Mind Over Metrics: How Cognitive Biases Hinder Analytics ROI

Through two decades of research, education and best practice implementation across inventory, pricing, supplier and category analytics, we at ACTvantage have seen a recurring pattern: many distributors are hesitant to implement cutting-edge analytics.

Why? 

Forward-thinking distributors actively leverage the power of data through applications ranging from cutting-edge artificial intelligence tools to must-have analytics scorecards. At this point, most distributors recognize the need to push beyond “go-to” spreadsheet analyses. However, many are hesitate to implement the latest AI tools or advanced analytics applications because they haven’t seen convincing ROI from data-driven efforts in the past.

The Subtle Art of Self-Deception

We recently walked through the current practices of an industrial distributor to understand decision-making in its sales, purchasing and supplier teams and unpack frustrations with existing analytics initiatives.

The discussion led officials at the company to express their disappointment with the implementation of one of the most essential best practices in distribution: customer stratification.

Customer stratification is the process of segmenting customers using 360-degree analysis of their purchasing behavior.

The distributor claimed they had already implemented this best practice but had not achieved the expected return on investment (ROI). To understand their frustration, we dove into the customer stratification model and how the use cases were customized and implemented.

The CFO launched customer stratification efforts shortly after he heard about the concept in a seminar he had attended. He crafted intricate customer stratification models using spreadsheets. He then set up Excel macros to rerun the model twice a year. In diving deeper into their desired use cases for the customer stratification model, the CFO explained to us that they had planned to use it in pricing applications, but it had been stuck on their internal to-do list for the past two years.

Staying the Distance

We’ve encountered similar frustrations from many other distributors in terms of not achieving ROI on analytics initiatives.

A vice president of IT at an electrical distributor cited challenges with executing customer stratification analysis he designed using the best practice book, “Customer Stratification: Best Practices for Boosting Profitability.” [Editor’s Note: Senthil Gunasekaran co-authored this book, which was published by NAW’s Institute for Distribution Excellence.]

He shared results with C-level executives and incorporated ranks into their standard quarterly reports. However, they never followed through and educated the sales, pricing or inventory teams on the underlying concepts of stratification. This oversight meant their teams weren’t comfortable discussing performance ranks with customers or using them in tactical decision-making. They also failed to integrate the results in core use cases such as pricing or inventory management.

They went the first mile in creating the customer stratification analytics, but they failed in the last mile in terms of sales or inventory team usage; hence, they failed to see ROI from what could have been an invaluable data-driven analytics initiative.

During our educational sessions, we’ve encountered many instances of distributors’ anguish over not getting traction or ROI from analytics. We believe the common root cause often lies within.

Behavioral economists believe human beings are unknowingly hamstrung by limited attention, cognitive biases, overconfidence and other psychological factors that inevitably cause judgment errors. The investigation of these instances uncovered a common thread: the chasm between developing and then using analytics often goes unacknowledged. There is a cognitive misstep where the illusion of progress masks actual achievement.  T

here are several critical cognitive biases that often keep distribution executives from achieving their data’s full potential and harnessing powerful, actionable insights.

Customer stratification is one of several potentially game-changing analytics in which cognitive bias can make or break ROI. It is essential for distribution leaders to understand and root out cognitive biases to successfully launch and integrate analytics and data-driven decision-making in day-to-day best practice.

6 Common Cognitive Biases in Distribution

1. The Illusory Progress in Analytics

Illusory progress is a cognitive bias that lulls businesses into a false sense of completion. By merely developing a stratification model, firms often believe they fully utilize its insights. However, its potency remains unrealized without integrating this model into the operational fabric of pricing, inventory and sales management.

2. The Dunning-Kruger Effect in Play

The Dunning–Kruger effect is a cognitive bias in which people with limited competence in a particular domain overestimate their abilities. A profound understanding of customer stratification or similar analytics requires more than the model — it demands practical application. The Dunning-Kruger effect is often at play here, where executives may overestimate their mastery of stratification due to their proficiency in creating a model. The actual depth of customer stratification is grasped only when the analytics inform real-time business decisions, something a static spreadsheet or standard quarterly report can never accomplish.

3. Checklist Mentality vs. Strategic Integration

Executives often fall prey to the checklist mentality, in which distributors treat the implementation of customer stratification or inventory analytics as a box to be ticked. This mentality overlooks the necessity for strategic integration, mistaking a rudimentary first step for the entire journey.

 4. Satisficing: ‘Good Enough’ Is Not Enough

 The strategy of satisficing, or settling for what seems “good enough,” often dictates the premature halt in implementing a thorough stratification approach. It is vital to recognize that good enough in today’s rapidly evolving marketplace is a mirage that obscures the pursuit of excellence.

5. Overcoming Inertia with Advanced Analytics

Resistance to change and inertia is a common psychological hurdle. The shift from spreadsheets to dynamic analytics scorecards can seem daunting. Yet, it is through this transition that data is transformed from static figures into dynamic, strategic assets.

6. The Value Perception Gap: Recognize What’s Unseen

The value perception gap, in which you understand the worth of something in theory but do not recognize its practical benefits, needs to be closed for distributors to realize the full potential of their data. The gap is created due to the unbalanced focus on two key components of analytics implementation: analytics development vs. analytics usage.

Typically, as you embark on data-driven initiatives, analytics development (which includes understanding the methodology, customizing the model, identifying and extracting the data, cleansing, pre-processing, analyzing, and creating visualizations) gets more attention than the last mile. The last mile entails using the analytics, which includes establishing use cases, educating stakeholders, integrating analytics into workflows, aligning stakeholders with the performance incentives, applying course corrections and more.

Distributors with this blind spot fall into one of two categories: those that simply mistake analytics development for analytics usage, as mentioned in our examples above, or those that underestimate the importance of the last mile of driving adoption of analytics. Distributors in either category routinely fall well short of expected analytics ROI. A dual approach to analytics implementation bridges the analytics development vs. usage gap by making the theoretical benefits of analytics (such as customer stratification) tangible and actionable.

Invitation to a Cognitive Reappraisal

Regardless of which cognitive bias resonated with you, the remedies are multifold:

1. Focus on the Foundation

Often, distribution executives appreciate the value of data but get overwhelmed by technology offerings and wonder where to start the analytics journey. Group all your data-driven or analytics needs into two groups:

1. 1. • must-have core analytics, such as inventory optimization, pricing optimization, customer stratification and supplier stratification, and;
      • supporting analytics.

This categorization of analytics needs can help distributors start on the right foot. When asked how one successful distribution CEO leverages data and adopts new technologies, he responded: “Clean your house before decorating.”

2. Understand the Road to ROI

Recognize the dual aspects of analytics implementation — the first mile (analytics development) and the last mile (analytics usage or adoption) – and their impact on ROI. Understanding these two components will help distributors learn what it takes and set the right ROI expectations. Without these best practices, distributors become more susceptible to cognitive biases, leading to sub-optimal decision-making.

3. Recalibrate Your Approach to Analytics

We extend an invitation for a cognitive reappraisal, a deliberate reevaluation of how distributors view and use must-have analytics such as customer stratification, inventory stratification or vendor stratification. Perform a deliberate reevaluation of core analytics in the first mile (analytics development) compared to the last mile (analytics usage or adoption).

Have you traveled the last mile in customer, inventory, pricing or supplier analytics? If not, get out, research, and witness the transformative power of integrated analytics that goes beyond the superficiality of spreadsheets.

4. Deploy Internal Resources Where They Matter

Plan scarce internal resources wisely by deploying them where they matter — the last mile.

The accessibility and affordability of technology or analytics tools have significantly improved in the last decade. Distributors don’t have to reinvent the wheel by manually creating core analytics from scratch in spreadsheets. Instead, collaborate with external resources or tools to expedite the first mile while actively preserving your stretched internal resources for the last mile.

For instance, a distribution executive deployed one of their emerging leaders to lead the last-mile journey while collaborating with the service provider for the first-mile journey. This became a win-win move for the distributor. While getting the ROI from analytics initiatives, the emerging leader has learned what it takes to travel the last mile.

A Journey Towards Data-Driven Distributorship

The first step towards truly mastering must-have analytics, such as customer stratification, is to understand and overcome cognitive biases. By embracing a dual approach (first and last miles) to advanced analytics, distributors can refine their view of their business (across customer, supplier and inventory insights) and supercharge all aspects of business strategy.

Are you ready to challenge your perceptions and unleash the true potential of your data?