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Realizing that you can only improve what you measure is a good way to think about KPIs. Often companies want to improve different aspects of their business all at once, but can’t put a finger on what will measure their progress towards overarching company goals. Does it come down to comparing the growth of last year to this year? Or, is it just about the cost of acquiring new customers?
If you’re nervously wondering now, “wait, what is my cost per deal”, don’t sweat it. Another growing pain of deciding on KPIs is discovering that there is a lot of missing information.
The good news is that in the business intelligence world, measuring performance can be especially precise, quick and easy. Yet, the first hurdle every data analyst faces is the initial struggle to choose and agree on company KPIs. If you are about to embark on a BI project, here’s a useful guide on how to decide what it is that you want to measure:
A lot of companies start by trying to quantify their current performance. But again, as a data analyst the beauty of your job and the power of business intelligence is that you can drill into an endless amount of very detailed metrics. From clicks, site traffic and conversion rates, to service call satisfaction and renewals, the list goes on. So ask yourself: What makes the company better at what they do?
You can approach this question by focusing on stage growth, where a startup would focus most on metrics that validate business models, whereas an enterprise company would focus on metrics like customer lifetime value. Or, you can examine this question by industry: a services company (consultancies) would focus more on quality of services rendered, whereas a company that develops products would focus on product usage.
Ready to dive in? Start by going from top-down through each department, and isolate the pain points and health factors for every department. Here are some examples of KPI metrics you may want to look at:
Once you choose a few important KPIs, then try to break it down even further. Remember, while there’s no magic number, less is almost always more when it comes to KPIs. That’s because if you track too many KPIs, as a data analyst you may start to lose your audience and the focus of the common business user. Choosing the top 7-10 KPIs is a great number to aim for and you can do that by breaking down your core business goals into a much more specific metric.
For example, in Sisense’s Professional Services Team, success is measured by our annual contract value (ACV), the number of opportunities that have been created, and by comparing renewals from this year to last year. Hila Kantor, the BI Consulting Team Leader at Sisense let us in on her thought-process when choosing KPIs:
“Managing a BI consultant group has some unique pain points. Initially I thought, how do I prevent tickets from being opened? Then that pointed to ensuring high quality work from the solutions architect. I discovered the best KPIs to measure is how many tickets were created following the a specific solution project, and measuring if the solution easily withstands a version upgrade.”
Remember, the point of a KPI is to gain focus and align goals for measurable improvement. Spend more time choosing the KPIs than simply throwing too many into the mix, which will just push the question of focus further down the road (and require more work!).
After you have your main 7-10 elements – you can start digging into the data and start some data modeling. A good question to ask at this point is: How does the business currently make decisions? Counterintuitively, in order to answer that question you may want to look at where the company is currently not making its decisions based on data, or not collecting the right data.
This is where you get to flex your muscles as a “data hero” or a good analyst! Take every KPI and present it as a business question. Then break the business questions into facts, dimensions, filters and order (example).
Not every business questions contains all of these elements – but there will always be a fact because you have to measure something. You’ll need to answer the following before moving on:
Do this by breaking each KPI into its data components, asking questions like: what do I need to count, what do I need to aggregate, which filters need to apply? For each of these questions you have to know which the data sources are being used and where the tables coming from.
Consider that data will often come from multiple, disparate data sources. For example, for information on a marketing or sales pipeline, you’ll probably need Google Analytics/Adwords data combined your CRM data. As a data analyst, it’s important to recognize that the most powerful KPIs often comes from a combination of multiple data sources. Make sure you are using the right tools, such as a BI tool that has built-in data connectors, to prepare and join data accurately easily.
Congrats! You’ve connected your KPI data to your business. Now you’ll need to find a way to represent the metrics in the most effective way. Check out some of these different BI dashboard examples for some inspiration.
One tip to keep mind is that the goal of your dashboard is to put everyone on the same page. Still, users will each will have their own questions and areas where they want to explore, which is why building an interactive, highly visual BI dashboards is important. Your BI solution should offer interactive dashboards that allow its users to perform basic analytical tasks, such as filtering the views, drilling down, and examining underlying data – all with little training.
As a data analyst you should always look for what other insights you can achieve with the data that the business never thought of asking. People are often entrenched in their own processes and as an analyst you offer an “outsider’s perspective” of sorts, since you only see the data, while others are clouded by their day-to-day business tasks. Don’t be afraid to ask the hard questions. Start with the most basic and you’ll be surprised how big companies don’t know the answers–and you’ll be a data hero just for asking.
This post was originally published by Elana Roth here and has been republished with permission.