised learning models (e.g., logistic regression, random forests) Supervised learning models use labeled historical data to predict engagement scores or the likelihood of specific engagement outcomes (e.g., churn, purchase). For example:
Logistic Regression: Can predict the probability of a customer being "engaged" based on various features.
Random Forests: Can handle complex interactions between features and provide feature importance rankings.
These models require a predefined engagement metric as the target variable.
b. Unsupervised learning models (e.g., clustering algorithms) Unsupervised learning models, like K-means clustering, group customers based on similar behavior patterns without predefined labels. This can reveal natural segments of engagement levels. For example, it might identify clusters of "highly engaged," "moderately engaged," and "disengaged" customers based on their behavior patterns.
c. Deep learning models (e.g., neural networks) Neural networks can capture complex, non-linear relationships in customer data. They're particularly useful for large datasets with many features. For engagement scoring, a neural network might take in various customer interactions and behaviors as inputs and output an engagement score or probability.
Time-decay models
Time-decay models assign more weight to recent interactions and less weight to older ones. The principle is that recent behaviors are more indicative of current engagement levels. For example, a login from yesterday might be worth 10 points, while a login from a month ago might only be worth 2 points. The decay rate can be linear, exponential, or follow other mathematical functions depending on the specific use case.
Multi-touch attribution models
These models are used to assess the impact of different marketing touchpoints on customer engagement or conversion. Common types include:
First Touch: Gives all credit to the first interaction
Last Touch: Gives all credit to the last interaction before conversion
Linear: Distributes credit equally across all touchpoints
Time Decay: Gives more credit to touchpoints closer to conversion
U-Shaped: Gives 40% credit each to first and last touch, 20% distributed among middle touchpoints
These can be used to calculate engagement scores by weighing the importance of different interaction types.
Sentiment analysis-based scoring
This method uses natural language processing to analyze the sentiment of customer communications (e.g., support tickets, social media posts, reviews). Sentiment can be categorized as positive, negative, or neutral, and assigned corresponding scores. These scores can then be incorporated into an overall engagement metric, providing insight into the emotional aspect of customer engagement.
Behavioral segmentation
This involves dividing customers into groups based on their behaviors, preferences, and decision-making patterns. For engagement scoring, you might create segments like:
Power Users: Frequent, in-depth product usage
Occasional Users: Sporadic, surface-level engagement
At-risk Users: Declining usage patterns
Each segment would have its own engagement scoring criteria, allowing for more nuanced and context-specific measurement.
Engagement mapping
Engagement mapping involves creating a comprehensive view of all customer touchpoints and interactions across their journey. This might include:
Product usage metrics
Customer service interactions
Marketing campaign responses
Social media interactions
Purchase history
Each interaction is assigned a score based on its perceived importance to engagement. The cumulative score across all touchpoints provides an overall engagement metric. This method provides a holistic view of engagement but requires extensive data integration.
Each of these methods has its strengths and is suited to different business contexts. Many companies use a combination of these approaches to get a comprehensive view of customer engagement. The choice of method(s) depends on factors like available data, business objectives, industry context, and technical resources.