[Originally posted 4/13/2023 on LinkedIn]
Teams, organizations and companies need to stay focused on how well and in what ways they’re executing on goals and objectives- the exact reason Key Performance Indicators (KPIs) exist. They’re the data points -metrics- used to measure these things.
But what are your Northstar KPIs, the one or two specific metrics that you rally around, that really show you above all others if you’re going the right direction at the needed velocity? With so much data available, it can be easy to get bogged down in metrics that aren’t truly indicative of success. Northstars provide a clear picture of what is most important and help prioritize efforts.
I think as an organization, it’s important to surface, examine, agree and socialize them- in some cases they might become be part of the culture or the mission. In others, they may change as the organization or strategy matures.
A year ago we brought home Oliver, a 10-week old puppy. For the first 2 months, our northstar KPI’s with him were the number of words of vocabulary learned (starting with his name, and “sit”, “come”, “no”…), and how many accidents he had inside (on a decreasing scale). After 2 months, we changed these up to more scaled goals.
And if you’re wondering, yes, I’m the guy who sets goals and metrics for a pet.
Proactive vs reactive measures
A northstar KPI should ideally be a leading indicator of success that is closely tied to an organization’s strategy and objectives, serving as a guiding star for the organization, helping it stay on course and make necessary adjustments to achieve its goals. Unlike lagging KPIs, which measure past performance, northstar KPIs provide early warning signs of potential issues and help organizations take corrective action before it’s too late. And, there are some areas of debate about whether a particular measure is proactive or re-active. Many organizations see NPS as proactive as it predicts customer loyalty and future likelihood to do business; others consider it reactive, since the customer motivation to respond as a promoter or detractor has already occurred. Both are probably right.
New data capabilities are massively changing the KPI landscape
What’s really interesting right now is that new data platforms are quickly evolving KPI’s into real-time measures. AI and data connectivity solutions that can connect and collate large datasets across business functions provide in-line capture of every interaction with prospects, customers and employees, connect it, and use it to produce contextualized, actionable information to any endpoint. One example among many now driving this space is theloops.io, which is an intelligent support operations platform that transforms the support experience by contextualizing support data with product and customer data to provide actionable insights to reps and managers within their tools and workspaces.
This is a monumental shift. As I’ve written about previously Why customer support is a vital part of brand promise, support is a frequent point of customer interaction relationships, and can generate a huge amount of valuable data used to impact things like retention (thus customer LTV) and product experience (thus, customer engagement).
Examples of the changing KPI landscape
Our product and operations teams were highly interested in knowing if (and what) customer issues were being encountered with each new feature release. Some of this was available with product instrumentation, and much of what customers were experiencing came through the various support channels and being captured in our CRM, where extracting, normalizing and reporting at any scale took days or weeks to produce actionable information. With data connected AI solutions like TheLoops, FullStory and NetSpring, these insights could be generated in near real-time and connected across support, customer, product, and operations data sets. This in turn would enable a product or operations team to see and respond to specific issues or opportunities as customers encounter them and intervene to address the customer impact, experience, satisfaction and sentiment very quickly to positively drive key cost and revenue drivers.
Here’s a fictitious, wonkish and, admittedly, simplified comparison of what this could look with traditional vs AI data connectivity capabilities:
Suppose an issue arises in which customers on a new product release, with databases over 800TB, hosted in the Denver datacenter and who upload a new image for a new item get a “413 Payload Too Large” error, and start to contact support (already a bad customer experience on the new release) to get it resolved (and how painful is that for them to achieve?).
With CRM support silo’d data extracts, normalization, reporting and analysis, the time to recognize the issue pattern, triage it with case by case info, pool related ops and/or product info, identify the root case and release a fix could span weeks (during which more customers continue to be impacted and increasing numbers of resources internally need to be engaged). With AI data connectivity spanning support, operations, product and customer data sets, the pattern and impacted components could be surfaced in real time, moving right to root cause and fix in days.
Data automations that can quickly knit together disparate data to surface actionable insights will cut the number of customer impacts, limit the duration of impact, and reduce the resources used to address the impact dramatically. Even more, something like this enables whole new views on KPI’s – northstar or others- such as highly contextual support contact volumes, shorter incident resolution times, different Csat influences, and more.
What are your northstar KPI’s?
Northstar KPIs are important because they help teams and organizations focus on what really matters to customers and to the business. Within the scores of things that can be measured, these are the very critical measures of most importance.
They might include metrics related to customer acquisition, customer experience and retention, or product quality. And it’s important to note that northstars are not one-size-fits-all; those that work for one organization may not work for another, and each organization must identify the KPIs that are most relevant to its business and align with its overall strategy. A company driving a customer base growth strategy might track the number of new customers acquired each month, while a company that wants to improve product quality might track the percentage of releases that cross quality thresholds.
If you’ve developed these for your team or organization and had some good learnings, or are using new data capabilities and solutions in your KPI toolkit, I’d love to hear about it, please share in the comments!