This content, written by Ross Barrett & Kim Frithiof, was initially posted in Looker Blog on Sep 9, 2020. The content is subject to limited support.
As we know, digital has become the heartbeat of modern marketing, and the amount of data marketers use is growing at a considerable rate. However, even with the increase of data-driven marketing, there are still challenges with marketing analytics, with one of the biggest being that data used to measure and improve marketing performance is siloed across numerous channels and tools. In order to make the right decisions and ensure budgets are not exceeded, marketers today need their data centralized and transformed so that it can be leveraged in real-time.
The processes to get from siloed data to centralized and transformed is usually a manual, time consuming one, prone to human error. Thankfully, there are now solutions on the market that aim to solve this problem for marketers and data teams alike. Modular analytics stacks allow you to choose any modern cloud-based data warehouse in combination with almost any visualisation tool, making it easier than ever to perform reliable data analyses.
But even with modular analytics solutions, there are still some remaining challenges to performing marketing analytics today.
Challenge 1) Data is still siloed
The increase of powerful data warehouses and visualization tools has not completely solved the fundamental problem of siloed data. Marketers still need a way to collect data from different platforms and consolidate it into one place. This isn’t always an easy task, and the increased number of data sources many teams work with only adds to the complexity of this challenge.
To help solve this problem there are new cloud based ETL tools along with data pipelines built for the cloud. These tools help move raw data from point A to point B with automated scheduling, handling API limits, and even performing some basic data cleaning.
By using these tools to help centralize the data, the next step is to prepare the data for analysis so individuals can start extracting real-time value from it.
Challenge 2) Preparing data for analysis
Making data business-ready requires technical resources and a deep understanding of each platform. Data teams must ensure that the data is cleaned, normalized, combined and aggregated into a model that supports all the data sources and unique business logic of the organization.
For instance, let’s say you’re a digital marketer and you want to compare how much you’re spending across channels. To do this, you would need to normalize the data into a single
cost metric. Unfortunately, this isn’t as straightforward as it sounds — for Facebook, this metric is called
amount spent, Google Ads calls this
cost, and for Twitter this is denoted as
spend. Given a consistent naming convention, you might also want to create new dimensions of measurement that would allow you to dig deeper into your metrics. For instance, say you choose to include the target market in your campaign names, you could extract that data and map it with your tracking data to create a specific segmentation for your analysis.
Even with the data team helping you get to this point, manually having to maintain this data model over time would be a time and resources intensive task.
Challenge 3) Keeping up with changes
When industry or organizational changes require adjustments to data, it’s oftentimes the data team that must pivot the most so that the changes are reflected in a timely and consistent manner.
Let’s take a look at a common scenario many data teams face with this challenge.
On the left you have the source teams, which is where the data is generated. This includes things like the marketing team setting up new campaigns in an advertising platform, the customer success team entering data into a CRM system, or the sales team working on deals. Data formats are continuously changed and new sources and platforms are added to solve new use cases. The teams are fairly happy using these systems as long as it gets the job done. However, they have little incentive to make sure that the changes are reflected well in the data platform.
As the source teams perform these tasks, data formats may change and new sources of data may be added to help solve for new business use cases. In the midst of these changes, these teams are usually content with using the existing systems as long as they get the job done. However, they have little incentive to make sure that any changes to the data source or format is reflected in the platform, causing bigger problems down the line.
These problems are faced by the group on the far right — the data consumers who are stuck waiting for the information they need. This group will always have new requirements for their data requests and new questions they need rapid answers to, as they want to use data to better inform their decisions and strategy planning. But because they’re reliant on the already busy engineering team to supply them with the insights they seek, data consumers will often become frustrated.
The difficult thing about this scenario is that the group in the middle, data engineers, are the folks responsible for both cleaning and preparing the data from the source teams and delivering data consumers what they need based on their current requirements. Because they have to constantly handle changes and new needs from both sides, there is little time left over for them to do more valuable work, like performing deep dive analytics or working on impactful strategies of their own.
Challenge 4) Different data consumer needs
The ways in which individuals want to access and view data varies depending on their role. Marketers may want to analyze aggregated data in a spreadsheet or as an easily-shared visualisation. A data analyst may prefer to have the data delivered to them as a SQL interface. Data scientists might opt for getting the data in full in a parquet file.
Whatever the preference, exposing data to consumers in their prefered formats and tools can end up being a massive undertaking. Some tools on the market today only support one use case/destination, which makes the whole solution rigid and difficult to change right from the get-go. This can then become exacerbated if the organization decides to use another data warehouse solution in addition to what is already in place.
In this case, you would end up needing to build a new set of logic and repeating that process over and over to support the new data destinations, all while addressing the other challenges outlined above — making switching out only one part of the overall tech stack a very expensive and strenuous process.
Overcoming these challenges with Looker + Funnel
To address these challenges of marketing analytics and overcome them requires a solution that can:
- Take siloed data from all marketing, advertising and sales platforms and automatically feed it to a centralized location
- Map and harmonize data in real time, whilst preserving the full granularity and raw data
- Do all of the cumbersome cleaning and mapping right out-of-the-box
and Looker have collaborated to build the as a solution to address these challenges. This not only promotes the acceleration of marketing analyses, but it provides a great starting point for anyone looking to visualize marketing data in Looker.
With this Block, you can view
- An overview of cross-channel KPIs such as cost, clicks, impressions, CPC, CPM and CTR
- Cost broken down by traffic source, media type, and individual campaigns
- Keyword performance
does the heavy lifting that otherwise would fall on source teams and data engineers, by providing a single, cleaned, and fully mapped table in your data warehouse(s). Funnel also generates the based on your customized data schema with the click of a button, so you can create dashboards instantly with no need to write a single line of code.
By implementing this Block, technical resources no longer have to be spent on tedious data collection and manipulation tasks and can instead be free to focus on higher-value activities and more advanced transformation.
Learn more about Looker + Funnel solutions for marketing analytics
Get a first-hand look of the in action during our on September 24, or to learn more about getting the most out of your marketing data.