{"id":32091,"date":"2018-11-13T14:44:58","date_gmt":"2018-11-13T19:44:58","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/"},"modified":"2018-11-13T14:44:58","modified_gmt":"2018-11-13T19:44:58","slug":"vodafone-leveraging-machine-learning-in-an-almost-5g-world","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/","title":{"rendered":"Vodafone: leveraging Machine Learning in an (almost) 5G world"},"content":{"rendered":"
<\/div>\n

Machine learning is one of the latest megatrends impacting industries worldwide, driven by the availability of large amounts of data, better algorithms, and increasingly more powerful computers[1].\u00a0 Vodafone Group plc, with 313MM customers across networks in 25 countries, <\/em>is uniquely positioned to leverage the large amounts of data at its disposal, including individual traffic patterns, app usage, and IoT data, to deploy machine learning across its Consumer (Mobile, Fixed broadband, TV, voice) and Enterprise (Carrier, Cloud, IoT) segments[2].\u00a0 <\/em>\u00a0YoY declines in revenues, from \u20ac49.8B in 2016 to \u20ac46.4B in 2018, evidence challenges the company is currently facing in the industry, and Big Data and predictive analytics are a key strategic focus to drive improved financial performance[2]. Competition is fierce, with cloud technology further blurring the line between Telecommunications players and Technology companies. Telecommunication companies are also on a race to leverage 5G, big data and AI to offer better, faster service to clients.<\/p>\n

On the Consumer side, Vodafone subsidiaries have primarily focused on deploying Big Data and predictive analytics to create better products\/plans, personalize marketing, and enhance customer service[3]. \u00a0According to David Gonzalez Martinez, Head of Big Data and AI at Vodafone Group Enterprise, customized recommendations have been launched in 15 markets, enhancing cross-sell and customer service, increasing revenues and reducing churn[2,3]. \u00a0For instance, Vodafone India is leveraging AI and Big Data to design and recommend better suited products and services based on both aggregate and individual prior consumption behavior[4]. \u00a0The company has also launched an AI chatbot named TOBi, designed to handle a wide range of customer queries in order provide a more seamless customer service.\u00a0 TOBi has seen early success; conversion rates are +100% compared to its website, with lower transaction times and abandonment rates [5,6].\u00a0 Vodafone also partnered with FaceMe to launch Kiri in New Zealand, the first Telco Digital Human, who is designed to carry out basic transactions in-store to free up sales representatives for more complex customer needs.\u00a0 Kiri can handle verbal interactions and is capable of reading and responding to human emotions[7].\u00a0 Vodafone plans to first pilot Kiri in a couple stores in New Zealand, with the potential for worldwide roll-out, enhancing customer service and reducing costs due to a smaller required workforce.<\/p>\n

\"\"<\/a>
Kiri, Vodafone’s Digital Human<\/figcaption><\/figure>\n

Furthermore, Vodafone is conducting machine learning tests in Germany and Ireland in C-SONs (Centralized Self-operating Networks) to improve the speed and connectivity of its networks.\u00a0 Initial tests have been promising; network optimization has increased by 45,000%, with an average 6% improvement in mobile download speed and savings of about 2.5 months in manual engineering labor[8]. <\/strong>The company expects to deploy this new technology commercially in 2019. \u00a0While reducing cost reduction and improving service in the short-term, the ability of the network to self-optimize also has medium-term implications as the company moves to 5G.<\/p>\n

On the Enterprise side, Vodafone takes advantage of its massive data on customer movement, app usage, and page visits to better service its Enterprise Customers.\u00a0 According to David Gonzalez Martinez, Vodafone Enterprise clients can leverage Vodafone\u2019s data and machine learning algorithms to improve their own companies.\u00a0 Retailers, for instance, can use Vodafone customer traffic data when deciding the best location to open a store.\u00a0 Governments can improve public transportation as it gets a better sense of peak transit times and routes[3]. Medium term applications of machine learning for Vodafone\u2019s Enterprise customers are also quite large when considering the expected launch of 5G in the next year. 5G can process even larger amounts of data more quickly, allowing Vodafone to better support clients across multiple industries \u2013 e.g. help accelerate autonomous car development, reduce manufacturing downtime, improve healthcare, and create smarter cities[9,10]. \u00a0<\/strong><\/p>\n

With 5G on the horizon, Vodafone has strong incentive to continue improving its machine learning capabilities.\u00a0 Currently, innovation seems to occur primarily in-house at a local level (for instance, Vodafone Germany conducting C-SON tests) and with external partners, supported by some M&A activity.\u00a0 Vodafone should consider whether a more centralized approach would speed the rate of innovation across the organization.\u00a0 I would also recommend Vodafone consider whether it can further accelerate innovations via investments or more acquisitions of early stage start-ups. Additionally, Vodafone has maintained a strict customer privacy policy[8], <\/strong>using aggregate rather than individual customer data to provide insights to Enterprise clients.\u00a0 Considering an \u201copt-in\u201d approach to utilize individual-level data could allow the company to better service both individual and enterprise clients by facilitating deeper connections between the two.\u00a0 Should Vodafone take advantage of individualized data to enhance its Enterprise product offerings or is there too much sensitivity given prior customer protection violations?\u00a0 Given strong competition in telecommunications, its access to data, and Vodafone\u2019s in-house 5G and machine learning capabilities, should they consider expanding into other industries?\u00a0 If so, what industries?<\/p>\n

Word Count: 793<\/p>\n

 <\/p>\n

\u00a0<\/em>Sources<\/strong><\/p>\n

[1] Brynjolfsson and A. McAfee. What\u2019s driving the machine learning explosion?<\/a>\u00a0性视界 Business Review Digital Articles<\/em>\u00a0(July 18, 2017). Accessed November 8, 2018.<\/p>\n

[2] Vodafone Group Plc. Annual Report 2018. \u00a0https:\/\/www.vodafone.com\/content\/annualreport\/annual_report18\/downloads\/Vodafone-full-annual-report-2018.pdf<\/a> (July 2018).\u00a0 Accessed November 8, 2018.<\/p>\n

[3] Gonzalez Martinez, David. Telephone interview. November 8, 2018.<\/p>\n

[4] Abbas, M.. Vodafone India leverages artificial intelligence and big data.\u00a0The Economic Times<\/em>\u00a0Retrieved from http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/2042496758?accountid=11311<\/a> (May 17, 2018).\u00a0 Accessed November 12, 2018.<\/p>\n

[5] Vodafone to use artificial intelligence to speed up online queries.\u00a0M2 Presswire<\/em>\u00a0Retrieved from http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1886648629?accountid=11311\u00a0<\/a>(April 12, 2017).\u00a0 Accessed November 12, 2018.<\/p>\n

[6] Davis, Ben. Vodafone\u2019s chatbot is delivering twice the conversion rate of its website. Econsultancy. <\/em>\u00a0https:\/\/econsultancy.com\/vodafones-chatbot-is-delivering-twice-the-conversion-rate-of-its-website\/<\/a> (October 11, 2018).\u00a0 Accessed November 8, 2018<\/p>\n

[7] Mack, B. Meet Kiri, Vodafone\u2019s new digital human.\u00a0https:\/\/global-factiva-com.prd2.ezproxy-prod.hbs.edu\/redir\/default.aspx?P=sa&NS=16&AID=9HAR000400&an=COMSMEA020181103eeav00001&cat=a&ep=ASI<\/a> (October 31, 2018).\u00a0 Accessed November 8, 2018.<\/p>\n

[8] Tenorio, Santiago. AI enabled augmented engineering increases network optimisation speed by over 45,000%.\u00a0 Vodafone Group. <\/em>\u00a0https:\/\/www.vodafone.com\/content\/index\/what\/technology-blog\/ai-enabled-augmented-engineering-increases-network-optimisation-.html<\/a> (September 26, 2017).\u00a0 Accessed November 8, 2018.<\/p>\n

[9] Krigsman, Michael. Vodafone exec talks 5G, autonomous vehicles, virtual surgery, security, and more. ZDnet. <\/em>https:\/\/www.zdnet.com\/article\/vodaphone-exec-talks-5g-autonomous-vehicles-virtual-surgery-security-and-more\/<\/a> (October 29, 2018). Accessed November 8, 2018.<\/p>\n

[10] Telcos and automakers increase connected car trials, partnerships. Mobile Europe. <\/em>https:\/\/www.mobileeurope.co.uk\/press-wire\/telcos-and-automakers-increase-connected-car-trials-partnerships<\/a> \u00a0(March 1, 2017). \u00a0Accessed November 8, 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

Vodafone's ability to deploy more predictive analytics given its access to large, global, and varied data pools will be a key competitive advantage as we approach a 5G world.<\/p>\n","protected":false},"author":11549,"featured_media":32120,"comment_status":"open","ping_status":"closed","template":"","categories":[4365,156,346,970,2373,344,4711],"class_list":["post-32091","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-artifical-intelligence","category-customer-service","category-machine-learning","category-personalization","category-process-improvement","category-product-development","category-telecomm","hck-taxonomy-organization-vodafone","hck-taxonomy-industry-telecommunications","hck-taxonomy-country-united-kingdom"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nVodafone: leveraging Machine Learning in an (almost) 5G world - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Vodafone: leveraging Machine Learning in an (almost) 5G world - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"Vodafone's ability to deploy more predictive analytics given its access to large, global, and varied data pools will be a key competitive advantage as we approach a 5G world.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/\" \/>\n<meta property=\"og:site_name\" content=\"Technology and Operations Management\" \/>\n<meta property=\"og:image\" content=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Vodafone-1.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"313\" \/>\n\t<meta property=\"og:image:height\" content=\"200\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/\",\"url\":\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/\",\"name\":\"Vodafone: leveraging Machine Learning in an (almost) 5G world - Technology and Operations Management\",\"isPartOf\":{\"@id\":\"https:\/\/d3.harvard.edu\/platform-rctom\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Vodafone-1.jpg\",\"datePublished\":\"2018-11-13T19:44:58+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/#primaryimage\",\"url\":\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Vodafone-1.jpg\",\"contentUrl\":\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Vodafone-1.jpg\",\"width\":313,\"height\":200},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/d3.harvard.edu\/platform-rctom\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Submissions\",\"item\":\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Vodafone: leveraging Machine Learning in an (almost) 5G world\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/d3.harvard.edu\/platform-rctom\/#website\",\"url\":\"https:\/\/d3.harvard.edu\/platform-rctom\/\",\"name\":\"Technology and Operations Management\",\"description\":\"MBA Student Perspectives\",\"potentialAction\":[{\"@type\":\"性视界Action\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/d3.harvard.edu\/platform-rctom\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Vodafone: leveraging Machine Learning in an (almost) 5G world - Technology and Operations Management","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/","og_locale":"en_US","og_type":"article","og_title":"Vodafone: leveraging Machine Learning in an (almost) 5G world - Technology and Operations Management","og_description":"Vodafone's ability to deploy more predictive analytics given its access to large, global, and varied data pools will be a key competitive advantage as we approach a 5G world.","og_url":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/","og_site_name":"Technology and Operations Management","og_image":[{"width":313,"height":200,"url":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Vodafone-1.jpg","type":"image\/jpeg"}],"twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/","url":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/","name":"Vodafone: leveraging Machine Learning in an (almost) 5G world - Technology and Operations Management","isPartOf":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/#website"},"primaryImageOfPage":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/#primaryimage"},"image":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/#primaryimage"},"thumbnailUrl":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Vodafone-1.jpg","datePublished":"2018-11-13T19:44:58+00:00","breadcrumb":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/#primaryimage","url":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Vodafone-1.jpg","contentUrl":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Vodafone-1.jpg","width":313,"height":200},{"@type":"BreadcrumbList","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/vodafone-leveraging-machine-learning-in-an-almost-5g-world\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/d3.harvard.edu\/platform-rctom\/"},{"@type":"ListItem","position":2,"name":"Submissions","item":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/"},{"@type":"ListItem","position":3,"name":"Vodafone: leveraging Machine Learning in an (almost) 5G world"}]},{"@type":"WebSite","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/#website","url":"https:\/\/d3.harvard.edu\/platform-rctom\/","name":"Technology and Operations Management","description":"MBA Student Perspectives","potentialAction":[{"@type":"性视界Action","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/d3.harvard.edu\/platform-rctom\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"_links":{"self":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission\/32091","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission"}],"about":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/types\/hck-submission"}],"author":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/users\/11549"}],"replies":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/comments?post=32091"}],"version-history":[{"count":0,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission\/32091\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/media\/32120"}],"wp:attachment":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/media?parent=32091"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/categories?post=32091"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}