Productivity Powerhouse

What Nobody Tells You About Making Data Work

My journey with data analytics began in the trenches of telecom loyalty programs, where I thought having millions of customer data points meant we had everything figured out. I couldn’t have been more wrong. Managing a loyalty program for 3.2M premium customers at Robi Axiata taught me that data without context is just noise – and sometimes very expensive noise.

I remember our first attempt at preventing customer churn through predictive analytics. We had mountains of data – call patterns, recharge behaviors, data usage, loyalty point redemptions – you name it. Our initial model looked impressive on paper: complex algorithms analyzing multiple touchpoints. Yet we were still losing high-value customers. The wake-up call came when we discovered that our most “engaged” customers, according to traditional metrics were showing subtle signs of dissatisfaction through their changing redemption patterns.

The reality check? Our sophisticated data models hadn’t picked up that customers who suddenly started redeeming all their loyalty points were preparing to leave. This wasn’t showing up in their usage patterns or NPS scores. It took a casual conversation with our customer service team to spot this trend. We had been so focused on the big data metrics that we missed these crucial behavioral signals.

One of our most valuable discoveries came from a failed campaign. We had launched what we thought was a perfectly targeted reward program based on usage patterns. The data suggested offering premium rewards to customers with high data usage during peak hours. The campaign flopped spectacularly. When we dug deeper, we found that our high-data users were price-sensitive young users sharing hotspots, not the affluent professionals we had assumed. This taught me that data patterns don’t always tell the story we think they’re telling.

Here’s what I wish someone had told me earlier: the value of data analytics isn’t in its ability to give you answers but in its power to help you ask better questions. When we implemented our automated documentation workflows, the reduction in reporting effort wasn’t the real win. The real value came from finally having the time to understand what our numbers were telling us about our customers’ behaviors.

Data analytics isn’t about being right all the time; it’s about being less wrong than you were yesterday. And sometimes, the most valuable insight is knowing when to look beyond the data altogether.