Today’s customers expect a timely, relevant, and personalized experience across every interaction. They have high expectations for when and how companies engage with them—meaning customers want communications on their terms, through their preferred channels, and with personalized, relevant offers. With the right data and analytics capabilities, organizations can deliver an engaging and tailored customer experience (CX) along each point on the customer journey to meet, if not exceed, expectations.

Those capabilities include segment analysis, which analyzes groups of customers who have common characteristics, and predictive analytics, which utilizes data to predict future events, like what action a customer is likely to take. Organizations can improve CX using segment analysis and predictive analytics with the following steps.

Elevating Customer Experiences Starts with Seven Key Steps

Use a Scalable Data Platform

Bringing together the large volumes of data needed to create customer 360-degree profiles and truly understand customers requires a modern and scalable data platform. The platform should easily unify, transform, and orchestrate data pipelines to ensure the organization has all the data needed for accurate and comprehensive analytics—and make the data readily available to the teams that need it. In addition, the platform must be able to perform advanced analytics to deliver the insights necessary to identify and meet customer needs, leading to improved CX.

Integrate the Required Data

Unifying customer data across purchasing history, social media, demographic information, website visits, and other interactions enables the granular analytic insights needed to nurture and influence customer journeys. The insights give businesses and marketers an accurate, real-time view of customers to understand their shopping preferences, purchasing behaviors, product usage, and more to know the customer better. Unified data is essential for a complete and consistent customer experience. A customer data management solution can acquire, store, organize, and analyze customer data for CX and other uses.

Segment Customers into Groups

Customer segmentation allows organizations to optimize market strategies by delivering tailored offers to groups of customers that have specific criteria in common. Criteria can include similar demographics, number of purchases, buying behaviors, product preferences, or other commonalities. For example, a telco can make a custom offer to a customer segment based on the group’s mobile usage habits. Organizations identify the criteria for segmentation, assign customers into groups, give each group a persona, then leverage segment analysis to better understand each group. The analysis helps determine which products and services best match each persona’s needs, which then informs the most appropriate offers and messaging. A modern platform can create personalized offers to a customer segment of just one single person—or any other number of customers.

Predict what each Segment Wants

Elevating CX requires the ability to understand what customers want or need. With predictive analytics, organizations can oftentimes know what a customer wants before the customer does. As a McKinsey article noted, “Designing great customer experiences is getting easier with the rise of predictive analytics.” Companies that know their customers in granular detail can nurture their journeys by predicting their actions, and then proactively delivering timely and relevant next best offers. Predictive analytics can entail artificial intelligence and machine learning to forecast the customer journey and predict a customer’s lifetime value. This helps better understand customer pain points, prioritize high-value customer needs, and identify the interactions that are most rewarding for customers. These details can be leveraged to enhance CX.

Craft the Right Offer

One goal of segment analysis and predictive analytics is to determine the right offer at the right time through the right channel to the right customers. The offer can be recommending a product customers want, a limited time discount on an item they’re likely to buy, giving an exclusive deal on a new product, or providing incentives to sign up for loyalty programs. It’s important to understand each customer’s appetite for offers. Too much and it’s a turn off. Too little and it may result in missed opportunities. Data analytics can help determine the optimal timing and content of offers.

Perform Customer Analytics at Scale

Once customers are segmented into groups and organizations are optimizing data and analytics to create personalized experiences, the next step is to scale analytics across the entire marketing organization. Expanding analytics can lead to hyper-personalization, which uses real-time data and advanced analytics to serve relevant offers to small groups of customers—or even individual customers. Analytics at scale can lead to tailored messaging and offers that improve CX. The analytics also helps organizations identify early indicators of customers at risk of churn so the business can take proactive actions to reengage them.

Continue Analysis for Ongoing CX Improvements

Customer needs, behaviors, and preferences can change over time, which is why continual analysis is needed. Ongoing analysis can identify customer likes and dislikes, uncover drivers of customer satisfaction, and nurture customers along their journeys. Organizations can use data analytics to continually improve CX while strengthening customer loyalty.

Make Data Easily Accessible

To improve CX with data and analytics, organizations need a platform that makes data easy to use and access for everyone. For example, the Avalanche Cloud Data Platform offers enterprise-proven data integration, data management, and analytics in a trusted, flexible, and easy-to-use solution.

The platform unifies all relevant data to create a single, accurate, real-time view of customers. It makes the customer data available to everyone across marketing and the business who needs it to engage customers and improve each customer experience.

Related resources:

6 Predictive Analytics Steps to Reduce Customer Churn

7 Ways Market Basket Analysis Can Make You More Money

How Application Analytics Can Optimize Your Customer Experience Strategy

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