![\"Sentiment](\"http:\/\/19squx2sqzlk2w3lh726rs88.wpengine.netdna-cdn.com\/wp-content\/uploads\/2015\/11\/Sentiment-Impact.png\")
<\/p>\nHave you ever filled out a customer satisfaction survey and thought: \u201cThey are never going to read this [do something about it\/respond\/etc. etc.].\u201d For many companies, that\u2019s exactly what happens.\u00a0 Companies collect feedback and all it spits out is a number \u2013 how do they know what to do when I tell them that, on a scale from 1 to 10, I was a \u201c7\u201d satisfied?\u00a0 They could read my individual comments, but that\u2019s hard to scale when you have 1,000\u2019s to 10,000\u2019s+ of transaction occurring at any time.\u00a0<\/p>\n
Enter Medallia, a company that brings big data analytics to customer experience.
\nTheir software platform allows their clients to take in data from a range of sources (surveys, social media, mobile, and ERP\/CRM), visualize the data, and act on results.\u00a0 But how?\u00a0 Machine learning tools perform sentiment analysis on unstructured feedback (like comment boxes on surveys or reviews on TripAdvisor) to determine what and how customers are talking about your company.<\/p>\n
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In addition to these machine learning tools, Medallia employs a small \u201cInsights\u201d team that consults with major clients to better utilize their big data collected on the Medallia platform.\u00a0 One common use case is to help companies identify the most significant areas for improvement based on survey results.\u00a0 Imagine, for example, you were presented with thousands of rows of the below survey results?![\"Survey](\"http:\/\/19squx2sqzlk2w3lh726rs88.wpengine.netdna-cdn.com\/wp-content\/uploads\/2015\/11\/Survey-example.png\")
\u00a0 It\u2019s hard to draw any insights (and thus any plans of action) without using supervised regression techniques.\u00a0 Medallia\u2019s insights team could, however, tell you that a customer\u2019s overall satisfaction is most greatly impacted by your room cleanliness and internet speeds.\u00a0 Armed with such information, managers can allocate resources and investments to operations that most impact their customers.<\/p>\n
This process can create a ton of value.\u00a0 Many sources of research suggest that overall customer satisfaction increases can be directly tied to loyalty, reduced churn, higher average basket sizes, etc.\u00a0 Medallia\u2019s tools help managers supervise and act on customer satisfaction \u2013 leading to higher revenues, reduced costs, and better ROI on decision-making.<\/p>\n
However, Medallia has so far struggled with the Value Capture in this model.\u00a0 As a SaaS provider, the system is priced as a subscription that is often tied to the size of the organization or relative number of transactions\/customers.\u00a0 Further, the Insights work \u2013 which can derive significant strategic value \u2013 is priced like most consulting arrangements using hourly rates.\u00a0 With this model, the company leaves a lot of value on the table.<\/p>\n
This calls into question some of their operating model decisions.\u00a0 SaaS companies are valuable because of high margin, recurring revenue streams.\u00a0 Medallia, alternatively, has built itself up with a significant services department \u2013 like the Insights team and implementation\/management services.\u00a0 These lower margin businesses are not scalable like software.<\/p>\n
Accordingly, I think the company will face many challenges going forward. First and foremost is the question of how Medallia can capture more of the value that it creates.\u00a0 One suggestion is to turn to a value-based pricing model \u2013 companies pay more as the software helps identify clear benefits in customer satisfaction (i.e. improvements in Net Promoter Score result in increasing prices).\u00a0 But even with those changes, the company will have to solve other operational questions like: How do you alter the business model to rely on the power and scalability of big data and technology? What happens if\/when your biggest clients choose to build these competencies in house?\u00a0 Hopefully Medallia\u2019s sophisticated algorithms push them past the competition and make this a winner take all market.<\/p>\n
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<\/p>\n","protected":false},"excerpt":{"rendered":"
Medallia uses machine learning and regression to help its Fortune 1000 clients collect, analyze, and act on customer feedback – in turn increasing customer loyalty and spend. That value creation made this startup into one of Silicon Valley’s newest Unicorns.<\/p>\n","protected":false},"author":30,"featured_media":2484,"comment_status":"open","ping_status":"closed","template":"","categories":[34,366,870],"class_list":["post-2482","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-customer-loyalty","category-machine-learning","category-regression"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/data-driven-value-creation-value-capture-and-operating-models\/","yoast_head":"\n