{"id":35085,"date":"2018-11-13T18:44:28","date_gmt":"2018-11-13T23:44:28","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/how-salesforce-is-using-machine-learning-to-unlock-more-value-for-its-customers-and-their-customers\/"},"modified":"2018-11-13T18:44:28","modified_gmt":"2018-11-13T23:44:28","slug":"how-salesforce-is-using-machine-learning-to-unlock-more-value-for-its-customers-and-their-customers","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/how-salesforce-is-using-machine-learning-to-unlock-more-value-for-its-customers-and-their-customers\/","title":{"rendered":"How Salesforce is using machine learning to unlock more value for its customers (and their customers)"},"content":{"rendered":"

Salesforce is a software company that was founded by Marc Benioff in 1999. Salesforce\u2019s core business focuses on Customer Relationship Management (CRM) software solutions. Companies, and salespeople in particular, typically rely on CRM tools to help them manage their entire the sales process, from customer sourcing to suggested next steps and pipeline management. In recent years, machine learning has become a paramount technology for CRM products, and Salesforce is currently working on implementing an innovative machine learning strategy to fuel its future growth.<\/p>\n

Machine learning is critical to Salesforce\u2019s product offering because it can help salespeople be better at their jobs. Machine learning can solve one of the most difficult questions that salespeople must answer: which potential customers are most likely to buy? Using the vast data sets that Salesforce\u2019s CRM software collects on potential customers, machine learning can identify patterns of customer behavior which are too complex or obscure for a human salesperson to detect. Increasingly, customers will look to Salesforce not only to help them manage their sales process, but also to help them make decisions about which customers to invest in and which ones to de-prioritize. Machine learning can also help salespeople decide what the next step with a customer should be. Should we email the customer? Is a phone call more appropriate? Is an in-person meeting with the customer likely to yield a good outcome? These are decisions that salespeople currently make relying on their instincts, and statistics-based machine learning can significantly reduce the human inefficiencies in this process.<\/p>\n

To address this issue, Salesforce\u2019s management team has rolled out machine learning capabilities to their customers via a new analytics platform called \u201cEinstein.\u201d Their machine learning model uses \u201cadvanced machine learning, deep learning, predictive analytics, natural language processing and smart data discovery\u201d[1]<\/a> to help clients better identify good sales leads. Critically, Einstein is custom-tailored to each client\u2019s unique needs and is constantly learning and self-improving. This means that, as time goes on, Einstein will get better at making predictions about the quality of a particular sales lead and recommending best next actions. To date, Salesforce has deployed Einstein to bolster their cloud offerings in Sales, Customer Service, Marketing, and Community Management.[2]<\/a><\/p>\n

In the short term, Salesforce is focused on improving these offerings for customers and working directly with customers to tailor the Einstein platform to their needs (e.g. they have worked individually with Airbus[3]<\/a> and Marriott[4]<\/a> on such initiatives). In the medium term, Salesforce is going to heavily focus on acquisition of AI acquisitions for its future growth. Historically, Salesforce has always leaned more towards acquiring companies to fill gaps in its service offerings to clients, as opposed to developing software to fill those gaps from scratch. According to Keith Block, Salesforce\u2019s recently appointed co-CEO, Salesforce will continue to employ his strategy to boost their capabilities in the machine learning and AI space[5]<\/a>. In the last year alone, Salesforce acquired Datorama, an Israeli startup offering \u201can artificial intelligence service to marketers that was integrated into the Salesforce Marketing Cloud.\u201d[6]<\/a> In the age of big data analytics and machine learning, such acquisitions are particularly valuable because of the volume of high quality customer data that they bring in. By combining the data of newly acquired companies with their own, Salesforce can unlock important synergies and improve the quality of the predictions made by their machine learning algorithms.<\/p>\n

As Salesforce looks to improve its machine learning capabilities, I would recommend for it to be careful not to overly emphasize a certain archetypical consumer\u2019s past purchasing history as a predictor for future purchases. Often times, the reason why a potential customer may not decide to buy a product or service is not because they are a bad lead, but because the product does not solve their problem effectively. By overly indexing past purchasing behavior and ignoring customers that have not historically purchased a product, companies are guaranteed to miss out on opportunities for product improvement and innovation. For example, for a company that sells solar-panels, a great predictor of a purchasing customer may be whether the customer has a tilted roof which is amenable for solar-panel installation. However, by overly indexing the type of roof that each potential customer have, the company may miss out on the opportunity to improve their product to also work well on flat roofs.[7]<\/a><\/p>\n

Open ended question:<\/p>\n