Medallia’s Approach to Customer Feedback Data

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.

Have you ever filled out a customer satisfaction survey and thought: “They are never going to read this [do something about it/respond/etc. etc.].” For many companies, that’s exactly what happens.  Companies collect feedback and all it spits out is a number – how do they know what to do when I tell them that, on a scale from 1 to 10, I was a “7” satisfied?  They could read my individual comments, but that’s hard to scale when you have 1,000’s to 10,000’s+ of transaction occurring at any time. Sentiment Impact

Enter Medallia, a company that brings big data analytics to customer experience.
Their 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.  But how?  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.


In addition to these machine learning tools, Medallia employs a small “Insights” team that consults with major clients to better utilize their big data collected on the Medallia platform.  One common use case is to help companies identify the most significant areas for improvement based on survey results.  Imagine, for example, you were presented with thousands of rows of the below survey results?Survey example  It’s hard to draw any insights (and thus any plans of action) without using supervised regression techniques.  Medallia’s insights team could, however, tell you that a customer’s overall satisfaction is most greatly impacted by your room cleanliness and internet speeds.  Armed with such information, managers can allocate resources and investments to operations that most impact their customers.

This process can create a ton of value.  Many sources of research suggest that overall customer satisfaction increases can be directly tied to loyalty, reduced churn, higher average basket sizes, etc.  Medallia’s tools help managers supervise and act on customer satisfaction – leading to higher revenues, reduced costs, and better ROI on decision-making.

However, Medallia has so far struggled with the Value Capture in this model.  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.  Further, the Insights work – which can derive significant strategic value – is priced like most consulting arrangements using hourly rates.  With this model, the company leaves a lot of value on the table.

This calls into question some of their operating model decisions.  SaaS companies are valuable because of high margin, recurring revenue streams.  Medallia, alternatively, has built itself up with a significant services department – like the Insights team and implementation/management services.  These lower margin businesses are not scalable like software.

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.  One suggestion is to turn to a value-based pricing model – companies pay more as the software helps identify clear benefits in customer satisfaction (i.e. improvements in Net Promoter Score result in increasing prices).  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?  Hopefully Medallia’s sophisticated algorithms push them past the competition and make this a winner take all market.




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Student comments on Medallia’s Approach to Customer Feedback Data

  1. The challenges you mentioned were the ones I also thought of when thinking about the viability of a tech business that has an army of sales people and consultant like people to deliver on a relatively simple product. Yes their platform can decipher unstructured data and assess sentiment of customers towards a brand, but it did not seem like anything a company couldn’t really do on their own if they cared about customer service (i.e. Caesar’s Entertainment has a team of statisticians to figure out logistics and customer service aspects, so nothing a good old regression could not do.) Also, once a customer learns the techniques of data analysis (i.e. what your software looks at), the value they get out of the product is likely to decrease. This would only work in companies that assess very methodically and rigorously the customer feedback, and for the vast majority of companies this is not a monthly task. I am sort of bearish on their ability to prove and get compensated for their value add.

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