How to Use Customer Insights Metrics More Effectively
Customer insights metrics are important in their own right, but a customer data platform (CDP) adds more value by using Machine Learning and predictive capabilities to translate metrics into actionable insights. Let’s look at how a CDP uses customer metrics to benefit both brands and their audiences.
What Are Customer Insights Metrics?
Customer insights metrics are quantitative interpretations of consumer behavior, attitudes, habits, and other information as it relates to a specific brand and its initiatives. They are used to guide a brand’s efforts in meeting the needs of its customers. When coupled with a customer data platform’s capabilities, brands can use customer insights to:
Increase Customer Acquisition
Brands use customer insights metrics from digital and mobile analytics to track audience behavior on online platforms including websites, social media, mobile apps, and others. These metrics include:
- Web traffic
- Average sessions
- Average session length
- Number of user pageviews
- Pages most viewed
- Bounce rate
- Mobile screen time
- Device information
Customer Data Platforms can add more value to these customer insights metrics by using them to target new leads. For example, a CDP builds a lookalike audience based on existing customers’ actions on online platforms, which are statistically likely to lead to conversion.
Best-in-breed CDPs also use digital analytics with advanced attribution models to pinpoint which channels and touchpoints contribute the most to customer conversion. With a comprehensive customer journey overview, a CDP is able to orchestrate the right actions (such as a web pop-ups or a limited-time discount) in the right channels (e.g., website, mobile app, email, etc.) for maximum effect.
Enhance Customer Experience
Marketers use customer insights metrics such as Customer Effort Score (CES) to identify pain points in the customer journey. The CES gives marketers a sense of how easy or difficult it is for customers to:
- Buy a product
- Return a product
- Get an answer to an inquiry
- Resolve a complaint
- Reach the right person in customer support
A CDP can take these customer insight metrics and use it to improve the client’s experience. For example, a brand’s CES survey shows that customers are reporting long and complicated customer support transactions. Their CDP can equip support agents with updated client interaction history across different channels so callers don’t have to repeat themselves. The CDP can also recommend the Next-Best Action (NBA) for agents to help them resolve issues and complaints in the most efficient way possible, freeing team members from unnecessary delays and keeping customers happy.
Customer insights metrics from churn and Net Promoter Score (NPS) surveys identify areas for improvement. Sample questions include:
- What are the top three reasons for canceling your account?
- What areas for improvement can you suggest for our product/service?
- On a scale of 0-10, how likely are you to recommend [Brand Name] to your family or friends?
- How likely are you to recommend [product/service] to your co-workers?
Using churn and NPS metrics as a starting point, a CDP can help brands identify pain points in the customer journey through accurate attribution models. Furthermore, the CDP enables early intervention by predicting which users are likely to churn based on similar profiles and historical data. The platform then alerts customer service and support to take action. A CDP further increases effectiveness by using NBA recommendations to specify what steps to take to stop churn in its tracks.
Upsell / Cross-Sell
A CDP can use customer insights metrics from product surveys and layer them with Machine Learning-powered recommendations to boost a company’s bottom line. Product/service satisfaction surveys typically include the following questions:
- How long have you been using our product/service?
- Which product features do you like the most?
- How often do you use our service?
- What would you improve in our product/service?
Based on customer answers and trends revealed by Machine Learning, the CDP can recommend upsell and cross-sell products to shoppers. Sales and marketing teams can also use a CDP to fine-tune recommendations for specific audience segments as necessary.
Create New Products
In the same way, a CDP can take customer insights metrics from product/service surveys to identify new opportunities for brands.
For example, imagine a large portion of a brand’s customer base indicates they would support a more eco-friendly version of their favorite hair care products. A CDP can help determine the segment to which these environmentally-aware customers belong, what characteristics they have in common, on which channels they are most active, what hours they usually respond to offers, and so on.
The brand can then shape a new product offering (a shampoo bar instead of bottled shampoo) to the preferences of its targeted users and offer it to them using the right channels at the right time. This way, the CDP helps brands create and launch new product lines/service offerings to a built-in audience based on customer insights metrics and predictive analytics.
In summary, a CDP adds more value to customer insights metrics by using Machine Learning, audience segmentation, and predictive analytics capabilities to translate metrics into actionable insights. With a CDP, brands can use customer metrics to increase acquisition, enhance experiences, combat churn, find upsell/cross-sell opportunities, and create new products with a built-in target audience in mind. A CDP’s customer data foundation, analytical prowess, and journey orchestration are the keys to leveraging customer metrics to benefit both brands and their customers.
Translate Customer Insights Metrics Into Actionable Insights
Treasure Data helps brands address crucial consumer needs from omnichannel service to real-time journey orchestration. Fortune 500 and Global 2000 companies use our capabilities to transform their businesses and serve customers worldwide. Treasure Data’s capabilities include:
- Multi-Attribution Modeling
- Next-Best-Action (NBA) Recommendation System
- Machine Learning
- Predictive Profile and Scoring
- And more
Treasure Data takes customer insights metrics and turns them into actionable insights that drive revenue and build customer relationships.