In the dynamic digital marketing landscape, data reporting tools like Supermetrics have been pivotal by enabling businesses to extract, analyze, and visualize data effortlessly. With little effort, you are able to extract data from platform A and B, then get it into a spreadsheet or reporting dashboard – traditionally referred to as an extract-transform-load (ETL) or point-to-point process.
While point-to-point reporting solutions excel at simplifying data retrieval and basic data transformation and analysis, the data volumes and explosive growth in the number of platforms used by organizations have pushed this type of reporting to, and often beyond, its limits.
The advent of marketing intelligence platforms promises a new era in sophisticated data management and utilization. This brings us to the pivotal question: when should businesses transition from a point-to-point solution to a marketing intelligence platform that facilitates advanced data management, traceability, and sending data to multiple destinations?
Challenges with point-to-point reporting
As organizations and marketing efforts grow, data management and reporting needs inevitably expand and become more complex. This is true for:
- Number of platforms
- Data transformation needs
- The volume of data
Point-to-point solutions fall short in efficiently managing these multifaceted data requirements, leaving broken or inaccurate reports in their wake. This is particularly stressful when the data needs to be shared with senior stakeholders internally or, in the case of agencies, external clients.
The primary stakeholder for marketing reporting tends to be the marketing department itself. However, there are often a slew of other stakeholders that also need access to the data marketing generates, such as leadership, the finance team, revenue operations, IT, and so on.
Without a central platform that allows the marketing team to share its data to multiple destinations, organizations must set up additional point-to-point data transfers. The end result, of course, is that the organization ends up with many buckets of siloed data and no single source of truth.
Ensuring data accuracy and consistency across multiple reports and dashboards is paramount. Connected to the point just above, with point-to-point solutions, disparate data sets and individualized reporting can sometimes pave the way for inconsistencies, posing significant challenges in forming coherent strategies. How can an organization have trust between departments if everyone reports on numbers that don’t exactly match?
4. Data storage
In recent years, many platforms have decreased the amount of historical data they keep in their reports. Google Analytics 4, for instance, only keeps up to 14 months of historical data, which means you can’t even perform full year-on-year analysis in the interface. By extension, your reports might also suffer from a lack of historical data if you request it from platforms on an adhoc basis. You can read more about the importance of owning your reporting data here.
5. Case study - Arm Candy
Arm Candy, a digital marketing agency based in Texas, had reached the end of the line with its point-to-point reporting. Broken reports and disappointed customers were commonplace, and even when things worked, the quality of insights they were able to provide was not very impressive. Switching to Funnel allowed them to remove instances of broken reports, centralize data management, and send clean and accurate data to multiple destinations at once. Read the full story here.
Advantages of marketing intelligence platforms
1. Built for scale
Marketing intelligence platforms are purposefully built to handle complex data transformation and data storage, both for small and large use cases. Since reporting data is stored in the cloud, it remains available even if there is a temporary problem with a platform's reporting API, decimating instances of broken reports.
2. Centralized data management
The capability to centrally manage data (ensuring it is consistent, accurate, and accessible across all departments) represents a tangible advantage of marketing intelligence platforms. As opposed to a data warehouse, most marketing intelligence platforms offer a no/low-code interface that is easy for business users to manage without compromising on robustness.
3. Supports multiple destinations
Instead of recreating the same process two or more times, marketing intelligence platforms allow you to send the same data set to multiple destinations. This allows stakeholders to find common ground by looking at the same KPIs, which increases trust throughout the organization.
Marketing intelligence platforms are designed to grow with your business, offering the flexibility to integrate new data sources and destinations seamlessly while providing a scalable solution for evolving data needs. Once new data has been collected, it is automatically reflected in all destinations, minimizing the manual work traditionally involved when new data or a new platform is added.
5. Case study - Power Digital
Power Digital, a San Diego-based agency, saw firsthand how a reporting layer based on spreadsheets can become unruly due to increasing platforms and data volumes. Forget providing great insights, the primary challenge was keeping things together for baseline reporting, which often broke despite their best efforts.
Their transition to Funnel provided a massive boost for the entire agency. With broken reports no longer an issue, the team could spend more time providing great insights to clients. You can watch the full case study here.
While point-to-point solutions like Supermetrics have historically proven beneficial in small use cases where ad-hoc reporting has been sufficient, the expanding marketing landscape and growing data volumes have started to highlight the shortcomings of this approach.
The undeniable advantages of marketing intelligence platforms in terms of scalability, consistency, and advanced analytics make them a formidable contender for businesses eyeing future growth and improved data management. It’s vital for businesses to weigh their unique needs, scalability expectations, and resource availability in deciding the path forward in their data management journey.