{"id":12004,"date":"2020-04-19T19:20:01","date_gmt":"2020-04-19T23:20:01","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/zoba-an-ai-driven-demand-forecasting-and-optimization-saas-for-shared-mobility-companies\/"},"modified":"2020-04-19T19:41:53","modified_gmt":"2020-04-19T23:41:53","slug":"zoba-an-ai-driven-demand-forecasting-and-optimization-saas-for-shared-mobility-companies","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/zoba-an-ai-driven-demand-forecasting-and-optimization-saas-for-shared-mobility-companies\/","title":{"rendered":"Zoba: an AI-driven demand forecasting and optimization SaaS for shared mobility companies"},"content":{"rendered":"<p><strong>Background<\/strong><\/p>\n<p>Zoba is an early-stage spatial analytics start-up that offers demand-forecasting and optimization software to Micromobility and Car-Sharing companies. Customers can plug-and-play Zoba\u2019s machine learning algorithms and combine their own data together with Zoba\u2019s expertise (and featured data) to predict demand and optimize their fleet distribution to increase its utilization rate.<\/p>\n<figure id=\"attachment_12002\" aria-describedby=\"caption-attachment-12002\" style=\"width: 875px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2020\/04\/Austin-Trips-Map.png\"><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-12002\" src=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2020\/04\/Austin-Trips-Map.png\" alt=\"\" width=\"875\" height=\"621\" srcset=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2020\/04\/Austin-Trips-Map.png 875w, https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2020\/04\/Austin-Trips-Map-300x213.png 300w, https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2020\/04\/Austin-Trips-Map-768x545.png 768w, https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2020\/04\/Austin-Trips-Map-600x426.png 600w\" sizes=\"auto, (max-width: 875px) 100vw, 875px\" \/><\/a><figcaption id=\"caption-attachment-12002\" class=\"wp-caption-text\">Austin scooter trips start (white) and end points (blue) visualized with kepler.gl for morning commute trips in January 2019 \/ Source: Zoba Blog<\/figcaption><\/figure>\n<p>Micromobility and Car-Sharing solutions are expected to grow with a CAGR between 20 and 25% in the coming 5 years<a href=\"#_ftn1\" name=\"_ftnref1\">[1]<\/a>, with the Micromobility market reaching 300 USD billion by 2030<a href=\"#_ftn2\" name=\"_ftnref2\">[2]<\/a>. The attractiveness of these two markets has brought together big OEM\u2019s, mobility giants and an overwhelming quantity of local start-ups: all of them trying to validate a multifaceted business model in which operational excellence is critical to surviving in a fierce and thin-margins market.<\/p>\n<p>Operational excellence requires adequate demand forecasting, disentangling utilization (the rides that consumers take) from actual demand (the rides that users would take if provided with proper supply). Only then, companies can optimize demand-supply tradeoffs by implementing rebalancing or variable pricing tactics.<\/p>\n<p><strong>A Scalable, Best-in-class and Real-Time Solution<\/strong><\/p>\n<p>Zoba offers its customers a scalable, best-in-class and real-time software that offsets the drawbacks of outsourcing the demand forecasting and fleet distribution algorithms.<\/p>\n<ul>\n<li><strong>Flexible scalability:<\/strong> Through a simple API call, Zoba\u2019s solution can handle the customer\u2019s event data from day-one. Building those capabilities in-house is particularly challenging for companies in the space, where limited funding or human capital hinders the scalability of developing forecasting and optimization tools and slows down its development. Instead, Zoba offers a \u201cturn-key\u201d solution.<\/li>\n<li><strong>Best-in-class: <\/strong>Zoba can develop highly accurate demand forecasting solutions by feeding its machine learning algorithms with larger sets of data than the ones that in-house solutions can access to. In fact, Zoba captures multiple information sources, including both customer\u2019s generated data and external featured data. For example, a common pitfall of in-house built demand forecasting methods of Micromobility operators is to assume that high usage areas are the ones with high demand. In reality, \u201cthey are actually observing what is in large part a byproduct of where they have historically placed supply.\u201d<a href=\"#_ftn3\" name=\"_ftnref3\">[3]<\/a>To operators navigating these demand-supply feedback loops, Zoba can bring best-in-class solutions to predict actual demand and boost their utilization rates.<\/li>\n<li><strong>Real-time decision-making: <\/strong>In addition to offering more accurate models in \u201cstable\u201d conditions, Zoba\u2019s prediction and optimization package provides customers with the capability of capturing sudden variations (such as weather) in demand and redefine their fleet distribution to better adapt to these events and maximize their profitability.<\/li>\n<\/ul>\n<figure id=\"attachment_12003\" aria-describedby=\"caption-attachment-12003\" style=\"width: 640px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2020\/04\/Zobas-Model.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"size-large wp-image-12003\" src=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2020\/04\/Zobas-Model-1019x1024.jpg\" alt=\"\" width=\"640\" height=\"643\" srcset=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2020\/04\/Zobas-Model-1019x1024.jpg 1019w, https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2020\/04\/Zobas-Model-300x300.jpg 300w, https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2020\/04\/Zobas-Model-150x150.jpg 150w, https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2020\/04\/Zobas-Model-768x772.jpg 768w, https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2020\/04\/Zobas-Model-597x600.jpg 597w, https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2020\/04\/Zobas-Model.jpg 1286w\" sizes=\"auto, (max-width: 640px) 100vw, 640px\" \/><\/a><figcaption id=\"caption-attachment-12003\" class=\"wp-caption-text\">Zoba&#8217;s Product Flowchart \/ Source: Zoba website<\/figcaption><\/figure>\n<p><strong>SaaS value capturing<\/strong><\/p>\n<p>Zoba charges shared mobility companies based on the metered access to its service. Since the company needs to navigate a wide array of customers (and their different sales cycles), going from small operators to OEM\u2019s and large mobility players, its pricing scheme is probably developed on a case-by-case approach.<\/p>\n<p><strong>Main opportunities to expand value creation and value capture<\/strong><\/p>\n<ul>\n<li><strong>Profit-sharing agreements to boost data flywheel:<\/strong> on top of offering free-trials of its solution (up to one location selected by prospective customers), Zoba could expand its market penetration by engaging in cost-savings\/profit sharing agreements (with upper and lower limits) based on the results generated by Zoba\u2019s software implementation. This could reduce customer acquisition friction and speed up the data flywheel to improve its machine learning algorithms.<\/li>\n<li><strong>Expand customer base to aggregators and planners: <\/strong>since the sharing mobility sector is part of a larger urban network, the estimation of actual demand (<strong>not supply!<\/strong>) for each vertical mode of transportation is highly valuable for mobility end-to-end platforms, city planners, public transportation authorities and routing planners and mapping services. Putting in place proper confidentiality agreements with its current customers, Zoba could capture value by offering the aforementioned players an updated and accurate aggregated demand data-platform that can overperform current mobility data specification programs implemented by cities.<\/li>\n<\/ul>\n<p><strong>Main challenges and risk mitigation alternatives<\/strong><\/p>\n<ul>\n<li><strong>Increasing consolidation of shared mobility players: <\/strong>particularly in the micro-mobility landscape, there is an increasing consolidation (driven by regulation scrutiny and market forces) trend that will end with 3-4 main players per each market. As players become larger and their business models more developed, it could be feasible for these players to build Zoba\u2019s capabilities in-house. In order to mitigate this risk and increase the stickiness and robustness of the solution, Zoba could double down on its prediction and optimization capabilities by including additional variables related to other areas of its customer operations and by modelling, for example, the impact of Marketing or Product changes on customer behavior and demand.<\/li>\n<li><strong>Local environments and volatile regulatory landscape: <\/strong>as the company starts to expand both in the U.S. and internationally, its solution will need to adapt its prediction capabilities to the particularities of each local market and the volatile regulatory landscape of each location. In this scheme, ad-hoc partnerships with local software providers and global contracts with major OEM\u2019s and global mobility players could help Zoba expand its business in a sustainable and scalable way.<\/li>\n<\/ul>\n<p><strong><em>Sources<\/em><\/strong><\/p>\n<p><a href=\"#_ftnref1\" name=\"_ftn1\">[1]<\/a> Preeti Wadhwani and Prasenjit Saha, \u201cCar Sharing Market Size By Model (P2P, Station-Based, Free-Floating), By Business Model (Round Trip, One Way), By Application (Business, Private), Industry Analysis Report, Regional Outlook, Application Potential, Price Trend, Competitive Market Share &amp; Forecast, 2020 \u2013 2026\u201d, Global Market Insights, April, 2020 \u00a0<a href=\"https:\/\/www.gminsights.com\/industry-analysis\/carsharing-market\">https:\/\/www.gminsights.com\/industry-analysis\/carsharing-market<\/a>, accessed April 2020.<\/p>\n<p><a href=\"#_ftnref2\" name=\"_ftn2\">[2]<\/a>Kersten Heineke, Benedikt Kloss, Darius Scurtu, and Florian Weig. \u201cSizing the Micromobility market\u201d, McKinsey and Company<em>, <\/em>January, 2019, <a href=\"https:\/\/www.mckinsey.com\/industries\/automotive-and-assembly\/our-insights\/micromobilitys-15000-mile-checkup\">https:\/\/www.mckinsey.com\/industries\/automotive-and-assembly\/our-insights\/micromobilitys-15000-mile-checkup<\/a>, accessed April 2020.<\/p>\n<p><a href=\"#_ftnref3\" name=\"_ftn3\">[3]<\/a> Zoba, \u201cBeware the heatmap: feedback loops in shared mobility\u201d, <em>Medium (blog), <\/em>September 30, 2019, <a href=\"https:\/\/medium.com\/@zobatech\/beware-the-heatmap-feedback-loops-in-shared-mobility-9e675414dd73\">https:\/\/medium.com\/@zobatech\/beware-the-heatmap-feedback-loops-in-shared-mobility-9e675414dd73<\/a>, accessed April 2020.<\/p>\n<p>[4]\u00a0 Zoba, &#8220;Product&#8221;, <a href=\"https:\/\/www.zoba.com\/product\">https:\/\/www.zoba.com\/product<\/a>, accessed April 2020.<\/p>\n<p>[5] Zoba, &#8220;Blog&#8221;, <a href=\"https:\/\/www.zoba.com\/blog\">https:\/\/www.zoba.com\/blog<\/a>, accessed April 2020<\/p>\n<p>[6] Megan Rose Dickey, &#8220;Zoba raises $3 million to help mobility companies predict demand&#8221;, TechCrunch, Feb. 2019, <a href=\"https:\/\/techcrunch.com\/2019\/02\/21\/zoba-raises-3-million-to-help-mobility-companies-predict-demand\/\">https:\/\/techcrunch.com\/2019\/02\/21\/zoba-raises-3-million-to-help-mobility-companies-predict-demand\/<\/a>, accessed April 2020.<\/p>\n<p>[7] Frederick Daso, &#8220;Zoba, A 性视界 Spatial Analytics Startup, Offers Logistics-As-A-Service To All&#8221;, Forbes, Feb. 2020, <a href=\"https:\/\/www.forbes.com\/sites\/frederickdaso\/2020\/02\/04\/zoba-a-harvard-spatial-analytics-startup-offers-logistics-as-a-service-to-all\/#7fa9883a65d3\">https:\/\/www.forbes.com\/sites\/frederickdaso\/2020\/02\/04\/zoba-a-harvard-spatial-analytics-startup-offers-logistics-as-a-service-to-all\/#7fa9883a65d3<\/a>, accessed April 2020.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>As shared mobility providers grow and need to validate multifaceted business models, Zoba brings a modular solution to increase the companies&#039; ability to predict demand and optimize their fleets.<\/p>\n","protected":false},"author":11909,"featured_media":12007,"comment_status":"open","ping_status":"closed","template":"","categories":[381,366,563,1395,1077,2767],"class_list":["post-12004","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-future-of-mobility","category-machine-learning","category-mobility","category-prediction","category-sharing-economy","category-spatial-analytics","hck-taxonomy-organization-zoba","hck-taxonomy-industry-transportation","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/competing-with-data-and-ai-challenge\/","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - 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