A customer engagement score is a quantitative measure used by businesses to assess the level of interaction, involvement, and satisfaction of individual customers with your brand, products, or services.
In this article, we’ll be discussing:
- The importance of customer engagement scores.
- Factors that impact customer engagement scores.
- Ways of measuring and optimizing your customer engagement scores.
Let’s get going!
The importance of customer engagement scores
There are several perks to monitoring customer engagement as a customer marketer. This score is an easy way to monitor how customers interact with your brand, improving your understanding of your customer’s preferences, interests, and pain points.
Attributing a numerical value to these customer behaviors makes customer satisfaction and loyalty far easier to measure, allowing for the overall success of customer satisfaction easier to manage.
Customer engagement scores can also be a good way of segmenting your audience. Those with low engagement scores will need different tailoring in your marketing than those with high engagement scores.
And, over time, customer engagement scores can be used as a success metric, like a KPI, to assess the effectiveness of campaigns, identify areas for improvement, and optimize strategies for better results.
Eve Chatfield
Factors contributing to customer engagement scores
Behavioral metrics
A behavioral metric is used to assess how customers interact with a brand or product. These metrics track actions, behaviors, and interactions that customers exhibit across your websites, apps, emails, social media platforms, and more.
This’ll include things like:
- Frequency of interactions
- Time spent
- Recency of interactions
- Depth of interactions
- Conversion rate
- Click-through rate (CTR)
- Social media engagement
Let’s take the example of ‘frequency of interactions.’ This metric measures how often a customer interacts with a brand across the various touchpoints.
Let's say a customer frequently visits your company's website, engages with your social media posts, and regularly opens your marketing emails. In this case, the frequency of interactions indicates a high level of interest and involvement with the brand. As a result, this customer is likely to receive a higher engagement score compared to someone who interacts with the brand less frequently.
Attitudinal metrics
Attitudinal metrics refer to metrics that offer insight into customers' perceptions, feelings, and attitudes toward your brand. They’ll help you understand the emotional connection and loyalty that customers have, which will influence the success of your long-term engagement and retention strategies.
These metrics include:
- Customer satisfaction
- Net promoter score (NPS)
- Customer loyalty
- Brand perception
- Customer sentiment
- Brand affinity
- Purchase intent
If we consider the attitudinal metric, ‘customer satisfaction’, we can glean a bit more. This metric measures the overall satisfaction level of customers with your brand and service.
If a customer completes a satisfaction survey after making a purchase and provides high ratings for product quality, customer service, and overall experience, the customer's positive attitude indicates a high level of engagement and satisfaction.
As a result, this customer is likely to receive a higher engagement score compared to someone who expresses lower satisfaction levels.
Transactional metrics
Transactional metrics are all about your customers' buying behaviors and patterns. They help marketers identify highly engaged customers who are more likely to make repeat purchases, advocate for the brand, and contribute to revenue growth.
This differs from behavioral metrics as these metrics are all about the frequency of actual purchases or other advocacy activities.
Transactional metrics will be things like:
- Purchase frequency
- Average order value (AOV)
- Customer lifetime value (CLV)
- Repeat purchase rate
- Basket size
- Churn rate
- Subscription renewal rate
For an example, let’s look at ‘purchase frequency.’ This metric measures how often a customer makes purchases from the brand within a specific time period.
Consider a scenario where a customer regularly buys products from your brand, making multiple purchases each month. Their frequent transactions indicate a high level of engagement and loyalty.
This, in turn, with result in a higher engagement score compared to someone who makes purchases less frequently or sporadically.
Analyzing and optimizing customer engagement scores
Continually optimizing and analyzing customer engagement scores is crucial if you want to maintain relevance in today's market landscape.
Regularly assessing engagement metrics can enable you the flexibility to adapt your strategies and offerings to the changes occurring within your target audience.
This ongoing optimization and analysis of engagement scores helps identify areas for improvement, optimize resource allocation, and prioritize initiatives that have the greatest impact on customer engagement and business performance.
Let’s look at a few examples of how this analysis and optimization should occur.
Segmentation analysis
Analysis
Utilize analytics tools to segment customers based on their engagement scores and other relevant variables such as demographics, purchase history, and geographic location. Identify high-engagement segments that exhibit consistent patterns of interaction with the brand.
Optimization
Tailor marketing campaigns and communication strategies to target each segment effectively. Implement personalized messaging, offers, and incentives to further enhance engagement and encourage desired behaviors.
Eve Chatfield
Journey mapping and funnel analysis
Analysis
Map out the customer journey from initial interaction to conversion using analytics tools. Identify key touchpoints and stages in the customer funnel where engagement levels may vary.
Optimization
Optimize the customer journey by identifying potential friction points or drop-off areas where engagement scores decrease. Implement improvements such as streamlining the checkout process, optimizing landing pages, or enhancing product recommendations to keep customers engaged and moving through the funnel.
Predictive modeling and forecasting
Analysis
Use advanced analytics techniques such as predictive modeling and machine learning to forecast future engagement levels based on historical data and customer attributes.
Optimization
Leverage predictive insights to proactively identify at-risk customers who may exhibit declining engagement in the future. Implement targeted retention strategies such as personalized re-engagement campaigns or exclusive offers to prevent churn and maintain high engagement levels.
Eve Chatfield
A/B testing and experimentation
Analysis
Conduct A/B tests and experiments using analytics tools to measure the impact of different strategies and interventions on customer engagement scores. Test variables such as messaging, design elements, and promotional offers to identify what resonates most with customers.
Optimization
Use insights from A/B testing to refine marketing strategies and optimize engagement scores over time. Continuously iterate and experiment with new approaches to identify the most effective tactics for driving engagement and achieving business objectives.
Other customer marketing metrics
Want to learn about other KPI metrics that'll help you prove your team successes? Kevin Lau, Senior Director of Global Customer Engagement at F5, shares his insights on metrics that matter, and how to use them to transform your customer programs into strategic assets that impact business growth.
Kevin Lau
