Part II: Why the “Imperfect LOI” Overcomes Obstacles to a LMM Close

In Part I, I shared an observation regarding process dynamics: In the Lower Middle Market (LMM), ease beats alignment. When a seller is overwhelmed by "data gymnastics," they will often migrate toward a buyer who asks fewer questions, even if that buyer is a competitor that is not concerned about their legacy. This creates a paradox. If you wait for perfect data to submit a "perfect" LOI, you’ll be too slow. If you submit a firm, high-price LOI based on messy data, you’ll likely have to re-trade later—destroying trust and killing the deal anyway. Data from Axial further supports our experience. According to Axial’s research, the most common causes of deal failure are: • Seller financial under-preparedness • Unsuccessful renegotiations • Seller cold feet Essentially, this is a sequence of dominos that fall due to the reality that most LMM companies do not have well-organized financial reporting systems. The solution is counterintuitive: The Imperfect LOI. A. Create "Certainty" with a “Collaborative Framework" In institutional-grade deals, we are taught that an LOI should be as precise as possible. But in the LMM, precision is often impossible. If the data is far from perfect, we must adapt, rather than fight it, and create an “Imperfect LOI.” An Imperfect LOI isn't a sloppy one; it is a Transparent Framework for Collaborative Discovery. Instead of a rigid price with a list of contingencies that will likely break under the weight of "tribal knowledge" during diligence, we propose a Formulaic Bridge. We acknowledge the "Black Boxes" upfront and define the process to find reality—and mathematically how that reality will affect the final price. Very importantly, we establish the relationship and, through our actions, clearly communicate that we are not "auditing the seller"; we are "solving a puzzle with them." By using a formulaic LOI, you are essentially saying: "I am not here to catch you; I am here to help you package the truth for the lenders and the equity." When you solve the "data puzzle" alongside the seller, you aren't just a buyer—you are their first partner in the new era of the business. As our efforts demonstrate our commitment to close, the seller feels certainty. B. Psychological Safety The conclusions of thought leaders support our experience. Consider the Theory of "Benevolent Trust." Research from the Kellogg School of Management identifies three pillars of trust in buyer-seller relationships: Competence, Honesty, and Benevolence. Most acquirers only demonstrate Competence (modeling) and Honesty (price) but do not create the "difference that makes the difference": Benevolence. Our "Transparent Framework for Collaborative Discovery" signals Benevolence; the belief that you will not use "information asymmetry" (finding a mess) to exploit the seller. By proposing a formula before you find the mess, you prove you aren't looking for a reason to re-trade; you are looking for a reason to close. This builds a level of relational equity that price alone cannot buy. It effectively neutralizes "shopping" risk: a seller is unlikely to take your framework to a competitor when they finally feel understood by a partner, rather than merely appraised by a buyer. Professor Amy Edmondson (Harvard Business School) defines psychological safety as a "shared belief that the team is safe for interpersonal risk-taking." In a typical audit, the seller feels "on trial." If they make a mistake or admit they don't have a report, they fear "punishment" (a lower price). Our approach moves the work from a Performance Test (Is your data good?) to a Learning Opportunity (Let's solve the inventory puzzle together). In economic theory (Akerlof’s "Market for Lemons"), deals fail when the buyer assumes the seller is hiding something. A fixed-price LOI creates a "zero-sum" game. If the buyer finds a problem, they win a lower price; the seller loses. A "Process-Based LOI" creates Incentive Alignment. Everyone acknowledges the asymmetry upfront. Instead of the buyer trying to "catch" the seller, both parties become "Co-Investigators." C. The Seller’s Experience: No Data Gymnastics As we identified in Part I, the process must respect the seller’s bandwidth. Our primary job isn't "analyzing"—it's de-risking the seller’s experience. If you ask a founder for an AR aging report and they don't have one, don't send a follow-up email. Send a resource. This is where we utilize what I call "Forensic Calibration." We don't just ask for data; we provide the "Institutional Infrastructure" to help the seller extract it. We move the heavy lifting from the seller’s desk to our own "Deal Lab." In our segment, you aren't just buying a business; you are engineering a transition. The searcher who makes the process "Buyer-Grade" for the seller is the one who wins. D. The Result: Deal Certainty You mitigate the deal failure modes quoted by Axial not by being the highest bidder, but by being the most prepared architect of the close. By acknowledging the unknowns upfront and collaboratively filling in the blanks while calculating the agreed-upon formula, we mitigate the three deal killers that Axial identified: • Financial Under-Preparedness: They no longer have to "hide" the mess. • Unsuccessful Renegotiations: They are pre-solved. By agreeing to the formula upfront, the "re-trade" is transformed into a mathematical adjustment that both parties expected, preserving the relationship. • Seller Cold Feet: The seller owns part of the process and is helping you to architect the future of their legacy. By adopting this methodology and frame of mind, you also eliminate the threat of low-friction bidders that we explored in Part I. In your current pipeline, which 'Black Box' is causing the most anxiety for your seller?