How to optimize your loyalty program through data and technology: PwC

While not all data fields are useful for each analysis, having more data is often a better option than finding yourself with too little data to effectively understand your program.

• Availability: Data availability varies significantly across loyalty programs. For example, we have observed that larger, more mature programs tend to have direct access to their data (i.e., “in house”) while smaller or more recently launched programs often use an external data vendor to store and manage the data. Some programs have many years of detailed historical customer level data. Other programs may only have access to aggregate data or may only have access to a limited number of years of data due to historical data retention decisions (e.g., if the program structure has changed through a merger or if information technology systems have changed).

Recent improvements in storage capacity and a better understanding of the potential value of data as an asset is pushing programs toward conserving more data for longer periods. The complexities of data management and large storage volumes have led more and more companies to use third party external vendors to manage their data. Programs also have access to cloud storage to allow users to scale up data needs with agility.

While recent data may be more representative of the current realities of a program, the benefits of historical information should be weighed against data and storage costs. Programs rely on historical information to help understand the impact of prior significant events (e.g., macroeconomic shocks, program promotions) and anticipate how future events may impact them. Historical data is also helpful to better understand the long term value of customers for the program.

While having data storage in the right environment is one aspect to availability, another equally critical aspect is having the right personnel to access that data. Anecdotally, Business Intelligence (BI) and IT teams often have many conflicting professional demands, making it difficult to secure nuanced or one-off data requests in a timely fashion. For example, for a one-off breakage analysis where data is sent to a third party consultant, getting the necessary resources who have the requisite coding skillset, knowledge of the data, and ability to transfer data can be an impediment to such analyses. Program managers should be cognizant of that constraint and consider the appropriateness of bringing BI skill sets into their teams to minimize the friction around accessing data when it is needed.

• Quality: While data may be widely available, the quality and usefulness of it varies widely across programs. Ideally, there is a single, easily accessed and well maintained source of truth. In practice, we have observed that detailed data used for program analyses (e.g., breakage analysis) may not always perfectly tie to accounting data. Understanding this situation prior to launching an analysis may take up a disproportionate amount of time or effort, rendering otherwise useful information difficult to use for financial reporting analyses. Other programs may have a single data source, but internal analysts or external data vendors may not sufficiently understand the data which may lend it to be potentially misused. The program’s management team can provide significant insights into possible data issues, which is often critical in the early stages of any statistical analysis. Meaningful analysis can only begin if the data fields are understood and the data has been accurately reconciled and validated.