Member data analysis is all about getting a crystal-clear understanding of who your members are at a detailed level through data analysis.
By investing in better data analysis techniques, your organization is committing to collecting and scrutinizing large volumes of data to paint a clear picture of each individual member (their demographics, behaviors, preferences, major life events, etc.) so smarter, targeted actions can be taken.
Using time-tested techniques to analyze member data is the key to building a rock-solid member relationship that maximizes acquisition, on-boarding and retention. In short, superior data analysis helps you:
- Know if you have the right data to answer key action-oriented questions.
- Draw accurate conclusions from your data.
- Informs your decision-making process.
Follow these steps, that we’ll outline over multiple posts, and you’ll be on your way to a successful data analytics strategy where you get to know your data, the right way.
Step 1: Listen to your data.
“Successful analytics” is all about finding patterns and anomalies across the KPIs identified as important to your credit union’s business objectives. Do not try to shape the data into the story you want it to tell. Rather, let the data do the storytelling.
Start with analyzing the “4Cs” of data analytics: Collect, Cleanse, Compute, Consume.
Collect: Eliminate silos of data by collecting data from multiple sources, both internal and external, to create a single source of truth. The Knowlton Group’s VeriCU Data Platform gets your credit union up and running with their industry-leading platform in a matter of days – not months.
Cleanse: Resolve inconsistencies and maximize the accuracy of your data by cleansing, integrating, and relating to data.
Compute: Statistically relevant data points don’t simply exist in your data system – they need to be computed in your data platform. For example, computing an attrition likelihood based on a missed direct deposit or summing total deposits for engaged members to calculate a proxy for monthly income are valuable data points that need to be calculated to exist.
Consume: This step refers to how the information is presented and used. The output results should empower data-driven-decisions and action-lists that integrate directly to your marketing-action-platform to generate a message to a member to then implement the best call to action, timing and message.
Step 2: Identify the best metrics.
If you are looking to improve data analysis, a clear understanding of valuable business metrics is essential.
Vague metrics result in vague results.
Ill-defined metrics rarely align with key business goals meaning your organization is focusing on moving levers that provide no tangible value. Clarity around what actions truly need to be improved and then identifying what metrics need to be tracked for those actions is essential.
In our next post, we’ll complete this five-step list to help your organization improve its data analysis capabilities. Stay tuned…