Journey Data

Sometimes we don’t truly understand the value of knowledge until an event awakens our senses. Remember 1998 when Y2K was going to impact systems, and people who were once tagged as out of date legacy programmers were in high demand for their knowledge to ensure those legacy systems were not impacted by the date change? Perfect example of the value of knowledge.  

Last week I experienced that same awakening while meeting with a client to discuss their desire to invest in Journey Analytics. The goal of the meeting was to execute a data discovery session by identifying the most impactful data sources to support their use case. After a short discussion on their infrastructure, the client was quickly guided to the data sources for the project. While it seemed simple to me, the client was amazed at the depth of knowledge around infrastructure components and data sources.

My experience in research and development (AT&T Bell Labs), Unix System sales engineering (AT&T and HP), and call center technology (Genesys, Rockwell and Transera); has provided a wealth of knowledge not anticipated to be leveraged in journey analytics. Add to that past experience, 9 years of reviewing, auditing and validating over 200 unique data sources from telecom, financial services, utilities, insurance companies, retailers and that knowledge base increases exponentially.  I will share this knowledge through a series of posts on journey data to empower more people to be journey data aware.

Let’s start with data classification. Most journey data can be placed into 4 categories:

·        Interaction Data – paths customers take during a journey (web, IVR, agent, retail visit, etc.).

·        Transaction Data – outcome of an interaction journey with a transaction (make a payment, withdrawal funds, purchase an item, etc.).

·        Business Data – impact to the business as a potential result of an interaction or transaction (low NPS score, churn, complaints, etc.).

·        Segmentation Data – attributes about the customer (tenure, products, services, etc.).

These 4 data categories comprise the foundation of journey analytics. This data needs to be connected across all events within a source as well as across sources. The key is to provide enough data where the “data” can guide you to a business objective or outcome through path analysis. Long gone should be the days where a Business Analyst has a hypothesis and validates or invalidates that hypothesis in a test and learn method.

Next post – Finding journey data in the Enterprise