Summary:<\/em><\/strong> \u00a0McKinsey says platform companies will represent 30% of global business revenue by next year (2020).\u00a0 In Part 1 of this article we started to lay out some important lessons learned and examples for you to follow.\u00a0 Here are the rest.<\/em><\/p>\n \u00a0 McKinsey says platform companies will represent 30% of global business revenue by next year (2020).\u00a0 In Part 1 of this article we started to lay out some important lessons learned and examples for you to follow.\u00a0 Here are the rest.<\/p>\n The goal here is to describe how mature companies can benefit from platform strategy either by building a parallel platform offering or by executing a complete pivot to this new approach.\u00a0 If you\u2019ve pretty much exhausted the standard strategies of line and channel extension, and verticalizing your supply chain, these lessons are for you.<\/p>\n In Part 1 we described these lessons:<\/p>\n Now let\u2019s continue:<\/p>\n New and Used Cars<\/em><\/strong><\/p>\n The travel insurance example above illustrates information asymmetry, but for an even simpler example look no further than new and used car sales.<\/p>\n In new car sales such a large percentage of wholesale discounts are now based on dealer volume and not reflected on the invoice that dealers can sell cars at invoice all day long and still make a profit.\u00a0 Only the dealer knows what a \u2018fair price\u2019 is and it\u2019s their job to try to get more than that.<\/p>\n Similarly on the used car side, only the dealer knows what they paid for their used inventory and what\u2019s its true condition is relative to price.<\/p>\n TrueCar.com<\/strong> has been able to aggregate actual retail prices from a large percentage of dealers and parlay that information into a \u2018fair price\u2019 gauge available to buyers, adjusting even for the level of equipment and locale.\u00a0 On the supply side, the dealers wanting to be represented must supply their retail sales information and can also buy valuable information about their competitor\u2019s pricing.<\/p>\n CarFax<\/strong> has become the defacto quality bar in used car sales, reporting prior accidents and repairs.\u00a0 Similarly the pricing portals such as Kelly Blue Book (KBB.com)<\/strong> play a role in setting fair expectations about the value of trade-ins.<\/p>\n \u00a0<\/p>\n Although B2C networks have more users, the users in B2B networks often control more resources and therefore can be just as valuable.\u00a0 The same rules about information imbalance between buyers and sellers apply.<\/p>\n Take for example the complex supplier-dominated ecosystems around container shipping, logistics, trading, regulations and tariffs, and ports.\u00a0 Most transportation suppliers are moving to platforms to capture the relationship with their shipping customers by making the transaction transparent and presumably save cost and time.<\/p>\n MassMutual Insurance<\/em><\/strong><\/p>\n Company sponsored defined benefit retirement plans are complex to administer and to optimize.\u00a0 MassMutual and Matrix Financial Solutions partnered on a platform aimed at corporate DB Plan managers offering simplified evaluation and selection of a broader array of investment options, self-service reporting, simplified administration, can generate custom reports on individual plans, and provides customized pension yield curves so that plan sponsors can calculate and report pension liabilities.\u00a0 MassMutual directly serves 2,600 DB plans which may seem like a small network, but one that controls a very large quantity of assets.<\/p>\n \u00a0<\/p>\n John Deere<\/em><\/strong><\/p>\n As all elements of agriculture and farming have become more reliant on AI\/ML it was a natural extension for John Deere to platformize its My John Deere Operations Center.\u00a0 This is a platform devoted to all things technology in farming from predictive maintenance to GIS\/GPS monitoring of crop yields, chemical application, and remote operation of equipment.\u00a0 It\u2019s worth noting that there\u2019s a natural affinity between all things IoT and platform strategy.<\/p>\n Of particular note here is how John Deere opened itself up to other suppliers of gear related to AI\/ML based monitoring and control by exposing APIs.\u00a0 This led to one of the most desirable features of any platform strategy in which additional suppliers, managing their own contributions to the network, create a virtuous loop of drawing in more customer-users.<\/p>\n So far John Deere has attracted 114 platform partners that provide data, calibration, or add-on features to Deere\u2019s equipment ranging from weather forecasting, to soil testing, to farm management accounting.<\/p>\n \u00a0<\/p>\n Our final lessons are these:<\/strong><\/p>\n LinkedIn remains valuable for recruiters looking for a wide variety of skills, but less so for recruiters and job seekers in a narrow specialty.<\/p>\n A platform strategy that optimizes a specific type of value trade is not immune from disruption.\u00a0 Take for example the platforms created by American Express, Visa, and MasterCard bringing shopkeepers and shoppers together.<\/p>\n All of these traditional successful platforms have had to hustle to keep up with burgeoning fintech mobile payment systems that can be owned by mobile phone providers like Apple or ecommerce giants like Alibaba.\u00a0 As awareness of platform strategies becomes mainstream, expect waves of competition.\u00a0 But above all, don\u2019t wait to evaluate and if possible act on your own platform strategy.<\/p>\n \u00a0<\/p>\n \u00a0<\/p>\n Other articles on AI Strategy<\/strong><\/p>\n AI\/ML Lessons for Creating a Platform Strategy \u2013 Part 1<\/u><\/em><\/a><\/p>\n A Radical AI Strategy – Platformication<\/u><\/em><\/a><\/p>\n Now that We\u2019ve Got AI What do We do with It?<\/u><\/em><\/a><\/p>\n Capturing the Value of ML\/AI \u2013 the Challenge of Offensive versus Defensive Data Strategies<\/u><\/em><\/a><\/p>\n The Case for Just Getting Your Feet Wet with AI<\/u><\/em><\/a><\/p>\n The Fourth Way to Practice Data Science \u2013 Purpose Built Analytic Modules<\/u><\/em><\/a><\/p>\n From Strategy to Implementation \u2013 Planning an AI-First Company<\/u><\/em><\/a><\/p>\n Comparing the Four Major AI Strategies<\/u><\/em><\/a><\/p>\n Comparing AI Strategies \u2013 Systems of Intelligence<\/u><\/em><\/a><\/p>\n Comparing AI Strategies \u2013 Vertical versus Horizontal.<\/u><\/em><\/a><\/p>\n What Makes a Successful AI Company<\/u><\/em><\/a> \u2013 Data Dominance<\/u><\/em><\/span><\/p>\n AI Strategies \u2013 Incremental and Fundamental Improvements<\/u><\/em><\/a><\/p>\n \u00a0<\/p>\n Other articles by Bill Vorhies<\/u><\/em><\/a><\/p>\n \u00a0<\/p>\n About the author:<\/strong> \u00a0Bill is Contributing Editor for Data Science Central.\u00a0 Bill is also President & Chief Data Scientist at Data-Magnum and has practiced as a data scientist since 2001.\u00a0\u00a0\u00a0 He can be reached at:<\/p>\n Bill@DataScienceCentral.com<\/a> or<\/span> Bill@Data-Magnum.com<\/a><\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a><\/p>\n
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<\/a>Creating a platform strategy is not a guaranty of freedom from competition.\u00a0 Since networks are most valuable when users have similar needs, it follows that broader general purpose platforms will be succeeded by more narrowly defined micro platforms where users are more similar.\u00a0 A good example is the LinkedIn platform for sharing of all types of work experiences versus special purpose recruiting platforms focusing on a rare but narrow group of talents like data science.<\/p>\n