Last month I posted "Build a better business by choosing the right metrics" to give the fundamentals of setting metrics to track for a data-driven team. I pitched this as the key to accelerating growth but the truth is that it is only the first step. Value is truly unlocked when the data is activated through operations — that is it is being used by business teams to drive actions. Data without operations is like having an oil field without drills and pipelines. The liquid gold is right beneath your feet but you can’t access it.
But it’s not just the drills and equipment that’s needed to extract the oil — there’s also a coordinated team around it to operate it to the fullest potential. It’s the same in data. Collaboration and a like-minded data culture is integral to a business putting the value of it’s data into practice and, surprisingly for many, this is the part where a large number of companies fail.
Fortunately, there are a few simple principles you can put into practice to get your team started in collaborating to activate data. Read on to find out more. Let’s break down how to operationalise your metrics!
What is operationalisation?
Apart from being a word that’s difficult to pronounce, operationalisation is the key to activating data. In simple terms, it is the process of making something functional, or putting something into effect. In the case of metrics, operationalisation is processes we have in place that turns data into action.
Common examples of data operations
Here are some examples of typical ways to operationalise data in order of increasing complexity:
Goal tracking: Translating metrics into goals unifies teams around your core metrics. Consider a sales team tracking the metric “Quarterly new sales $”. Instead of stating “Quarterly new sales is $25k” they should connect the metric to a goal in such a way as “Quarterly new sales so far is $25k. This is 50% towards our goal of $50k. We only have one month left in the quarter so we need to accelerate our efforts to achieve our goal.” This framing would motivate the sales team to make changes needed to accelerate their progress.
Alerts: Setting alerts for metrics gives an operational team peace of mind that they will know when something changes for the worse in their data. Typical alerts could be “daily sales dropped by > 20%” or “weekly churn spiked by 30%”. Notifications like this can drive a team to immediate action.
Recommendation systems: These are similar to alerts but add a recommendation off the back of the alert. Instead of programming an alert such as “Customer X hasn’t used the product this month” add the proposed action “Call customer X to check in on their usage of the product and make sure everything is okay.”
Ticketing systems: Once you have a recommendation system in place you want to make sure you track the actions taken off of the back of them. This can take the form of a simple ticketing system. That is, once a recommendation is provided to Operations, a ticket is created that then moves through different stages: Created -> Action underway -> Resolved. This allows you to prioritise tasks and track how effectively actions are being taken off of the data recommendations.
A/B testing: A/B testing is worth its own blog post but, in short, it is a method used to test hypotheses and allow you to make more informed decisions using data. It involves rolling out two versions of a solution to customers / prospects and analyzing the effect on your metrics. For example, for website-optimisation you could rollout two versions of your website with slightly different designs / features and measure the difference in traffic and conversion rates for each version. This will help you understand which version is best so you can implement the best features and optimise your web-traffic conversion.
What the best teams do to turn data into action
Some of the above may sound as simple as setting up some basic processes and tracking around them but the truth is in my 8+ years of consulting over 30 companies large and small, only a handful have got this stuff right. It’s very difficult if you have a lack of collaboration or poor data culture in place. Here are some of the key characteristics of teams that I have seen effectively collaborate to activate their data:
Clear definitions and understanding of metrics: In order for team members to take actions from data they need to have understanding and alignment around the core metrics. It is common for metrics to be defined differently across departments. An example in SaaS is customer churn which a Customer Success team often records when a customer gives notice they want to cancel, whereas I tend to see finance teams report churn only when a customer subscription ends and they stops paying for a service.
Data governance: Trust is another essential aspect needed in your data for it to inspire action. If someone doesn’t believe the numbers they see, they are not going to take any notice. Ensure your there is strict governance in how your metrics are pulled together to maintain data quality and trust in outputs
Open-mindedness: Sometimes data analytics will tell you something you don’t believe that goes against your gut. The best teams embrace these moments as opportunities to learn more about what is really happening in their business rather than disregarding because it goes against what they expect.
Great operational systems: To execute best-in-class operational practices teams need to have effective and reliable systems in place and the valuable data needs to be streamlined into these systems. For Sales teams this is usually a CRM platform such as Hubspot or Salesforce which are market leaders in managing the flow of customer and prospect data through a business. Data teams need to make sure that insights from the data warehouse are pushed back into systems like the CRM for regular consumption. This process has recently been labelled as “reverse ETL”
Streamlined feedback loops: How do you know that the actions you’re taking off the back of tracking metrics are having an effect? The answer is by streamlining feedback loops; that is creating processes for gathering feedback on the actions being taken. For example, ask questions to your team such as: was the insight received from the data helpful? Did the insight inspire any action? Was the action effective? Collecting this data allows the best teams to optimise the way they operationalise and know instinctively how best to react when new data insight is provided to them.
Getting started in operationalisation of data
If all the above seems overwhelming and you want to know how to get started, let me outline how we’re thinking about this at Calliper, where we’re on a mission to make data accessible and actionable for early stage teams using our collaborative analytics platform.
Below is a template for setting up alerts on core metrics for a B2B SaaS within Calliper. Our philosophy is simplicity-first when it comes to data and so we let you choose which metrics you want to track and operationalise, then data science and statistics takes care of alerting you when a significant trend emerges. We let you configure auto-alerts as well as one-off events of interest such as “Achieved £100k ARR”, “Over 10 customers churned this month”, etc. These insights then get pushed to a Slack channel of choice so they can be actioned upon by the right people. All of this can be set up in less than 10 minutes. Not bad for a process that takes many larger companies months or years!
Do you know anyone that has great data but struggling to turn insight into action? If so, and if you think they would find this content helpful, please go ahead and share!
Also, please comment below any tips for how you have operationalised data insights in your business. We’d love to hear!