Marketing teams succeed or fail based on their ability to assess the needs of their customers. Doing that in an increasingly data-driven environment requires better tools for marketing analysis. Unfortunately for demand generation and marketing analysts, much of that data about their campaigns is hard to access and needs to be manipulated manually. Marketing tech stacks are typically made up of many loosely connected applications that make it difficult to work with information across sources. When data can be gathered and parsed, the story it often tells is not clear, recent or accurate enough to guide planning. What marketers are really after is clarity around the performance of their programs and the value of their spend. But by 2026, one-half of marketing
Marketers are not data scientists. They don’t have deep expertise in predictive or statistical analysis, nor in the nuances of integrating and cleansing data from multiple systems. They are experts at using data insights to make decisions about where to put resources and how to approach customer segments. So organizations face a dilemma—how do they provide marketing teams with the necessary information to make decisions and measure results, without making that information hard to extract and manage that the process is counterproductive?
To address this problem, organizations should insist on several important features or characteristics of an analytics platform that serves marketers. Above all else, marketing analytics needs to meld data coming from a wide variety of sources. Customer data, for example, lives in many different enterprise systems, much of it duplicative or contradictory. The priority is to aggregate it from wherever it is collected into a format that can be cleansed, potentially enriched and made accessible based on the role of the user.
The second key element is automation: any analytics tool should be able to perform the ingestion process without manual intervention by a marketer. Operations teams still spend a lot of time manually pulling together data on an ad hoc basis. Ventana Research has found that analysts spend the bulk of their time on manual tasks such as preparing data for analysis (47%) and checking quality and consistency (45%) in the data rather than doing actual analysis. This should be unacceptable to most enterprises.
This reliance on manual methods means that marketers are always several beats behind in assessing the strength of their campaigns and the impact of their spend. It keeps them from making sound proactive decisions about resource planning. If you cannot clearly see the results of the marketing efforts you’ve paid for, chances are you’re not going to understand what works, what doesn’t work, and where to invest (or reduce investment).
The need for better analytics goes deeper than that. Organizations want to provide better experiences for their customers. Better experiences do more than make for happy customers—they foster loyalty, which encourages people to recommend a business, or to buy more. CX has an impact on customer value, longevity, and on revenue. But unless you measure and analyze these connections you can’t move them in the desired direction.
For example, consider how to personalize messaging to individuals or small groups. Thanks to advancements in AI and machine learning, it is now possible to create precisely targeted campaigns that can vary based on the context (e.g., mobile, in-store, advertising or websites). But that degree of fine control over messaging requires a constant back-and-forth of assessing the customer, identifying the segment, measuring performance and recalibrating. When it works, it’s a constantly iterative process that relies on data that’s accurate, fast and continually changing.
Another important use case is attribution—determining what message or “touch” contributed to a sale or revenue moment, and by how much. This is key to determining the profitability of a campaign or strategy, which in turn is key to getting upper management to commit resources to marketing. There are many models for determining attribution, all of them complex, all of them requiring a complex analytic system to interpret. Or consider lead optimization. Marketing determines the strength of a lead, but ultimately it’s the sales team that passes judgement about whether that lead is qualified and generates revenue. The process of scoring leads, uncovering lookalikes and determining buying interest is almost entirely dependent on a robust and automated analytic system.
Better analytic management can also improve the relationship between sales and marketing efforts. It’s hard for teams to disagree about the value of leads, for example, when there is empirical evidence that shows the truth one way or another. One fortunate outcome of a better analytic approach to marketing operations is the amount of time you can recover. Marketers, who have many activity threads to manage, have the flexibility to apply the insights they gain from data analysis to come up with more optimal campaigns and strategies. Those campaigns are more likely to be relevant to smaller segments and on down to individuals. Marketers can put that recovered time to good use exploring new ways to use insights to make better predictions and other advanced use cases for their data.
The overall goal is to give marketers a better understanding of customer behavior, awareness of what levers they can pull to influence that behavior, and the ability to act on this in real time with content and product displays that are predicted algorithmically to be most appealing to the customer. Without that extra help, they will be in the dark about where their resources go and whether they are moving the revenue needle.