Juxt india onlinestudy_cybercafe_report_30072014


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Understanding Cybercafe users in India in terms of - the universe and the spread, their lifestyle, buying behavior and online behavior.

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Juxt india onlinestudy_cybercafe_report_30072014

  1. 1. India Cybercafe Landscape 2014 – Key Findings Report Understanding Cybercafe Users in India July 2014
  2. 2.  The Universe & Spread  Profile of the typical Cyber café user  Ownership and Usage of Products & Services  Lifestyle Insights  Buying Behaviour  Online Behaviour  Methodology Overview Table of Contents 2Confidential
  3. 3. The Universe & Spread 3Confidential
  4. 4. Access exclusively from cybercafes 13.8 mn Access Internet from Cybercafes 29.3 mn Total Internet users 106.9 mn Cyber café Users: The Universe 4  There are 29.3 million users who access Internet from Cybercafes  The total no of Internet users in India is 106.9 million  Nearly half of these (13.8 million) access exclusively from Cybercafes, meaning they do not access from anywhere else Confidential
  5. 5. Home OfficeCybercafe Cybercafe Users: A Detailed Look 5 Cybercafe users account for 27.4% of India’s total Internet users Home, Office & Cybercafe– 7% Only Home – 38% Only Cybercafe – 14% Only Office – 18% Home & Cybercafe – 5% Home & Office – 15% Office & Cybercafe – 4% Sample base – 36,464 Projected base -1069,00,232 all internet users Confidential
  6. 6.  Significant difference in regional distribution: Cybercafé users more consolidated in the West & South of India  73.5% of all Cybercafé users are in West & South; only 60.7% of Non-Cyber café users are in these regions  More democratic spread across townclasses  Smaller towns (below 5 lakh population) account for a higher share (40.2%) among Cybercafé users as compared to Non-Cybercafe users, only 36.2% of who are in these towns The Spread 6Confidential
  7. 7.  There’s no major difference between Cybercafe users and non-Cybercafe users when it comes to their regional and city-class wise distribution. South & West account for the lion’s share. The Spread: A Detailed Look 7 18% 8% 32% 42% North East South West Regional distribution of Cybercafe users Townclass-wise distribution of Cybercafe users 27% 14% 5% 54% Upto 1 Lakh 1-5 Lakh 5 - 10 Lakh 10 Lakh+ Sample base – 6,166 Projected base -292,84,347Confidential
  8. 8. Cybercafe User – Profile 8Confidential
  9. 9. Individual Profile 9 94% 83% 6% 17% Female Male Cybercafe Non-usersCybercafe Users Gender 30% 26% 45% 52% 19-24 yrs 25-35 yrs Cybercafe Non-usersCybercafe Users 30% Age Marital status 20% 18% 80% 81% Single/Unmar ried Married Cybercafe Non-usersCybercafe Users Occupation 45% 47% 16% 18% 17% 13% Self- employed/Busi nessman Salaried Student Cybercafe Users Cybercafe Non-users Cybercafe Users Cybercafe Non-users Sample base 6,166 16,815 Projected base 292,84,347 776,15,885Confidential  Cybercafes are still mostly frequented by men  Monthly individual Income : Cybercafe Users- Rs.18,000 ; Cybercafe Non - users – Rs.21,000  Contrary to popular perception, more of 25-35 age group (the spending class) frequent Cybercafes
  10. 10. Ownership & Usage Products & Services 10
  11. 11. Cybercafe Users Asset Ownership (Household) 11 Cybercafe Users Cybercafe Non-users Sample base 2,890 9,562 Projected base 259,43,120 809,57,171 26% 25% 48% 21% 34% 36% 35% 71% 24% 67% 34% 23% 29% 35% 35% 47% Cybercafe Non-users  You don’t need a research to know that there would be considerable difference in PC base among Cybercafe users homes and non-users homes. But besides that, there’s not too much of a difference TV Fridge Electric Iron Camera VCD/DVD Player Music System PC/Laptop Washing Machine Confidential
  12. 12. Cybercafe Users Asset Ownership (Automobile) 12 Cybercafe Users Cybercafe Non-users Sample base 2,890 9,562 Projected base 259,43,120 809,57,171 27% 20% 52% 15% 23% 20% 46% 22% Cybercafe Non-users Cars Motorcycle Scooter Bicycle Confidential  An apt TG for 2 wheelers (Tap into 1st time “Users”, “Brand Switchers”, “Upgraders”). Perhaps for 4 wheelers too!
  13. 13. Cybercafe Users Asset Ownership (Financial) 13 Cybercafe Users Cybercafe Non-users Sample base 2,890 9,562 Projected base 259,43,120 809,57,171 13% 12% 14% 26% 37% 61% 12% 12% 36% 56% 11% 25% Cybercafe Non-users  This is revealing. The Cybercafe users seem to be far more mature users of financial services than the non-Cybercafe users Confidential Saving bank account Credit/ Debit/ Cash card Life Insurance Loans General insurance Medical insurance
  14. 14. Lifestyle Insights 14
  15. 15.  A higher percentage of Cybercafe users visit Gyms (28%) & Spas (6.5%), as compared to non-Cybercafe Internet users: Gym (25.1%) and Spas (5.3%)  A significantly higher percentage of Cybercafe users play sports (40.8%), as compared to non-Cybercafe Internet users (32.7%)  Cybercafe users watch and more action/adventures movies as compared to non-Cybercafe Internet users  A higher percentage (10.5%) of Cybercafe users watch movies in theaters as compared to non-Cybercafe Internet users (8.2%) Lifestyle Insights 15Confidential
  16. 16.  A higher percentage of Cybercafe users watch English movies channels (14%) on TV as compared to non-Cybercafe Internet users (11.9%)  A higher percentage of Cybercafe users read business magazines (14.4%) as compared to non-Cybercafe Internet users (12%) Lifestyle Insights (Cont’d) 16Confidential
  17. 17. Activities undertaken regularly 17 41% 20% 18% 18% 13% 13% 9% 29% 33% 19% 19% 16% 12% 11% 9% 29% Cyber Cafe Users Cybercafe Non-users Play sports Cybercafe Users Cybercafe Non-users Sample base 4743 14,072 Projected base 275,99,178 793,01,066 Confidential Go for weekend trips Dine out watch plays/drama Home delivery of food attend music concerts Go on leisure holidays None  The Cybercafe users and non-users are into similar Activities-except on ‘sports’
  18. 18. Favourite Hobbies 18 16% 13% 10% 6% 5% 5% 16% 11% 10% 5% 6% 5% Cybercafe Users Cybercafe Non-users DancingAdventure sports (racing Play computer gamesCinema/filmsListening to music Acting/Drama/Theater Cybercafe Users Cybercafe Non-users Sample base 6166 16,815 Projected base 292,84,347 776,15,885 Confidential  The Cybercafe users and non-users have similar hobbies
  19. 19. 19 49% 23% 13% 6% 10% 48% 29% 11% 5% 8% Cybercafe Users Cybercafe Non-users NoneWestern formal (Suit Shirt-Trouser) Indian traditional (Kurta Pyjama/Dhoti) Western casual (Jeans/Shorts-T- shirt/Shirt) Western semi-formal (Jacket Cybercafe Users Cybercafe Non-users Sample base 4743 14,072 Projected base 275,99,178 793,01,066 Confidential Favourite Clothes
  20. 20. Buying Behaviour 20
  21. 21. Buying Behaviour Insights 21  5.4 million Cybercafe users are online shoppers (18.5%) which is a sizable segment, and a further 9 million Cybercafe users (31%) search online but havent bought online, which presents a significant opportunity for the online marketers.  The Cybercafe users and non-users are quite similar in terms of their buying behaviour. The top attributes that they give importance to while buying a product are – Brand image, Price, Design and Looks and the Performance Quality.  Their reasons for buying from a particular shop is also similar, with the shop being closest to their home/place of work, the shop gives the best deals/discounts being the the top reasons.  They show similarities in the reasons for buying online as well, with more variety/product choices, deals/discount vouchers being the top reasons for shopping online. However, a greater proportion of Cybercafe users gave importance to better prices than offline stores as a factor. Confidential
  22. 22.  Clearly, brand matters more than price to Cybercafe users when they buy a product or service Most Important Factor While Buying for a Cybercafe User 22 Sample base – 4,753 Projected base -275,99,178 Brand Image(25%) Price (19%) Design & Looks (11%) Performance Quality (10%) Confidential
  23. 23.  Convenience is as important as cost. Other factors come way behind Reasons for Buying Product/Services from a Place/Shop for a Cybercafe User 23 12% Offers non-cash payment options 13% 29%Gives the best deals/discounts Closest to your home/place of work Stocks the specific brand you wish to buy 30% Offers the best range to choose from 8% 7% Offers home delivery service Sample base 4,753 Projected base 275,99,178 Confidential
  24. 24.  Well, people still don’t buy cars and bikes online but this shows how much research they do online before buying one. Categories Bought/Searched Online by Cybercafe Users 24 Sample base – 2,092 Projected base -156,06,841 online searchers/buyers Beauty, Cosmetics & Grooming Product (5%) Appliances (15%) CD/VCD/DVDs(6%) Automobiles (12%) Mobile Phones, PDA & Accessories (6%) Books & Magazines (7%) Computer Hardware (7%) Bags & Luggage(9%) Art, Antiques & Collectibles (7%) Confidential
  25. 25.  In offline shopping, it is convenience (store location close to home) but in online, where it doesn’t matter, variety of choice matter the most. And why not? Isn’t long tailing the most important advantage of a web store? Reasons for Shopping Online for Cybercafe Users 25 29%Product reviews available 15%Takes least time to get delivered 55%More variety/product choices 24% 48%Deal/discount voucher 32%Ease of buying/placing order Multiple payment options available 31%Better price than loacal stores/market Sample base – 723 Projected base - 54,24,696 online shoppers Confidential
  26. 26.  As consolidation seems imminent in online retail, the users have already declared that. The Rule of Three is evident. Shopping Sites used by Cybercafe Users 26 Sample base – 1970 Projected base - 146,95,726 online searchers /buyers Confidential
  27. 27.  As the answers show, it is lack of familiarity that come as the major reason than any real issue. Except for fear of credit card misuse and longer delivery time, rest all suggest unfamiliarity/lack of knowledge Reasons for not Buying Online for Cybercafe Users 27 Lack of fun of physical shopping Lots of hidden & shipment charges 18%Cant have a physical look & feel of the product online Nobody stays at home in the day time to receive Prices on the net are higher than local market 5% 5% 6% Lack of choices and variety of products Fear of misuse of credit card 9% 9% Don’t trust online medium for buying Don’t have credit card/Online banking 15% 12% 4% Never felt the need to do so 6%Product delivery time is very high 6% Sample base – 1,220 Projected base - 90,54,450 people who don’t shop online Confidential
  28. 28. Online Behaviour Cybercafe Users 28
  29. 29.  Not very different from Internet users in general, as many as 70% watch videos Top 10 Activities Online for Cybercafe Users 29 38%Instant messaging/chatting 39% Mobile contents 42% Train Ticketing 55% Job Search 56% Download music 65% Watch videos 70% Social networking 87% Web info search 88% E-mailing 89% Joined online community Sample base – 4,188 Projected base - 271,33,112 Confidential
  30. 30.  Cybercafe users seem to be more seasoned users of different online activities than non- Cybercafe users Selected Activities: Cybercafe Users vs Non-users 30 Cybercafe Users Cybercafe Non-users 30%30% 36%35% 39% 31% 42% 64% 70% 26% 18% 24% 16% 21% 17% 26% 21% 26% 23% 29% 23% 31% Watch videos 16% 19% 19% 14% 18% 11% 16% 13% Mobile contents (Ring tones / games etc.) Listen/ stream music online Post your own Tweet Online games Share/upload pictures Joined online community Bus Ticketing Dating/ Friendship Share/ upload music Check Cinema Content Check business/ Financial news Read product rating/reviews Screensavers/ wallpapers Check cricket content/score Cybercafe Users Cybercafe Non-users Sample base 4,188 12,722 Projected base 271,33,112 797,67,174 Confidential
  31. 31. Top Websites for Cybercafe Users 31 27% 28% 89% 68% 42% 40% 34% 29% 96% 84% 66% 42% Sample base – 4,188 Projected base - 271,33,112 Confidential
  32. 32. Cyber cafe Users on Social Media 32 260 The average number of friends on facebook for a Cybercafe user 14 The average friends interacted (chat/tag/post on wall/play with etc.) with by a Cybercafe user on facebook each day 20 The average number of people followed by a Cybercafe user on twitter 18 The average number of twitter followers of a Cybercafe user 12.8 million The number of people share product reviews on social channels from Cybercafes 24 million The number of Cybercafe users on facebook 7.5 million The number of Cybercafe users on twitter Confidential
  33. 33. Reasons for Following A Brand on Social Media for Cybercafe Users 33 16% 10% 15% 15% 24% 16% 17% 19% 23% 23% 28% 40% To participate in contests Seeing friends are already following the brand To share my personal experiences To show that I support the brand To get regular updates from the brands I like Gain access to exclusive content To research brands when looking for a specific product To share my intersets with others Recommendation from a friend Brand advertisement (TV/online/magazine) I don’t follow any brand To get coupons/discounts Sample base - 3,067 Projected base - 262,88,848 Confidential
  34. 34.  Very secular distribution; not too much difference between top and bottom reasons Extent of Trust in Different Types of Online Advertisements for Cybercafe Users 34 38% 36% 36% 38% 39% 41% 43% 43% 46% 48% Posts by other people on company/brand pages Professionally written online reviews Information on company/brand official websites Natural search engine result (google,Yahoo,Bing etc) E-mail from companies/brands Posts by companies/brands on social media sites Consumer written online reviews Ads on website (banner ads etc) Sponsored search engine results Recommendations from friends & family Sample base - 3,067 Projected base - 262,88,848 Confidential
  35. 35. Methodology Overview 35
  36. 36.  The objective of the online sampling and survey was to reach out to the actual Internet users and collect the ‘in-depth’ information on their Internet access and usage behavior and preferences directly from them through a ‘self-administrable’ online questionnaire.  The online sampling this year was done using Juxt’s 50million strong ‘online access panel’, ‘GetCounted’ (www.getcounted.net) and its associate partner’s panels.  The details about their more recent internet usage behavior, like the ‘online activities’ they undertake, the websites they visit the most to undertake these activities, their online shopping status and behavior, etc. were captured by conducting an ‘online survey’ among the active GetCounted and associate partner panel members. A reportable sample of 36,464 internet users, including 6,166 Cybercafe users was collected among these active panel members.  The e-questionnaire used in the ‘online survey’ was pre-tested and timed to take approximately 30-40 minutes for a respondent to complete depending on the speed of comprehension and answering of the questions. A new responsive UI was designed to minimize the level of ‘respondent fatigue’ to an extent that was practically possible. The Online Sampling and Survey 36Confidential
  37. 37.  To finally report the cleaned and tabulated data as part of the findings of the India Cybercafe Landscape Study we needed to ensure that we make it ‘representative’ of the entire Cybercafe users population and not just of those who are ‘more likely’ to register in an online panel or to fill up an online surveys (because of factors like convenience of access, regularity of usage, attractiveness of the incentive prize offered for becoming a panel member or filling the survey, etc).  To bring in the ‘representativeness’, a set of appropriate demographic weights or ‘multipliers’ of Internet users as derived from the land survey, and using the base population statistics, needed to be applied to the online data. Making the Online Survey Findings ‘Representative’ 37Confidential
  38. 38.  Firstly, we estimated the internet users in urban and rural India from the offline enumeration.  To do so we constructed ‘individual’ level multiplier. We did this based on 3 key demographic factors Geographic Zone, Town Class and SEC. This resulted in 100 combinations (4 zones * 5 Town/Village class * 5 Urban SECs) of homogenous cells. Once we applied these multipliers as a second step we applied a correction factors on top of it based on the proportion of Gender and language preference within each States of India.  This resulted in deriving 124 distinct (31 States * 2 Gender * 2Language preference) individual level proportion correction factors,which were applied on the projected sample to bring in further corrections.  After estimating the total number of ‘internet using households’ in urban and rural India like this, thereafter, the ‘individual level’ responses received from our land survey on usage of ‘internet’ from anywhere in the last one year as well as in last one month were analyzed. This resulted in arriving at both the ‘average internet users per household’, and from them, the number of ‘internet using individuals’ within these internet using households. Our Estimation Process 38Confidential
  39. 39.  However, before arriving at the final estimated numbers of ‘internet using individuals’, from the internet using households in urban and rural India, we made two relevant ‘corrections’ to account for two possible ‘underreporting biases’ present in the land survey:  Correction for possible underreporting of internet users in the survey ‘per se’, as internet usage is not merely a ‘home’ based activity (can even be undertaken from place of work and cybercafé). Therefore, in a land survey fieldwork scenario where very often the ‘respondent’ of the survey may be a less educated/less aware member of the households there is a good likelihood of underreporting of an apparently ‘non-visible’ activity (at home) like internet usage.  When we check with ‘actual internet users’ in an online survey about ‘the likelihood of other members of their household being aware of their internet usage’, there is a good 20% or so who say ‘not very likely’. Our Estimation Process 39Confidential
  40. 40.  Correction for underreporting of number of ‘internet users per household’ arrived at from such a land survey where the majority of respondents are not internet users themselves.  Again when we check with ‘actual internet users’ in an online survey about ‘how many members in their household including themselves use internet’ the number on ‘average users per household’ we get is significantly higher than from the land survey, almost to the extent of 50-60% higher.  The Cybercafe numbers then come as a natural fallout from the respondent responses to Where all do you access internet from?”  Therefore, only after making these ‘underreporting’ corrections and ensuring consistency with our last year’s data, we arrived at and reported our final estimates for both the internet using population as well as the Cybercafe using population in both urban as well as rural India for 2013-14, as contained in the findings of the India Online Landscape study and India Cybercafe Landscape study. Our Estimation Process 40Confidential