AI-Powered Customer Insights

We as humans are not great predictors of our own behavior. What we do is often contrary to what we say we do and we rarely can predict our own future choices. So how can as designers and business leaders truly know our customers when common biases cloud our feedback loops? The good news is that service data can help us fill in some of these research gaps.

Customer serving teams often sit on a gold mine of data. When tapped, daily communications from customers offer a view into their true sentiments and behavioral patterns. Research and Design teams can leverage AI to use this data to meet unspoken customer needs and positively influence engagement behaviors for better customer experiences and business outcomes.

AI-Powered Customer Insights: Maximizing Retention with Actionable Data

Company: Farfetch, Global Luxury Marketplace & Saas Platform

Role: Senior Global Director, Service Design

Focus: Data, AI, Voice of the Customer, Journey Metrics

Customer: B2C, B2B2C, Enterprise

Industry: Tech/ eCommerce Platform/ Digital Marketplace

The Customer Experience Index

Nominated for the AIconics AI Innovator of the Year Award in 2022

Business Challenge:

As the company was about to IPO in 2018, it faced several challenges in the customer experience. One being that parts of the customer journey were completely opaque. There was no data or feedback to support decision-making for or to measure impact to the post-purchase customer experience. The purchase journey could be measured with onsite metrics, but post purchase for e-commerce occurs largely off screen and while the onsite journey is absolutely critical to customer acquisition and conversion, post purchase is where the customer promise is fulfilled, directly impacting customer retention. Riding blind at this critical phase was proving to be costly in more ways than one.

The Opportunity:

Fortunately, data existed that could shed light on post-purchase, it just hadn’t been tapped. My team of expert data scientists and analysts were able to change that. Starting manually to build the concept for a post-purchase journey metric, we dug into Customer Service data to find patterns that aligned to volumes of contacts along critical customer journey moments, then read hundred of emails to evaluate the general sentiment and anxiety levels of each of those moments. Even in it’s early manual phases, we were able to identify critical friction points and root causes to surface highly actionable insights for strategic decision making in the post-purchase journey. The reports were instantly adopted by company executives to invest in customer self-service and customer service Robotic Process Automation initiatives. This early research served as the foundation of the Customer Experience Index.

Data was sporadic and hard to access. Sources were spread across the organization. Decisions were made without customer input. CX suffered as a result.

Important customer feedback knowledge was held within Customer Service teams as a compilation of individual experiences, making it hard to extract patterns or validate.

Customer verbatims were researched manually to find patterns and root causes of negative customer sentiment. Human-power was limited and reports rolled out slowly.

Machine Learning and Sentiment at Scale:

In partnership with the Marketing department and an external software partner, we were able to scale this research and further develop use cases that would continue to shed light not only on the post purchase journey, but the total customer lifecycle. Leveraging AI, the team the software to read customer sentiment in the thousands of weekly emails as well as comments from customer surveys - offering real time insights into the holistic customer experience.

Expanded global view.

We were able to pull data from across global regions, reading feedback in 12 customer languages, allowing us to identify regional customer patterns and compare insights to reveal cultural nuances.

Surfaced actionable insights.

We taught the machine to read customer sentiment and identify patterns that led to root cause identification. These root causes enabled business action for continuous improvement and transformative business initiatives.

Increased pace of change.

It expanded human workforce capacity, capabilities in pattern recognition. We could produce more reports more quickly, to respond to customer expectations and behavioral changes.

Customer feedback languages monitored for global insights.

Customer interactions monitored for sentiment each week.

Business cases supported for customer-centric strategies & business initiatives.

Development of the “Customer Experience Index (CXI)”

Leveraging emerging technologies, we continued to evolve metrics that would support understanding of the customer’s experience and behaviors. The CXI was developed as a way to better understand the impacts of our business’ performance on the customer behavioral response (conversion, cancellations, repeat purchasing and retention). By linking the service KPIs to the customer sentiment and contact volume data, we were able to monitor the customer journey moments, identifying which moments could make or break our relationships, to make better business decisions that would ultimately drive repurchase and retention.

This research and metric development led to the nomination of my direct report, Senior Head of Voice of the Customer, for the AIconics AI Innovator of the Year award in 2022 for he and the team’s brilliant and ground-breaking work.