Marketing is a data-driven business as it's all about maximizing ROI
You build your Martech stack based on your specific business needs.
Here we won't present an exhaustive list of tech tools. Instead, we’ll hone into the data and analytics category. If you aim to build a data-driven marketing operation, you should read on.
Many of these tools offer a free trial period, and for many of them, you can sign up for a demo to learn more.
Marketing analytics tools
Marketing analytics is a broader category, and there is a wide range of tools compared to ETLs. In a nutshell, marketing analytics are software platforms that help marketers understand how their marketing is performing, enabling them to manage and measure the performance of their campaigns. These tools allow marketers to improve their marketing efforts and prove the value that marketing brings to the business, i.e., the return on investment.
What do marketing analytics tools do?
Marketing analytics tools can collect data from all marketing channels in your marketing mix and report on them holistically or by individual channels. In your MarTech stack, these tools make it easy for marketers to generate reports (without depending on data scientists) and can track campaigns and perform competitive benchmark analysis.
Some of the data these tools can capture are:
- Website analytics - data on web pages, landing pages
- User behavior
- Paid campaign performance data
- Customer purchase data
- Email marketing data
The must-have marketing analytics tools out there
The Martech stack is continuing to expand, and our objective here is to not present an exhaustive list of every tool out there - nor do you need all of them.
Here we list some of the big players and explain why you will need some of this tech in each category, whether web tools, automation, social, or email.
Web analytics tools - Google Analytics, SEMrush, Moz, SimilarWeb, and others
Web analytics is vital to growing your business. If you want to understand what visitors are doing on your website and how they interact with your content, it's a must. You can use any web analytic tool to collect website data, but the most popular and free version is Google Analytics.
Google Analytics is by far the most popular and most used web analytics tool. It enables you to track and understand your customer's behavior, user experience, online content, device functionality, and more.
Google Analytics comes in a free and paid plan. The free version is ideal for SMBs, and you can be up and running straight away. Meanwhile, the paid plan, Analytics 360, is perfect for enterprise-level companies.
With the insights you gain from Google Analytics, you will also want to combine it with one or more SEO tools to optimize your site and ensure you can increase organic traffic. Some essential tools are SEMrush, Moz Pro, SimilarWeb, Ahrefs, to name a few.
Marketing automation tools
Marketing automation is when you use technology to automate several repetitive tasks you usually do in a marketing campaign. It's a tool that allows a marketer to design, execute and automate a time-bound marketing workflow.
Marketing automation assists significantly in lead generation, segmentation, lead nurturing and lead scoring, relationship marketing, cross-sell and upsell, retention, and ROI measurement. Also, most tools leverage data from a separate or integrated CRM to understand customer impact and preferences.
There is an array of marketing automation tools out there. The most popular being:
Social analytics tools
Social media analytics is when you track, collect and analyze data from your various social media accounts. With the right tools in place for social media marketers, analyzing your performance should be a breeze. Most social media platforms have their own free in-tool analytics. Then some tools help you collect performance data from every social network in one place, with easy-to-understand reports.
Social media tools help you measure key metrics such as:
- Brand sentiment
- Share of voice / Mentions
To name some of the most common social media tools out there, they are:
- Hootsuite Analytics
- Google Analytics
- Sprout Social
What are ETL platforms?
ETL stands for extract, transform, and load and is part of a 3-step data management process.
- Extract unstructured data from multiple sources
- Transform it into a business-ready format
- Load it to a target destination
ETLs play a crucial role in data analysis. They allow businesses to gather data from multiple sources and consolidate it into a single location. ETLs also make it possible for different types of data to work together or blend. So your Facebook data can be used for analysis with your CRM data.
Many data-driven companies typically turn to an ETL tool, as it’s the first step in getting all of the business essential data into one centralized repository. There are other ways to do this, and there are tools that perhaps don’t fall in this category but do the same thing.
Here are the key steps that an ETL performs:
1. Extract your data
Most companies manage data from various sources and use many data analysis tools to get marketing and business insights.
To unlock value from data and optimize marketing campaigns, you’ll need data to travel freely between your systems and tools. Before you can move data to a new destination, though, you must extract it from its source.
In this first step, structured and unstructured data is imported and consolidated into a single repository. You can extract raw data from a wide range of sources, including:
- Sales and marketing applications (e.g., Shopify, Facebook, Google Ads, etc.)
- Mobile devices and mobile apps
- CRM systems (e.g. Salesforce, HubSpot)
- Data storage platforms with your backend data
- Finance systems
- Analytics tools (e.g., Google Analytics)
Although you can do this manually, hand-coded data extraction is time-intensive and prone to errors. ETL tools automate the extraction process and create a more efficient and reliable workflow.
2. Transform your data
You can apply various rules and logic to get your data in good shape and meet whichever reporting and analysis requirements you may have. With data transformation, here are the specific things one can do:
- Clean - fix any inconsistencies and missing values. For example, maybe campaign names were named differently on various platforms, and you want to clean this up and make it one consistent campaign name.
- Standardize - apply specific formatting rules to the data set. For example, this could be anything from using the same date format to standardizing how you name your markets like the US or the United States.
- Deduplicate - exclude redundant data.
- Verify - remove irrelevant or unusable data.
- Sort - organize your data and create groupings for you to analyze better, e.g., product groups.
Transformation is an essential part of an ETL. However, ETLs are becoming increasingly data loaders, where the typical data transformation occurs in the data warehouse.
3. Load your data
The last step is to send your data to a storage or data warehouse to start answering questions with your data using SQL. And you can prepare that data in advance before sending it to your visualization tool.
The critical thing to note here is that the data is nice and tidy but still in silos. So your Facebook data is cleaned and transformed, but you don’t have it unified yet with your Google Ads or Google Analytics data. You’ll need to do this step in a data warehouse.
ETLs are, therefore, just piping data through. And as data transformation is complex and requires a specific set of skills, you’ll be dependent on a data analyst, engineer, or scientist to help out.
What is a data warehouse, and why would you need one?
A data warehouse is a system used for reporting and data analysis used by BI and marketing teams. Some of the benefits of having a data warehouse are: it helps combine data from many sources, reduces the load on an organization's operational systems, and helps keep track of historical data.
Generally speaking, data warehouses help improve access to information, speed up query-response times, and allow businesses to fetch insights from big data. In essence, data warehouses provide a single source of truth - as you have one central place for company-wide data.
Companies before had to invest a lot in infrastructure to build a data warehouse. Thanks to cloud technology, the cost of data warehousing is significantly less. Depending on company size and data needs, you may or may not need a data warehouse.
However, data warehouses do come in handy for those businesses looking to scale their operations and those organizations that manage huge volumes of big data. Today, there are cloud-based data warehousing tools that are fast and highly scalable. Here is a hand-picked list of some of the biggest players:
- Amazon Redshift
- Microsoft Azure
- Google BigQuery
What are data visualization tools?
Data visualization tools provide marketers, data scientists, and BI teams an easier way to create visual representations of large data sets to draw insights from data and make decisions more quickly. When dealing with data sets that include hundreds of thousands or millions of data points, automating the data visualization process makes whoever is in charge of marketing reporting and the dashboards' job significantly more manageable.
You can use data visualization tools for various purposes: dashboards, annual reports, an overview of sales and marketing KPIs, investor slide decks, and any other use case where information needs to be understood immediately.
Why is data visualization important?
Data visualization is essential because of how our brains are wired and how we best take in and understand what someone presents to us. Imagine trying to draw quick decisions from sifting through numerous spreadsheets with a vast amount of complex data. With data visualization, you can convey a point you want to make more compellingly.
Data visualization can help you to:
- Identify areas that need attention or improvement
- Understand which aspects are influencing customer behavior/li>
- Help you understand which products or campaigns are driving growth
- Predict sales volumes and revenue
Some of the best data visualization tools out there are
- Power BI
- Google Data studio
- Qlik Sense
- SAP Analytics Cloud
- Zoho Analytics
- IBM Cognos Analytics
- Google Charts
Funnel’s unique place as a data marketing platform
With Funnel, you get all the benefits of a typical ETL and more.
Funnel helps digital marketers do the 3-step process to extract, transform, and load data, but we do it more simply for digital marketers and more agnostic than other tools.
You can also use Funnel as a data warehouse of sorts where you always have access to your historical and raw data.
Unlike working with spreadsheets, which is manual, slow, tedious, and error-prone, or working with emerging tools that require coding, Funnel makes working with marketing data easy.
Funnel gives control back to the marketing team and frees up their time to focus on higher-value activities. Digital marketers are free to experiment with new platforms and unify their marketing data without relying on IT or BI departments.
- Unlimited data sources
- Pre-built, contextual, constantly updated data model
- Point-and-click logic, no coding skills needed
- Send data to any tool in your ecosystem
The benefits of a data marketing platform
A data platform helps you to collect, transform, and stitch together data automatically. Thus, you save tremendous time and effort otherwise spent on amassing information manually.
Handle complex data without a hitch
Your marketing department and business will have to handle vast amounts of data, both intricate and diverse. Multinational companies will have data from numerous markets, campaigns, and products and will most likely want to map their customer acquisition costs and customer lifetime value.
If you've managed a range of dimensions and metrics, you may end up formatting data all day long. A data platform can help streamline your workflow by simplifying data cleaning and prep.
Kiss errors goodbye
Even if you're meticulous by nature, you're prone to making errors when handling data manually. And, even a tiny misstep can have a domino effect. For example, enter the wrong currency conversion, sales figures, commission rate, and your entire marketing report will be wrong.
A data platform automates essential steps in data processing, reducing manual intervention and hence the likelihood of errors occurring.
Gain one source of truth
Manually managing multiple datasets is heavy work, inefficient, and time-consuming. A data platform takes away the headache of a task by combining databases and different data types into a single, unified view.
The business outcome - a digital marketer, can more easily analyze, visualize, and make sense of large data sets.
Keep historical context
You have the flexibility to combine legacy data with data collected from new platforms and applications. You can view older alongside new data sets and gain a broader perspective.
Improve efficiency and productivity
Data platforms automate the process of data collection, transformation, and migration. Instead of spending internal resources to get your development team to do this, they can focus their effort on corporate innovation.
Skyrocket your marketing intelligence and ROI
A data platform ensures that the data you get for analysis is of high quality. Thanks to this, you can make better decisions and increase your marketing ROI.