08-07-2025
Unlocking The Potential Of Advanced Analytics To Ignite Growth
Helping CMOs Aggregate, Organize and Monetize Knowledge In Marketing Actions, Conversations and Decisions
Digital marketing has blessed modern marketers with a wealth of customer engagement data. This data pours out of web sites, digital media campaigns, Customer Data Platforms (CDPs), ecommerce channels, CRM systems and mobile devices. In theory, the current revolution in advanced analytics and AI should multiply the ability of marketing leaders to use this data to unlock more growth for their businesses. Data-driven marketing can improve the performance of marketing campaigns, personalize customer experiences, and multiply the return on brand, relationship and channel assets. But in practice, most marketing teams are far from realizing the potential of big marketing data to ignite growth. And many are not satisfied with efforts to maximize AI's value – despite the hype.
For example, only 31% of marketers are fully satisfied with their ability to unify customer data sources, according to a survey of 5,000 marketers by Salesforce. Less than one third (32%) of marketers surveyed by Salesforce were completely satisfied with how they use customer data to create relevant experiences. And while almost every CMO surveyed by the Fuqua School of Business has invested in a wide variety of tools to manage and monetize this data, the majority of them are unsatisfied with the results they are getting from this technology. Over three quarters of growth leaders say they face talent gaps and operational constraints that limit their ability to effectively harness AI technology, according to a survey of 1,800 marketing and sales executives by The IBM Institute for Business Value.
This begs the question: why are marketers struggling to unlock the full potential of advanced analytics to ignite Growth? Is it the data? Is it the way it is organized? Are the tools and techniques being used to analyze it inadequate? Or the skills of the analysts? Or is it the inherent risks of relying on big data that are holding them back?
The answer is likely all of the above, according to Frank Findley, Executive Director of the Marketing Accountability Standards Board (MASB), who is actively studying the problem with a team of top academics in the field. 'There are no clear standards for the methods, tools and procedures that businesses use to collect, structure, analyze and monetize the wealth of 'marketing big data' that they generate,' says Findley, who is leading the MASB Big Data in Marketing Study. 'It is difficult for CMOs to know which analytical and statistical approaches still work in a world of big marketing data, and many have not yet figured out what what new AI enabled analytics methods they can rely upon to make better, faster and more reliable business decisions about their marketing actions and investments.'
For example, tried and true traditional statistical based approaches like regression analysis and reporting tools and dashboards that have been proven to be reliable for analyzing customer data in the past. But these proven tools may not be fast or cost effective enough to support the large data sets and analytical needs of a modern marketing organization. Likewise, while many marketers are quick to experiment with new AI enabled analytics approaches, such as Natural Language Processing (NLP), Machine Learning, and Generative Artificial Intelligence (GAI) – their efficacy, trustworthiness, reliability, and integrity remain in question.
In public forums, Marketers regularly tout the potential of advanced analytics to better segment audiences, plan media, forecast demand, optimize pricing, and allocate growth resources. For example, CMOs expect a full 44% of their work to be AI enabled in the next three years, according to the CMO Survey by the Fuqua School of Business. But behind closed doors they worry about whether AI will give them the wrong answers, introduce biases into their decision making, and create brand and compliance risks due to hallucinations and privacy breaches. In addition, the reliability, availability and cost advantages of these tools are not well understood.
'If marketers don't figure out the best ways to structure, analyze, and monetize their marketing big data, their role as the voice of the customer and the leader of customer insight will be called into question,' says Purush Papatla, a Professor of Marketing in the Sheldon B. Lubar School of Business who helped design the MASB Big Data in Marketing Study. Purush makes a prescient point. For the first time, fewer than half of CMOs report they have customer insights as one of their primary responsibilities in the latest CMO survey. If the marketing team does not own customer insights, which functional organization does? – and do they have the business acumen, analytical skills, and customer context to use those insights to improve go to market performance?
Getting an answer to the questions MASB is asking is important. Why? Because today, the knowledge that resides in this customer big data can be the largest and most valuable financial asset in the business. When you consider that over 80% of the value of any B2B organization is made up of 'intangible assets' like intellectual property, innovations, 'know how', customer insights, and codified information, and brand equity - the knowledge in your customer database could be worth millions or billions of dollars, according to analysis in the book Capitalism without Capital.
In that light, much of the value of marketing creates lies in their unique ability to capture, organize, and analyze all this customer engagement data to create useful information to support marketing decisions and actions. 'Our goal with the MASB Big Data Study is to help CMOs learn from the collective intelligence of other CMOs on how to best squeeze the most value out of investing in AI and how to avoid the roadblocks and landmines, so to speak, they will face along the way,' says Professor Papatla, who is also the Co-Director of the Northwestern Mutual Data Science Institute.
For example, the best marketing organizations are creating value by connecting and consolidating their customer engagement data assets in ways that turn it into knowledge that can support marketing decisions and plans. There are many ways that marketers can use advanced analytics and AI agents to turn messy and disorganized marketing big data into valuable go-to-market knowledge that supports marketing actions and actions. They include:
A bigger opportunity to create financial value from this data is to turn it into codified knowledge that can be monetized in marketing channels, selling situations, commerce transactions, and customer responses. Codified, or explicit knowledge, is defined as information that is captured and cataloged and converted into knowledge that is ready for people to use – ideally to make more money. It appears in documents, procedures, and databases. In contrast with tacit or implicit knowledge, explicit knowledge is codified and articulated. The most important aspect of codified knowledge is that it is acknowledged by the accounting profession as an asset with financial value. In accounting, a knowledge asset refers to the intellectual resources and expertise an organization possesses that can be used to create value and gain a competitive advantage. These assets can include things like patents, trade secrets, customer databases, employee skills, and company processes. In the context of revenue generation, codified knowledge assets are useable materials in a customer database or knowledgebase such as codified/explicit knowledge such as instructions, AI agents, algorithms, models, software that can be used sales and marketing to generate future revenues. There are many ways that marketers can use advanced analytics and AI agents to turn insights into codified knowledge that can be monetized in marketing, media, and sales channels, according to a Certification program for marketers by Revenue Operations Associates. The list includes:
One problem is that in most organization, knowledge is spread around in many systems, brains and silos. Much of it lies in on average eight systems such as CDPs, marketing automation, CRM, digital media, content management, and first party web sites. Much of this knowledge still lives in adjacent systems like word processing, collaboration software, emails, SharePoint drives, or in spreadsheets. 'Marketers are tasked with analyzing a variety of first party data structures, that track behavior across web sites, mobile devices, commerce platforms and social media channels – but also increasingly unstructured data from audio recordings, video and images,' says Findley.
Another problem is there is often no single function, or role with responsibility to organize and analyze that data. Often big customer data is managed by marketing, or sales, or a third party vendor, according to Findley. In some cases a center of excellence is put in place to coordinate and analyze data. This challenge is multiplied by the growing variety of people who are actively analyzing that data. For example, in any given organization analysts from marketing, sales or finance can be evaluating the same data sets, in a different context, with different purposes (e.g. targeting audiences, defining account coverage, forecasting cash flow). Different players will have different levels of skills and use different types of tools. For examples marketers may use path analysis to optimize resources and attribute revenues, cluster analysis to segment markets objectively, and computer vision to evaluate, categorize and personalize creative. 'Specific tools have specific usefulness, so having a wide variety of proven tools in your belt can be a real advantage,' says Findley. 'But marketers also need to understand the unique risks and deficiencies of the tools they are using. For example AI agents create a range of compliance, bias, and privacy risks which are not well understood – while other traditional BI and reporting tools may be too slow or labor intensive to manage the large data sets marketers have at their disposal.' By examining the current practices for collecting, managing and analyzing customer data across hundreds of businesses – and cross referencing the associated risk and financial implications of those approaches – his team hopes to provide CMOs a complete picture of how they need to organize their approach to big data.
