What Insights Can Business Schools Get from an Elective Demand Estimation Survey?

The short answer: a demand estimation survey asks students, before the bidding round opens, which electives they intend to take. The results tell the programme office which courses will be oversubscribed, which will struggle to fill, how many sections to run, which courses must not share a time slot and how intense the bidding is likely to be. Acted on early, that data produces a timetable built around real demand rather than last year's guesswork.

Every digitised academic process leaves a trail of data. Once elective registration, bidding and Add/Drop run on a platform instead of spreadsheets and email, each cycle produces information the programme office can analyse at the level of an individual student, a programme, or any custom parameter it defines. The demand survey is where that analysis should start, because it is the one point at which the offering can still change.

What is an elective demand estimation survey?

It is a short pre-bidding round in which students indicate the electives they plan to take in the coming term. No seats are allocated and no bid points are spent. The purpose is to measure intent early enough that the programme office can still add a section, cap a course, move a slot or drop an offering before students commit.

Some schools run it as a standalone survey. Others build it into the bidding cycle as a formal demand estimation round. IIM Calcutta runs MBA demand estimation rounds on Registro alongside its bidding and structured Add/Drop rounds, and has delivered 21 term-level elective bidding cycles for 3,000+ students since 2017, with manual effort for the PGP office down 95% and student queries down 99%. Read the IIM Calcutta bidding case study.

Why run a survey before bidding opens?

Because the bidding round is the wrong time to discover demand. By the time bids are in, sections are fixed, rooms are booked and faculty have planned their term. If a popular course has one small section, most of the students who wanted it lose out. If an elective attracts only a handful of bids, it may be cancelled, and the students who chose it have to find other credits at short notice.

A survey moves that discovery a few weeks earlier, when the fixes are still cheap: a room swap instead of a timetable rebuild, an extra section instead of a queue of complaints in the first week of term.

What should the survey ask students?

Keep it short. Students rush through long surveys, and the data suffers. A useful survey covers:

• Intended electives: the courses each student expects to take in the term, up to their credit requirement.
• Strength of preference: a ranking, so the programme office can tell a first choice from a filler.
• Term or trimester: where electives run across multiple terms, which term the student plans to take each one in.
• Concentration or specialisation: where the programme has concentration rules, the track the student is aiming for.
• Workshops: if workshops are registered through the same process, include them.

The combination of courses each student picks matters as much as the individual choices. That combination is what builds the clash matrix.

What does the programme office learn from the results?

Which electives are in high and low demand

The first output is an elective-wise demand estimate. It shows which courses and workshops students want most, and which have low demand and may be candidates for dropping from future offerings.

How many sections and seats to plan

For high-demand electives, the programme office can plan additional sessions and adjust capacity before bidding opens, rather than leaving most interested students without a seat.

Which courses must not clash

A course clash matrix records which pairs of courses students commonly take together. Built from survey data, it tells the scheduler which courses should never share a time slot. NMIMS Mumbai uses Registro to identify high-demand electives and generate clash matrix data automatically ahead of each bidding cycle, and timetable conflicts are detected at the point of bidding rather than after allocation. Across 5 multi-term bidding cycles and 2,500+ students, manual effort fell by 95% and student queries during registration fell by 99%. Read the NMIMS case study.

How intense the bidding will be

Pre-bidding demand parameters also help measure auction intensity: which electives are likely to clear at high bid prices once the bidding round opens. That is useful when briefing students, and when deciding whether a course needs another section after all.

How does demand data shape the timetable?

Demand data, the clash matrix and faculty time-slot preferences are the three inputs of a data-driven timetable. Together they let the scheduler place popular courses in suitably sized rooms at high-availability time slots, keep commonly paired courses apart and still respect when faculty want to teach.

IIM Calcutta built its automated timetabling on exactly these inputs. Historical enrolment data feeds a demand estimation model that predicts course sizes and assigns popular courses to appropriately sized venues. A clash matrix of common course pairings keeps courses frequently taken together out of the same slot. Faculty submit their preferred teaching windows. The result was a 70% reduction in time spent creating timetables, 95% of potential scheduling conflicts resolved by the system and 85% of faculty preferences incorporated into the final schedule. Read the IIM Calcutta timetabling case study.

One lesson from that project applies to every school: data quality comes first. Demand estimates and faculty preferences had to be cleaned and verified before the system could perform well. A survey that half the cohort ignores will mislead the scheduler, so it is worth chasing responses. For the wider timetabling picture, see what timetable scheduling automation is.

What can you track once bidding is over?

The survey is the start of the analysis, not the end. When bidding and Add/Drop run on the same platform, the programme office can also track:

• Credit registration: how successfully students register for the minimum elective credits they need each semester, and who is falling short.
• Waitlists: which electives are likely to build long waitlists.
• Add/Drop: which courses see high drop rates after the first class.
• Demand trends: anonymised bidding outcomes, analysed term after term, show how demand for each elective is moving and how closely it matched the survey.

How do you act on the findings?

1. Add sections for electives where demand clearly exceeds the capacity of one section.
2. Review low-demand electives: merge them, move them to another term, open them to students from other programmes through cross-programme bidding, or drop them.
3. Feed the clash matrix into the timetable before any slots are published.
4. Collect faculty preferences at the same time, so the scheduler has all three inputs together.
5. Brief students on which courses are likely to be competitive, so they plan their bid points with open eyes.
6. Compare survey and bids after the round, and tighten next term's survey where it missed.

Frequently asked questions

How is a demand survey different from a bidding round?

A demand survey measures intent and allocates nothing. A bidding round allocates seats, with students spending a fixed budget of bid points. The survey comes first so that sections, rooms and the timetable can be adjusted before seats are allocated.

When should the demand survey run?

Ahead of each bidding cycle, early enough that sections, room bookings and the timetable can still change. At NMIMS Mumbai, demand and clash matrix data are generated before every bidding cycle.

What is a course clash matrix?

A table of which pairs of courses students commonly take together. The scheduler uses it to keep those courses out of the same time slot.

Can survey data predict clearing prices?

It indicates which electives are likely to clear at high bid prices. The actual clearing prices only emerge in the live round, where Registro gives students clearing-price visibility.

Should the survey run in the same system as bidding?

It helps. When the survey, bidding and timetabling share one platform, demand data flows straight into the clash matrix and the schedule, as it does at NMIMS Mumbai and IIM Calcutta, with no re-keying between spreadsheets.

Related reading

• How real-time demand insights improve elective bidding
• How to conduct elective bidding across multiple programmes
• Registro Timetable Automation
• Registro Course Bidding

To design an elective demand survey and bidding process for your institution, write to us at registro@edtex.in or request a demo.