4 Types of Analytics Every Business Should Track

Businesses today are inundated with data from a wide range of sources: accounting, manufacturing, websites, CRM, etc. The larger the company, the more data. Too often management can be so overwhelmed with data and analytics that it begins to lose its value.

For CEOs, CFOs, and owners, developing processes and investing in solutions to take full advantage of available analytics is key to successfully managing business growth. This is particularly true when preparing for a potential transaction or capital raise, in the short- or long-term. The first step to implementing a system is understanding the range of analytics that exist. Below is an overview of the four basic types and how they are used.

Descriptive

The most basic and widely used form of analytics, descriptive analytics summarize what has happened for a period of time. By nature these analytics look backward in time (e.g., last month, last quarter, last year).

Your monthly financial statements are a good example, as well as when you compare this month’s financials to the previous month or year-to-date numbers. Over longer periods of time, several months or years, you also use descriptive analytics to identify and track trends. These analytics may be tracked in programs like Excel, Quickbooks, and Salesforce, and used throughout your company in a variety of ways including sums, averages, percentages, changes, etc.

You may already be using trending in Excel for creating budgets and forecasts as well as for sales reporting and forecasting, tracking customers or even personnel management. Your CFO may use a more sophisticated model for forecasting 4-5 years out, creating full financial statements, and capital needs. These models often allow you to run simulations based on adding debt, issuing common or preferred stock, warrants or any combination of new capital.

Predictive

The next category of analytics is called predictive, which is a bit misleading since no analytics can predict the future with 100% accuracy. However, predictive analytics try to predict the probability of a given event using statistics, data mining, machine learning, and other methods (as such these analytics rely on a specialized business intelligence team and/or software solution). These analytics use historical data (descriptive analytics) to look for patterns, trends, or relationships.

A good example of how predictive analytics is used is in the way credit scores are calculated. Some of your vendors report payment information to a credit agency. They apply predictive analytics to this data to determine or try and predict if your payment pattern will continue in the same pattern of paying on time or change. This is reflected in your company’s credit score, the higher the score the more they predict you will pay your vendors on time. When you apply for a loan, the bank will pull up your credit score and use that information, along with other data, to determine if you get the loan.

For many companies, sales and marketing is the biggest user of predictive analytics. Your marketing department may be using them right now to mine data coming from your website and social media accounts. They’ll look through this unstructured data to see customer needs, behaviors, and buying patterns; identify customers with the highest propensity to buy; and determine what product(s) to recommend.  These analytics help you target marketing campaigns to the right audience at the right time.

Your CFO may also be using predictive modeling to more accurately forecast revenues based on the nuances and dynamics of your sales. This becomes very important when evaluating an M&A transaction or potential capital raise.

There are a wide range of companies offering predictive analytic platforms segmented for specific applications:

•       Sales and marketing: Everstring, Infer, Radius
•       Industry specific: Halo, Board, Statistica
•       Data scientists: SAS, Rapidminer, IBM SPSS, SAP

Prescriptive

Prescriptive analytics go one step further than predictive analytics, in that they identify relationships within data and then recommend the best course of action for a given business issue. Like predictive analytics, prescriptive analytics require a business intelligence group and/or software solution to gather the data, develop models, and manage any relevant tools.

Implemented correctly, prescriptive analytics can help optimize a business’s decision-making to maximize profits and growth.  

If your company sells through the internet, prescriptive analytics might analyze data from your company website and social media accounts.  Based on a customer’s internet page access patterns, comment postings and buying habits it would prescribe specific products to suggest while they are shopping online. For a manufacturing company, your purchasing department could be using prescriptive analytics to determine when materials should be ordered and shipped for just-in-time production scheduling.

Your CFO may also use prescriptive analytics in capital planning, to better understand the real impact on capacity, production and service levels. Through what-if analysis, your CFO can make better decisions on financial planning and reduce company risk exposure.

Companies like Frontline Systems offer Excel add-ons for predictive analytics, while other companies offer stand-alone platforms (e.g., Ayata, IBM,  River Logic,  SAS, and others).

Benchmarking

Benchmarking is a method of analysis for descriptive and prescriptive analytics, by which your company’s financial data is compared against an established standard for companies in your industry and size range.

When you run a benchmark, you can see where there are performance gaps between your company and the industry performance standard. You may find some critical issues that are proving costly to your business and require immediate action, such as excessively high inventory levels while other gaps may be smaller and not need immediate attention. You can also see how different what-if scenarios might impact your company — for instance, if you reduce your inventory by 30 days how much cash can you free up? The information from benchmarking helps you prioritize which performance gaps should be tackled first.

You’ll find benchmarking is a very useful add-on tool that addresses several business concerns, including cash management, budgeting and forecasting, short and long-term planning, identifying and preparing for capital needs, evaluating M&A transactions, or even exit planning to maximize business valuation.

There are several companies offering benchmarking data on public companies, such as Thomson Reuters. My company, CxO Analytics, offers benchmarking for private companies.

Summary

No one type of analytics is better than the other. Each has its own purpose and use. When you are looking to use analytics, start by asking questions like: What data do you have available? How are you going to use this data? What kind of questions do you want to answer? Equally important is whether you have the people resources to analyze and make sense of the data you’re collecting.

A descriptive analytic solution may meet all of a company’s needs for quite some time. As your company grows, you may be interested in maximizing the use of the information derived from data analysis to further drive company growth, profitability, and customer satisfaction.