Meeting the OTT challenge


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How should telcos meet the "over the top" service provider challenge? By taking full control over the trading space that exists inside every network.

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Meeting the OTT challenge

  1. 1. Meeting the OTT challenge Martin Geddes Martin Geddes Consulting Ltd © 2013 All Rights Reserved
  3. 3. Core thesis 1. Networks are (option) trading spaces – That match supply and demand across all timescales 2. Your business is statistical multiplexing for fun and profit – Supply and demand meet here, and trades are made 3. Success primarily depends on how well you do this – Regardless of the (OTT) business model on top or who pays 4. Your current business is mathematically unsustainable – Because you have not taken full control over your network trading space 5. There is a way to take control – Get away from supply-push “bandwidth” approach & purpose-for-fitness 6. Move to a sustainable demand-driven “quality” model
  4. 4. What to do? 1. Characterise demand and create fit- for-purpose supply 2. Align your design, marketing, operations to deliver 3. Execute to create differentiation in cost and QoE 4. Enable new OTT business models
  5. 5. The Facts Situation
  6. 6. OTT voice and messaging are hurting telephony and SMS revenue
  7. 7. Selling data speed and mechanisms
  8. 8. Value is measured in data volume Revenue model: Proportional to average volumetric demand
  9. 9. Cost model Size to peak demand Planned upgrades Volume-driven capacity planning rules Unplanned upgrades Driven by churn and complaints
  10. 10. Key properties of data demand • User have a sense of entitlement – Want properties of circuits – Uncontended, on-demand un-impaired capacity • Ability to attach any device or application – Demand shocks can and do happen (iPhone, Olympics, emergency events, etc.) • Distribution of use is shifting – Not just the average; peaks are getting “peakier”
  11. 11. Key properties of data supply offer • One-size-fits-all: Single class of service • One-sided market: End user pays – No “toll free” data or upstream revenue • No quality assurance or performance SLAs • Little visibility of actual user experience Supply-push model: Purpose-for-fitness
  12. 12. The market is evolving • Rapid growth in demand – SaaS/cloud, mobile workers, tablets, automotive, small cells, M2M, smart grids, etc. • These require new supply capabilities – Very different cost and quality profiles
  13. 13. The market is evolving • Government and regulatory focus shifting to “digital dividend” – Tackling economic/social issues – It’s not going to be about negotiating roaming and termination rates in future
  14. 14. All operators are facing tough questions 1. How to sustain voice and messaging revenue and differentiation positioning? 2. How to relate to OTTs (block, bundle, ignore, service, join in, partner…)? 3. How to address growing market needs at an affordable cost?
  15. 15. What’s wrong Complication
  16. 16. Speed (and volume) are not value Dangerous myth: More Speed is Always Better
  17. 17. Contention exists! Need to consider variability, not just speed. Source: Predictable Network Solutions Ltd
  18. 18. Black cygnets: small “bad coincidences” create bad experiences
  19. 19. These coalesce under high load
  20. 20. And create ever more ‘black swan’ application failures
  21. 21. The application Hierarchy of Need 3. Reasonable bounds on loss and delay 2. Sufficient stationarity 1. Sufficient capacity Note: exact requirements are application-dependent
  22. 22. So 4G won’t solve your problems Downstream delay over a 3G connection – 4G doesn’t change this unwanted variability Too much variability for TCP to work well. Source: Predictable Network Solutions Ltd
  23. 23. What you need to know Some theory
  24. 24. Capacity demand TWO sources of network demand Schedulability demand
  25. 25. Capacity demand LOW HIGH Feasible Infeasible MAX CAPACITY TWO fundamental resource limits Feasible MAX SCHEDULABILITY Schedulability demand Infeasible LOW HIGH
  26. 26. Problem Schedulability demand is growing fast VoIP, gaming, 2-way video, UC, HTML5 web, WebRTC…
  27. 27. Problem Solving schedulability issues (i.e. non-stationarity) with capacity is inefficient and ineffective
  28. 28. Problem Monoservice network means costs track the worst-case schedulability limit of loading
  29. 29. Summary so far • “Bandwidth” is your current input and output – This is not a good proxy for fitness-for-purpose – Other factors also matter to QoE • Revenue is from fit-for-purpose experiences – But you have stopped paying attention to user needs – Dependability is not on sale, at any price • Costs are being driven by schedulability issues – Every flow has the same cost structure as your most quality-demanding users/flows – But schedulability isn’t part of costing & ops model
  30. 30. The consequence Undesirable future
  31. 31. Telecoms is a capital killer ($60bn/year shortfall, every year) Source: PwC
  32. 32. Failure of technology to keep up with ever rising demand forces shorter upgrade cycles Rising load makes service quality fall, forcing upgrades ServiceQuality Time UndepreciatedAssetValue Time Mathematically unsustainable
  33. 33. More, more, more (aka 2G/3G/4G/5G cycle of doom) More supply More elastic demand Faster saturation of backhaul More non- stationarity More complaints and churn
  34. 34. Race to the bottom?
  35. 35. The alternative Desirable future
  36. 36. What do we want? • Demand – increased benefits – Able to match a wide range of quantity, quality and cost needs – Can package offers to fit segments • Supply – decreased costs – Costs scale sub-linearly with users – Predictable in-life operational costs
  37. 37. Packaged (OTT) cloud applications • Available when and where you need it • Right quantity and quality • At a cost you can afford • Easy to consume
  38. 38. How to get there? The Question
  39. 39. The big question How can we exploit the trades (and demand-shift by scheduling) and match supply to demand to create the right QoE and cost trade-offs? Then, given that capability, what should our OTT strategy be?
  40. 40. What do I need to do? The Answer
  41. 41. Bandwidth Quality Need to frame the problem differently to make it soluble
  42. 42. What has to change? NOW FUTURE PURPOSE-FOR- FITNESS FITNESS-FOR- PURPOSE Focus on enabling outcomes – not shifting data Make bad experiences rare(r). Lower cost of delivering good experiences.
  43. 43. TELCOEND USER Manage benefits, costs and risks across supply chain BENEFIT COST RISK (failed call) Made the sales call Price of phone call Didn’t make sale Second car Revenue Tin, opex SLA breach or churn Unplanned capacity upgrade Time wasted Reputational loss INSURANCE Contingency fund (lawsuit, PR) Frustration Excess risk has to be (self-)insured
  44. 44. Manage QoE risk through network resource “trades” The “tails” of loss and delay + their structure are what cause application QoE failure, and whose mitigation drives cost. Source: Predictable Network Solutions Ltd
  45. 45. Lower cost of good experiences by time-shifting delay-insensitive traffic • Reduce cost by lowering peaks – Currently encouraging people not to time-shift. – Users behave in a predatory way. • Mark bulk traffic – Cheaper to post bulk mail if pre-sorted. Microseconds to minutes Peak demand
  46. 46. Summary: Do’s and Don’ts • Do: – Explore the nature of the market – who is paying for what? – Think systemically; optimise globally – Become aware your implicit bandwidth thinking and its dangers – Exploit packet-based statistical multiplexing • Don’ts: – Focus on supply inputs and volume; it’s about outcomes – Mistake trades for QoS – Sell circuits – you will be arbitraged (cf ISPs in 1990s) – Think you can solve this without differentiation
  47. 47. It’s all about the trading space The logistics companies out-competed the shipping companies because they controlled the resource trading space
  48. 48. Get in touch to discuss the necessary changes to network design, operations, marketing & product management to meet OTT challenge Martin Geddes