Applying Customer Journey Analytics To Upgrade Your Customer Experience

High-performing companies are 1.7 times more likely to use customer journey analytics than underperformers, according to Pointillist’s 2021 State of Customer Journey Management and CX Measurement Report. This approach enables high performers to optimize customer experience and measure its impact on business outcomes. In this article, we look at the benefits of customer journey analytics and how to use it to upgrade customer experience.

Benefits of Customer Journey Analytics

More and more companies are recognizing the value of customer journey analytics. In fact, Pointillist reports high performing enterprises rank customer journey analytics as the second highest priority on their list of customer experience (CX) investments. Customer journey analytics offers the following benefits:

Customer centricity. The ability to see and analyze customer journeys at scale helps brands orchestrate journeys around customers’ actual needs. Using analytics, teams can identify and correct discrepancies between channels to create unified, consumer-centric brand experiences.

Real-time picture. Customer journey analytics, especially when paired with an omnichannel data collection solution like a customer data platform (CDP), flags issues in customer experience as they happen. A continuous flow of event-level data ensures teams have the most recent snapshot of customer journeys for accurate analysis.

Centralized feedback. Customer journey analytics centralizes direct and indirect feedback in one place. This helps teams correlate customer-provided information (from surveys or interviews) with indirect or inferred feedback (such as engagement rates or social sentiment) to better understand factors that have the most impact on CX.

How To Use Customer Journey Analytics

Customer journey analytics helps businesses quantify the performance of CX initiatives and identify opportunities for improvement. Here are three easy steps for using customer journey analytics:

1. Aggregate Customer Data

First, brands must collect as much customer data as possible and centralize it in one place. Customer data platform solutions are designed to do exactly that: they collect and aggregate customer data into a single environment. CDPs collect data from multiple sources including:

• Brand and e-commerce websites
• Mobile apps
• Social media
• Point-of-Sale systems
• Surveys
• Customer records
• Second- and third-party sources

As a centralized platform, a CDP provides organizations with a single view of customer data analytics for faster activation. Enterprise CDPs also offer the advantage of schemaless ingestion, which expedites analysis and time-to-value.

2. Analyze Customer Behavior and Data

With a rich, centralized customer data foundation in place, brands can analyze the data to uncover insights about customer behavior and experience. CDPs often deploy Machine Learning (ML) techniques to process millions of data points and discover behavioral trends.

A common application is determining how many customers reached a certain stage in their journey and experienced issues that caused them to disengage. In the same way, analytics can determine key decision moments in the journey by analyzing customer behavior and outcomes at specific stages.

Brands can also apply attribution models to customer journeys to analyze how much influence key channels and brand actions have on conversion. Teams may use first-touch, last-touch, W-shaped, or multi-touch attribution models. These models help brands identify high-performing touchpoints and amplify certain actions on specific channels to drive results.

3. Activate Insights To Improve CX

The last step is to activate insights derived from customer journey analytics to improve CX. Once analytics have identified key decision moments, critical channels, or pain points, teams can make data-driven decisions about which CX initiatives to prioritize and invest in.

Brands can also apply insights to tailor experiences to specific audiences. Coupled with ML techniques, teams can use customer journey analytics to segment precise audiences for hyper-personalized, connected customer experiences. For example, customers who prefer to engage with mobile touchpoints can be targeted with optimized mobile experiences or shorter checkout processes on mobile apps.

Lastly, insights derived from customer journey analytics can sharpen the accuracy of recommendation engines. Machine Learning techniques can compare customers’ journeys, evaluate outcomes, and calculate the action with the highest chance of success. Consider a scenario where a customer has viewed a product’s technical details several times. They are further along on the purchasing journey and can be targeted with a product review video that answers common questions. This could resolve lingering doubts and nudge the customer closer to making a purchase decision. Such an approach enables brands to apply Next-best Actions consistently to enhance experience and lead customers to conversion.

In summary, customer journey analytics can improve CX by identifying pain points, key decision moments, and critical factors that affect experience (such as specific channels or brand actions). Applying analytics also allows brands to tailor experiences to specific audience segments and deploy the right activations at the right time. Brands can apply customer journey analytics by aggregating data, analyzing behavior and audience data, and activating insights to upgrade customer experience.

Improve Customer Experience with Treasure Data

Treasure Data’s enterprise-grade customer data platform is trusted by Fortune 500 and Global 2000 companies around the world. We help brands analyze and transform customer journeys to deliver exceptional experiences at the right place and time to the right audiences.

Treasure Data Customer Data Cloud is an integrated suite of cloud-based customer data platform solutions that helps companies improve campaign performance, achieve operational efficiency, and create connected customer experiences. See what you can do with Treasure Data:

• Collect and centralize customer data from all sources
• Unify customer profiles using online + offline data
• Segment audiences by attributes, user behavior, and others
• Analyze customer journeys and derive actionable insights using machine learning techniques
• Personalize customer experience at all customer journey stages
• Predict customer behavior
• Activate insights across channels and touchpoints for a unified brand experience
• And more

To learn more about how Treasure Data Customer Data Cloud helps brands improve customer experience with customer journey analytics, consult an expert today. Want to learn more? Request a demo, call 1.866.899.5386, or contact us for more information.