Data + Analytics = Dollars

Its now possible to free data from operational silos to explore relationships that would be difficult or impossible with point solutions. Business-level drivers keep analytics on target.

In this presentation given at NYC Data Driven Business Meetup I did a quick high-level snapshot of the analytics landscape and finished with a few examples of explicitly connecting analytics (metrics) to net income.
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A Customer Support Analytics Model

Scaling and optimizing customer support has many moving parts. Customer support leaders have to find the right mix of staff, technology, and support channels to deliver the highest quality service, at the lowest possible cost as, quickly as possible.

In A Process for Scaling & Optimizing Customer Support I wrote about using process, goals, and benchmarked KPIs as a framework for becoming data driven.

We put this concept into practice with an analytics model that reports a few dimensions of customer support, including cost per resolution.
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A Process for Scaling & Optimizing Customer Support

Customer support performance optimization boils down to delivering the highest quality service at the lowest possible cost as quickly as possible.

The 3 dimensions of customer support: Quality, Cost, Speed

Considering the importance of customer service, it’s amazing how hard it is to answer two basic questions: how are we doing—as an organization—and how can we improve? Support leaders rely on transaction-level metrics available from tools such as Zendesk and Intercom to piece together insights that can improve performance.

The problem with this approach is that not knowing which metrics most matter leads to following random metrics and to management by gut feeling.

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