Advice From A Top VC: Big Data Insights From 'Cloud' Companies And The 'Death Of McKinsey'
Silicon Valley VC firm Emergence Capital Partners was an early investor in Salesforce.com and now works exclusively with cloud-based startups primarily focused on business customers. Gordon Ritter writes that they can learn a lot from consumer services companies.
By Gordon Ritter
Consumer internet companies have always been good at harnessing behavioral data from their customers to serve their customers better (and help themselves in the process). Many enterprise cloud companies have the same opportunity but have not focused on the importance of this data.
Maybe it is because there is not the direct correlation between product changes and resulting advertising monetization.
Or that the scale of user interactions seems too small to warrant focusing on it. Whatever the reason, the value of using aggregated customer data to help avoid churn, increase upsell, and ultimately create a more strategic relationship with customers, is at stake.
Here are three steps I encourage all our companies to take as they transform from being "tools" companies to thinking of themselves as "insight" companies:
1. Embed your employee insights into your offering.
Start by getting your key product, marketing and sales teams in a room and ask a simple question:
"What's the most important piece of advice that we tell our customers every day about how to get more value out of our service?"
It could be a common wisdom that your customer success people mention every day when they talk with customers. It could be the little secret that diligent customers find buried in your FAQs.
In my experience every company has these little gems. The key is to embed the insights directly in your service and to use this to build your data strategy.
At Salesforce.com, they saw early on that customers who forecast their sales pipeline were much more likely to stay as customers and add seats. Armed with this knowledge, the product team focused attention on making forecasting easier and encouraged sales reps to predict what a sales opportunity might be worth in the future.
This discovery about the value of forecasting led to a dramatic decrease in churn. I bet your company has 5 or 10 of these same insights but you have not codified them into changes in your offering.
2. Automated benchmark reports bundled with enhanced versions.
Gather aggregate behavioral data from your customers, and create reports that quantify how your customers are doing, compared to other customers. This is commonly called "benchmarking," but for a cloud company, the data is much richer because it is more granular.
Cloud companies can report detailed data about precise usage patterns and the correlation between those patterns and customer success with the product.
At Lithium Technologies, their depth of knowledge on how to build successful customer and marketing communities, led to a "Community Health Index" (CHI). A customer's CHI score is based on detailed usage patterns and is highly predictive about whether a community will succeed or fail.
Once you have automated the creation of these reports, bundle them with more expensive versions of your service as a way to begin to monetize this valuable data.
3. "The Death of McKinsey": C-level recognition that only your company has the data-driven insights.
The reason I call this final stage "The Death of McKinsey" is because you may already have what a strategic consulting firm uses to come up with their CEO-level advice.
Typically, a consulting firm will start their project by interviewing employees and gathering data from a client's IT systems, and then look for patterns to support some strategic change.
You may already have the employee, or end-customer usage patterns, and be able to provide the insights that a consulting firm charges hundreds of thousands of dollars for.
Getting to this final stage will take some time for a young company, but know that some of the largest public cloud venders are starting to think of themselves as "insight-engines" for their customers.
The end goal for your data strategy is to continuously derive so many insights from customer usage patterns, correlated with success metrics, that you can help predict more about their success than your customers can. This will lead to higher productivity for your customers, and far higher ASPs and lower churn for your company.
Whatever industry domain you are in, you have the opportunity to be the best provider of insights for your customers. Take this step to break the "tools" mindset in your company.
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