Welcome to the first part of our two-part series on decoding the complexities of analytics implementation in distribution.

In this installment, we'll delve into the cognitive biases that hinder the realization of substantial ROI from analytics initiatives. As distributors increasingly harness the power of data-driven insights, the gap between aspiration and actualization looms large, fueled by a myriad of psychological barriers. 

Forward-thinking distributors are actively leveraging the power of data through many applications ranging from cutting-edge AI tools to must-have analytics scorecards. At this point, most distributors recognize the need to push beyond “go-to” spreadsheet analyses. However, through two decades of researching, educating, and implementing analytics best practices across inventory, pricing, supplier, and category analytics, we at ACTvantage have seen a recurring pattern: many distributors are hesitant to implement cutting-edge analytics (whether it’s the latest AI tools or advanced analytics applications) because they haven’t seen convincing ROI from data-driven efforts in the past. 

Unmasking Hidden Barriers to Analytics Success

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 to their disappointment with implementing one of the most essential best practices in distribution – customer stratification (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 expected return on investment (ROI). To understand their frustration, we dove into how the customer stratification model and 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.  

Ensuring Long-Term Success  

We’ve encountered similar frustrations from many other distributors in terms of not achieving ROI on analytics initiatives. A VP of IT at an electrical distributor cited challenges with executing customer stratification analysis he designed using the best practice book, “Customer Stratification: Best Pactices for Boosting Profitability”. 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, which meant that 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 in cognitive barriers. Behavioral economists believe that 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—a cognitive misstep were illusion of progress masks actual achievement. There 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.  

Six 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—treating 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, 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: Recognizing What's Unseen 
 
The value perception gap—understanding the worth of something in theory but not recognizing 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 your 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—actually using the analytics (which includes establishing use cases, educating stakeholders, integrating analytics into workflows, aligning stakeholders with the performance incentives, applying course corrections, etc.).  
 
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. 

Navigating Cognitive Biases: The First Steps 

As we embark on this journey through the labyrinth of cognitive biases hindering analytics ROI, we invite you to challenge your perceptions and confront the illusions masking genuine progress.  

In part two of this series, we will chart a course towards actionable insights and transformative outcomes. In the meantime, get in touch with us today if you’d like to get a head start on moving from analytics to action! 

*This article was originally written for and published under the title, Mind Over Metrics: How Cognitive Biases Hinder Analytics ROI, by Modern Distribution Management, the leading research, analytical and educational provider in wholesale distribution.