Managing the stack: Analytics, data management and the new era of marketing
Analytics tools aren't just another level of your martech stack. Contributor Jose Cebrian argues that they should be interwoven throughout the technologies used in the marketing process.
One of the few universally agreed-upon truths in our industry today is that data is the key to more effective marketing. Data leads to deeper customer insights, which lead to more personalized and relevant marketing, which leads to revenue growth.
I am a believer in this fundamental truth: Data is king.
Because of this primacy of data -– and because sources of data have exploded in our hyper-connected era –- we have built a dizzying assortment of technology tools to collect, consolidate, manage, and act upon that data. Just managing these tools comprises the bulk of a marketer’s increasing responsibilities.
This means that marketing is no longer just about creative messaging, but rather about managing an entire customer journey through a stack of technology that incorporates data management, integration, orchestration, activation, and insights. And in this environment, analytics is the only discipline that is prepared to validate the capability and effectiveness of every one of these layers.
There is no such thing as a universal marketing tech stack anymore. But there are, in general, categories of tools that are common to enterprise-level marketing departments.
These include tools for planning and developing, attracting and engaging customers, managing identity and data, and decisioning, as well as database tools and consumer-facing platforms. Add to this stack ad tech platforms like demand-side platforms (DSPs) and ad networks, and you have a truly broad range of technologies to implement, manage, and track.
You might often see an analytics platform included as one of these tools, usually deployed at the end of a process or cycle. But analytics are necessary at every layer of the marketing stack — and they are crucial to assessing not just what results these interconnected technologies are generating, but the quality of the data generated by each component.
The importance of analytics
Unfortunately, analytics are often considered as an afterthought -– a method of justifying the outcomes of a marketing system, rather than a tool to fine-tune the contributing aspects of that system.
The challenge is partly one of availability; not every marketer has access to the data science needed to properly apply analytics at every point of the tech stack. But every marketer should realize that analytics are an effective validating mechanism, providing insight into whether they are integrating the correct data inputs and variables to be of value.
There are literally thousands of data sources that can be integrated into a marketing system, and those sources can be activated in multiples of different channels. The goal is to capture data, create a single customer view, apply automation layers to that data, and leverage it for effective marketing that influences customer behavior. Paramount to success is making sure that data is accurate and being manipulated correctly at every point in the process. Layer-by-layer analytics, then, is what marketers really need to meet their goals.
Analytics also validate the accuracy and integrity of the data that is being collected by the martech tools in place. This is critical, because incorrect, invalid, or muddled data is as detrimental to executing marketing initiatives as no data at all.
Validation, not just insights
Because having the right data is so important, marketers should embrace the validation capabilities of analytics at every layer of the stack. It’s much easier to pinpoint erroneous or misleading data if there’s already a process in place for analyzing the inputs and outputs of each system, rather than simply looking at overall efficacy and tracking back any possible fail points.
Moreover, having analytics in place can prevent errors in the first place, as it can flag (or discard) invalid data before it flows into the system. In complex marketing stacks, ensuring “clean” inputs is essential.
As marketers become more entrenched in their roles as managers of automated tech stacks, the importance of having an analytics process for every layer in the stack will become more obvious to them.
In the meantime, forward-looking marketers will be assessing their current stacks and analytics systems and putting the capabilities in place to more effectively manage the data flowing through them. And in doing that, those marketers will be positioning themselves and their companies for greater success in a complex and crowded marketing environment.