How to Analyze Data: 30+ Experts on Making Sense of Your Performance

Do you look at spreadsheets of data with analysis paralysis? In this guide, we share 13 techniques that will help you analyze data (the easy way).

Elise Dopson Elise Dopson on June 26, 2021 (last modified on February 15, 2022) • 21 minute read

Do your eyes roll when you see you’ve been assigned the task “analyze data” or “create report”?

Research has shown that analyzing data doesn’t come natural to most people.

Creating awesome marketing campaigns? Great.

But when it comes to analyzing whether that campaign was a success, it’s where most companies fall short.

We wanted to help solve that problem–especially when data-driven companies are three times more likely to report significant improvements in decision-making.

So, how do you overcome the fear (or struggle) of analyzing data?

In this guide, we’ll share the results of our survey that help us understand how difficult data analysis to master, along with 14 expert data analysis techniques to help you get started.

Google Analytics Website Engagement Dashboard Template by Databox

What is Data Analysis?

Data analysis refers to the process of collecting, cleaning, defining, and processing raw data to uncover valuable, actionable insights that will enable you (and your team) to make better-informed decisions backed by facts rather than assumptions.

Collecting data alone doesn’t amount to much unless you take the time to dig through and interpret it. By analyzing data consistently, you can drastically improve your business performance.

Why Is It Important to Analyze Your Data?

Data analysis is pivotal for business success both in the short and long term.

On a deeper level, analyzing your data makes it easier for you to determine the ROI of your marketing and sales efforts, understand customer behavior patterns, market trends, make data-driven decisions, and more.

 Here are some of the top reasons why you should analyze data:

  • Improved customer experience: Analyzing your data helps you understand your customers better (behavior and actions), their needs, and how you can deliver better, more personalized customer support.
  • Better decision making: Data analysis helps boost your confidence as a business owner and make better-informed, data-driven decisions. By analyzing data you’ll be able to get a snapshot of all aspects of your business, including what’s working, and what’s not working, the risks and potential opportunities for improvement, and much more.
  • Understand customer behavior: Stay up to speed with everything that pertains to your customers with data analysis. Learn and easily predict customer behavior based on data, follow-up by taking action or making changes if necessary promptly. 
  • Helps with competitors’ research: Data analysis makes it easy to conduct competitor’s research. It provides you with all the information you need to know about your competitors, including insights into their strengths, weaknesses, sales tactics, and marketing strategies.

How Hard Is It to Analyze Data?

The words “analyze data” can make any marketers’ skin crawl. You’re a creative person working on writing snappy copy and piece together clever campaigns… Not dig into a bunch of numbers, right?

We wanted to find out why marketers struggle with data analysis.

The results of our survey could be the answer. Although the majority of companies we polled said they have several employees:

company employees number survey data

Almost half don’t employ a full-time data analyst. Companies that do employ several data analysts fall into the minority:

umber of full time data analysts in your company survey results

…That means regular members of your team are left to fill the gaps. Those people might not be data experts, making analysis seem like a tricky task. (Or, one that staff attempt to pass along.)

But it’s not just a lack of skill that makes data analysis so hard. Mensa’s Jenny Rankin explains: “I conducted a quantitative study in which 211 college-educated adults participated and found their accuracy when interpreting data averaged just 11% correct, and that those misinterpreting data were typically unaware that they were misinterpreting the data.”

“This is on par with similar studies that find using data is more difficult than most people realize.”

13 Effective Ways to Analyze Your Data

Are you ready to overcome so-called “analysis paralysis” and start understanding the numbers that fill your spreadsheets?

We’ll share our experts’ best tips for analyzing data, such as:

  1. Cleaning your data
  2. Aiming to answer a question
  3. Creating basic data descriptions
  4. Checking the context is correct
  5. Pooling data from various sources
  6. Niching down to your key metrics
  7. …But comparing those with other KPIs
  8. Searching for data that goes against your hypothesis
  9. Investigating anomalies
  10. Categorizing qualitative data
  11. Visualizing your data
  12. Color-coding trends and patterns
  13. Using cohort analysis
  14. Making use of data analysis tools

1. Start with clean data

Before diving in with the figures, Eve Lyons-Berg of Data Leaders Brief thinks you should “make sure that you’re working with good, clean, thorough data!”

“Data analysis is built on the fundamental assumption that the data you’re analyzing is trustworthy. If you’re looking at unreliable data, or insignificant (ie too small) data, or even just inconsistent data (ie a metric that’s usually measured daily, but with several week-long gaps at random intervals), your results won’t be reliable.”

Omar Fonseca of Medicare Plan Finder explains how to clean your data: “First, you want to de-duplicate your data to ensure you remove duplicates if that is something that will cause issues in your dataset.”

“You also want to delete unnecessary fields (or columns) which you can manually delete after saving your data set as a separate set.”

Fonesca also advises to “make sure that you title and format all of your fields (or cells) properly. That way when you’re knee-deep in data, you are able to sift through the granularity of the data in search of valuable information and insights into both opportunities and threats you might otherwise have missed!”

See also: How to clean up your bad CRM data

2. Aim to answer a question

Chosen Data‘s Branko Kral thinks “it is very easy to get lost in the analytics tools, such as Google Analytics, if you open them without a specific question in mind. It is desirable to dig around and explore new reports or report modifications, but you want to keep coming back to the main motivation for the analysis. The question will keep you focused.”

Kral’s team put this into action when they experienced a drop in organic traffic: “The main question was – what caused the drop and what can we do bring the traffic back up?”

“There were some nuances in the data, but overall, we discovered that organic traffic was affected site-wide, as well as without us making any major changes to the site’s SEO qualities for at least a few weeks before the drop. That gave us the confidence to state that the cause for the drop was external.”

“We researched SEO news and learned that the early June algorithm update favors big publishers. We’ve also been noticing the external factor of featured snippets pushing page 1 results further down,” Kral adds.

Fundera‘s Lizzie Dunn, who adds: “An important tip for analyzing and using data is whether or not your business can justify a business case for it in the first place.”

“Asking the question: “why is this data collected and what can we use it for?” is an important question, but an even more important question to ask is, “is this data creating value for customers and the business?”

“By answering these questions, you are more likely to able to determine what data is the most important to look at, and what data can be directly associated with value creation for the customer or monetization for the business,” Dunn writes.

Summarizing, Tony Mastri of Search Engine Coach says: “It’s rare that a data set will just raise its hand and tell you the insights it holds. When you begin your analysis with a question, you can quickly determine which metrics and dimensions are needed to distill an answer.”

3. Check the context is correct

Earlier, we mentioned how cleaning your data is the first step in data analysis. Without accurate data, you can’t get accurate analysis.

For that reason,Hexe Data‘s Krzysztof Surowiecki thinks it’s important “to double-check the data you are working with. It is a common mistake we often encounter. Therefore, make sure the data you want to prepare for further investigation is correct.”

Trade Finance Global‘s Robin Abrams explains: “An example would be when you are looking at website data across two years. It’s important to realize that, during the time period, the amount of traffic and/or structure/number of pages, products, and competitors may have changed so much that the two years are no longer comparable.”

Surowiecki’s team put this into practice when they “conducted a deep analysis for one of the leading fashion e-commerce. They reported that they have a lot of traffic, users are looking at clothes, add them to the basket but they do not shop (convert).”

“Our client was puzzled and suspected an error and the reason behind it in their service. We looked for reasons there, researches the service, took a closer look at data. And our research showed that actually service is fine, everything works there properly.”

“However, our research showed that users treat service as a fitting room. They try on new clothes, add them to the basket, but they actually buy them only on paydays. […] It is just longer users’ path that leads to purchase decision on the last or 10th day of the month when they receive their salary.”

“I think this case is interesting because it shows that the e-commerce service works, but users’ behaviors are just different from the clients suspected, so they did not research the audience thoroughly in the first place,” Surowiecki summarizes.

Colibri Digital Marketing‘s Andrew McLoughlin agrees and recommends to “always put your data in its proper context. Without an understanding of the broader conditions, benchmarks, trends, or details then your data will be unhelpful and any conclusions you draw will be unreliable.”

“Try to treat the raw information as just one node in as broad a net as possible. The analysis will be more comprehensive, and your conclusions will be infinitely more actionable,” McLoughlin adds.

4. Pool data from various sources

“The best tip I give our clients is to stop looking at data in siloes and work with a data aggregation/visualization tool,” writes Kiwi Creative‘s Giselle Bardwell.

“Leverage a platform, like Databox, to combine multiple sources and metrics to tell a full story of how marketing and sales are performing (or not!) Bringing all the data together makes it easier to find correlations, similarities and areas to improve.”

Bardwell continues: “We recently looked at overall engagement on our blog in terms of the initial landing page, interactions with various calls-to-action (CTAs) on the page, and the journey the user takes through the website after reading a post.”

“Looking at the difference between user interaction on the blog versus sales-specific pages, helped us to revise our content strategy to include more relevant CTAs to boost lead growth.”

It’s a data analysis tactic also used by Simon Rodgers of WebSitePulse: “I overlaid the server log files with the XML site map and found out the Google bot did not visit some of the URLs. It turned out they had nofollow meta tag by mistake.”

LSEO‘s Kyle Kozie explains how they did this recently when they analyzed “keyword rankings and traffic coming into those pages. We had just recently revamped and redesigned our website.”

“At a holistic view based on traffic and rankings, everything looked fine and dandy, but that wasn’t exactly true.”

“What we noticed when we dug into the Google Analytics data is that we were missing huge opportunities by not having all of the correct content on our site to target terms we should be going after based on all of our services. We noticed this by specifically analyzing landing page data and traffic coming into those pages.”

“We then cross-checked that with SEMrush keyword data to notice that we weren’t being specific enough in our content,” Kozie adds.

As Jason Parks Parks of The Media Captain summarizes: “Don’t be afraid to spend money on great software. It can save your team a lot of time and benefit your business or your clients business.”

For example, Mobile Device Management (MDM) software is a tool relevant to the current enterprise mobility scenario. MDM solutions have the capability to enable over-the-air document sharing which plays a vital role in pooling the data together for analysis, despite the diverse locations of employees.

5. Niche down to your most relevant metrics

“Gathering data is just step one of a data-driven strategy; the real work comes in sorting through the data to decide what to include and what to disregard or deprioritize,” says Futurety‘s Sam Underwood.

“It’s really easy to get lost in terabytes of data when just a couple of those columns would tell you the main story you need to make a business change.”

Niche down to your most relevant metrics

But similar to starting with a question, Harris Schachter of OptimizePrime thinks you should “start with the end in mind” by asking questions like: “Where does your revenue come from? What channels, and from what types of customers? Start there and work backward into the meaningful metrics that get you there.”

Schachter adds: “This is normal for a finance person to do, but as marketers, our default setting is to start at the top with vanity metrics like SEO rankings, unique page views, or worst of all, share/like/follow counts. Your business doesn’t keep the lights on with those metrics, so focus on what does, and you’ll be surprised at the insights you can find.”

Related: 39 Most Important KPIs to Track Across Your Company

6. …But compare several other KPIs

Ward van Gasteren of Tools with Ward recommends to “segment based on various relevant metrics for your company [because] a single metric will not give you insights, but multiple metrics combined can yield a wealth of insights.”

“For example, you can segment the number of send forms based on the country of origin, acquisition channel or the user’s device. Is there something somewhere that is not ‘normal’? For example, a specific channel that is lagging behind compared to last month and the other channels.”

van Gasteren continues: “Maybe you’ve changed the ad on that channel and it happened to cause a misfit with the registration form or those people don’t feel like the form is meant for them.”

“If you are new to a company, you can always ask a more experienced colleague to watch with you at the data. Perhaps they immediately notice that one of the most important channels is lagging behind or that something is missing,” van Gasteren adds.

But which metrics do you pick to monitor? According to Romy Fuchs, BEE Inbound AG answer this question: “Which key figures are critical and relevant to our growth?”

“From this, we develop an analysis concept and compile the necessary live dashboards with Databox. We not only measure but also search for optimization potential. The measures with good performance are strengthened, unsuccessful ones are discontinued.

“In our experience, it’s worth starting a campaign small, then measuring and optimizing it, and then expanding it.”

7. Look for data that goes against your hypothesis

Regardless of the question you’re starting with, you’ll have some idea of the trends you’re hoping to pull from the data.

(For example: If you’re asking “why did Facebook traffic increase last month?”, you might expect the answer to be through your increased ad spend.)

HubSpot‘s Alex Birkett recommends that “instead of looking for data to support a previous hypothesis, try looking for counterfactuals or pieces of data that reject your hypothesis.”

“We’re all armored with enough cognitive bias to steer the ship in a totally disastrous direction, so to combat confirmation bias, we need to consciously seek out alternative explanations.”

8. Investigate your anomalies

When searching for data that rejects your initial question, you might spot some unusual differences.

Those are anomalies–something you should be investigating, according to G2‘s Lauren Pope: “Listening to the data is important but it’s not infallible. If the data is suddenly telling you something VERY different from what it did just a week ago, take the time to see if everything is running the way it should.”

“There’s a chance that a module has been turned off, a UTM code has been corrupted, or something else has gone wrong. Don’t blindly follow the data, trust your gut,” Pope adds.

It’s a tactic also used by the team at Web Canopy Studio , as Kenny Lange explains: “I find it most helpful to drill down into anomalies – even if they’re small. It’s easy to rationalize the change in the patterns and assume that whatever you’re seeing isn’t statistically significant.”

“In addition to drilling down into anomalies, always be asking ‘why?’. I know up and to the right is good but if you never understand what levers are controlling your growth, you’ll be unable to fix them when they break.”

Investigate your anomalies

9. Categorize qualitative data

The majority of data analysis tips we’ve covered so far relate to quantitive data (i.e. data in numerical form.)

But how can you analyze qualitative data–such as characters, symbols or words–that you’re collecting through channels like surveys?

“Often, people get nervous about how to analyze survey responses where people fill in their answers instead of multiple choice,” writes FYI‘s Marie Prokopets.

However, Prokopets has a simple step-by-step process to analyze this type of data: “First read all the responses. Next, classify each of the responses into categories (it’s OK if each response fits into multiple categories), and tally up the numbers to see which categories are the largest.”

“Pick the best quotes that illustrate the findings for each category and organize those in your analysis.”

10. Visualize your data

Visualize your data whenever possible,” adds Eastside Co‘s Will King. “It’s difficult to notice emerging trends when looking at a spreadsheet full of numbers. But as soon as that data is represented in a visual format, those insights become easier to find.”

Software Path‘s Tom Feltham agrees: “One of the best solutions […] is to use collaborative visualization or reporting systems.”

“Ideally, these systems should allow you to share reports or create performance dashboards for simple communication of results and document the analysis to allow discovery or reference at a later date.”

*Editor’s note: With Databox’s customizable templates, you can easily view (and share) your most important metrics with your team. Simply connect your data analysis tool, pick your most relevant data, and share the link with your co-workers:

Databox's customizable templates example

11. Color-code your data

“I love exporting my data from all different resources and then color-coding it,” writes Anne-Marie Hays of BestCompany. “It is so much easier for me, as a visual learner, to make sense out of data that is color-coded and color scaled.”

“On a regular basis, I do keyword research, with data from multiple sources. I import it all into the same spreadsheet and then color scale my data. It is so much easier to see where my opportunities are.”

12. Use cohort analysis

“My top tip for analyzing data is to make sure to use cohorts, and compare different marketing strategies using comparable time and qualification strategies,” says Bonjoro‘s Casey Hill.

Cohort analysis is a method usually used within Google Analytics to group together data with similar trends to make it easier to interpret what the data shows.

Hill continues: “For instance, one key problem I see in data analysis with a lot of companies is they see a 15% growth in clicks and they attribute it to a new campaign or initiative they ran. In actuality, that growth could of been mirroring their natural growth curve, given the other drivers in the business (organic, referral etc.)”

“An example of this cohort analysis can be seen in looking at churn numbers, where we took a comparative look at utilizing Bonjoro’s (personalized video messages) vs. standard email for getting people through trial periods.”

“We took a 3-month swath of data using these two different methodologies for follow-ups during trial periods and kept all other variables constant. This allowed us to feel confident when deciding that the Bonjoro’s were effective in increasing conversion rates,” Hill adds.

13. Use data analysis tools

Hands up if you’re currently using a spreadsheet to manage your data.

(Go on, I’ll wait.)

I’ll bet you raised your hand. Why? Because almost every single company we polled said it’s the tool they use for data analysis:

data analysis software surveys results

However, these experts recommend taking your spreadsheets to the next level, as Fiona Kay of Nigel Wright Group notes: “Use affordable analysis tools where possible.”

“I’m constantly analyzing our organic search rankings using Ahrefs. I use the rank tracker to keep an eye on any movements in our rankings and also use this as well as the keywords explorer to identify new opportunities for keywords that we should be targeting, both in existing and new content.”

Storylead‘s Romina Buchle also says they “analyze our Marketing and Sales activities for our own company (as well as for our clients) regularly within the HubSpot tool. Basic metrics are Number of Sessions, Session to Contact Rate, Number of new Contacts, of new Qualified Leads (MQLs/SQLs), new added Deals and Closed Deals.”

DIY Digital Strategy‘s Ben Lund “make[s] sure Google Analytics is set up to track all valuable events. This includes orders, leads, and even calls (through the CallRail platform).”

“Once you setup these goals within Google Analytics, you can see which traffic sources are most profitable for your business, and push those channels appropriately.”

Lund also says: “On a daily basis, I’m looking at how our Google Ads are driving qualified traffic via Google Analytics. This is reviewing the session duration, bounce rate, pages visited, and goals completed on site (leads, orders, or calls).”

“I review this data to then optimize our Google Ads campaigns to push on the top-performing campaigns and pull on the underperformers.”

How Often Should I Analyze My Data?

Now you understand how to analyze your data, it’s worth touching on the time you should set aside for this task in your work routine.

Our survey found that companies with more employees tend to conduct in-depth data analysis several times per week–compared to companies with 1-5 staff, who only dive into their data several times per month:

The bad news? There is no “right” answer here. So long as you’re analyzing data frequently enough to catch (or prevent) problems in the future, you’re on the right lines.

Google Analytics Website Engagement Dashboard Template by Databox

As you can see, data analysis shouldn’t be two words that make your toes curl, as G2‘s Lauren Pope summarizes: “The data can’t tell us anything if we don’t take the time to analyze.”

Digging through your reports is a great way to find trends that could help you (or your clients) improve–making it a small time investment for the advantages you’ll get in return.

About the author
Elise Dopson
Elise Dopson Elise Dopson is a freelance B2B writer for SaaS and marketing companies. With a focus on data-driven ideas that truly provide value, she helps brands to get noticed online--and drive targeted website visitors that transform into raving fans.
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