My rudimentary knowledge of SEO tells me that you’re probably somewhere in the middle of our marketing funnel and are comparing alternatives to Supermetrics. Otherwise, why would you be reading this?
If you’ve made it this far into the evaluation process, it’s time you learn about our philosophy for how we approach data at Supermetrics. This philosophy guides how we think about which connectors to prioritize, how we enable access to data, and our approach to the data ecosystem. Most importantly, it’ll highlight how and why we’re different from competitive solutions. Let’s dive in.
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- Centralized data
- Decentralized data
- Zoomed out or big picture data
- Zoomed in or little picture data
- Stages of growth
Data centralization is a key trend these days. Thanks to the hardware advancements of petabit networks, cloud-based data warehousing vendors spent the last decade making it essentially trivial to spin up a cloud-based data warehouse. Today, every person on the planet is a few clicks and a credit card away from spinning up a data warehouse in the cloud that’s capable of storing and processing an inconceivable amount of data. This has sparked a revolution in the way we use business data to drive tangible economic outcomes.
The marketing data landscape is no different. A CMO’s budget continues to increase even though marketing and advertising performance are harder to measure than ever before. The need for internal visibility of marketing performance is driving companies of all shapes and sizes to centralize their marketing data into a data lake or data warehouse.
The old way of logging into half a dozen paid media platforms, downloading reports, and pivot-tabelling them to death is so last decade. Centralization has a number of key benefits for marketing data, and I absolutely encourage you to embrace it as a marketing leader. You’ll build trust within your organization, you’ll have a repository full of historical benchmarks and much more.
In this, we fully agree. Supermetrics was born as a spreadsheet tool but has been operating in the data warehousing space since 2018. The foundation that we’ve laid for processing massive query loads in parallel has translated exceedingly well to the data warehouse and data lake product side from a technical perspective. The benefits of a centralized data warehouse for marketing data are numerous, but we won’t go into them here.
Alas, centralization isn’t unique to Supermetrics. There are hundreds of vendors that can and will promise to do what we do and move your marketing data into a centralized destination. In my experience, only about half a dozen of these vendors are even worth considering, but hey, it’s a free market. This next section is where we start to delineate from the competition in significantly unique ways.
Amidst the growing popularity of centralized data models, the merits of decentralized data models seem to have been forgotten. Decentralized data access models don’t require the data to be stored in a centralized warehouse but rather enable users to self-serve the raw data they need in a very raw form.
Early in the data journey
Decentralized data access models are remarkably helpful for organizations early on in their data journey. Building a data warehouse requires planning, architecture, and a very good idea of what the underlying data already looks like. Without these things, the time spent on data work will be high, and the value delivered will be lower.
Intuitively this makes sense. It’s much easier and quicker to pull data into a Google Sheet than push it into a data warehouse. It also enables a higher level of interactivity with the data when you can see it, touch it, feel it, and build charts and graphs right on top of it.
You really get a chance to know and understand your data before committing to a larger technical architecture. Supermetrics uniquely excels in this way.
In addition to, not in place of
As great as centralized data access models are, they do have some drawbacks. Companies with high data fluency that are far along in their data journey actually reap significant benefits from a decentralized data access model, in addition to their centralized model.
What use-cases does a decentralized data access model have when paired with a centralized model? The list is surprisingly long:
- Enables rapid tactical experimentation on existing and new advertising channels
- Gives ownership of the marketing data back to the marketing teams that are generating it
- Enables timely, ad-hoc analysis in a way that’s hard to design for in centralized systems
- Reduces the marketing team’s dependence on a central BI/Data team for time-critical insights
- Enhances the ability to rapidly prototype new data flows that can eventually be centralized
- Adds an extra layer of redundancy for any downtime associated with centralized models
At Supermetrics, we find that maintaining a decentralized data access method adds a significant tactical element to the marketing data stack.Evan Kaeding, Lead Sales Engineer, Supermetrics
In this, I can say that Supermetrics is uniquely capable of delivering solutions that fit both centralized and decentralized data access models.
Zoomed out data
We have a saying here at Supermetrics: are we looking at the big picture or the little picture? Fortunately, by combining the merits of centralized and decentralized data access models, you can do both with relative ease.
For big picture questions, you’re usually going to want a centralized model. This is largely because the volume of data needed to answer those questions is going to be higher and probably not fit in a single spreadsheet or in a dashboard’s local memory. Essentially, you want a zoomed out view of your data. Understanding something like the profitability of different marketing strategies over a several-year time frame is definitely a big picture question and will require centralized tools to answer it.
Supermetrics helps answer big picture questions with our solutions for data warehouses, data lakes, and our API. To make getting started with marketing data projects even easier, we’ve built what we call “Standard Schemas” that help tackle the most frequently asked questions right out of the gate. You can really get started in 5 minutes and start moving pre-modeled data directly into your warehouse of choice, hook it up to your favorite BI tool and get rolling immediately.
Competitors that I personally respect, Funnel, Adverity, and Fivetran, only handle big picture marketing data. While they can serve these zoomed out needs well, the tactical benefits of decentralized data access models are significantly encumbered or non-existent.
The evidence for this can be found in our own sales organization. We frequently engage with members from the marketing department who are fed up with waiting on their BI/Data team to produce a specific visualization or add a new data pipeline.
Your data needs to operate at the same speed as your marketing.Evan Kaeding, Lead Sales Engineer, Supermetrics
It’s also worth mentioning that many of the data models provided by these customers are not dashboard-ready right out of the box. Most of the time, data models will need to be created on top of the raw data generated by these tools to ensure that they’re ready and performant.
This adds a pretty significant time delay for spinning up new pipelines, and that’s if you’re lucky enough to have engineering resources. If you don’t know what “converting a normalized schema into a denormalized schema” means, then you might have some challenges making this data useful.
Zoomed in data
Now, for zoomed in data. The data that is quick to access, small in volume, but absolutely irreplaceable in a marketing data workflow. Supermetrics excels—pun intended—here in many ways.
Supermetrics has the ability to link your marketing data directly to your Google Sheets Spreadsheet, Google Data Studio Dashboard, or Microsoft Excel workbook. Our connections are live, meaning you can get the freshest data right from the source without worrying about latency or stored data being out of date.
The quick and easy-to-access nature of these tools makes them utterly invaluable to marketing teams that want to operate independently of their BI/Data team and maintain a high cadence. The depth of our individual connectors and breadth of supported sources are cited as key reasons that Supermetrics is the preferable alternative to many of our competitors in this space.
Competitors like PMA and TapClicks, only handle zoomed in data—meaning companies have to stop using them once they mature and need a data warehouse.
For companies early on in their data journey, these tools can provide immense value. However, when companies are ready to make the transition to a marketing data warehouse, Supermetrics has offerings that are readily available and a whole team that can help ease the transition, no matter how complex the implementation is.
Supermetrics has specific features dedicated to this use case. Customers who use our new “Custom fields” functionality can define transformations on their data and use them to manipulate their data before it hits their GS/DS/XL destination. These same custom fields can be used to apply these same transformations to their data warehouse or data lake destination.
Similarly, data source access can be significantly streamlined with what we call “Shared tokens”. This means that data source tokens to platforms like Google and Facebook can be easily shared across our destinations. A user who has provided their data source credentials for a Google Sheet can easily begin moving that same data to BigQuery without having to reauthenticate to the underlying data source.
Stages of growth
Choosing a tool to manage your marketing data is an exercise in humility. You must accurately assess how comfortable your company is with data during your vendor selection process.
We have a lot of experience helping companies level up their data practice. Most of the time, it starts with a subscription to one of our core products, GS/DS/XL. This gets them going for a while, sometimes years, until they hit a point where they are ready to enhance their reporting capabilities with a marketing data warehouse.
Our competitors are largely split into two groups—those who deal with data warehouses and those who don’t. You can imagine that crossing the chasm between spreadsheets and a data warehouse with multiple vendors isn’t a pretty situation.
I said we’d be talking about philosophy here, so I’ll go ahead and bring Plato into the discussion:
“Each man is capable of doing one thing well. If he attempts several, he will fail to achieve distinction in any.”
2,000 years later, this fact holds true for practitioners in the data industry—for all people, not just men of course. What are the implications of this?
Supermetrics decided early on to specialize in marketing and sales data. We decided that the best way to compete in a fragmented market was to specialize, which has paid off. We’ve actually retired connectors that were not aligned with our vision and didn’t add value to marketing and sales teams because we’re so dedicated to this mission.
When you specialize, the number of things you need to worry about simultaneously increases and decreases. When we build connectors, we have a checklist with over 100 steps to ensure that it meets the needs of even the savviest marketing user. This includes things that, to some, could be considered highly trivial—things like customizable attribution windows, metrics and dimensions that are uncommonly used, and support for different authentication methods.
We have no doubt that you’ll find Supermetrics’ marketing and sales data connectors are best-in-class.
Many of our competitors have taken a “broad brush” approach to connector development. Competitors that advertise 500+ connectors often fail to produce or maintain high-quality connectors.
Keep in mind that all of the APIs we’re connecting to are constantly changing. Maintaining connectors within a set of specific domains is challenging—trust us, we know the struggle— but imagine having to do that across domains and for hundreds of connectors with maybe only a few users? It’s an unfortunate recipe for stale connector configurations that don’t evolve with the underlying APIs and sometimes break altogether and aren’t fixed for months.
To sum up our product philosophy in a single sentence, I’d say that:
The companies that provide domain-specific, specialized solutions for customers at every point in their data journey will provide the most value.Evan Kaeding, Lead Sales Engineer, Supermetrics
Whether you’re just starting out or a seasoned data expert, Supermetrics has a solution for you. Let’s get started!
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