MEDIA & ENTERTAINMENT

Movie Theatre vs Home Viewing Preference Survey

Quantify how general audiences evaluate ticket pricing, content availability, and viewing comfort when choosing between theatres and home platforms, so you can sharpen acquisition targeting, refine pricing tiers, and fix retention gaps across both channels.

Pan-India sample
General audiences (Active Moviegoers, 18-45)
15-20 min
Talk to a Survey Consultant
Channel conversion gapsIdentify which friction points push audiences from theatres to home platforms.
Pricing & WTP signalsBenchmark willingness-to-pay thresholds across ticket tiers and subscription plans.
TRUSTED BY LEADING BRANDS
Brand 0Brand 1Brand 2Brand 3Brand 4Brand 5Brand 6Brand 7Brand 8Brand 9Brand 10Brand 11Brand 12Brand 13Brand 14Brand 15Brand 16Brand 17Brand 18Brand 19Brand 20Brand 21Brand 22Brand 23Brand 24Brand 25Brand 26Brand 27Brand 28Brand 29Brand 30Brand 31

CONTEXT & RELEVANCE

Why run this survey now

Most exhibitors and streaming executives don't lose audiences purely on ticket price or subscription cost. They lose them due to shifting occasion triggers, screen quality expectations, social viewing habits, content release timing, and comfort trade-offs, none of which fully show up in box office reports or platform engagement dashboards.

If you are...

  • Theatre chain or multiplex operator
  • Streaming platform content head
  • Studio theatrical vs digital release planner
  • Cinema advertising revenue lead
  • OTT subscriber acquisition strategist

You're likely facing...

  • Occasion confusion: event film vs everyday viewing
  • Footfall drop: post-release window shrinkage
  • Theatres = premium/infrequent perception
  • Streaming = convenient/low-stakes perception
  • Hybrid audience: splits spend across both

This will help answer...

  • Primary occasion drivers by format
  • Switching triggers: theatre to home
  • Segment preference by genre and age
  • Willingness to pay: premium vs convenience
  • Loyalty signals and repeat visit intent

RESEARCH THEMES

What This Survey Investigates

Eight interconnected research themes that map the complete viewer journey from content discovery to post-screening loyalty.

TENETS 01

Discovery & Triggers

  • Content discovery channels used
  • Decision triggers by format type
TENETS 02

Preference Drivers

  • Theatre vs. home viewing motivators
  • Genre-specific format preferences
TENETS 03

Experience & Atmosphere

  • In-theatre sensory experience ratings
  • Home setup quality benchmarks
TENETS 04

Pricing & Spend

  • Ticket price sensitivity by segment
  • Streaming subscription spend benchmarks
TENETS 05

Friction & Barriers

  • Drop-off points in theatre visit planning
  • Home viewing friction by household type
TENETS 06

Social & Occasion

  • Group viewing occasion frequency
  • Solo vs. social format split
TENETS 07

Streaming & Loyalty

  • Platform stickiness by content type
  • Theatre loyalty program engagement
TENETS 08

Format & Future

  • Premium format adoption intent
  • Hybrid release window preferences

SAMPLING STRATEGY

Tell us about your ideal sample

Help us understand your target respondent profile. Select what applies, we'll design the optimal sample plan based on your inputs.

Sample size
How many respondents do you need?
Not Selected
Target audience
Who should we survey?
Not Selected
Region
Which regions should we cover?
Not Selected
Segments
How should we slice the data?
Not Selected
Discuss sample plan

METHODOLOGY

Survey approach

For the Movie Theatre vs Home Viewing Preference Survey, we recommend a quant-first design with flexible data-collection modes to balance reach, depth, and verification across viewer segments.

PRIMARY
Online web surveySelf-administered survey shared via email / panels to capture structured responses at scale.
Best for
1
Ranking theatre vs home viewing preference drivers
2
Mapping spend patterns by genre and format
3
Comparing segments by age, city tier, and income
Deliverables
Preference driver ranking
Segment preference matrix
Occasion frequency bands
OPTIONAL
CATI (phone survey)Interviewer-led telephone interviews to reach owners who are harder to get online.
Best for
1
Older or low-digital viewers in smaller cities
2
Quick coverage across Tier 2 and Tier 3 markets
Deliverables
Tier-wise viewer coverage
Call-log diagnostics
SELECTIVE
Face-to-faceOn-ground surveys or interviews in key industrial clusters or high-value cohorts.
Best for
1
High-frequency moviegoers at multiplex locations
2
Households with premium streaming subscriptions
Deliverables
Cohort viewing maps
Occasion context notes
OPTIONAL
FGDs
Deliverables
Themes and quotes
Concept feedback
OPTIONAL
Mixed surveysAny 4-mode combo Online + CATI + F2F + FGDs to maximise reach and representation. Mode-specific quotas and weighting for clean comparisons.
Deliverables
Unified dataset
Mode-adjusted analytics
Our Recommendation
Start with: Online web survey as the core quant layer, targeting streaming subscribers and multiplex visitors across metro and Tier 1 cities, supported by CATI for Tier 2 and Tier 3 viewer segments with lower digital access.
Consider adding: Face-to-face intercepts at high-footfall multiplexes for high-frequency moviegoers, and a focused FGD layer to isolate the social and emotional triggers that drive theatre attendance decisions.

EXECUTION PROCESS

How we execute

A proven 9-step process from scoping to delivery, designed to ensure quality, speed, and actionable insights.

Define the decision frame

Confirm objectives, target cohorts, geographies, and reporting cuts

Step 01

Define the decision frame

Design the instrument

Build workstream modules mapped to outputs (drivers, friction, pricing, retention, trust)

Step 02

Design the instrument

Lock the questionnaire

Review wording, sequencing, LOI, and competitive context; approve final version

Step 03

Lock the questionnaire

Pilot and calibrate

Test comprehension and ease quality; refine quotas and remove friction where needed

Step 04

Pilot and calibrate

Run fieldwork

Execute collection with active quota management and feasibility controls

Step 05

Run fieldwork

Assure quality

Dedupe, attention checks, speed/consistency rules, removals with audit trail

Step 06

Assure quality

Prepare the dataset

Clean data and deliver codebook/variable definitions

Step 07

Prepare the dataset

Analyse and synthesise

Driver ranking, leakage diagnostics, pricing bands, segment insights

Step 08

Analyse and synthesise

Deliver and align

Executive deck (optional dashboard) and leadership readout with recommendations

Step 09

Deliver and align

COMMERCIAL TERMS

Request a Commercial Proposal

Pricing depends on cohort, geography, sample size, approach, LOI, and deliverables. Configure below for an indicative estimate.

Select Sample Size

100

Geography

  • India
  • APAC (Singapore, Vietnam, Philippines, Indonesia, Australia, NZ, Japan, Thailand)
  • Middle East (UAE, KSA, Qatar, Bahrain, Oman, Kuwait)
  • North America (US, Canada)
  • Europe
  • Africa (South Africa, Kenya, Nigeria, Egypt, Algeria)
  • LATAM (Brazil, Mexico)

Select Mode of Survey

  • Online
  • CATI
  • Online FGD (5 people per FGD)
  • F2F

Length of the Interview

  • Select
  • 0-15
  • 16-20
  • 21-30
  • 31-45
  • 46-60
  • Custom
Indicative Estimate
  • Indian Rupee (INR)
  • United Arab Emirates Dirham (AED)
  • Afghan Afghani (AFN)
  • Albanian Lek (ALL)
  • Armenian Dram (AMD)
  • Netherlands Antillean Guilder (ANG)
  • Angolan Kwanza (AOA)
  • Argentine Peso (ARS)
  • Australian Dollar (AUD)
  • Aruban Florin (AWG)
  • Azerbaijani Manat (AZN)
  • Bosnia-Herzegovina Convertible Mark (BAM)
  • Barbadian Dollar (BBD)
  • Bangladeshi Taka (BDT)
  • Bulgarian Lev (BGN)
  • Bahraini Dinar (BHD)
  • Burundian Franc (BIF)
  • Bermudian Dollar (BMD)
  • Brunei Dollar (BND)
  • Bolivian Boliviano (BOB)
  • Brazilian Real (BRL)
  • Bahamian Dollar (BSD)
  • Bhutanese Ngultrum (BTN)
  • Botswana Pula (BWP)
  • Belarusian Ruble (BYN)
  • Belize Dollar (BZD)
  • Canadian Dollar (CAD)
  • Congolese Franc (CDF)
  • Swiss Franc (CHF)
  • Chilean Peso (CLP)
  • Chinese Yuan (CNY)
  • Colombian Peso (COP)
  • Costa Rican Colón (CRC)
  • Cuban Peso (CUP)
  • Cape Verdean Escudo (CVE)
  • Czech Koruna (CZK)
  • Djiboutian Franc (DJF)
  • Danish Krone (DKK)
  • Dominican Peso (DOP)
  • Algerian Dinar (DZD)
  • Egyptian Pound (EGP)
  • Eritrean Nakfa (ERN)
  • Ethiopian Birr (ETB)
  • Euro (EUR)
  • Fijian Dollar (FJD)
  • Falkland Islands Pound (FKP)
  • British Pound (GBP)
  • Georgian Lari (GEL)
  • Ghanaian Cedi (GHS)
  • Gibraltar Pound (GIP)
  • Gambian Dalasi (GMD)
  • Guinean Franc (GNF)
  • Guatemalan Quetzal (GTQ)
  • Guyanese Dollar (GYD)
  • Hong Kong Dollar (HKD)
  • Honduran Lempira (HNL)
  • Croatian Kuna (HRK)
  • Haitian Gourde (HTG)
  • Hungarian Forint (HUF)
  • Indonesian Rupiah (IDR)
  • Israeli New Shekel (ILS)
  • Iraqi Dinar (IQD)
  • Iranian Rial (IRR)
  • Icelandic Króna (ISK)
  • Jamaican Dollar (JMD)
  • Jordanian Dinar (JOD)
  • Japanese Yen (JPY)
  • Kenyan Shilling (KES)
  • Kyrgyzstani Som (KGS)
  • Cambodian Riel (KHR)
  • Comorian Franc (KMF)
  • South Korean Won (KRW)
  • Kuwaiti Dinar (KWD)
  • Cayman Islands Dollar (KYD)
  • Kazakhstani Tenge (KZT)
  • Lao Kip (LAK)
  • Lebanese Pound (LBP)
  • Sri Lankan Rupee (LKR)
  • Liberian Dollar (LRD)
  • Lesotho Loti (LSL)
  • Libyan Dinar (LYD)
  • Moroccan Dirham (MAD)
  • Moldovan Leu (MDL)
  • Malagasy Ariary (MGA)
  • Macedonian Denar (MKD)
  • Burmese Kyat (MMK)
  • Mongolian Tögrög (MNT)
  • Macanese Pataca (MOP)
  • Mauritian Rupee (MUR)
  • Maldivian Rufiyaa (MVR)
  • Malawian Kwacha (MWK)
  • Mexican Peso (MXN)
  • Malaysian Ringgit (MYR)
  • Mozambican Metical (MZN)
  • Namibian Dollar (NAD)
  • Nigerian Naira (NGN)
  • Nicaraguan Córdoba (NIO)
  • Norwegian Krone (NOK)
  • Nepalese Rupee (NPR)
  • New Zealand Dollar (NZD)
  • Omani Rial (OMR)
  • Panamanian Balboa (PAB)
  • Peruvian Sol (PEN)
  • Papua New Guinean Kina (PGK)
  • Philippine Peso (PHP)
  • Pakistani Rupee (PKR)
  • Polish Złoty (PLN)
  • Paraguayan Guaraní (PYG)
  • Qatari Riyal (QAR)
  • Romanian Leu (RON)
  • Serbian Dinar (RSD)
  • Russian Ruble (RUB)
  • Rwandan Franc (RWF)
  • Saudi Riyal (SAR)
  • Solomon Islands Dollar (SBD)
  • Seychellois Rupee (SCR)
  • Sudanese Pound (SDG)
  • Swedish Krona (SEK)
  • Singapore Dollar (SGD)
  • Saint Helena Pound (SHP)
  • Sierra Leonean Leone (SLL)
  • Somali Shilling (SOS)
  • Surinamese Dollar (SRD)
  • São Tomé and Príncipe Dobra (STD)
  • Syrian Pound (SYP)
  • Swazi Lilangeni (SZL)
  • Thai Baht (THB)
  • Tajikistani Somoni (TJS)
  • Turkmenistani Manat (TMT)
  • Tunisian Dinar (TND)
  • Tongan Paʻanga (TOP)
  • Turkish Lira (TRY)
  • Trinidad and Tobago Dollar (TTD)
  • New Taiwan Dollar (TWD)
  • Tanzanian Shilling (TZS)
  • Ukrainian Hryvnia (UAH)
  • Ugandan Shilling (UGX)
  • United States Dollar (USD)
  • Uruguayan Peso (UYU)
  • Uzbekistani Som (UZS)
  • Vietnamese Đồng (VND)
  • Vanuatu Vatu (VUV)
  • Samoan Tālā (WST)
  • Central African CFA Franc (XAF)
  • East Caribbean Dollar (XCD)
  • West African CFA franc (XOF)
  • CFP Franc (XPF)
  • Yemeni Rial (YER)
  • South African Rand (ZAR)
  • Zambian Kwacha (ZMW)
  • Zimbabwean Dollar (ZWL)

$0.00

+ applicable taxes

Proposal turnaround typically 24–48 hours

Note: Estimate is indicative only. Final pricing is subject to scope finalization after discovery call.

REFERENCE CASELETS

Reference

Real-world examples of survey work in the entertainment viewing preferences space.

CASELET 1

Streaming platform tier preference & pricing sensitivity (India)

CASELET 2

Out-of-home entertainment occasion mapping & social viewing triggers (India)

Streaming platform tier preference & pricing sensitivity (India)

OBJECTIVE

A mid-size streaming platform needed to map how urban multiplex-goers and suburban casual viewers weigh subscription cost against content freshness when choosing between paid tiers and free ad-supported options.

WHAT WE DID

Ran a structured quant survey across 6 cities with 800 respondents, capturing willingness-to-pay thresholds , content genre priorities , device preference by viewing occasion , and stated reasons for tier upgrades or downgrades in the prior 6 months.

DELIVERED

A pricing corridor by viewer segment , a content-value trade-off map ranking genre categories against price sensitivity, and a tier migration framework identifying the 3 triggers most likely to convert free users to paid subscribers.
CASELET 1

Streaming platform tier preference & pricing sensitivity (India)

CASELET 2

Out-of-home entertainment occasion mapping & social viewing triggers (India)

Streaming platform tier preference & pricing sensitivity (India)

OBJECTIVE

A mid-size streaming platform needed to map how urban multiplex-goers and suburban casual viewers weigh subscription cost against content freshness when choosing between paid tiers and free ad-supported options.

WHAT WE DID

Ran a structured quant survey across 6 cities with 800 respondents, capturing willingness-to-pay thresholds , content genre priorities , device preference by viewing occasion , and stated reasons for tier upgrades or downgrades in the prior 6 months.

DELIVERED

A pricing corridor by viewer segment , a content-value trade-off map ranking genre categories against price sensitivity, and a tier migration framework identifying the 3 triggers most likely to convert free users to paid subscribers.

FREQUENTLY ASKED QUESTIONS

Common Questions

Answers to frequently asked questions about this survey mandate.

What decisions will this survey enable?

Who is the buyer vs who are the respondents?

Can we see differences between frequent theatregoers, occasional theatregoers and home-only streamers?

How will you measure viewing format preference beyond simple ratings?

Will the survey map the full viewing decision journey and drop-offs?

Can this survey inform product and pricing strategy?

How will findings improve our theatre attendance and subscriber conversion rates?

Still have questions?

Schedule a discovery call to discuss your specific needs and get a custom quote.

Book a Discovery Call