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. Vodafone Group plc, with 313MM customers across networks in 25 countries, 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. YoY declines in revenues, from €49.8B in 2016 to €46.4B 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. 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.
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. According 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]. For 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. The 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. TOBi has seen early success; conversion rates are +100% compared to its website, with lower transaction times and abandonment rates [5,6]. 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. Kiri can handle verbal interactions and is capable of reading and responding to human emotions. 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.
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. 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. The company expects to deploy this new technology commercially in 2019. While 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.
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. According to David Gonzalez Martinez, Vodafone Enterprise clients can leverage Vodafone’s data and machine learning algorithms to improve their own companies. Retailers, for instance, can use Vodafone customer traffic data when deciding the best location to open a store. Governments can improve public transportation as it gets a better sense of peak transit times and routes. Medium term applications of machine learning for Vodafone’s 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 – e.g. help accelerate autonomous car development, reduce manufacturing downtime, improve healthcare, and create smarter cities[9,10].
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 Vodafone Group Plc. Annual Report 2018. https://www.vodafone.com/content/annualreport/annual_report18/downloads/Vodafone-full-annual-report-2018.pdf (July 2018). Accessed November 8, 2018.
 Gonzalez Martinez, David. Telephone interview. November 8, 2018.
 Abbas, M.. Vodafone India leverages artificial intelligence and big data. The Economic Times Retrieved from http://search.proquest.com.ezp-prod1.hul.harvard.edu/docview/2042496758?accountid=11311 (May 17, 2018). Accessed November 12, 2018.
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 Davis, Ben. Vodafone’s chatbot is delivering twice the conversion rate of its website. Econsultancy. https://econsultancy.com/vodafones-chatbot-is-delivering-twice-the-conversion-rate-of-its-website/ (October 11, 2018). Accessed November 8, 2018
 Mack, B. Meet Kiri, Vodafone’s new digital human. https://global-factiva-com.prd2.ezproxy-prod.hbs.edu/redir/default.aspx?P=sa&NS=16&AID=9HAR000400&an=COMSMEA020181103eeav00001&cat=a&ep=ASI (October 31, 2018). Accessed November 8, 2018.
 Tenorio, Santiago. AI enabled augmented engineering increases network optimisation speed by over 45,000%. Vodafone Group. https://www.vodafone.com/content/index/what/technology-blog/ai-enabled-augmented-engineering-increases-network-optimisation-.html (September 26, 2017). Accessed November 8, 2018.
 Krigsman, Michael. Vodafone exec talks 5G, autonomous vehicles, virtual surgery, security, and more. ZDnet. https://www.zdnet.com/article/vodaphone-exec-talks-5g-autonomous-vehicles-virtual-surgery-security-and-more/ (October 29, 2018). Accessed November 8, 2018.
 Telcos and automakers increase connected car trials, partnerships. Mobile Europe. https://www.mobileeurope.co.uk/press-wire/telcos-and-automakers-increase-connected-car-trials-partnerships (March 1, 2017). Accessed November 8, 2018.