From Business Goals to Outcomes – How to Frame your Approach to Business Analytics


To sustain and grow, companies set business goals they want to accomplish each year. A company, depending on its vertical, its size, its operating market, or other business variable, will pursue set of goals that is unique to that organization. Revenue and profit are the most ubiquitous, but others include cost stabilization and control, customer and employee retention, or productivity and efficiency maximization, to name a few.

Beyond business goals, companies develop business strategies for how to accomplish them.

Goal: Revenue Growth ⇒ Market Penetration & Development

In the case of revenue growth, a company can pursue a strategy like market penetration or market development, where they sell their existing products in their current market or adjacent markets. A great example of market development is Ike’s Sandwich Shops. The original Ike’s opened in San Francisco in 2007, and after finding a successful formula, opened additional restaurants in the SF bay area before expanding to markets outside the bay area and outside of California.

Goal: Revenue Growth ⇒ Channel & Product Development

A company can also pursue a business strategy of channel or product development to achieve its goals, where they sell existing products in new channels or create new products for existing or new customers.

An example of this type of business strategy, and one of my favorite female-led success stories is that of Pepsico and it’s recently retired CEO, Indra Nooyi. She was CEO for 12 years before retiring in 2018. In her tenure, Pepsico’s revenues increased by 80% as it introduced new products, including healthier foods targeted to female buyers, to counter declining interest in sugary beverages.

The Role of Analytics

What do business goals and business strategy have to do with analytics? Analytics is the process for measuring how well business strategies are succeeding and driving outcomes. Analytics also helps identify business opportunities for achieving goals that the company hadn’t previously strategized about.

In my last blog post, I provided a great example of exactly this for the restaurant chain industry, where an analysis of the breakdown of sales within and across restaurants exposed opportunities to offer dining incentives to existing customers and to eliminate unprofitable shifts like mid-day lunch, where there wasn’t enough business to support them, thus reducing restaurant operating costs.

Cycle of Analytics (1)

As companies define business goals and strategies, they implement a series of tactical organizational and business process changes to accommodate the strategies. These could be organizational or hierarchical changes, new product launches, new or updated internal systems (ERP, Finance, HR, Go-to-Market, CRM, for example). Each of these changes creates data within its proprietary application. The data, when integrated across systems, provides the foundation for analytics and is used to measure effectiveness of tactics and strategy, in order to drive business outcomes.

Based on analysis of the measures (data aggregation) companies can make changes to their tactical implementation or to their business strategy, or both, to get the business outcomes they desire.

Analytics Strategy

Having a well defined analytics strategy enables you to reap the greatest rewards of using analytics to measure effectiveness of strategy and tactics. An analytics strategy ensures that you are measuring the right activities for the right users using the right technology for your organization. With an analytics strategy in place, that defines the who, why, what, and how of your analytics program, as well as prioritization of delivery and the technical program that delivers it, you will have a numbers-driven approach towards achieving goals.

In my next blog post, I’ll talk about the role of data and data platform technology (collection, integration, visualization) on the actualization of business strategy and the achievement of business goals using Cohere Insights’ Analytics Value Pyramid. The Analytics Value Pyramid hierarchically categorizes the process of achieving business goals through data, where there is a continuous feedback and information flow up and down the hierarchy (data to goal and vice versa).

The pyramid is a great instrument for demonstrating the value relationship between data, technology, analytic apps, and what the business wants to accomplish. When articulated across teams, it creates buy-in on approach and facilitates delivery of successful analytics applications.

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


How Analytic Scorecards identify new revenue growth and cost savings opportunities

[et_pb_section admin_label=”section”] [et_pb_row admin_label=”row”] [et_pb_column type=”4_4″][et_pb_text admin_label=”Text”]analytics-business-chart-920116.jpg 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.

charts   Lagging 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.

discount  Leading Indicators

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. pexels-photo-262978.jpeg 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. Analytical findings:
  1. 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.
  2. In the locations where there is low cover count during business lunch, we can reduce staff or close during lunch hours.
  3. 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: Scorecard-process-2.png
    • 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

Untitled-drawing-1.pngAs with any analytics endeavor, the best place to start is with a high level strategy for the who (users), why (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.

Final thoughts

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[/et_pb_text][/et_pb_column] [/et_pb_row] [/et_pb_section]