Turning Data Insights into Negotiation Leverage

#Negotiation Tip Number 4: Gather and Leverage the Data.   In his book “Moneyball,” Michael Lewis quotes John Henry, renowned investment manager and owner of the Boston Red Sox, in reference to a comparison between professional baseball and the financial markets, “People in both fields operate with beliefs and biases. To the extent you can eliminate both and replace them with data, you gain a clear advantage.” Since that book was published, data analytics has become a vital part of how almost every major professional sports team makes decisions. Data is equally important in commercial real estate negotiations. Most CRE professionals realize the importance of obtaining data, but few understand how to fully use it to achieve a successful outcome. In a negotiation while representing a buyer of a low-rise office building in a submarket with dozens of similar-sized office buildings, my team cherry-picked comparable sales and sent them to the seller’s representative, making a case for a purchase price around $90 per square foot. On the contrary, the seller’s representative made the case that the purchase price should be closer to $100 per square foot — submitting their own version of comparable sales as justification. At this point, our team was certainly tempted to accept the invitation from the seller’s broker to play the high-low game. Instead, we evaluated the seller’s comp set to determine how we could either work toward bridging the gap or defend our original position all while trying to achieve our client’s goals. As we dissected both data sets, we were able to see that many of the seller’s comparable sales had already been renovated, while the property being bought still needed cosmetic renovation. That was telling from a qualitative analysis, but the most convincing case came when we put both sets of sales comps on a line graph to show the trend in sale price per square foot over time. This line graph was very helpful for both the buyer and the seller to understand the current value of the property as the next data point in a trendline. Ultimately, they agreed on a purchase price that equated to $87 per square foot. Both sides had data, but it wasn’t until it was dissected and brought to life that anyone truly understood how it brought relevance to the negotiation. #CapitalMarkets, #InvestmentSales, #CRE, #CommercialRealEstate

Most negotiators lose deals because they divide value wrong.    In every deal, the big question is:    Who gets what?   Most people decide based on:    ❌ Who speaks the loudest  ❌ Who has more power  ❌ Who simply asks for more    But the best negotiators don’t guess.    They use Shapley Value:   A game theory concept that shows exactly how much each person should get based on their real contribution.    Here’s the problem:    Most negotiators assume their value is obvious.    It’s not.     Let’s say three companies form a partnership:    - One brings technology  - One brings customers - One brings funding   Who deserves the biggest share?    Instead of arguing, Shapley Value calculates each partner’s real impact.   ✅ What happens if one partner leaves?  ✅ How much does each person’s role increase the total success?  ✅ What’s their actual contribution in numbers?    This shifts the conversation from opinion to logic.    How to use this in negotiations:   (Step-by-Step)    🔹 Step 1: Identify all contributors   List out everyone involved in the deal:   - partners, - suppliers, - team members - anyone adding value.    🔹 Step 2: Define measurable contributions   Ask:   What does each person bring to the table?   Focus on revenue impact, risk reduction, efficiency, or access to key resources.    🔹 Step 3: Calculate impact if one party is removed   For each contributor, ask:    “If this person/company walked away, how much value would be lost?”   🔹 Step 4: Assign value based on actual impact   If one party is responsible for 40% of the success, they should get a 40% share.   Not just an equal split.    🔹 Step 5: Use this data to justify your position   Instead of saying, “I want 30%,”* say:    “Based on our contribution analysis, our role increases revenue by 30%, reduces risk by 20%, and improves efficiency by 25%. Our fair share should reflect that.”   This eliminates emotional arguments and forces negotiations to focus on real impact.    Bottom line:   Most people negotiate based on feelings.    The best negotiators prove their worth.   If you’re not using game theory in negotiations, you’re leaving money on the table.   P.S. How do you ensure fairness in your deals?    Drop your insights below. I’d love to hear your take.    ---------------------- Hi, I’m Scott Harrison and I help executive and leaders master negotiation & communication in high-pressure, high-stakes situations. - ICF Coach and EQ-i Practitioner - 24 yrs | 19 countries | 150+ clients  - Negotiation | Conflict resolution | Closing deals 📩 DM me or book a discovery call (link in the Featured section)

AI won’t make you a better negotiator by default. But used correctly, it can make negotiations more rational, fair, and far more valuable. I’ve shared a short PDF with practical principles for using AI in negotiation—not theory, not hype, and not tech for tech’s sake. Here’s a bit of context to help you use it well. 1. Start with the type of negotiation AI behaves very differently in positional versus collaborative negotiations. Feeding data into AI without first agreeing on the negotiation context is like calculating numbers without knowing the rules of the game. Precision without shared context rarely leads to good outcomes. 2. Use data to replace opinions, not people In collaborative negotiation, data becomes a shared language. I’ve seen teams stuck for weeks because both sides defended opinions instead of agreeing on the facts. AI helps most when it reduces bias and emotion—not when it’s used as a weapon to “win arguments.” 3. Model value, not just price Using AI only to optimize price is a missed opportunity. The real power is in uncovering asymmetric value, trade-offs, and joint gains—the areas where one side’s cost is another side’s benefit. That’s where negotiations evolve from squeezing to creating. 4. Let AI optimize—humans decide AI can simulate scenarios and highlight options. But it can’t own relationships, accountabilities, or consequences. I’ve seen negotiators hide behind AI recommendations instead of leading with judgment and ethics. That’s a mistake. 5. Protect trust like currency Ask yourself: Are you willing to share the same data you expect from the other side? If the answer is no, AI will amplify mistrust instead of value creation. Trust isn’t soft—it’s a measurable economic driver. If you want to go deeper: Watch my LinkedIn Learning course “Boosting Your Negotiation Skills with Generative AI” — the step-by-step framework that helps you use AI to support your negotiation thinking and outcomes (not just automate tasks). https://lnkd.in/ggx5AExQ (LinkedIn) Get my new book 'The SMART Negotiator': Unlocking the Power of AI and Human Insight in Effective Deal-Making(Wiley) — the first book that combines behavioral negotiation research with practical guidance on how humans and AI can create more value together. https://lnkd.in/gK-z5cwz (LinkedIn) The PDF is designed as a practical checklist for leaders, procurement, sales, legal, and anyone experimenting with AI in negotiations. AI doesn’t negotiate. People do. AI just makes their choices more visible. #negotiation #AI BMI Executive Institute The Program on Negotiation at Harvard Law School #procurement #contract World Commerce & Contracting AAU Executive - MBA and HD at Aalborg University Tine Anneberg Moïse NOUBISSI Juan Manuel García P. Gražvydas Jukna Jason Myrowitz Tiffany Kemp Said A. ,(MBA, EFQM)

Over two decades of legal work spanning disputes, transactions, and tech, I’ve seen recurring issues in how legal teams work. When Adarsh S. and I began building solutions at Ad Idem, it became clear: Automation gets the spotlight, but few legal departments are tapping into the deeper value hidden in their data. Most discussions around legal AI focus on efficiency: faster contract review, automated workflows, reduced counsel spend. But a transformative opportunity lies in something more hidden—leveraging data embedded in an organization’s dispute history. I often ask In-house counsel: “Have you ever surveyed your disputes to identify patterns that consistently impact outcomes?” The consistent answer? No. The reason? “It would take thousands of hours.” This exposes the gap: legal teams are stewards of rich, complex data—but without tools to make it accessible, strategic insight stays locked in old case files. Ask yourself: -What factual patterns increase the likelihood of favourable outcomes? -Where do procedural delays consistently emerge? -What systemic organizational gaps do your disputes reveal—across product, sales, compliance, or customer experience? Currently, most legal departments see disputes as operational burdens to manage efficiently. Forward-thinking teams are reframing this. They're not just solving each case—they're studying the portfolio. The difference isn’t tech savviness—it’s conceptual framing. Consider these potential real-world shifts: -A tech firm discovers 80% of wrongful terminations come from two departments with poor documentation habits. After targeted training, litigation costs dropped 40%. -A real estate firm uses AI to analyse years of construction disputes. Subcontractors from one vendor caused 65% more litigation. Adjusting selection protocols halved future issues. -An online services company finds that slow response times in two regions correlated with higher customer disputes. By optimizing service response, they reduced escalations by 28%. These insights weren’t obvious. But they became visible with data analysis. The real opportunity in legal AI is predictive intelligence—not just faster workflows. It’s the ability to inform new strategies using old experience. To tap this potential, legal departments must: Assess current dispute data—organizations may not store data in a way that helps analytics Identify insights that impact outcomes — different industries have different points Begin implementation pilots — engage with legal AI to apply analytics to a defined subset of disputes Prepare to operationalize insights—tech without application creates limited value Create improvement mechanisms—outcomes should inform and enhance predictive capabilities Legal teams that lead this shift will gain more than efficiency—they’ll reshape how their organizations anticipate and avoid risk altogether. In a field where one dispute can alter strategic trajectory, this isn't optional transformation. It's imperative.

People don’t save money by negotiating harder. They save money by being better informed — before the conversation even starts. Whether you’re buying, selling, or managing a contract, the quality of your decisions depends on the quality of your information. That’s why I love the example from Santa Clara County’s procurement team. They used credible market intelligence to cut sourcing time by up to 50% and saved $380,000 on a single project. Not because they got lucky. But because they used insight to clarify what they were actually buying — total cost of ownership, supplier competition, and quality controls — then leveraged that knowledge to secure stronger pricing. The outcome wasn’t just savings. It was a faster, smarter, more credible process that reduced risk and built internal trust. That’s the real ROI of market intelligence: Better RFPs. Better negotiations. Better outcomes for everyone. #Procurement #MarketIntelligence #Leadership #StrategicSourcing

Several times M&A negotiations stall not because of unrealistic demands from the other side but because of ❗️uncommunicated assumptions about business performance and risks and ❗️lack of a data-driven negotiation process. Two examples from the past six months: 1️⃣ We represented a buyer team, involving a complex earnout scenario in an acquisition. Negotiations around valuation and deal structure began to stall because the target felt we were pushing for a structure that would penalize the target unfairly for post-close performance. 2️⃣ We represented a seller that believed in their forward performance. However the top few potential buyers were skeptical and didn't believe our client could hit the numbers. ✅ Solutions to both scenarios: 🎯Instead of negotiating with the other side in a vacuum, we built and shared a more detailed model with them with our drivers and our assumptions based on historical data and the customer pipeline. 🎯 The discussions moved from the abstract to collaboration on a shared set of numbers and business assumptions. 🎯 We were able to step through specific scenarios and have a much more nuanced, granular discussion, with each side using the same framework and model structure. 🎯And we were able to drive to a successful resolution in both deals. (This is NOT saying that you open everything to the other side. Knowing the parts of the model that should be shared for negotiation purposes and those that shouldn't be is critical). But sometimes the best path forward in "stuck deals" is throwing light on the data. #mergersandacquisitions