Future of Broadband in Africa Dr Christoph Stork. researchictsolutions

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1 Future of Broadband in Africa Dr Christoph Stork researchictsolutions

2 Approach Nationally representative household and individual surveys in 12 African countries (Census Sample Frame) Basket methodology for Fixed-wired and mobile broadband data prices Annual financial reports Quality of Service

3 Where does the math come in? Example: Household survey methodology

4 Sampling Step 1: Split Enumerator Areas into Urban and Rural EAs (Census Sample Frame of NSO) Step 2: PPS Sampling of EAs Step 3: Simple Random Sampling of Households through Listing Step 4: Simple Random Sampling of Household Members (Birthday method)

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7 Listing in Tanzania

8 Listing Nigeria

9 GPS Cameras to verify boundaries of EAs

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11 Sample Size: UNSD Desired level of accuracy typically confidence level of 95% Absolute precision (relative margin of error) typically 5%. Population proportion P conservatively would be 0.5 (yields the largest sample size) n = Z p(1 p) a C p 2 n = (1 0.5) = 384

12 Country Target sample Actual sample 1 Cameroon 1,200 1,200 2 Ethiopia 1,600 1,608 3 Ghana 1,200 1,203 4 Kenya 1,200 1,239 5 Nigeria 1,600 1,552 6 Rwanda 1,200 1,200 7 Tanzania 1,200 1,201 8 Uganda 1,200 1,200 9 Namibia South Africa 1,600 1, Mozambique 1,200 1, Botswana Total 15,000 15,077

13 HHw = household weight INDw = Individual weight 16+ PHH = Household Selection Probability PEA = EA Selection Probability PI = Individual Selection Probability P HH = N HHw = DW * 1 P HH * P EA P EA = m * HH EA HH EA HH STRATA INDw = DW * 1 P I = 1 P HH * P EA * P I HH m16 +

14 Household & Individuals 15+ Survey Results

15 Share of households with fixed-lines South Africa Namibia Ethiopia Ghana Kenya Tanzania 2007/8 2011/ % 7.6% 4.0% 2.6% 1.8% 2.3% 0.6% 0.9% 0.4% 18.2% 18.0% 17.4% Nigeria 0.3% Botswana Cameroon Uganda Rwanda 0.2% 1.8% 2.2% 0.3% 1.5% 0.1% 11.0% 15.0% Residential fixed-lines on the way out except Botswana, Cameroon, Uganda and Rwanda

16 Share of households with a working computer Share of households with a working Internet connection South Africa 24.5% South Africa 19.7% Botswana 15.7% Kenya 12.7% Namibia 14.7% Namibia 11.5% Kenya 12.7% Botswana 8.6% Cameroon 8.6% Nigeria 3.4% Ghana 8.5% Ghana 2.7% Nigeria 6.6% Cameroon 1.3% Uganda 2.2% Uganda 0.9% Rwanda 2.0% Tanzania 0.8% Tanzania 1.6% Rwanda 0.7% Ethiopia 0.7% Ethiopia 0.5% Less than a quarter of households have a computer and even fewer Internet access, but more households have Internet access than have fixed-lines

17 ITU data: Percentage of individuals using the Internet!30!!!25!!!20!! Botswana! Ethiopia! Kenya! Namibia! Rwanda! Tanzania! Cameroon! Ghana! Mozambique! Nigeria! South!Africa! Uganda!!15!!!10!!!5!!!"!! 1996! 1997! 1998! 1999! 2000! 2001! 2002! 2003! 2004! 2005! 2006! 2007! 2008! 2009! 2010!

18 2007/8 2011/12 Ethiopia 3% 1% Internet Tanzania Rwanda 2% 2% 4% 6% use (15+) more than doubled Uganda 2% 8% within 4 Ghana 6% 13% years Cameroon Namibia Nigeria Kenya Botswana 13% 9% 15% 6% 14% 16% 18% 26% 29% South Africa 15% 34%

19 Internet Access Models Old Internet New Internet Hardware Computer / Laptop Mobile Billing Skill requirement Electricity Postpaid (monthly Internet subscription) High (Windows + Internet explorer + Viruses) electricity mostly required at location of Internet use Prepaid Low not required at home Location Work, school, Internet cafe Anywhere

20 Among Internet users: Where was the Internet used first? Computer Mobile phone Cameroon Rwanda Botswana Ghana Kenya South Africa Namibia Tanzania Nigeria Ethiopia 82.1% 70.8% 70.6% 70.5% 68.9% 65.1% 50.1% 45.8% 45.2% 33.3% 17.9% 29.2% 29.4% 29.5% 31.1% 34.9% 49.9% 54.2% 54.8% 66.7% Uganda 28.2% 71.8%

21 Where the Internet was used in past 12 months Mobile phone Work Place of education Internet cafe 74% 80% 85% 51% 35% 58% 32% 51% 33% 21% 36% 50% 31% 52% 63% 24% 45% 45% 20% 29% 72% 39% 31% 42% 21% 17% 51% 55% 23% 36% 48% 20% 10% 61% 64% 71% 71% 75% 75% 78% 81% 81% 87% 30% Cameroon Ghana Botswana South Africa Rwanda Tanzania Nigeria Kenya Ethiopia Uganda Namibia

22 Mobile Internet at the BOP Cost saving strategy: Zero or very low cost to send a text message Whatsapp, Facebook Zero, Skype Prepaid still too expensive to use for more advanced features such as video and music

23 Broadband Pricing

24 Baskets of Use Monthly cost of 1 GB use per month with at least 256kbps connection for a period of 24 months (ITU basket) Monthly cost of 5 GB use per month with at least 256kbps connection for a period of 24 months Monthly cost of uncapped use per month with at least 256kbps connection for a period of 24 months

25 1 GB per month basket Fixed Mobile Postpaid Mobile prepaid Botswana Mozambique Cameroon South Africa Namibia Ghana Kenya Uganda Ethiopia Tanzania Nigeria Rwanda

26 5 GB per month basket Botswana prepaid mobile not displayed: 380 USD Fixed Mobile Postpaid Mobile prepaid Botswana Mozambique Cameroon Uganda South Africa Namibia Ghana Ethiopia Kenya Tanzania Nigeria Rwanda

27 uncapped GB per month basket Fixed Mobile Postpaid Ghana South Africa Cameroon Namibia Kenya Tanzania Uncapped: Yes for some countries Rwanda 29.4 but 384kbps to 1Mbps compared Uganda 37.8 to between 21 and 100 Mbps

28 Quality of Service Example South Africa

29 South Africa: Broadband.co.za 75,000 measurements ,227 Fixed Mobile Fixed-wireless 4,753 2, Download Speed (kbps) Upload Speed (kbps) Latency (ms)

30 Options for fixedline incumbents?

31 1) Focus on Corporate Developmentally, the least desirable option for African Leaves the residential market to mobile operators and hence to less competition and poorer quality of service offerings A strategy already adopted by some incumbents but also by new kids on the block such a Neotel in South Africa

32 2) Data flat rate pricing: voice free app Focus on data only through flat rate pricing, and ignore traditional voice revenues altogether Converting all existing fixed-lines into data lines would increase broadband penetration drastically in Botswana, Namibia and South Africa Flat rate pricing also solves the billing problem, something mobile operators are good in and fixedline incumbents are not

33 Example Namibia Telecom Namibia s revenues by segment in million N$ Data Voice Prepaid Voice Postpaid

34 Data and voice flat rate simulation based on current users number Technology 512kbps Speedlink Home Current prices N$ 24 months (Speedlink Business) New Price N$ Price drop No of current Lines New revenue in million N$ % 20, Mbps Speedlink Home % 20, Mbps Speedlink Business 10 Mbps Speedlink Business % 10, % 100, Total per month 73 Total per Year 876 * Based on10mbps product of TN

35 Data and voice flat rate simulation assuming price elasticity Technology 512kbps Speedlink Home 1Mbps Speedlink Home 4 Mbps Speedlink Business 10 Mbps Speedlink Business Current prices N$ 24 months New Price N$ Price change Lines Revenue in million N$ % 37, % 35, % 18, %191, Total per month 139 Total per Year 1,670 * based on10mbps product of TN

36 3) Triple Play - Premium TV content Add premium content TV and replace copper with fibre Fibre to the home (FTTH) may be able to compete if it is able to match satellite TV contentwise The competition for premium TV content would also reduce prices, bringing them closer to European levels instead of the current USD 80+ An average ARPU of USD50 still good enough for premium content and uncapped fast Internet Botswana 68.1 Cameroon 78.0 Ethiopia 78.0 Ghana 84.4 Kenya 84.6 Mozambique 63.4 Namibia 72.0 Nigeria 69.8 Rwanda 78.0 South Africa 72.0 Tanzania 83.5 Uganda 88.3 DSTV Premium Package in USD per month

37 Conclusions

38 Fixed-Internet on the way out Fixed lines = value add for ADSL, contrary to Europe were ADSL has been a reason not to cut the cord The voice battle was lost by fixed-line in Africa in the last decade, the data battle may be lost soon as well ADSL cannot compete with mobile 3G/4G speeds Fixed-line operators need to invest into new technologies, VDSL2 or fibre to the home to stand a chance against mobile broadband

39 By choice not default The response of fixed-line incumbents is often to leave the sinking ship and start mobile services Other options exist that may be more profitable More beneficial to broader economic development goals

40 Regulatory initiatives Carrier pre-selection and local loop unbundling will not increase competition for most countries Who wants to be part of a dying business? Structural separation of fixed-line incumbent and open access model for national backbone may remove the biggest competitive bottle neck in Africa at present