Introduction
In the ever-evolving marketing landscape where fast-paced digital media is becoming more prominent, companies are constantly seeking innovative ways to improve their marketing strategies. Among the most powerful tools at the disposal of marketing analytics experts and marketers alike are Artificial Intelligence (AI) and its subset, Machine Learning (ML). They have been revolutionizing the marketing space for less than a decade.
Simultaneously, GDPR and CCPA privacy restrictions and the abolition of third-party cookies has led to a renaissance in Marketing Mix Modeling (MMM) - one of the most long-standing and holistic marketing effectiveness measurement approaches.
In this blog post, we explore the impact of AI in marketing analytics, focusing on the various ways it is transforming the process and application of MMM in driving faster, data-driven decisions for businesses. We also highlight its limitations and areas where caution is needed in its use.
The traditional approach to analyzing the effectiveness of marketing investments
Marketing Mix Modeling has long been the gold standard for marketing measurement. This statistical technique helps companies understand the impact of online and offline marketing channels on their business Key Performance Indicators (KPIs) such as sales, web activity, enquiries, etc.
Historically, this process involved the manual collection of data from various sources, often requiring extensive time and resources. Analysts would then use statistical models to disentangle the business contribution of different marketing channels like TV, Print, Digital, but also Promotions and other internal and external factors. Outputs would only be available in static PowerPoint format.
While traditional MMM has been valuable as a holistic and privacy-proof measurement technique, it has had limitations that prevented it from keeping pace with the dynamic nature of modern marketing. Slow data collection and modeling, high resource commitment and lack of granularity are among the frequently cited weaknesses that initially drew marketers towards “novel” but incomplete techniques like Multi-Touch Attribution. Those are now fading away due to strict privacy regulations.
The introduction of AI in Marketing Analytics
Traditional marketing analytics such as media mix models have come a long way in providing valuable insights, but with the advent of AI, things are changing. AI is revolutionizing the analytics space, enabling businesses to harness the power of big data like never before.
So, what precisely is AI? In simple terms, AI refers to the simulation of human intelligence in machines, allowing them to perform tasks that typically require human intelligence, such as problem-solving, learning, and decision-making.
As far as marketing effectiveness analysis is concerned, AI-driven technologies use advanced algorithms and data processing to provide marketing teams with deeper and faster insights, improved decision-making capabilities, and greater personalization.
Practical applications of AI/ML in Marketing Analytics and Modeling
Smart KPIs: A survey conducted by MIT Sloan Management Review and Boston Consulting Group, involving over 3,000 managers worldwide, revealed that executives in various sectors are employing artificial intelligence to improve the prioritization, organization, and sharing of KPIs. Additionally, AI is being utilized to enhance the accuracy and predictive capabilities of these KPIs. As it relates to marketing, companies are leveraging AI to establish a stronger link between two key drivers of business growth: profit margin and market share. They deploy AI to deliver insights into how commercial and marketing investments which contribute to profit improvements (such as media or in-store activity) also impact market share objectives, and vice versa.
Enhanced Data Processing: AI and cloud computing have made data extraction, processing, and storage faster and more flexible. AI and ML routines do extremely well at processing large volumes of high-frequency data quickly and accurately and perform data reduction routines. This helps analysts to quickly spot any missing or erroneous data and identify patterns and correlations that will aid the subsequent modeling process. A pharmaceutical brand wanted to understand the impact of thousands of different search terms in their MMM and leveraged GPT-4 in order to identify keyword groups to focus on that would drive increased patient interest and prescriptions.
Personalization and Content Optimization: By leveraging generative AI, businesses can tailor their marketing efforts to individual customer preferences. Using customer data and sentiment analysis, AI can identify the most effective channels and messages for each customer, leading to more personalized and effective marketing campaigns. Whether it involves recommending products, sending personalized emails, or showing tailored content, AI ensures that customers receive messages that resonate with them.
Dynamic Pricing: Brands can use AI algorithms to set and adjust the prices of their products in real-time given changes to market conditions and consumer demand. This practice is being utilized particularly by online retailers. Companies are thereby able to provide a specialized shopping experience, outperform competitors and maximize profits. A growing online apparel retailer had been experimenting with dynamic pricing over the past couple of years and wanted to assess the impact of using dynamic versus fixed pricing on their winter collection. Leveraging Dynamic MMM the retailer uncovered a 15% increase in incremental sales from using dynamic pricing accounting for all other factors impacting demand.
Near Real-Time Analysis and Prediction: The growing sophistication of supervised ML techniques and access to real-time data has led to a proliferation in automated MMM solutions and near real-time marketing analytics at scale. Such solutions are ideal if the goal is simply a set of best-fitting predictive models for accurate sales forecasting, facilitating the supply chain planning process and management of product inventory. However, for meaningful ROI and budget allocation analysis, ‘structural’ cause and effect relationships are required. Under these circumstances, all types of MMM solution – traditional or AI-based – need to come with a transparent identification mechanism to ensure the model accurately identifies which marketing actions truly drive sales.
Granular Insights: Modeling using hourly or daily data from various sources including digital, social platforms and CRM leads to more granular campaign and creative insights generation. These insights are particularly valuable in the current digital era. For example, DRTV insights can go down to the station, genre, creative, ad length and day part level. Audience learnings can also be gathered for Digital and Social Media channels including CTV. Paid Search can uncover platform and keyword insights. This in turn provides marketers with more targeted budget optimizations.
End-To-End Process Improvement: AI software allows for end-to-end data collection and visualization of marketing insights. Furthermore, automated model refreshes enable marketers to seamlessly track and monitor the performance of their campaigns. In turn, project turnaround time and project costs are positively impacted. Updates which used to take months can now be done in a week with the right data feeds and processes in place.
Agile Budget Optimization: AI tools enable the rapid development and refinement of marketing budget optimization scenarios by analyzing many response curves at a speed which surpasses human ability. This software is usually embedded with a set of response adjustment factors (e.g., costs changes, audience changes, event and promotional calendars, and media planning constraints) for greater planning accuracy. Along with working closely with a client’s media agency to ensure strategic and operational alignment, these tools help businesses allocate their marketing resources efficiently, thus maximizing profitability. For an automotive company AI-powered optimization software was used to trade off 100s of campaigns, media channels, nameplates and geographies to optimize spend.
For more tactical digital optimizations, AI can recommend optimal budget allocations across different marketing channels based on real-time performance, ensuring that resources are invested where they generate the most value.
Challenges and Watchouts
While AI offers significant opportunities in marketing analytics, there are some challenges and considerations that marketers need to keep in mind in order to leverage the full potential of this technology ethically and responsibly.
Causal Inference: AI can certainly be a participant in the marketing analytics process but cannot replace it. At its heart, MMM strives to identify causal (incremental) relationships between sales and marketing investments for accurate ROI and budget allocation recommendations. However, the selection bias or endogeneity inherent in much online media confounds correlation and causation, leading to over-attribution of the impact of channels such as paid search where people who search for a product were already interested in buying it anyway. The growing popularity of AI and automated machine learning (ML) techniques has only exacerbated this problem, where rigorous causal analysis is sacrificed for the promise of rapid ‘real-time’ MMM delivery at scale. This is fine if the goal is simply a set of best-fitting predictive models, however, if the aim is to uncover the ‘structural’ cause and effect relationships necessary for accurate budget allocation, standard AI/ML approaches simply don’t cut it.
This has led to the introduction of ‘causal’ AI techniques, which attempt to discover the causal pathways present in a data set, based on Directed Acyclic Graph (DAG) structures and causal assumptions. However, since human judgement and an understanding of the market, brand and media strategy is always required, such techniques cannot serve as stand-alone methods for causal learning. Furthermore, there is rarely one unique chain. Endogeneity bias stems from ignoring the simultaneous likelihood of all plausible causal chains in the data. As such, AI techniques are useful for uncovering sets of competing causal chains, but cannot automatically solve the selection bias problem per se.
Further watchouts in terms of analytical robustness and output quality can be found in our blog post.
Data Quality and Bias: AI relies on high-quality data. If the data used is inaccurate or biased, the analytical models will perpetuate this bias and it can lead to flawed insights and decisions. Similarly, they might be biases in feature selection or algorithmic choices depending on the type of model chosen. Marketers should invest in data quality and cleansing efforts, as well as apply human judgement when developing an analysis.
Data Privacy and Ethics: AI often relies on vast amounts of data, which can raise concerns about privacy and data security. Marketers must ensure that they collect and handle customer data responsibly and comply with privacy regulations like GDPR and CCPA.
Skill Gap: The rapid evolution of AI technology can create a skills gap, as marketers may lack the necessary expertise to implement AI effectively. Implementing AI in marketing analytics may require organizations to upskill their teams or hire individuals with AI expertise. Fostering a culture of continuous learning will also ensure businesses are able to keep pace with the constantly evolving nature of this cutting-edge technology.
Cost: Developing and maintaining AI-powered marketing analytics tools can be expensive, both in terms of technology and access to resources. In order to succeed, companies need to invest in the relevant technology, ongoing education, collaborate with data scientists and AI experts, and develop a clear AI strategy that aligns with their marketing goals. Small businesses may need to weigh the costs against the potential benefits.
Conclusion
Artificial Intelligence is reshaping marketing analytics, providing businesses with powerful tools to optimize their marketing strategies, increase ROI, and stay competitive in a rapidly changing marketplace. While the benefits of AI in marketing analytics in modern MMM solutions are undeniable, there are challenges to overcome. As technology continues to advance, businesses that embrace AI-powered analytics will have a distinct advantage in understanding and reaching their target audience effectively. However, the need for human input and business understanding of the brand, data and task on hand will still be crucial in the application of marketing analytics in the foreseeable future.
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