How to Define KPIs for Successful Business Intelligence

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.

Defining Your KPIs

Choosing the right KPI is crucial to make effective, data-driven decisions. If you choose the right KPI, it will help to concentrate the efforts of employees towards a meaningful goal, however, choose incorrectly and you could waste significant resources chasing after vanity metrics.

In order to rally the efforts of your team and achieve your long-term objectives, you have to measure the right things. For example, if the goal is to increase revenue at a SaaS company by 25% over the next two quarters, you couldn’t determine success by focusing on the number of likes your Facebook page got. Instead, we could ask questions like: Are we protecting our ARR by retaining our existing customers? Do we want to look at the outreach efforts of our sales development representatives, and whether that results in increased demos and signups? Should we look at the impact of increased training for the sales team on closed deals?

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Similarly, if we wanted to evaluate the effectiveness of various marketing channels, we need to determine more than an end goal of increasing sales or brand awareness. Instead, we’ll need a more precise definition of success. This might include ad impressions, click through rates, conversion numbers, new email list subscribers, page visits, bounce rates, and much more.

Looking at all these factors will allow us to determine which channels are driving the most traffic and revenue. If we dig a bit deeper, there will be even more insights to discover. In addition to discovering which channels produce traffic most likely to translate into a conversion, we can also learn if other factors such as timing make a difference to reach our target audience.

Of course, every industry and business are different. To establish meaningful KPIs, you’ll need to determine what most clearly correlates with your company’s goals. Here are a few examples:

  • Finance – Working capital, Operating cash flow, Return on equity, Quick ratio, Debt to equity ratio, Inventory turnover, Accounts receivable turnover, Gross profit margin
  • Marketing – Customer acquisition cost, Conversion rate of a particular channel, Percentage of leads generated from a particular channel, Customer Churn, Dormant customers, Average spend per customer
  • Healthcare – Inpatient mortality rate, Bed turnover, Readmission rate, Average length of stay, Patient satisfaction, Total operating margin, Average cost per discharge, Cash receipt to bad debt, Claims denial rate
  • Retail – Gross margin (as a percentage of selling price), Inventory turnover, Sell-through percentage, Average sales per transaction, Percentage of total stock not displayed

If your business is committed to data-driven decision making, establishing the right KPIs is crucial. Although the process of building a performance-driven culture is iterative, clearly defining the desired end result will go a long way towards help you establish effective KPIs that will help focus the efforts of your team towards that goal, whether it’s to move product off shelves faster, create better patient outcomes, or increase your revenue per customer.

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 & KPI tracking. 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:

Step 1: Isolate Pain Points, Identify Core Business Goals

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 analysis. 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 to elicit requirements and isolate the pain points and health factors for every department. Here are some examples of KPI metrics you may want to look at:


  • Product related tickets
  • Customer satisfaction
  • Usage statistics (SaaS products)

Marketing KPIs

  • Brand awareness
  • Conversion rate
  • Site traffic
  • Social shares


  • Number of bugs
  • Length of development cycle
  • App usage

Step 2: Break It Down to A Few KPIs

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.

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!).

Step 3: Carefully Assess Your Data


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 contain 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:

  • What are the data sources
  • Predict the complexity of your data model
  • Tools to prepare, manage and analyze data (BI solution)

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.

Step 4: Represent KPIs in an Accurate and Effective Fashion

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 have their own questions and areas where they want to explore, which is why building an interactive, highly visual BI dashboards are 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.

See an example:

Profit & Loss - Financial Dashboard


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.

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Source: How to Define KPIs for Successful Business Intelligence by analyticsweek

Follow the Money: The Demand for Deep Learning

Numbers don’t lie.

According to CB Insights, 100 of the most promising private startups focused on Artificial Intelligence raised $11.7 billion in equity funding in 367 deals during 2017. Several of those companies focus on deep learning technologies, including the most well-funded, ByteDance, which accounts for over a fourth of 2017’s private startup funding with 3.1 billion dollars raised.

In the first half of last year alone, corporate venture capitalists contributed nearly 2 billion dollars of disclosed equity funding in 88 deals to AI startups, which surpassed the total financing for AI startups for all of 2016. The single largest corporate venture capitalist deal in the early part of 2017 was the $600 million Series D funding awarded to NIO, an organization based in China that specializes in autonomous vehicles (among other types of craft), which relies on aspects of deep learning.

According to Forrester, venture capital funding activity in computer vision increased at a CAGR of 137% from 2015 to 2017. Most aspects of advanced pattern recognition, including speech, image, facial recognition and others, hinge on deep learning. A Forbes post noted, “Google, Baidu, Microsoft, Facebook, Salesforce, Amazon, and all other major players are talking about – and investing heavily in – what has become known as “deep learning”. Indeed, both Microsoft and Google have created specific entities to fund companies specializing in AI.

According to Razorthink CEO Gary Oliver, these developments are indicative of a larger trend in which, “If you look at where the investments are going from the venture community, if you look at some of the recent reports that have come out, the vast majority are focused on companies that are doing deep learning.”

Endless Learning
Deep learning is directly responsible for many of the valuable insights organizations can access via AI, since it can rapidly parse through data at scale to discern patterns that are otherwise too difficult to see or take too long to notice. In particular, deep learning actuates the unsupervised prowess of machine learning by detecting data-driven correlations to business objectives for variables on which it wasn’t specifically trained. “That’s what’s kind of remarkable about deep learning,” maintained Tom Wilde, CEO of indico, which recently announced $4 million in new equity seed funding. “That’s why when we see it in action we’re always like whoa, that’s pretty cool that the math can decipher that.” Deep learning’s capacity for unsupervised learning makes is extremely suitable for analyzing semi-structured and unstructured data. Moreover, when it’s leveraged on the enormous datasets required for speech, image, or even video analysis, it provides these benefits at scale at speeds equal to modern business timeframes.

Although this unsupervised aspect of deep learning is one of its more renowned, it’s important to realize that deep learning is actually an advanced form of classic machine learning. As such, it was spawned from the latter despite the fact that its learning capabilities vastly exceed those of traditional machine learning. Nonetheless, there are still enterprise tasks which are suitable for traditional machine learning, and others which require deep learning. “People are aware now that there’s a difference between machine learning and deep learning, and they’re excited about the use cases deep learning can help,” Razorthink VP of Marketing Barbara Reichert posited. “We understand the value of hybrid models and how to apply both deep learning and machine learning so you get the right model for whatever problem you’re trying to solve.”

Whereas deep learning is ideal for analyzing big data sets with vast amounts of variables, classic machine learning persists in simpler tasks. A good example of this fact is its utility in data management staples such as data discovery, in which it can determine relationships between data and use cases. “Once the data is sent through those [machine learning algorithms] the relationships are predicted,” commented Io-Tahoe Chief Technology Officer Rohit Mahajan. “That’s where we have to fine-tune a patented data set that will actually predict the right relationships with the right confidence.”

Data Science
An examination of the spending on AI companies and their technologies certainly illustrates a prioritization of deep learning’s worth to contemporary organizations. It directly impacts some of the more sophisticated elements of AI including robotics, computer vision, and user interfaces based on natural language and speech. However, it also provides unequivocally tangible business value in its analysis of unstructured data, sizable data sets, and the conflation of the two. Additionally, by applying these assets of deep learning to common data modeling needs, it can automate and accelerate certain facets of data science that had previously proved perplexing to organizations.

“Applications in the AI space are making it such that you don’t need to be a data science expert,” Wilde said. “It’s helpful if you kind of understand it at a high level, and that’s actually improved a lot. But today, you don’t need to be a data scientist to use these technologies.”

Source by jelaniharper

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Are You Asking the Right Predictive Questions?

Predictive analytics works by learning the patterns that exist in your historical data, then using those patterns to predict future outcomes. For example, if you need to predict if a customer will pay late, you’ll feed data samples from customers who paid on time and data from those who have paid late into your predictive analytics algorithm.

>> Related: Predictive Analytics 101 <<

The process of feeding in historical data for different outcomes and enabling the algorithm to learn how to predict is called the training process. Once your algorithm determines a pattern, you pass on information about a new customer and it will make a prediction. But the first step is deciding what predictive questions you want to answer.


How do you know which predictive questions to ask?

When determining a predictive question, the rule of thumb is to base it on what you want to do with the answer.Following that logic, if we want to predict the number of late payments in a certain time frame—instead of if a particular person will pay late (as in the above example)—our predictive question should be: “How many customers will make late payments next month?”

Let’s look at a slightly more complex example. If we’re forecasting volume for a call center, our predictive question might be: “How many calls will I get tomorrow?” That is a forecasting/regression question (like the one in the example above). However, we could also ask a binary question such as: “Will I get more than 200 calls tomorrow?” That is a classification question because the answer will either be yes or no.

The predictive question you should ask will depend on what you are going to do with the information. If you have the staff to handle 200 calls, then you will likely want to know if you’ll get 200 calls or not (so you’d ask the classification question). But if your goal is to identify how many calls you are going to get tomorrow so that you can staff accordingly, you would ask the forecasting question.

Let’s apply this rule to a different industry. If you’re in sales and your monthly goal is 250 sales referrals, you would ask a classification question such as: “Will I get 250 referrals or more next month?” But if you simply want to know your expected referral volume, without taking into consideration any monthly goals, then you’d ask the forecasting/regression question: “How many sales referrals will I get in the next month?”

Over time, you’ll be able to run multiple algorithms to pick the one that works best with your data, or even use an ensemble of algorithms. You’ll also want to regularly retrain your learning model to keep up with fluctuations in your data based on based on the time of year, what activities your business has underway, and other factors. Set a timeline—maybe once a month or once a quarter—to regularly retrain your predictive analytics learning module to update the information.

To learn more about how predictive analytics can work for you, sign up for a free demo of Logi Predict >


Originally Posted at: Are You Asking the Right Predictive Questions?