Nov 07, 2018
Given the importance of web analytics data, it is essential to conduct a web analytics audit every year.
Apr 03, 2018
by Saurabh Kumar
The spending of online advertising is expected to grow from USD $266 billion in 2018 to nearly USD $376 billion in 2021, as per a report by eMarketer. In just three years this clocks to a growth rate of 41%, of which 50% of all advertising spending will be digital. This unprecedented growth means that marketers need to measure a lot more of their campaign results to stay ahead and embrace the right technologies and reporting solutions.
Many marketers understand the value of business intelligence tools that replaces siloed reporting tools. Until last year, only large organizations opted for automated marketing tools. However, this year, we’ve noticed that companies with annual budgets starting at USD $1 million are also considering these new solutions. The key drivers being:
If you can measure your marketing efforts correctly, you are able to make smarter business decisions, acquire more customers, and drive significant growth in your business.
Companies are offering automation of data import and mapping as services along with simplified tools for data imports.
A number of marketers are focused on building a smart marketing setup with tagging campaigns and URLs and using third-party analytics tools, which helps in effective reporting.
Traditionally, most marketers used siloed reporting tools or manual reporting with a spreadsheet. However, due to the shortcomings of manual tools, marketers are looking forward to new and improved reporting techniques.
Conventionally, marketing tools are vertical solutions that import data and visualize it in one application. This is done in siloes, where you can only view data from one source at a time. Though there are hundreds of such reporting tools that help create marketing reports, they treat data from different platforms separately. So, you can create a separate AdWords report, a Facebook report, and a Google Analytics report. However, you can’t combine the three and get an all-inclusive report that will pick cost data from AdWords and revenue data from Google Analytics.
When you’re looking for conversion tracking, you will only know about ads from a specific platform and you can’t get a comprehensive report on the performance of all advertising channels used in your campaign. Sophisticated advertisers thus tend to resort to manual reporting to account for the results.
Data-driven marketers tend to assess their marketing efforts based on the results from analytics, CRM, and other backend systems. However, to enjoy the better flexibility to support the customer acquisition model, they need to export their data into a spreadsheet and opt for manual reporting. There are many shortcomings of manual tools:
•It can be time-consuming, tedious, and prone to human error as large amounts of data are imported from various data sources. When the business grows, this becomes difficult, especially if you have to keep track of several markets, websites, or brands.
•You tend to know the problems only when you do the reporting, which happens only once a week. By then, you lose out on many optimizations and corrective action opportunities.
•The model in a spreadsheet has limitations as you can’t see data and trends developing on a daily level. Plus, if you want to slice the data by device, it may not be possible to do the analysis by yourself.
Marketers are now building an automated and flexible marketing reporting stack using the best of breed components. The objective is to ensure all data is always available and up-to-date. The stack consists of three layers, wherein each layer consists of products from different companies. This helps you to leverage the best components that fit your individual needs.
The role of the layers are as follows:
Visualization tools include Google Data Studio, Looker, Tableau, Microsoft Power BI, and Qlik. Larger organizations are relying on highly advanced business intelligence tools, but smaller and medium-sized companies are embracing the free tools like Google Data Studio for better reporting. Marketing teams of all sizes are now able to afford reporting solutions and benefit with automated marketing tools.
Like the name suggests, a data warehouse is a scalable data repository that is used to report and analyse data. Modern visualization tools are connected to the data warehouse so that marketers can always pull up the required data to feed dashboards and facilitate analysis. Conventionally, companies like Oracle, IBM, and SAP dominated the market for data warehouses. However, in recent times, cloud-based companies like Google and Amazon are making data warehouses available to the masses with affordable prices and self-service offerings.
One of the biggest challenges for building a marketing reporting stack has been to get data into the data warehouse. As maximum marketing data stays in third-party advertising and marketing platforms, there have been no good tools to solve this unique problem.
Now, you can either find one tool to solve the entire problem of data integration or use a combination of tools to import all your data into the warehouse. The two common types of tools include:
Data-as-a-service platforms that pull in marketing data (either all of it or some of it) and offer it in a clean and compact way.
•Extract Transform Load tools that allow integration with new platforms. These need a little technical knowledge as you are responsible to maintain and connect the platforms.
Remember that not all connections are equal as many platforms change their recent data and implements, so you need an implementation that backfills the warehouse with historical data as well.
Lastly, getting your data into the data warehouse isn’t the only challenge. If you pull up all the data you have into the warehouse, it can be tough to find and use data that you actually require. You need a highly structured data warehouse that’s cleaned and has a mapped set of data that everyone can use as per their requirements.
One of the greatest advantages of a marketing reporting stack is flexibility. Since all businesses are unique, automation of data and your reporting stack helps you create customized reports as per your distinctive needs. If you have a new data source that requires access, you can add it to your data warehouse and map it along with the other data. This will help you update reports with the new data.
Examples of types of reporting in a data warehouse and their benefits:
If you happen to meet a marketer, ask them (or yourself) this question: What percentage of your marketing budget have you spent this month and is your spending above or below budget as of today?
Most probably, you won’t get a straight answer as most marketers wouldn’t know. Their spending is usually tracked in various tools or siloed reports and isn’t added on a daily basis. A marketing stack helps to collect all data automatically so that you can visualize the overall marketing spends on a regular basis.
For instance, if you’re using 10 advertising platforms and have six different markets. For four platforms, you have one account for every market and one common account for all other markets. So, you’re using, 4*6 + 6 = 30 advertising accounts. So, every day, you can get a sum of the advertising spend in all 30 advertising accounts. You can also get spending forecasts for the month and put in budget lines to track if you’re on a budget.
In siloed reports, the performance of every advertising platform is shown on a separate report. However, performance-driven marketers have a preference for seeing cost data from advertising platforms and revenue data from third-party analytics solutions. When you use automated data and reporting stacks, all your data is properly mapped before it is put into the warehouse.
Sep 04, 2018
Although Google Analytics is an amazing service that helps in tracking e-commerce, many times it counts a transaction more than once.