4 Types of Data Analysis Every Startup Employee Should Know (+Examples)

Devon Tackels 

Senior Content Marketing Manager, Sigma

To reach your growth goals as a startup, you must be selective about spending your energy and resources. There is an infinite number of marketing campaigns to run, features to build, and service improvements to make. But you need to focus on the things that will deliver the highest and fastest ROI. Without data-driven insights into your business, your market, and your customers, it’s impossible to know where to direct resources.

Data analytics can help you better diagnose problems, determine why they’re happening, and identify solutions. It can also help you spot opportunities that could make all the difference in your success. To benefit from what analytics and BI offers, you need to understand the four main types of data analysis and how your team can use them to boost your growth.

Sigma’s A&BI solution allowed us to create an entirely new revenue stream and now plays a key role in generating and closing new business.

Chris Lambert 

CTO, Payload

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The 4 types of data analytics

The four main types of analysis range in complexity and the level of insights they can generate for your company. While you can use any one of these types individually, you’ll unlock more profound and actionable insights when working together.

 Descriptive analytics

Descriptive analysis helps you answer the question, “What is happening?” in any given situation. This type serves as the foundation for all the other kinds of analytics. Typically, descriptive analytics comes in dashboards and quarterly reports that provide a snapshot of performance to date. Descriptive analytics helps to track key performance indicators. Examples of descriptive analytics in action include tracking lead conversion rate, measuring marketing campaign ROI, and following cash flow.

 Diagnostic analytics

Once you know what’s happening, you’ll probably next ask, “Why is this happening?” Diagnostic analytics answers the why questions. To find these answers, you must drill down into the data behind the dashboard to identify the root causes of the situation or trend. When doing diagnostic analysis, you may be seeking to understand questions like:

  • Which ad campaigns drove the most qualified leads last quarter?
  • Which outbound sales emails resulted in the most demo requests last month?
  • What the key value drivers are for a newly-released product?

 Predictive analytics

Once you know what’s happening and why, you’ll want to know, “What’s likely to happen in the future?” — especially, “What’s likely to happen if we take this action versus that action?” Predictive analysis builds on the insights found using descriptive and diagnostic analytics, using historical data to predict future outcomes. It uses statistical modeling to forecast the most likely scenario in the future based on specific criteria.

For example, you can use predictive analytics to find out how many MQLs you’re likely to generate with your next ad campaign, what percentage of your demos will likely request a proposal or the dollar amount of sales you can expect in Q4.

“Sigma has ultimately helped us achieve a number of key business outcomes, including the ability to lower manufacturing costs for customers and more effectively prioritize product features based on demand. Actionable insights like these make a big difference in customer satisfaction and Fictiv’s overall success.”

-Jim Ruga

Chief Architect at Fictiv

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 Prescriptive analytics

Prescriptive analysis is arguably the most valuable type of analysis you can do. It answers the question, “What should we do next?” To fix a problem, improve your metrics, or jump on a valuable opportunity, you must know what actions have the highest potential for the best outcome.

Many companies fall short with prescriptive analysis because it’s the most challenging type of analysis to accomplish. It requires advanced data science and artificial intelligence to digest massive amounts of information and propose solutions — impossible without the right tools.

You can use prescriptive analytics to learn how to keep specific prospects moving through the buyer’s journey, how a sales rep can adjust their approach for a higher win rate, and how to mitigate financial risk.

The whole is greater than the sum of its parts

You can see why you need to combine these types of analysis to understand a situation thoroughly, deal with it effectively, prevent issues in the future, and fully optimize your operations. And because most companies stop at the diagnostic analysis or predictive level, you can gain a significant competitive advantage by using all types of analytics to make strong decisions quickly.

Learn more about how big data can help you grow faster and smarter. Check out Big Data Analytics – The Definitive Guide.

3 examples of analyses you can do with Sigma

Multi-hierarchy analysis

Data hierarchies are used by finance and sales teams to roll up totals across different dimensions. For example, when you have several product lines with similar sales numbers, you can identify which to focus on and dedicate resources. To do this in other BI tools is demanding, requiring complex manual modeling. But with Sigma, anyone can upload a CSV to the company’s cloud data warehouse, join it to an existing table, and perform a multi-hierarchy analysis with just a few clicks. See exactly how it works in this video.

Cohort analysis

Cohort analysis places customers into related groups. You can use cohort analysis to get a clear picture of customer retention, engagement, and loyalty, which will help you identify your ideal customer profile, decide which markets to invest in, and target your offerings better. Typically, this type of analysis would require several steps, technical skills, and extensive coding. With Sigma, however, you can do a cohort analysis in mere seconds (and without technical SQL skills). Here’s a video that explains how.

Regional sales analysis

Most web applications generate data in JSON format, a semi-structured data type. Your marketing, sales, and finance software programs are all generating JSON. To make use of the valuable information these systems hold, you need to extract data fields from JSON repositories. Traditional BI tools require complex SQL coding to prepare JSON data for analysis. But with Sigma, anyone in your company, even those without coding experience, can quickly parse fields and incorporate JSON into analyses. Here’s a video that explains how it works.

Get ahead with full-spectrum data analysis

As a startup, you’re racing toward product-market fit, scale, and, ultimately, a high valuation. But you can’t get there without making the right strategic decisions. You need all four of these types of data analysis to know what actions you need to take and how to move forward with confidence.

Learn how to make data your startup’s competitive advantage by reading our free eBook, Level the Playing Field with Cloud BI and Analytics.

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