Defining KPIs for Successful Data Analysis and Business Intelligence

Defining KPIs for Successful Data Analysis and Business Intelligence

Defining KPIs Right

Identifying the right Key Performance Indicators (KPI) for your business’s data analytics initiative is a subtle but important endeavour. Pick the wrong number sets as the base and you would fail to gauge anything useful. KPIs need to be carefully modelled according to the factors that you are planning on assessing for a clearer picture of organisational progress. This becomes a harder task when a broader sense of progress is sought from the use of KPIs. Is it applicable to measure this through the number of new people following your company through social media? Or should a traditional client acquisition ratio be the main consideration for finding out the right results? KPIs themselves are a powerful data-driven asset, but knowing how to use them right, is a different skill set altogether. As one of the primary decision-makers in the company and as a member of the C-suite, you will benefit from knowing how to go about picking the right KPIs for your organisation. Here we will explore just that.

Classifying KPIs According to Industry

A crucial step in determining the right KPI number sets lies with the recognition of certain factors that are exclusive to the industry that your company functions in. These factors are what powers the different layers of your company into functioning as a unified entity. Here we have outlined some of the relevant data points that constitute KPI sets for the different industries.

Marketing: Customer acquisition cost, New customers, the Conversion rate of a particular channel, Average spend per consumer, Inactive customers

Retail: Sell through percentage, Gross margin, Percentage of total stock not displayed, Average sales per transaction, Inventory turnover

Finance: Accounts receivable turnover, Gross profit margin, Quick ratio, Debt to equity ratio, Inventory turnover, Operating cash flow, Working capital, Return on equity

Health Care: Inpatient mortality rate, Average cost per discharge, Readmission rate, Total operating margin, Bed turnover, Cash receipt to bad debt, Claims denial rate

The delicate step, however, is in identifying the right KPIs and using them for the right calculations that are overarching to your company’s goals and visions.

Isolating Primary Objectives

In a purely data-driven pursuit to quantify progress, it becomes fairly easy to get lost in the details and be bogged down by the sheer number of participating factors. This is where it gets important to specify that the factors that get identified as KPIs vary according to the nature of the business. A startup would be feeling out the market with its newly minted business model, hence, the priority would be naturally laid on metrics that help corroborate the effectiveness of the proposed business model. Already established enterprise companies, on the other hand, are a different story altogether. The burning KPI metrics on that side of the fence would involve customer lifetime value. Hence, any viable structure that is focused on identifying the correct KPIs would have to be reasonably aware of how changing business objectives will also be an integral part of the process.

Crowd Control

Even after classifying KPIs according to their relevance, coherent information gathering would still be required to use fewer KPIs. This is because adding too many KPIs will convolute your data-driven analytics and make it tortuous to explain the results in layman terms to your audience. On an average, employing 8 to 10 KPIs would be perfectly suited for whatever matrix you are planning on using them under. The obvious emphasis in this context should lie on choosing the right KPIs that will adequately point to an objective and data-driven form of quantitative assessment. Using too many KPIs will only result in rampant counterproductivity in this regard.

Value of Business Questions

Less number of KPIs will be a value addition in terms of avoiding convolution, however, it is very important that the KPIs themselves consist of many business questions as possible. KPIs should consist of important business questions that can be further broken down with already available data points that pertain to the questions themselves. Further, even these can be further broken down by data sources that are related to the original premise but ultimately, originating from a different source. This kind of analytic modelling through the use of broken down KPIs will help you strengthen your decision making processes that are not reliant on data-driven information models.

Emphasis on Business Intelligence

The ultimate end product of a KPI powered analytic model is to prolifically create Business Intelligence platforms that can intuitively analyse the areas where your business might be lagging behind. Problem is, this will be impossible without flexibility. Keep in mind, the state KPIs themselves are defined by streams of data packets emerging from multiple layers of business activity. But, they are defined by what they are chosen to represent at the current moment for a specific performance-oriented calculation. Keeping this in mind and with heavy significance laid out to different businesses dealing in different sets of KPIs that are not interrelated in any way, calculating the final product will not be accurate unless customisable Business Intelligence platforms are built. As a process that consists of highly moving parts that are constantly evolving in value, this would be a critical feature.

Defining the right KPIs perfectly will go a long way in creating analytic models that are useful for organisations, both large and small. KPIs offer a demanding data-oriented framework to effectively project the current state of an existing business. The requirement for accuracy calls for simple considerations that will go a long way in a highly result-oriented endeavour such as this.