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An analytics scorecard is one of the best ways business leaders can use data to measure business performance, predict future outcomes, and identify revenue growth and cost savings opportunities.
It’s effective because it’s numbers driven, fact-based, and uses a straightforward format that makes the information easy to consume and digest. When defined well, it creates actionable insight.
What a scorecard is
A scorecard is an analytical instrument containing a handful of important KPI’s, which are metrics aggregated from data the business has sourced from its internal systems, or data it can acquire from outside the company.
The format for scorecards varies from industry to industry, business to business, and even from department to department, depending on who the scorecard user is and how they will use it. A C-level executive will require a set of KPI’s that covers all business lines of the company across operating regions/countries within his or her purview. A department lead, like a retail buyer for example, will require a different set of KPI’s, such as those on a vendor scorecard, that enable the buyer to evaluate vendors and negotiate favorable buying terms.
KPI’s to include
In my experience, there are two main types of indicators to use in a scorecard: 1) lagging indicators
, and 2) leading indicators.
Lagging indicators are the top level metrics that provide the initial view of what’s happening in the business. A lagging indicator measures numerical output of business activities. The most common lagging indicators are sales, cost, or profit — measured in units, whole currency, or as a percentage to total. A lagging indicator could also be a count, average, minimum, or maximum, based on factual data. This is not a definitive list, there are others, depending on the business.
While lagging metrics measure output, leading indicators are aggregations of activities that drive the lagging metric. A lagging metric has one or more metrics that lead into it. For retailers for example, a lagging metric is sales, and some of the leading metrics that drive it are customer or transaction count, average basket size, and basket assortment.
Deeper dive analysis and actionable insights
Any of the above leading indicators can drive revenue, and the scorecard should support a deeper dive analysis, so that the user can drill down and identify opportunities or estimate root cause of a problem, and take action where necessary.
Last week I spoke with Teresa Curtis
, retail expert, and former Senior Manager for Gap, Inc, to talk about how she uses data in retail. Her response: “I would look at the numbers to tell a story of what happened in the past, and then use that analysis to modify the story for the future.”
Here’s a great example of exactly this, from the chain restaurant industry.
Using sales data on a scorecard, we can drill down to analyze guest preferences and habits. Through analysis, we can identify that in some regions, Sunday family brunch is more popular than in other regions.
We can also identify where business lunches occur with greater or less frequency than in other regions.
- In the locations where family brunch is more popular, we can drive sales by advertising with features like a kids menu, holidays celebrations, or special birthday events.
- In the locations where there is low cover count during business lunch, we can reduce staff or close during lunch hours.
- In the locations where there is high cover count during business lunch, we can entice guests with offerings to come in for dinner or visit on the weekend, to extend the momentum of the weekday business lunch.
These findings are actionable, and the impact of the action can be measured. That’s the virtual cycle of analytics.
Let’s break this down by scorecard component:
- Lagging indicators: Sales by restaurant, region
- Leading indicators: Customer count by service type, time of day, day of week
- Analysis: Identify target sales growth or cost reduction opportunities by restaurant, region
- Actions: Targeted advertising based on sales growth opportunities; staff and/or operating hours reduction
- Outcome: Increased sales in target growth areas, reduced operating costs
How to create a great scorecard
As with any analytics endeavor, the best place to start is with a high level strategy for the who
(why are we analyzing, what do we expect to gain), what
(what data do we have available), and how
(high level technical componentry for how it all comes together).
From these four questions, the next step is to decompose it all into a program that includes detailed requirements and technical delivery of the solution.
Getting agreement and alignment from stakeholders on KPI’s and how they are calculated is of utmost importance, and from experience, can be the most challenging part of the process. If KPI’s are not meaningful to the user audience or if the audience doesn’t trust the calculations behind the metrics, the scorecard won’t get used, and users instead will develop manual workarounds in Excel spreadsheets, which is not a sound, sustainable business practice.
There is tendency to have analytics and data technology and engineering capabilities be the central focus for how a scorecard comes together, but a business scorecard is a business instrument built for the business. The technology and engineering provide the means to create it. To ensure success, it’s best to have a clear view of what the scorecard is supposed to be, why you need it, and how you will use it from a business perspective, and then dispatch the right engineering teams and the technology to build it.
In the end, it should be scalable, sustainable, and supportable, and of course accurate and easy-to-use.
Andrea Amaraggi is Founder and Principal of Cohere Insights. She helps companies leverage technology and engineering to build business focused analytics applications that drive revenue, reduce costs, achieve operational excellence, and create customer successes. Contact her at firstname.lastname@example.org