Algorithmic move to Smart KPIs: Marketing Efficiency Ratio 2.0

In the present business landscape, the term "Key Performance Indicators" (KPIs) has become omnipresent. KPIs is a vital tool for organizations, offering insight into performance, productivity, and progress towards strategic goals. However, as industries evolve and technology advances, there is a growing imperative to move beyond traditional KPI frameworks close to more adaptive metrics driven by algorithms.

This shift represents a pivotal transition in how businesses measure success, optimize processes, and achieve sustainable growth.

"When a measure becomes a target, it ceases to be a good measure" – Goodhart's law

Understanding traditional KPIs

KPIs have been static metrics often defined on historical data, industry benchmarks, or organizational goals. While these metrics provide valuable snapshots of performance, they often lack agility and fail to adapt to dynamic business environments. Moreover, traditional KPIs may overlook interconnectedness between different aspects of operations, leading to suboptimal decision-making and missed opportunities for improvement.

The rise of algorithmic Smart KPIs

In contrast to static indicators, the algorithmic intelligence makes KPIs smarter that can adapt to change better for dynamic predictions.

Smart KPIs leverages advanced machine learning and analytics to generate actionable insights in real-time. By analyzing vast datasets and detecting patterns, algorithms can uncover hidden correlations, predict future trends, and identify optimization opportunities that traditional metrics may overlook.

This open up new boundaries for marketing teams to design or redefine metrics within the core business context with the help of AI, based on market trends and strategic goals.

Characteristics of Smart KPIs

“SMART” is known as an acronym – for “specific, measurable, achievable (or assignable), relevant and time-bound (or timely)” – but that is another topic. The concept being explored on this article is not this framework (or others related), but how the outcome of certain indicators can be powered and augmented by AI, and ultimately lead to revisited those frameworks for better.

Smart KPIs have key characteristics that distinguish them from their traditional counterparts:

a. Deep real-time monitoring: Continuous monitoring at a deep level of performance metrics, providing up-to-the-minute insights into operational efficiency and effectiveness;

b. Advanced predictive analytics: Leveraging predictive algorithms, Smart KPIs forecast future trends and potential outcomes, allowing organizations to proactively address challenges and capitalize on opportunities;

c. Agile and adaptive optimization: Smart KPIs adapt to changing circumstances and evolving business objectives, ensuring relevance and alignment with strategic priorities;

d. Cross-Functional integration: By integrating data from various departments and systems, Smart KPIs offer a holistic view of organizational performance, fostering collaboration and synergy across functions

Bringing data governance to the frontline

Data governance plays a critical role in the success of Smart KPIs by ensuring the accuracy, integrity, and reliability of the data used to generate insights. Without robust data governance practices in place, organizations may lead to suboptimal outcomes and missed opportunities.

Effective data governance involves establishing policies, processes, and controls to manage the collection, storage, management, and use of data throughout its lifecycle:

• Data quality and accessibility: Reliable data is the foundation of algorithmic intelligence. Organizations must ensure data accuracy, completeness, and accessibility to derive meaningful insights from smart KPIs;
• Talent and expertise: Implementing algorithmic solutions requires specialized skills in AI, machine learning and data science. Organizations may need to invest in training or hiring talent to effectively leverage Smart KPIs;
• Ethical and Regulatory considerations: As algorithms play an increasingly influential role in decision-making, organizations must address ethical and regulatory concerns related to data privacy, bias, and transparency;
• Organizational culture: Embracing algorithmic intelligence requires a cultural shift towards data-driven decision-making and continuous learning. Change management strategies are essential to foster buy-in and adoption across the organization;
• Democratization of Data: Empowering employees at all levels with access to actionable insights and self-service analytics tools, democratizing decision-making and driving innovation

The algorithmic move to produce Smart KPIs represents a paradigm shift in how organizations measure and manage performance that requires a strategic approach, investment in technology and talent, and a commitment to fostering a data-driven culture.

By harnessing the power of algorithmic intelligence, businesses can unlock new opportunities for innovation, agility, and competitive advantage.

Those that embrace the shift towards Smart KPIs will emerge as leaders in the age of AI-driven businesses

Marketing Efficiency Ratio review: From “performance tracking” to redefining performance

Historically, measuring the marketing efforts has been ambiguous and challenging. The industry relied on traditional metrics but these often provided a limited understanding of true impact. With digital age a bunch of new metrics has brought complexity to marketing stack with old and new indicators altogether.

The recent moves of AI are giving marketers new options to delve deeper into data, uncovering nuanced insights and correlations, providing a reset on the industry measure mindset.

It’s time to revisit established KPI fundamentals, check their consistency and explore how AI brings the most out of it

The Marketing Efficiency Ratio (MER) is a KPI used to assess the effectiveness and efficiency of marketing efforts in generating revenue. It is calculated by dividing the revenue generated by marketing activities by the total marketing expenses incurred. The higher the MER, the more efficient the marketing efforts are in driving revenue.

The purpose of this metric is to measure the impact of marketing activities on revenue generation, based on their ability to drive business outcomes.

MER (effectiveness) provides distinct insights compared with ROI (profitability), but both are critical to evaluate marketing performance aligned with business:

• MER measures the efficiency of marketing activities by comparing the cost of those activities to the desired outcome, such as leads generated, conversions, or sales – helps marketers understand how efficiently their resources are being utilized to achieve specific marketing goals on different channels or campaigns;
• ROI measures the profitability of marketing investments by comparing the net profit generated from those investments to the cost of the investments – It helps top decision to understand the overall profitability and contribution of marketing efforts to the bottom line providing a broader perspective on the financial performance of marketing initiatives

Having strategic KPIs interconnected became even more relevant today, like the example of Pernod Ricard , a $10 billion global spirits company, that used to have 2 of its most important KPIs: profit margins and market share siloed (each with its own set of measures): Finance focused on profitability, while Sales and Marketing focused on market share.

The company now deploys AI to deliver insights into how commercial and marketing investments that improve profits — such as media or in-store activation — also influence market-share objectives and vice versa.

Instead of seeking to maximize each individual KPI, the company optimize both KPIs in concert with each other.

“AI is going to give you that information. With AI, we can better align market share KPIs, margin KPIs, and required investments to reach them” – Pierre-Yves Calloc’h, Pernod Ricard’s chief digital officer

Applying the Smart KPI concept to review the MER structure can enhance their rationale on driving revenue generation, resulting in a higher indicator and improved business performance:

• Attribution modelling and return analysis
• Customer lifetime value prediction
• Predictive revenue forecasting

By leveraging these processes, AI engines can analyze complex marketing datasets at scale, uncovering insights that were previously hidden or impractical to discover manually. Ultimately, it's this combination of advanced algorithms and automation capabilities that underpin AI's transformative impact on marketing measurement.

Since AI can improve productivity, the way to measure it should be reviewed starting with a simple question:

Regarding the improvements AI will bring: The fundamentals behind every metric remains realistic with today goals?