Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.



Published on

Презентация для студентов стипендиальной программы "Выше Мечты" (Тройка Диалог)

Published in: Business
  • Be the first to comment


  1. 1. Digital Economy Denis Afanasev CleverDATA
  2. 2. Company Profile Make your data clever Digital economy
  3. 3. | Make your data clever Business development on interna?onal markets One of top-3 leading Russian IT companies; 43 branches in Russia and abroad; 7000+ employees. Marke?ng Data PlaForm Data collec@on, clients profiles building, data enrichment, extend clients analy@cs; Data Marke?ng Cloud Cloud solu@on for comfortable interac@on of data suppliers and consumers. Development and implementa@on of predic@ve analy@c models and Big Data processing solu@ons; In-house centers of development; Partnership with global leading companies; Centre of exper?se for Big Data and Digital Marke@ng technologies. 1DMP
  4. 4. | Business solu?ons Data Management Platforms Customer base and experience management Marketing automation Building of analy@cal DWs, development of solu@ons for raw data collec@on based on Hadoop stack and other Big Data technologies, knowledge extrac@on and auditory segmenta@on. Building of predic@ve models of data processing for effec@ve retail customer rela@ons management, churn management, cross-sales, micro-segmenta@on and client profile 360° and scoring models building. Implementa@on of global leading vendors’ solu@ons for omni-channel targeted marke@ng (campaigns, event-based marke@ng, customer web experience management). Implementa@on of global leading solu@on Splunk Enterprise for opera@onal analy@cs in real-@me. Our target is to make your business more effec?ve with a help of DATA Operational analytics 4
  5. 5. | Our customers Online ads | Media | Telco •  Development of DMP plaVorms for targeted adver@sing and new business models crea@on; •  Development of solu@ons for traffic quality and web-content analysis. Finance market| Public sector •  Development of DWs and Big Data DWs; •  Customers data enrichment with external data; •  Data management plaVorm development, machine logs real-@me analysis for cri@cal business process monitoring; •  Implementa@on of ACRM systems for detailed customer analy@cs, based on Big Data driven marke@ng and customer experience; •  Development of models for predic@on of product response and churn management, scoring models, past-due amounts collec@on; •  Collec@on and analysis of Internet texts with the use of machine learning algorithms for media ac@vity monitoring. Retail •  Marke@ng automa@on systems implementa@on (Campaign Management); •  Predic@ve model development for business process op@miza@on. 5
  6. 6. | The Fourth Industrial Revolu?on
  7. 7. | The Fourth Industrial Revolu?on
  8. 8. | «Индустрия 4.0», получила свое название от инициативы 2011 года, возглавляемой бизнесменами, политиками и учеными, которые определили ее как средство повышения конкурентоспособности обрабатывающей промышленности Германии через усиленную интеграцию «киберфизических систем», или CPS, в заводские процессы. Производственная сторона, эквивалентная ориентированному на потребителей «Интернету вещей», в котором предметы быта, от автомобилей до тостеров, будут подключены к Интернету. Слияние технологий и стирание граней между физическими, цифровыми и биологическими сферами. The Fourth Industrial Revolu?on
  9. 9. | Профессор Шваб видит три причины, по которым сегодняшние перемены следует считать не простым продолжением третьей промышленной революции, а началом четвертой: •  скорость, с которой происходят перемены; •  их размах •  системный характер последствий. The Fourth Industrial Revolu?on
  10. 10. | В первую очередь от очередной технологической революции должны выиграть развитые государства, у которых есть задел в компьютерных исследованиях. Сингапур Швейцария Китай США Германия •  к 2025 году развитие технологий беспилотных автомобилей может сохранить до 1 млн жизней, сокращение выбросов выхлопных газов автомобилями — сэкономить до $867 млрд, а общая прибыль от прогресса составит $100 трлн. •  развивающиеся страны перестанут быть локомотивами развития, которыми они были на протяжении последних десятилетий, поскольку дешевая рабочая сила перестанет считаться конкурентным преимуществом The Fourth Industrial Revolu?on
  11. 11. | The Fourth Industrial Revolu?on
  12. 12. | •  Новая темпоральность •  Пространство потоков •  Глобальная деревня •  Длинный хвост (С) Сергей Медведев, профессор ГУ ВШЭ The Fourth Industrial Revolu?on
  13. 13. | The Fourth Industrial Revolu?on
  14. 14. | Hype Cycle for Emerging Technologies 2015 Решайте практические задачи!
  15. 15. | Exponen?al organiza?on
  16. 16. | Exponen?al organiza?on
  17. 17. | Exponen?al organiza?on
  18. 18. | New business models
  19. 19. | Moore's law is the observa@on that the number of transistors in a dense integrated circuit doubles approximately every two years. Exponen?al organiza?on
  20. 20. | Exponen?al organiza?on
  21. 21. | Exponen?al organiza?on
  22. 22. | Exponen?al organiza?on
  23. 23. | Exponen?al organiza?on Разработка Производство Доставка
  24. 24. | Exponen?al organiza?on Разработка Производство Доставка Crowdsoursing
  25. 25. | Exponen?al organiza?on Разработка Производство Доставка Robots/AI
  26. 26. | Exponen?al organiza?on
  27. 27. | Causality in ?mes of big data In the past, only half of the customers received a catalog. And it only went to loyal customers. Taking a look at historical data shows that only 6% of the customers who didn’t receive a catalog visited the shop, while 30% of those who received one did. This sounds like a huge marke@ng success... but it isn’t. But that is precisely the issue that we need to address. Which customers will be moved to purchase by the catalog? The result: With only half the budget (25% of all customers), the same adver@sing effect and the same result were achieved as shipping to 50% of all customers based on the old selec@on criteria. In other words: with 75% of the budget, customer ac@vity can be increased by 5%. The circle can now be squared: more sales with fewer costs!
  28. 28. | Accurately plan sales The key ques?on is: Which factors influence the sale of seafood? •  Factor 1 − Seasonality, or the ‘tourism effect’ •  Factor 2 − the weather •  Factor 3 − loca@on •  Factor 4 − prices and promo@ons •  Factor 5 − low stocks The result: All of this informa@on goes into predic@ve applica@ons, which turn them into accurate forecasts. This means the complex materials planning for sea- food can be automated for the supermarket chain’s many stores. You can imagine the cost savings that result.
  29. 29. | Customer buying behavior from their receipt data •  People buying pharmacy ar@cles will also − with a high probability − buy other pharmacy products (plausible). •  People who buy pharmacy ar@cles will also − with a high probability − buy perfume ar@cles (goes together). •  Interes?ngly, pharmacy ar?cles are also oven bought together with sta?onery products (this is not so obvious).
  30. 30. | Heatmap of Big Data Business Problems by Industry
  31. 31. | Exponen?al organiza?on Разработка Производство Доставка 3D Prin@ng/Drones
  32. 32. | Exponen?al organiza?on
  33. 33. | Exponen?al organiza?on