An independent study reference written by Dr Phuc V. Nguyen. It is not official subject material — for assessment requirements always follow your subject outline and vUWS.
Where analytics is applied
The same handful of moves reappear across every business function. Marketing segments customers, estimates who is about to leave, and attributes response to campaigns. Operations forecasts demand, positions inventory and schedules people. Finance scores credit, detects fraud and models cash. People teams analyse recruitment funnels and retention. Service operations route work and set staffing levels. What these share is a structure, not a technique: a decision that repeats, an outcome that becomes observable soon enough to learn from, and a data trail left by ordinary operations. Where that structure is missing, analytics has little to grip.
Why it matters
Ask three questions about any business problem. Does this decision come round again. Will we find out later whether it went well. Did the process leave a record. Three yes answers and analytics has something to work with. A single choice about entering a new country, with no comparable history and no fast feedback, is still an analytical decision, but it cannot be validated by the repeated pattern of rank, act and measure.
Which of these is least suited to the standard analytics pattern of rank, act and measure?
Formulas
Worked examples
A gym chain with 40,000 members loses about 3 per cent of them each month and wants to know where analytics fits.
At that rate roughly 31 per cent of a cohort is gone within a year, so retention deserves attention. The analytical decision is not to reduce churn. It is which members the retention team contacts in the next seven days, given it can reach about 500. That decision repeats on the same cadence, the outcome is visible within a month, and every entry swipe leaves a record. The chain can rank members by estimated risk, contact the top 500, hold back a comparable group, and measure the difference in cancellations between them.
A council wants to use analytics to decide where to place a new library.
This is a single decision with no repetition and no fast feedback, so the rank, act and measure pattern does not apply. Analytics still helps, in a descriptive role. Population, travel time, existing borrowing patterns and school locations can be mapped and compared across a shortlist of sites. What the council cannot do is validate its choice against outcomes, so the analysis should lay out trade-offs between sites rather than name one optimal answer.
Common mistakes
- ✗Analytics belongs to marketing and finance. Any function making repeated decisions with observable outcomes can use it, including rostering, maintenance, procurement and student support.
- ✗Each function needs its own special methods. The methods travel well. What does not travel is domain knowledge about what the data means, which is why analysts work alongside people who know the process.
- ✗If a business has data, analytics will help it. Analytics needs a decision that repeats and feedback arriving in time to learn from. Holding data creates neither of those on its own.
- ✗Public sector and not-for-profit work is too soft for analytics. Rostering, service demand, waiting lists and outreach targeting have the same repeated structure as commercial problems. The constraint is usually capacity rather than profit.
Revision bullets
- •The same moves recur: segment, forecast, score, rank, route, attribute
- •Analytics needs repetition, observable outcomes and a data trail
- •Methods travel between functions; domain knowledge does not
- •Churn compounds: 3 per cent monthly is about 31 per cent a year
- •One-off decisions still use evidence but cannot be validated against outcomes
Quick check
Which of these is least suited to the standard analytics pattern of rank, act and measure?
A subscription business cuts monthly churn from 3 per cent to 2 per cent. Roughly what happens to the share of a cohort still present after twelve months?
Connected topics
More in What Business Analytics Is
Sources
- Davenport & Harris (2007)Davenport, T. H., & Harris, J. G. Competing on Analytics: The New Science of Winning. Harvard Business School Press, 2007.Surveys analytics applied across marketing, supply chain, finance and human resources within the same organisations.
- Lewis (2003)Lewis, M. Moneyball: The Art of Winning an Unfair Game. W. W. Norton, 2003.A widely read account of a repeated, observable, record-leaving decision being reorganised around measurement.