Forem: Nadia Basaraba

5 Best Tools for Data Integration

Nadia Basaraba — Mon, 16 Jan 2023 19:37:34 +0000

A data integration tool is a software solution to transfer data from a source app to a destination app.

Data integration tools are meant to automate the process of data integration by eliminating manual routines. Instead of copying and pasting records or exporting CSV files with the required information, you can connect the source and destination apps. This connection will ensure a scheduled or triggered data flow that will save time and significantly improve the efficiency of your processes.

Below, is a list of the top 5 data integration tools you should consider.

1 – Coupler.io

Coupler.io is a data automation platform that allows you to integrate data to three destinations: Google Sheets, Microsoft Excel, and Google BigQuery. As for the sources to export data from, these include different apps for marketing, analytics, time-tracking, etc. The list of supported sources constantly grows, and you can even participate in it with your preferences.

Coupler.io can be considered a no-code ELT or ETL tool (depending on the data source). However, in a broader sense, it’s a data platform providing data analytics consulting services from simple data automation to setting up advanced analytics for your business. So, you can not only integrate your data from multiple sources but also get it visualized and analyzed with the help of a fancy dashboard.

2 – Hevodata.com

Hevodata, or simply Hevo, is a data pipeline platform that allows you to create ETL or ELT connections between apps and data warehouses. The number of supported sources is huge – more than 150 apps for different sorts of analytics. The list of destinations, data warehouses, is limited to 9, including Google BigQuery, MySQL, PostgreSQL, and others.

Hevo, being a platform, offers two products. Hevo Pipeline is meant to move data from apps to data warehouses. With Hevo Activate, users can reverse the data flow from a data warehouse to a marketing, sales, or business app. So, Hevo allows you to both integrate and disintegrate data without coding.

3 – Panoply.io

The word ‘panoply’ means an extensive or impressive collection. Panoply.io is a cloud data collection of code-free integrations to load and analyze raw data taken from apps, files, databases, and APIs. The loaded data is stored in Panoply’s centralized data warehouse, which you can connect to an analytical or BI tool for different data analysis tasks, such as building interactive dashboards.

The logic of using Panoply differs from the one at Coupler.io or Hevodata. You only need to select a source to get data from and, optionally, a BI tool for analytics. You’re not supposed to select a destination, since the data lands in a managed data warehouse.

4 – Integrate.io

Integrate.io is a data warehouse integration platform. It is a young brand, with the marketing being launched on December 15, 2021. However, Integrate.io was born from the merging of four companies Xplenty, DreamFactory, FlyData, and Intermix.io. Each of these companies represents offerings available on the platform:

ETL and Reverse ETL (Xplenty) to build easy low-code ETL & reverse ETL pipelines.
ELT and CDC (FlyData) to implement real-time data replication.
Data Warehouse Insights (Intermix.io) to run data warehouse analytics.
API Generation (DreamFactory) to generate instant APIs for enterprise data sources.

With Integrate.io, you can collect data from over 200 data sources to the public or private cloud, or on-premise infrastructure. From there, the data can be transferred to the preferred destination. Moreover, before the final transfer, you can transform and cleanse the data.

5 – Dataddo.com

This is another data integration platform that claims to grab your data from any source and send it to any destination, including BI tools and dashboarding apps like Power BI, Tableau, etc. In addition to integrating data and building ETL pipelines, Dataddo supports reverse ETL and data replication.

Currently, you can enjoy 200+ off-the-shelf connectors at Dataddo. However, if you haven’t found the one you need, you can tell them about it, and they’ll build it! They say that this will cost nothing and take around 10 business days.

Didn't find a tool that would fulfill your needs? Check out more tools here.

What are the 5 Types of Marketing Data Management?

Nadia Basaraba — Mon, 02 Jan 2023 19:32:17 +0000

There are multiple operations happening in an average digital marketing data management system. Here are the most common ones.

Data integration

As marketing typically involves dozens of data sources, from marketing channels to CRMs, data integration is a key process in the data management cycle. It involves taking data from multiple sources that may have different data types and formats and bringing it together in the data storage facility.

Executing it correctly takes a deep understanding of the data systems of each of the sources used and transforms the data to conform to a uniform standard.

Data refining

Refining data is a process that searches for and eliminates data that is incorrect or corrupted. During data refining, your analytics tools should check for data entry mistakes and other inconsistencies, and catch wrong formatting like mixed-up date formats, duplicate or confusing column names, etc.

This turns raw data into a format that is easy to parse and analyze.

Data enrichment

Datasets are rarely complete, but thanks to having access to multiple sources, there’s a chance there is plenty of duplicate data. While the bulk of it should be cleansed during the refinement process, you can use some duplicate data to enrich your dataset. This means fixing missing data.

This also means adding more columns of data to a dataset, for instance adding sales data to the Facebook ads dataset. The result is a dataset that may provide much more insight into the effectiveness of your marketing campaigns.

Data stitching

Data stitching operates on a similar idea. It’s the process of taking customer data that is present across different datasets and integrating it into a single one. This allows for a deeper understanding of how customers interact with different departments like marketing and sales.

Data mining

Data mining helps businesses make more sense of large volumes of data. This process involves analyzing datasets to find trends and correlations. These are used for further business analysis and can help you arrive at conclusions about your marketing performance that may not be achievable with a simple marketing dashboard.

Check this useful guide to learn how to manage your marketing data effectively.

Marketing Data Visualization: Definition and Example

Nadia Basaraba — Sat, 24 Dec 2022 14:54:05 +0000

Data visualization in digital marketing is the process of eliciting insights and visualizing them based on marketing raw data.

The marketing data that you can represent in the different forms of graphs and charts includes:

website analytics (page views, conversions, bounce rate, etc.)
email marketing (open rate, click-through rate, unsubscribes, etc.)
social media (shares, subscribers, approval rate, etc.)
advertising (clicks, impressions, cost per click, etc.)
video marketing (views, likes, sign-ups, etc.)
Other marketing activities

Example of data visualization for marketers

When talking about data visualization for marketing analysis, the first thing that comes to mind is a chart of any form: line chart, column chart, funnel chart, and so on.

However, data visualization is not only about charts and graphs. Pivot tables allow you to elicit valuable insights without actually visualizing data. So, we can include them in data visualization in digital marketing as well. Here is an example of marketing website performance in the form of a pivot table in Google Sheets:

And, of course, the most advanced way to present your marketing data visually is a dashboard. Dashboards can accumulate multiple metrics in different forms and even let you filter or group the data. Here is an example of a customer base built by data experts at Coupler.io using PowerBI.

So, as you understand, marketing data visualization can be very different depending on your goals. But is it possible to do analytics without any visualizing data? Check out the full article to dive into this.

4 Biggest Challenges of Data Analytics in Marketing

Nadia Basaraba — Mon, 19 Dec 2022 08:29:22 +0000

Marketing teams nowadays collect plenty of customer data. However, the data itself is useless unless it is analyzed the right way. Marketing data analytics is what helps businesses get customer insights and make data-driven decisions.

Every year marketing specialists need to aggregate more and more data, and therefore analyzing it becomes more difficult. Read on to find out 4 biggest challenges of modern marketing data analytics.

Problem of identifying and tracking meaningful analytics

This is one of the toughest challenges for marketers. The metric identification process must begin with something other than looking at whatever metrics are available or just following existing marketing processes. Instead, this process has to start with understanding the strategic objectives marketing is pursuing. There is often no easy, direct path to getting the best metrics for an analytics process. Marketers might find themselves juggling different sets of data, blending and calculating numbers to arrive at something that truly indicates how a process is performing.

Resources deficiency

Businesses need more resources to utilize data analytics properly. Staffing up employees who are trained in analytics will take priority. In addition, marketing data analytics requires time and money, but many businesses do not have the budget or staff to dedicate to data analytics, which can limit its usefulness.

Abundance of marketing data

The modern marketing landscape is diverse and complex. Most channels are digital, and campaigns produce content people consume across many devices. While this sheer volume of data is good, marketers find themselves overwhelmed in a data overload situation.

Lack of expertise

Many marketing organizations need more skills to use data analytics effectively. Marketing data analytics requires a specific set of skills, including statistical analysis, data visualization, and technical skills. Understanding the customers and products well is also important. These skills and knowledge are necessary to make sense of the data and glean actionable insights.

How to overcome these challenges?

Despite its difficulty, marketing data analytics is a powerful tool that can help you improve your marketing strategy. Thankfully, as the challenges of marketing analytics rise, so too are the BI tools that make marketing analytics more effortless to manage. Click here to learn how to use marketing data analytics in practice.

Data Stitching: what is it and why do you need it?

Nadia Basaraba — Sat, 17 Dec 2022 21:41:59 +0000

Businesses collect lots of customer data. To get customer insights and make data-driven decisions, marketing and sales teams need to acquire data from a range of different sources, channels, platforms, and touchpoints. Later on, it is often quite challenging to pull this data together and merge it in a single storage. This is where data stitching comes to the rescue.

Data stitching allows you to make sense of all the collected data and, in turn, helps you build more targeted strategies and offer a better experience to your customers.

Data stitching defined

Data stitching is the process of combining different sets of related data into one common destination. There, the data can be merged, aggregated, summarized, and processed in many different ways. The end goal, most often, is deriving valuable insights, building extensive customer profiles, or merging data from different business entities into a single report.

Marketing professionals, for example, can be interested in pulling data from all their channels and merging it all together for a general report. Accountants, on the other hand, would gladly combine reports from the different business entities they manage into a single destination. Check out this article to find a detailed case study of using data stitching for business.

While data stitching can be done manually by simply exporting data from apps regularly and uploading that into Excel, for example, it’s not feasible at all on a larger scale. Luckily, there are a number of tools capable of stitching the data from the apps you use on an automated basis, with little or no coding necessary.

Why does data stitching even matter?

While stitching data may seem like a nice addition, for many professions it has become nearly a necessity. Without it, they’re missing out on tons of valuable information and end up misinterpreting facts or guessing more often than they would like to.

To understand why, let’s look at a simple example. Marketers crave any information they can get about their customers. They want to know about every touchpoint, every click on an ad or a button, every interaction with their website, and so on. All this information is somewhere but it’s spread across different apps or accounts.

A potential customer, let’s name her Jane, clicks on a Facebook ad that pops up on her wall. Jane checks out the landing page an ad leads to, reads more about a business, and then leaves. A few days later Jane types in the website address one more time but this time on her mobile phone. She explores a bit further and eventually makes a purchase.

Many Janes later, the team looks at the data for each channel separately. Checking their Facebook Ads account, they see a very low conversion from that ad running lately so they decide to discontinue it and focus their efforts elsewhere. Meanwhile, Jane and many like her continue using the product thanks to that very ad they saw on Facebook.

Stitching data from Facebook Ads and Google Analytics would allow the marketing team to connect both visits and correctly attribute the user acquisition to the social media platform. The metrics they rely on would have looked differently and so would likely their business decisions.

Do SaaS businesses really need SaaS data analytics?

Nadia Basaraba — Wed, 14 Dec 2022 12:24:31 +0000

The SaaS market is evolving fast, adopting advanced technologies and promising huge growth potential. Because of the high competition in the field, SaaS companies have to be focused and creative to not get lost and die in the overcrowded ecosystem. Instead of hoping for pure luck and making decisions based on intuition, they need solid data to rely on. And this is where SaaS analytics come into play.

In simple words, SaaS analytics is how software-as-a-service companies track data in order to make data-driven decisions that help them grow and scale. To succeed, companies need to know everything about their SaaS business — from how it functions to where it fails.

Below in this article, you will learn four main reasons why SaaS businesses need data analytics.

Find growth blockers

A huge benefit of setting up a SaaS data analytics system that stores historical data from multiple sources is that you can find bottlenecks that stifle growth opportunities. Can you do this with the help of an analytical dashboard? You can if it displays proper metrics.

It’s important to not only select metrics, but also track their correlation and act on the results. For example, customer support tickets can provide you with many insights on how you can improve your SaaS product: enhanced features, additional functionalities, revamped UI, etc. However, this information is not directly connected to analytics. In this example, active user metrics as a part of SaaS analytics can give more valuable insights. When you see that usage is dropping on a particular feature, it signals there’s been a failure on your part that needs improvement.

Finding out what correlates with higher sales helps focus resources on improving aspects of your business that may lead to faster growth.

Therefore, the dashboard itself is not a magic pill. Not least important is the ability to read the data in the right way. That’s not a beginner-friendly task, so you may have to use the help of data experts.

Track growth dynamics

One of the most important things a business owner can do for their business is to monitor its growth. Month-over-month growth figures across the most important business metrics can show how well your business is doing or signal that a change in strategy is needed.

A well-managed SaaS analytics system turns gigabytes of sales and marketing data into a handy SaaS dashboard that shows all the important metrics.

You want to track the year-over-year and month-over-month change in revenue, sales volume, customer growth, and churn rate. The change in these is a strong indicator of how well a business is doing.

It’s even better if you can view these metrics in the quarterly or monthly form to see the change more clearly. Learn more details about what SaaS metrics you should track here.

See how well new strategies perform

Analytics for SaaS allows business owners and CEOs to not only monitor how the business is doing overall over long periods of time but also track the performance of specific business decisions.

You can track performance on select email campaigns in comparison with the ones you employ regularly. This can be done easily by comparing the number of conversions from different campaigns in a dashboard.

Or you can compare sales performance after you’ve implemented a new sales or marketing strategy. That would be a bit more complicated — you’d have to look not just for growth, but for month-over-month growth larger than in previous periods. This way, you’re checking if the new strategy is more effective than the previous one.

Forecast revenue potential

A feature of data analytics that is important for any business, and is of utmost importance for startups, is the ability to predict future revenue. You can do a rough estimate by going with the current average month-over-month revenue growth to see what the revenue is likely to be in six to twelve months.

Knowing that allows you to budget for the future — when to increase spending and when to cut it.

Of course, any prediction based on historical data is only a projection. There are multiple factors at play when it comes to the functioning of a business. You can arrive at a more accurate picture of the future if you run prediction calculations with different key metrics.

This allows you to figure out how much the sales increase or customer churn can change before it affects the bottom line significantly.