Top 20 Data Analysis Tools
Building a good marketing strategy begins with data analysis. After all, you can’t improve anything without assessing its starting point. Luckily, you don’t have to do it manually — there are a bunch of specialized tools that can help you automate data analysis to save time. In this article, we review 20 widely used data analysis tools and help you choose the most suitable one according to your needs.
With OWOX BI, you can easily collect data with no limits from your website, Google Analytics, advertising services, and your CRM system and merge it in GBQ to build marketing reports.
Table of contents
- What is data analysis?
- How should I analyze data?
- Top 20 data analysis tools
What is data analysis?
In marketing, data analysis is the process of organizing, explaining, and interpreting data to answer questions regarding a marketing strategy, resolve issues with that strategy, and multiply its advantages. You might ask the following questions as the first part of data analysis:
- What’s the most effective advertising channel?
- How many unique visitors does my website have on average per day, week, and month?
- What channels should we focus on to raise revenue?
Data analysis is a must for marketers. It’s how they can avoid guesswork and see what areas of their marketing performance require special attention. Marketers can use data analysis to uncover trends, patterns, and valuable insights in order to adjust advertising campaigns and get more revenue.
By analyzing data correctly, you can achieve the following marketing goals:
- Decrease ad spending. Find ineffective campaigns, stop investing in them, and focus on revenue-generating channels.
- Increase revenue from visitors. Predict LTV (customer lifetime value) and the conversion probability; offer products visitors really need by enhancing the level of personalization.
- Make data-driven decisions. Get high-quality data to have a solid basis for making timely decisions and be sure of your next steps.
Read also: Why is it important to check data quality for marketing analysis?
Data analysis tools for marketing differ in their level of detail and methods of interpretation. Generally, they focus on gathering, analyzing, or visualizing data. Still, the number of options on the market is quite large, and it’s not that simple to find the perfect tool. To help you narrow down your list, we suggest things you should consider when selecting a data analysis tool.
How should I analyze data?
The primary goal of data analysis is to find meaning in data and use this meaning to improve your marketing strategy.
Data analysis consists of the following steps:
- Define your goal. Figure out your expectations, specify the questions you’d like to answer with the help of data analysis, and determine which data you need to collect (data on advertising campaigns, visitors, orders, email openings, etc.).
- Collect data. Collect and merge data from different sources, then clean, arrange, and organize it.
- Ensure data quality. Make sure your data doesn’t have errors, gaps, or unnecessary parts. If it does, further interpretation won’t be trustworthy.
- Analyze data and interpret results. Build dashboards, reports, or charts and correlate them with your primary questions and expectations.
- Act on data. Based on your interpretation of the data, take actions that will improve your performance — reallocate your budget, adjust bids, create new audience segments, or turn off ineffective campaigns.
- Repeat the cycle to analyze how data changes over time.
Manually collecting, cleaning, and arranging data takes a lot of time, and the probability of human mistakes is high. Poor data quality can result in losing 21% of your marketing budget, and according to Gartner, businesses all over the world lose an average of $15 million per year for this reason. You can spend hours finding errors in piles of data with no guarantee that the results will be precise. To save time and make sure your data is accurate, you need to perform automated analysis using specialized tools.
Before selecting a tool, narrow down the list of possible options by defining:
- the types of data you want to analyze
- the goal you’d like to achieve with the help of analysis
- the level of detail you would like in your analysis
Now let’s take a look at twenty of the most popular data analysis tools.
Top 20 data analysis tools
We’ve prepared a list of the best tools to collect, analyze, and visualize marketing data. For each tool, we describe how it works and what its key characteristics are.
Tools to collect and analyze data
1. Google Analytics
Google Analytics is the most popular analytics service. It collects information from three sources: users’ HTTP requests, cookies, and data on browsers/operating systems using a Google Analytics Tracking Code. Once data is collected, it goes to Google Analytics servers as a list of parameters. Google Analytics then analyzes these parameters and creates reports you can find in your Google Analytics account.
- Limits on data collection — can collect up to 10 million hits per month per property with the free version; to collect more, upgrade to Google Analytics 360 or use another tool
- No data from CRM and non-Google services unless you import it manually
- Data is processed within 24 to 48 hours
Google Analytics is free to use, but if you operate with high data volumes, you should upgrade to Google Analytics 360 — which starts at $150,000 per year.
Mixpanel is a user analytics tool that allows you to track user behavior. You can do this by sending data from your server to Mixpanel, which will analyze it in real time. Mixpanel uses an event-based, user-centric model to analyze user actions with your product. The Mixpanel data model is built on the following key variables:
- event — a user action on a website
- property — a detail about an event
- user profile — a collection of information about an individual user
- Have to create a tracking plan with your business goals before collecting data
- Has a custom datastore with its own structure that’s different from other similar tools
- Limits on how much data you can collect — only 2,000 properties per user profile
Mixpanel offers a free plan for up to 100,000 monthly tracked users, and pricing for plans with unlimited reporting starts at $17 a month.
- Can track data in real time with the Kissmetrics Live feature
- Limited integration options, and it’s not possible to integrate Kissmetrics with Google products
- Suitable for small and medium-sized SaaS and ecommerce businesses
Pricing starts at $299 per month.
Weborama is a marketing technology company that provides behavioral targeting services and advertising analytics based on a semantic analysis engine. With the Weborama BigFish semantic AI platform, you can analyze website conversations, user behavior, and consumer insights. With the Weborama BigSea dataset, you can create a consumer database and build a relevant digital behavior score for each user.
- Allows you to unify CRM and behavioral data and build a predictive model of user behavior
- Performs direct marketing self-activation of audiences that have a high churn risk
- Need a separate tool to collect your data before uploading it to Weborama for further analysis
Pricing for Weborama services isn’t available on the official website. You must contact a representative to obtain current pricing.
5. OWOX BI
OWOX BI is an all-in-one marketing analytics platform that automatically collects data from multiple online and offline sources in one place — such as Google Analytics or Google BigQuery — where marketers and analysts can work with it. OWOX BI merges, cleans, and organizes data collected from ad services, websites, Google Analytics, CRMs, offline stores, and other sources. You can then use it to create reports based on this data from templates or build your own reports with custom metrics to measure what works best in your marketing. One of the big benefits of OWOX BI is that to do this, you don’t need to code or know SQL — you can build any report you want using natural language in a simple report builder.
Collects data in the same structure as Google Analytics and monitors data quality
- Can integrate with other marketing tools such as Google Analytics and Google BigQuery and can import your calculations to visualization services or BI tools — for example, Google Sheets, Looker, Tableau, Power BI, and Data Studio
- No limits on collected data, and it’s always unsampled and processed in real time
- Offers its own data-driven ML funnel based attribution model that evaluates how your marketing channels influence one another and conversions
You can start using OWOX BI with a free plan. Paid plans start from $42 a month, and you can request a plan to meet your specific needs.
With three or more data sources, Data Studio blends data and works slower. If you have more than two data sources, consider OWOX BI to speed up data analysis and get attribution reporting on top.
Datorama is an AI-powered business intelligence and analytics platform with built-in reporting, insights, and automated predictions on goal completion. It collects and merges marketing data in one centralized platform, analyzes data across different channels and campaigns, and builds reports based on the results.
- Suitable for B2B marketers and enterprises
- Allows for cross-platform management: adjust campaigns in Google, Facebook, and other tools connected to Datorama
- Plans are expensive and only billed annually
Pricing starts at $3,000 per month, billed annually.
Supermetrics is a cloud-based ETL platform that collects and analyzes your marketing data using APIs and sends it to storage or to a visualization, reporting, or BI tool of your choice. Supermetrics allows you to automate reporting and transfer data where you want it.
- Doesn’t offer data quality monitoring and attribution reporting
- Can connect Supermetrics to Google Data Studio, which is convenient
- Offers multiple integrations, including with data sources such as Google Analytics and Google Ads
Pricing starts at $39 a month and depends on the integrations and features you select.
Funnel is an ETL platform that allows you to collect data and automatically process it by cleaning, mapping, and grouping it. Based on this processed data, you can create reports and dashboards with key metrics you select and send the collected data to a data warehouse, Google Data Studio, Looker, or some other tool (Funnel offers about 500 integrations).
- Doesn’t support dynamic UTM parameters or streaming user data from a website
- No data quality monitoring or attribution reporting
- Gathers data in its own data warehouse, from which you can transfer it wherever you need to store it
- Can connect Funnel to Google Data Studio, which is convenient
Pricing starts at €499 per month.
Improvado is a cloud-based ETL platform that collects data from different sources and arranges it using a REST API. Next, it analyzes data by merging tables, making calculations, and modifying the data structure. Interpreted data then goes either to external data storage (Google BigQuery, Redshift, PostgreSQL, etc.) or to the internal Improvado database.
- Initial data collection takes about 24 hours, after which data is refreshed twice a day
- Can use Improvado with other marketing tools — for example, Google Analytics and Salesforce
- No free trial period
- Doesn’t collect data on user behavior and doesn’t perform cohort analysis
Pricing for data analysis in Improvado is determined on a case by case basis.
AppsFlyer is a mobile marketing analytics tool that allows you to create dynamic cohort, retention, and raw data reports as well as custom dashboards and live alerts. With AppsFlyer, you can track the performance of your paid mobile media sources and their organic activity in real time.
- Offers a secure storage locker hosted on Amazon S3 to ensure data security
- Offers localized language, time zone, and currency support
- To collect and analyze data from your website, ad services, CRM, and offline stores, you should consider using another tool such as OWOX BI
You can start with a free account, then pay from $6 per conversion as your business grows.
11. Adobe Analytics
Adobe Analytics allows you to merge and analyze data on customer journeys in real time based on online and offline sources. Adobe Analytics offers various types of analysis, such as ad hoc (custom), cohort, and flow analysis, which allows you to track the most important aspects of the user journey.
- Can set up alerts to detect anomalies using statistical modeling and machine learning
- Can integrate with other tools using APIs or a drag-and-drop UI
- No free trial available
Even though you can compare plan pricing for Adobe Analytics — Select, Prime, and Ultimate — you still must contact an Adobe representative to get the exact price for your company.
To learn more, see the official Youtube video on how Adobe Analytics collects data.
Fivetran is an automated data integration and analytics ETL tool that allows you to merge data from multiple sources in one platform.
- Thanks to log-based replication of databases, offers a capture deletes feature to analyze information that may no longer exist in the data source system
- Doesn’t store data but loads it into a data warehouse you select
- Offers history mode to analyze data from a particular point in time or track its changes over time, but this option isn’t available for all data connectors
Fivetran pricing is consumption-based and starts at $1 per credit. This means that each month, Fivetran calculates how many monthly active rows (MARs) you used during the month and what the price is for the month. Alternatively, you can purchase a certain number of credits in advance.
Data visualization tools
13. Google Data Studio
Perhaps the most popular data visualization tool is Google Data Studio — a visualization and reporting tool that allows you to set up a data connector to collect, filter, and compare data, set up automated interactive dashboards, and share reports with your colleagues. Also, you can create visualizations in Explorer — a separate space to explore collected data in detail.
- Can connect data from other Google accounts: Google Sheets, Analytics, BigQuery, etc.
- No official Google support
- Limitations on amount of data uploaded:
- 1000 datasets per user
- 2GB storage per user
- 100 uploads per dataset per day
- 100MB file size limit per dataset
Google Data Studio is free to use.
14. Microsoft Power BI
Microsoft Power BI is a reporting tool that allows you to collect and visualize data, build personalized reports, and securely share them with your colleagues. Microsoft Power BI offers more than 120 free connectors and cloud data sources such as Salesforce, Azure SQL Database, Excel, and Dynamics 365.
- Free desktop and mobile apps for Android, iOS, and Windows Mobile to collaborate on visualizations
- Similar features as other Microsoft products such as Excel — for example, self-service Power Query experience
- Can embed analytics and interactive reports into your applications
Pricing starts at $9.99 per month per user if you use a shared processing environment and $4,995 per month for a dedicated cloud.
Tableau is a visual analytics platform that collects and analyzes your data using machine learning. With Tableau, you can create visualizations using drag-and-drop functionality and employ AI-based statistical modeling.
- Partially based on VizQL, its core query language, which translates dashboard and visualization components into queries and reduces the need for manual optimizations
- Offers a mobile app for iOS and Android
- Allows you to integrate your own security protocols
Pricing starts at $70 per user per month.
Looker is a cloud-based data analytics and BI platform that collects and merges data from various sources in an automatically generated LookML model. Looker allows you to set up alerts, dynamic dashboards, and visualizations. A built-in code editor lets you modify automatically generated models if required.
- Can embed insights from Looker’s BI with Salesforce, Sharepoint, Confluence, PowerPoint, and other tools
- To use Looker, you must learn how the basic LookML model works
- Looker Blocks feature offers prebuilt code pieces that you can use to accelerate analytics
Looker pricing is completely custom and depends on your specific case, number of users, and the scale of the deployment.
Sisense is a cloud-native AI data analytics platform that allows you to process and visualize business data. It offers many drag-and-drop tools and interactive dashboards to collaborate with your teammates.
- Offers custom In-Chip technology to use fast CPU caching instead of RAM, which is slower
- Lets you create analytical apps based on your data to embed them externally
- Offers separate packages for cloud data teams, BI & analytics teams, and product teams
Sisense has custom pricing, so you must contact a representative to get a quote.
18. Qlik Sense
Qlik Sense is a data analytics platform based on artificial intelligence and an associative analytics engine that allows you to create reports and interactive dashboards. Qlik supports various environments, including the cloud (e. g. Azure and AWS), streaming platforms (e. g. Apache Kafka and Confluent), data warehouses (e. g. Exadata and Teradata), and others.
- Offers open APIs for REST and. NET
- Allows you to embed analytics into your web applications
- Lets you create map visualizations and perform geospatial analytics
Pricing for Qlik Sense data analytics starts at $30 per month, though it’s billed annually.
Adverity is an AI-based data analytics and reporting tool that allows you to automatically collect and merge your marketing data using a data integration module. You can create reports, dashboards, and visualizations using a marketing reporting module and use AI to find patterns and trends in your data.
- Can connect to affiliate networks, web tracking tools, offline files, TV audience metering systems, and other data sources
- Smart naming conventions allow you to avoid errors in reports by highlighting differences in campaign names and suggesting you manually update them
- Uses an augmented analytics module to analyze data, applying data mining, statistical modeling, machine learning, and complex algorithms to model user behavior and make predictions
Adverity doesn’t offer fixed pricing. The price depends on how many sources you need to integrate, how many accounts you’d like to have, and which manipulations you’d like to perform on the collected data.
20. SAS Business Intelligence
SAS Business Intelligence is a data analytics and BI platform that allows you to collect data and create interactive reports and dashboards with key metrics you select. SAS BI automatically analyzes your data based on machine learning and automatically highlights the most important parts in visual reports to help you find useful insights.
- Allows you to combine collected data with geographical data and perform visual location analysis
- Uses predictive analytics and algorithms to analyze and visualize data, reducing the need for manual optimization
- Can set up bots to reveal insights more easily or connect bots to external services
To get pricing for your company, contact a SAS BI representative.
Data analysis is an obligatory first step on the way to improving your marketing strategy. There’s a huge variety of analysis tools on the market that promise to do it all for you, and we’ve presented the most widely used ones to help you narrow down your search. What’s next?
To find something that will perfectly fit your needs, define your expectations from data analysis, your desired level of detail, and the types of data you’ll analyze. Combine tools that collect, analyze, and visualize data to achieve the best results in terms of quality, convenience, and cost.
If you still don’t know where to start, book a free demo to see how you can easily collect and analyze your data in real time right away.
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What is data analysis?In marketing, data analysis is the process of organizing, explaining, and interpreting data to answer questions regarding a marketing strategy, resolve issues with that strategy, and multiply its advantages.
How to do data analysis?
Data analysis consists of the following steps:
1. Define your goal.
2. Collect data.
3. Ensure data quality.
4. Analyze data and interpret results.
5. Act on data.
6. Repeat the cycle.