Traditionally, gathering survey feedback has been the sole approach to understanding customer experience. We now know that survey data alone is inadequate to deliver the business outcomes you seek. A combination of data types is necessary to provide a holistic picture of loyalty, enabling models predictive of behaviors and financials. There are four categories of data to be organized along the customer journey.

Relationship Data
Traditionally surveys have been the sole approach to gathering relational feedback. Relationship surveys measure the customer’s end-to-end journey experience with your brand or organization. This series of events, some very important and some not so important, shape a customer’s perception of the entire relationship. Relationship data enables touchpoint prioritization and journey optimization. Additionally, relationship feedback maps the path of Promoters, Passives and Detractors, enhancing your ability to develop winning customer strategies. The relationship survey format needs to be robust enough to cover Likelihood to Recommend, journey stage experiences, brand reputation drivers and open comments while being concise and relevant to the respondent.

Transactional Data
Transactional surveys provide a real time flow of feedback data on a specific interaction or event. Transactional feedback enables quick service recovery and bottom-up root cause learnings that direct touchpoint optimization. Improvements made can be tested and monitored. However, we do not recommend a transactional survey for every touchpoint along the customer journey as improving the experience of a particular touchpoint may not improve business outcomes. Analysis of the relative impact of a particular touchpoint on NPS informs where to implement transactional feedback measures. The survey format for transactional feedback needs to be short and focused solely on a specific experience.

Operational Data
Operational KPIs aligned with high-impact journey touchpoints are essential elements in building a data architecture to support decision making, including the establishment of minimum performance thresholds by segment/channel. When thinking about KPIs, keep in mind the distinction between internally focused KPIs and customer focused KPIs. Delivery for instance is often a key touchpoint. You may have an internal KPI to track delivery time which measures time from order getting to warehouse to shipment leaving warehouse. However, the only KPIs that may matter from the customer perspective is the time from placing the order to delivery.

Financial Data
Financials are an extremely rich source of data, offering multiple opportunities to engineer desired outcomes for your business. Layering revenue over various customer journeys, e.g. journey by segment, journey by NPS category, journey by account, sharpens your ability to make changes in areas that will deliver the greatest impact on revenue growth and competitive position.

All four data types, when properly calibrated, are the underpinnings of predictive modeling.

Far too many programs stop at the foundational step of collecting NPS scores through surveys. Even those programs that follow the demonstrated best practices in this guide by building out additional sources of attitudinal data usually stop before they take the transformative step of including operational data, which is a critical building block for predictive scoring. A complete, connected, and continuous set of data is a prerequisite for predictive scoring and a visionary NPS program. To get there, download our guide, take our comprehensive training course, or read about the future of NPS.