The Problem
The parent company sold its banking customers a single, commoditized product and had little relationship beyond it. It wanted to change that, by deepening those relationships into a broad data, analytics, and marketing-services business that could become significant over the long term. It already owned a marketing-services firm with a large pool of data, and that firm was the natural place to start. The questions it brought were what the firm's real assets were, whether a new business could be launched from its data, and whether the firm was the right foundation for the larger ambition.
The answer was not a given. The data pool was large in volume, but its value for analytics was unproven. The firm's way of working was people-intensive and custom to each client, and serving a large number of small banks at a profit had never been tested. Before committing to build a business around the firm, the parent needed a clear-eyed read on what the data was actually worth and what a business at scale would require.
The Results
The work gave the parent that read. A new business was buildable and the firm was a workable place to start, but only with a focused approach and a sober view of the data.
The first finding was about the data the parent already prized. The large internal pool was broad but shallow, covering many accounts at a surface level and few in any depth, so a new business could not lean on it. The leverage sat elsewhere. A bank's core system holds between 1,500 and 2,500 fields, and pulling transaction-level data out of it is slow and costly, enough to undermine the economics. Banks' month-end summary files are a different matter. They hold a few hundred fields, a bank can upload them to a secure site with little effort, and within them roughly fifty fields describe most of the customer behavior that marketing applications need. That smallest useful set was the key to a business that could scale.
From there the work set out what building the business would take. It mapped what banks needed, finding cross-sell targets, choosing channels, and proving marketing ROI, against what the firm and parent actually held, and recommended a deliberately simple model: a "data supply chain" to access, transform, and analyze client data cheaply and repeatably, standardized solutions instead of custom ones, the small data set as the way in so banks could adopt with little effort, and clear value propositions and target segments to manage against. The owned firm could anchor this, but its custom, people-intensive model had to give way to something built for scale. That defined what it would take to turn the firm into the core of the broader business the parent wanted, and the economics that would decide whether it became a real product line or stayed a consulting one.
Key Techniques
- An inventory of the firm's and parent's data assets, organized into a common taxonomy to judge their real analytic value and whether pooling them helped.
- A needs assessment across the customer lifecycle, from designing campaigns to measuring them, to define what banks were actually trying to do.
- A gap analysis that tested each gap on three counts: whether it was present, whether it mattered, and whether it could be closed by combining the parent's data or adding external data.
- Minimum-data-set identification, finding the smallest set of fields that supports the largest number of marketing applications.
- Secondary research on bank marketing priorities and budgets, used to ground the demand side in evidence.