A Path to Refine Your Data StrategyIntroducing an extended model, fit for the modern day that will help marketing teams and organisations assess where they are and how to advance.
Abstract: The history of marketing analytics maturity can be traced back to the early days of marketing, but as technology has advanced, so too has the ability to collect and analyse data. Analytics maturity models are frameworks for measuring an organisation's ability to utilise data and analytics to drive decision-making. Many different models are available, each with its own set of criteria and maturity levels, which measure the quality and sophistication of their operations. The Marketing Analytics Maturity Model has five distinct levels - providing a highly useful framework for all kinds of businesses: Descriptive, Diagnostic, Predictive, Prescriptive and Cognitive. The framework helps organisations and marketing teams understand their current level of analytics capabilities and identify areas for improvement and can help them understand how to effectively use data to inform their strategies and make data-driven marketing decisions. As a marketing team begins to rise through the different stages of the model, the business value of the extracted information also rises, as does the complexity of the techniques. So, in essence, as a company’s understanding of data analytics develops, so does its ability to employ more sophisticated approaches to gathering and analysing data, including the use of AI and technologies like OpenAI's GPT language model that can be used to create more sophisticated models capable of handling large amounts of data and make predictions with greater accuracy. Irrespective of the level of understanding, this model can support businesses to analyse data effectively, taking them from basic ad hoc analytics to deeper probing using AI capabilities.
Download the Marketing Analytics Maturity Model poster (PDF).
Marketing analytics has become an essential tool for businesses of all sizes and industries. It allows companies to track and measure the effectiveness of their marketing efforts, making data-driven decisions that improve ROI and drive growth. However, not all companies are created equal when it comes to their use of marketing analytics. This is where the marketing analytics maturity model comes in. At the core of the Marketing Analytics Maturity Model is the belief that every company can benefit from marketing analytics, regardless of their current level of expertise. Whether you're just starting to dip your toes into the world of marketing analytics, or you're a seasoned pro looking to take your program to the next level, the marketing analytics maturity model can help you get there.
But don't take my word for it, see for yourself how the Marketing Analytics Maturity Model can help your business. By understanding where you stand, and what steps you need to take to improve, you'll be able to make smarter, data-driven decisions that drive growth and improve ROI. So, if you want to stay ahead of the competition and make the most out of your marketing efforts, it's time to embrace the power of marketing analytics.
A Brief History of Analytics Maturity
The history of marketing analytics maturity can be traced back to the early days of marketing when businesses used simple metrics such as sales numbers and customer counts to measure the success of their marketing campaigns. Analytics maturity has its roots in the software capability maturity model (CMM), which describes the stages that an organisation moves through as they mature (St. Jeor, 2019).
Over time, as technology has advanced, so too has the ability to collect and analyse data. In the 1980s and 1990s, the advent of the internet allowed businesses to collect and store large amounts of data on customer behaviour, leading to the development of more advanced marketing analytics techniques such as customer segmentation and lifetime value analysis. For marketers, K'necht (2022) suggests, the constant value-based theme over a 30-year evolutionary period revolved around the evaluation of marketing campaign performance. However, the majority of analytics utilised until the late 90s were indeed performance-based, and not anywhere near predictive or prescriptive, Ross (2019) suggests. Early maturity models focused on the technical aspects of data management, such as data quality, data governance, and reporting capabilities and were designed to help organisations assess their current capabilities and identify areas for improvement. In the 2000s, the rise of digital marketing and the increasing amount of data available from digital channels, such as website traffic and social media interactions, led to the development of even more advanced analytics tools and techniques. This includes web analytics, multichannel attribution, A/B testing, and predictive analytics.
With the proliferation of big data and the increased use of machine learning algorithms, marketing analytics has become even more sophisticated in recent years. Businesses are now able to analyse vast amounts of data in real-time and make accurate predictions about future customer behaviour, but a new era of privacy, Perkin (2023) suggests, impacts marketing measurement and analytics as a whole. As technology evolves, marketing analytics is likely to become even more sophisticated and play an increasingly important role in helping businesses make data-driven decisions.
What is an Analytics Maturity Model?
Analytics maturity models are frameworks for measuring the progress and capabilities of an organisation in terms of its ability to leverage data and analytics to drive decision-making and are based on the idea that analytics should be viewed as an evolutionary process rather than a one-time event. Their goal is to help organisations develop an effective and sustainable analytics strategy by providing guidance on how to move through each stage, so organisations can ensure that they are taking full advantage of their data and using it to make informed decisions (Shykolovych, 2021).
Analytics Maturity and Data Governance Model Examples
Many different maturity models are available, each with its own set of criteria and levels of maturity. Some popular models include the Data Maturity Model (DMM) developed by Gartner, the Data Governance Maturity Model (DGMM) developed by the Data Governance Institute, and the Analytical Maturity Model (AMM) developed by the International Institute for Analytics. Despite the different models, all maturity models are based on the same principle: an organisation's ability to effectively use data and analytics is a function of its technical, organisational, and cultural capabilities. By assessing these capabilities and identifying areas for improvement, organisations can develop a roadmap for becoming more data-driven and utlilising analytics to drive business value.
The Marketing Analytics Maturity Model
Many organisations do not employ analytical techniques whatsoever, let alone marketing analytics. For many, there is no common reasoning, structure, or method beyond those utilised for particular initiatives, McConchie (2021) explains. Analytics is typically limited to description, such as quantifying historical and current data to characterise what has occurred and what is occurring. Reporting may provide basic visualisations and facilitate data exploration so that discoveries may be used in an ad-hoc fashion.
Based on Gartner's Analytics Ascendancy Model, the updated and extended Marketing Analytics Maturity Model describes the different levels of analytical techniques used by organisations as they become more sophisticated in their use of data and analytics. The five levels of the model are descriptive, diagnostic, predictive, prescriptive, and cognitive analytics. The framework helps organisations and marketing teams understand their current level of analytics capabilities and identify areas for improvement and can help them understand how to effectively use data to inform their strategies and make data-driven marketing decisions.
The Marketing Analytics Maturity Model
The first ad-hoc level in the maturity model is descriptive analytics, which focuses on understanding what is happening now or what has happened in the past. In this type of data analysis, two data-intensive processes are highlighted. The first is data aggregation, which is the act of collecting data and then presenting it in a way that highlights the most significant elements. It typically involves using basic statistical techniques, complemented by data visualisation and data mining. This type of analytics is mainly used for creating reports and dashboards that provide insights into past events and current performance and trends. Data mining is the act of sifting through big databases to identify and collect specific data points so that new information may be obtained from the stored collection by analysing the correlations and patterns between these data points. The datasets interrogated can often be huge, and multiple strategies are employed to discover these specific data points.
Descriptive analytics will answer questions such as:
- "What were the sales last quarter?"
- "What is the average customer satisfaction score?"
- "How many customers have we lost in the past year?"
This is the next level of the analytics maturity model, and it is focused on understanding why something has happened. On the basis of the insights gleaned by descriptive analysis, diagnostic analysis delves deeper into the causes. Finding the origin of the data can be as crucial as the data itself. It is mainly used for identifying patterns and trends in data, and it typically involves the use of more advanced statistical techniques that may include variance, standard deviation, skewness, kurtosis, bi-variate and multi-variate analysis to discover patterns in the data and discover trends in the data. Diagnostic analytics often focuses on deriving hidden insights from the data which are not captured during the descriptive analytics phase.
Diagnostic analytics will answer questions such as:
- "Why did sales decrease last quarter?"
- "What factors are driving customer churn?"
- "Which marketing campaign was most effective?"
The third level in the maturity model is predictive analytics, which focuses on understanding what will happen in the future, and it builds upon the foundations of descriptive and diagnostic analytical approaches. The efficacy of predictive analysis depends on a high-quality descriptive analysis, followed by the massive volumes of data acquired as a consequence of the diagnostic analysis. This type of analytics is mainly used for forecasting future performance and trends, and it typically involves the use of advanced statistical techniques such as probability, correlation and regression, along with time-series models, decision trees and clustering. Predictive analytics examines the hidden pattern in data and establishes the relationship between the outcome variable and the predictor variables in order to construct a mathematical/statistical model that can be used to predict the outcome variable for new data. This step includes data preparation, data cleansing, feature engineering, model construction, and model evaluation.
Predictive analytics will answer questions such as:
- "Which customers are most likely to churn?"
- "Which products are most likely to be in demand in the future?"
- "Which customers are most likely to respond to a new marketing campaign?"
The fourth level of the analytics maturity model is prescriptive analytics, which is focused on providing recommendations for action. This type of analytics is mainly used to identify the best course of action based on the insights and predictions generated by descriptive and predictive analytics and is more action-oriented than other forms of analysis, which are primarily concerned with data monitoring. It employs machine learning to assist organisations to choose a course of action based on the predictions of an algorithm. When utilised successfully, it may assist companies in making decisions based on facts and probability-weighted estimates as opposed to intuitive assumptions. It typically involves the use of advanced techniques such as optimisation, simulation hypothesis testing, heuristics and other forecasting techniques.
Prescriptive analytics will answer questions such as:
- "What is the optimal price for a product to maximise revenue?"
- "Which marketing strategy is most likely to increase customer acquisition?"
- "Which products should be recommended to a specific customer?"
The highest of the analytics maturity model is cognitive analytics, an emergent field which is focused on creating intelligent systems that can understand, reason, and learn from often unstructured data, such as text, images, and audio. This type of analytics is mainly used for creating intelligent systems that can make decisions and take actions autonomously and typically involves the use of advanced techniques such as natural language processing (NLP), machine learning and computer vision. It can be used to analyse large amounts of unstructured data and extract insights that would be difficult or impossible to discover using traditional methods.
Cognitive analytics will answer questions such as:
- "What are customers saying about our product on social media, and what is the sentiment?"
- "How can we extract insights from customer call centre transcripts to improve customer service?"
- "How can we use machine learning to segment and target customers based on their behaviour and preferences?"
The Impact on Marketing Growth
The model can help marketing teams to improve their data-driven decision making, identify new opportunities for growth and benchmark their analytics capabilities against industry peers. Some unique benefits include:
- Understanding their current analytics capabilities: The model provides a clear framework for evaluating where a team stands in terms of analytics maturity. This can help identify areas for improvement and set priorities for future investments.
- Identifying gaps in data collection and analysis: Uncovering absent capabilities that are preventing the marketing team from making data-driven decisions
- Improving data-driven decision-making: As teams progress through the levels of the model, they gain access to more advanced analytics techniques, such as predictive and prescriptive analytics. This can help them make more informed decisions based on data, rather than intuition or gut feeling.
- Benchmarking against industry peers: By understanding the model, teams can compare their analytics capabilities to others in their industry and identify areas where they can differentiate themselves.
- Identifying new opportunities for growth: The model can help teams identify areas where they can leverage analytics to create new business opportunities or improve existing ones.
Real World Examples
A retail company's marketing team is currently at the descriptive stage. By learning and implementing the model, they are able to identify that they need to invest in diagnostic analytics in order to better understand customer behaviour and improve their targeting and segmentation strategies. This allows the team to increase the effectiveness of their marketing campaigns and drive more sales.
A financial services company's marketing team is at the predictive stage. By applying the model, they are able to identify opportunities to use prescriptive analytics to optimise their marketing spending and improve ROI. The team is able to use advanced analytics to predict which customers are most likely to respond to certain marketing campaigns and allocate resources accordingly. This improves the efficiency and effectiveness of the team's marketing efforts.
A tech company's marketing team is currently at the diagnostic stage. By leveraging the model, they are able to identify that they need to invest in predictive analytics in order to better understand customer churn and identify at-risk customers. This allows the team to take proactive measures to retain customers and improve customer loyalty. Additionally, they can also use predictive analytics to identify new market opportunities and target new customer segments.
The Future of Analytics in Marketing
The advanced analytical approaches (machine learning, deep learning, GANs, etc.) already mentioned may be applied to new analytics challenges via the continual emergence of new analytical tools. With so many alternatives available, it can be difficult to know where to begin, suggests Marr (2022); thus, it is essential to grasp the possible advantages and disadvantages of each type of analytics, as well as the professions for which they are most suited. As AI and machine learning continue to evolve, they can be further integrated into the model to enhance its effectiveness and can enhance analytics reporting, predictive and prescriptive analytics, and propensity modelling in several ways. But data volume and veracity are the critical foundation of AI and machine learning analytical success, asserts Davenport and Mittal (2023). Potential future uses, and applications include:
- Automating the data collection and analysis process, reducing the need for manual labour and increasing efficiency
- Utilising machine learning algorithms to identify patterns and insights in the data that may not be immediately apparent to humans
- Incorporating natural language processing (NLP) to analyse unstructured data, such as social media posts and customer reviews
- Using AI-powered predictive analytics to forecast future customer behaviour and inform marketing strategies
- Implementing AI-powered personalisation and optimisation of marketing campaigns
Additionally, Beasley (2021) suggests they can be used to identify patterns and trends in data and unlock actionable insights that may not be immediately obvious to human analysts, leading to more accurate predictions and models. In terms of predictive analytics, AI and technologies like GPT can be used to create more sophisticated models that can handle large amounts of data and make predictions with greater accuracy. These technologies can be used to automate the process of building and testing models, reducing the time and resources required for this task. For teams, they can be used to analyse customer reviews, social media posts, and other forms of customer feedback to gain insight into customer sentiment and preferences. This information can then be used to inform marketing efforts and improve customer engagement. As data continues to grow in volume and complexity, these technologies will become increasingly important for companies looking to stay competitive in the marketing space.
AI technologies and business use cases (source: Gartner)
While the majority of algorithms used now are focused on maintaining or improving an existing condition, according to Econsultancy (2022), future algorithms may place greater emphasis on AI's capacity to successfully create new possibilities and on how this can interact with test and learn methods. Reinforcement learning has the potential to replace current, more scattergun approaches that rely on testing numerous alternatives at scale by understanding "what makes a good idea."
Future success will also depend on how human and machine oversight and learning interact. For instance, so-called "augmented analytics" illustrates how AI may complement rather than take the place of people at each stage of the data lifecycle. The tale of artificial intelligence in marketing will probably ultimately revolve around how it is interwoven into so many various aspects of the industry that it effectively blends into everything and becomes mostly invisible. We may have arrived at this point when it comes to "augmented marketing."
In conclusion, the analytics maturity model is incredibly useful for measuring the progress and capabilities of an organisation in terms of its ability to leverage data and analytics to drive decision-making. It has evolved over time to include not just the technical aspects of data management, but also the organisational and cultural factors, according to Nguyen (2022), that impact the ability to effectively use data and analytics, and it's been widely adopted by organisations of all sizes and across all industries to measure their progress and identify areas for improvement. We look forward to an exciting future in marketing analytics. Simply said, this means that it's incredibly simple to become mesmerised by technology itself and the incredible things that can be accomplished with it. When it comes to analytics and AI, Marr (2022) explains, many individuals begin by concentrating on the "what" and "how" issues when, in reality, they should be asking themselves "why".
- 1 - Beasley, K., 2021. Unlocking The Power Of Predictive Analytics With AI.
- 2 - Davenport, T., and Mittal, N., 2023. Stop Tinkering with AI.
- 3 - Econsultancy., 2022. Quick Guide to AI, Machine Learning and Predictive Analytics.
- 4 - K'necht, A., 2022. The evolution of digital analytics and marketing.
- 5 - Marr, B., 2022. Data Strategy.
- 6 - McConchie, D., 2021. Data Strategy - Selling the Value.
- 7 - Nguyen, M., 2022. Data Maturity Assessment: How-to Guide.
- 8 - Perkin, N., 2023. Digital and marketing trends for 2023.
- 9 - Ross, A., 2022. The History of Marketing Analytics.
- 10 - Shykolovych, O., 2021. Analytics Maturity Model: The Path to Analytics Perfection.
- 11 - St. Jeor, C., 2019. History Of The Data And Analytics Maturity Model.