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Profile of the rising African tech consumer

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Presented by Stephen Haggard, Executive chair Eneza Education at the Africa Technology Business Forum London, 22nd June 2016

Published in: Technology
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Profile of the rising African tech consumer

  1. 1. the rising African tech consumer ABTN 22 June 2016 Stephen Haggard Executive Chair Eneza Education, Nairobi
  2. 2. Real increases in African prosperity stronger prosperity growth than world, MENA, LatAm.
  3. 3. Where are they ? Different ways to reach the most dynamic segments. ● Cities ● “Lion” economies ● “Middle Class” ● Aspire to education McKinsey recommends clients to focus on Africa’s 65 >1mn cities
  4. 4. Meet Generation Z Africa Digital drivers ● Device dependence ● Information on demand ● Emotionally digital ● Trust digital sources ● Not impacted by geography Behaviours ● Snack Media habits ● Multiscreen ● Livestream ● Seek freedom of choice, total control of information environment thanks MTN (GH)
  5. 5. Concentration: in time, in age, in activity Most purchased digital product = downloads KE - 40%, NG - 60%
  6. 6. Mobile habits are polarised
  7. 7. How Gen Z behaves on Eneza platforms ⅓ of the user sessions yield 75% of the page views 15% of the users are visiting >20 times a week Sessions >30 min equally common in low and high frequency users Same number of long term loyal users, as one-session opportunists Churn (monthly) 4%-15%. Higher rates from higher acquisitions The loyal users exist and are worth targeting
  8. 8. Value vs marketing costs Direct Sales CoA $0.10 to $3.20 Wholesale Partners $0.05 per trial user Reseller Channels (revenue) $1.6 per user per month Direct Recruitment - high loyalty
  9. 9. Gen Z barriers to Digital fulfillment Connection quality, inflexible data plans Can be avoided in app and service design Affordability of data/airtime Growth in wifi use, pricing breakthroughs Personally relevant, local content
  10. 10. Failed subscriber study Nov & Dec 2015 2000+ customer calls to failed subscribers 100000+ demo/behavioural/channel datapoints Multivariate modeling Result: no significant patterning ● Services are generalisable ● Can’t pinpoint affordability BUT: sales channels are specialised
  11. 11. Thank you Stephen@enezaeducation.com @stephendh

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