Embedding Data Analytics in Your Negotiations Playbook

The playbook is your negotiating team’s plan for reaching a successful outcome. Business negotiations can be complex, high-stakes, time-critical events. A winning playbook, then, requires agility, intelligence, and speed to support different game day scenarios and counter-party moves. In our experience, powerful data analytics is often the missing or under-utilized element in the playbook. Fact-based modeling critically enhances preparations and informs real-time decision-making as negotiations progress. This Brief highlights the critical role of data analytics in a negotiations playbook and offers some best practices.

Complex deals call for more powerful data analytics

Routine agreements – for example, negotiating purchase price, delivery and payment terms for a component used in manufacturing – may require only a basic spreadsheet model to calculate total landed cost over the contract period, the key quantifiable outcome. But even in seemingly simple business deals, accurately modeling the expected outcomes can quickly become challenging. In the above example, complexity increases if the supplier proposes volume pricing discounts, minimum purchase commitments, product bundling constraints, or differing levels of price escalation protection tied to volume commitments. Add multiple bidders, quality differences, and uncertain future demand, and simple spreadsheet calculators can fall short.

Indeed, in our consulting work, we see increasing value in developing a negotiations playbook that incorporates powerful data analysis, optimization, and simulation tools. These data analytics tools can effectively model negotiation outcomes in real time despite increased complexity, numerous decision variables, and even uncertainty about what the future might look like over the life of the deal.

The business case

The ideal starting point for building playbook data analytics is within the business case. The modeling work done at this stage – assessing the merit of the business opportunity, testing sensitivity to various assumptions and world views, refining the business case and ultimately making the go/no-go decision – forms the core of data analytics needed for the negotiations playbook.

Beyond enabling the go/no-go decision, the best business case models also yield outputs that clearly define the measures of success for the negotiations and the critical walkaway conditions at which the negotiations will cease. These are critical inputs for your playbook.

Know thy counter-party

Modeling your counter-party’s business outcome is just as important as analyzing your own. Yet this is often overlooked, due to lack of information or inclination. Harnessing data analytics to achieve insights into your counter-party’s business case, probable measures of success, and desired negotiation outcomes is essential. For example, to renegotiate a critical sales and distribution agreement, we developed a playbook for our high-tech client that included modeling their key distributor’s P&L with and without the proposed deal. This model enabled the negotiation team to understand the impact of various proposed terms on the partner – invaluable to concluding an agreement that yielded acceptable results for all parties.

Developing such analytical models of your counter-party is challenging, but there are ways to do so. In our experience, these models can be built by:

Drawing upon company financial reports or other publicly-disclosed sources.
Reverse-engineering business models using industry benchmarks as approximations.
Simply asking your counter-party to provide key analytical elements can be surprisingly successful. This approach was successfully used in the distributor renegotiation example above.
In an RFP situation, inviting RFP respondents to enter into ‘open book’ negotiations, with full proforma P&L for the proposed business.

Incorporating a model of your counter-party’s business case into your negotiations playbook has another benefit – detecting potential ‘bait & switch’ bidding, whether in bad faith (rare) or inadvertently (more common). In such cases, a low-cost bidder might be awarded the business, but on unsustainable pricing terms. After launching the services and realizing operating losses, the bidder then seeks relief with demands for rate increases or reductions in service levels. Bad blood and animosity can result.

In cases where the negotiation involves outsourcing an existing business process, the customer can with relative ease insert a counter-party operating model into its negotiations playbook. The resultant knowledge can keep the providers from underbidding, or at least enable the customer to be in position to inquire about the methods or innovations the bidder will use to ensure that the pricing and services proposed are viable in the expected time frame.

Enable your negotiations playbook with real-time data analytics

Rapid assessment of revised proposals and counter-offers will be needed as negotiations progress. Several approaches can be effective in building fast-response models. These range from the simple – spreadsheet ‘what if’ calculators on top of a static business case model – to the complex. Striking the right balance between effort/complexity and speed/simplicity is crucial to a successful playbook.

Here are two of the more sophisticated approaches we have used:

Building an optimization engine. For example, to select freight carriers for a distribution network. A shipper engaged in annual freight contract negotiations with multiple bidders may include thousands of origin-destination lanes and transport modes/service levels in its RFP. Carrier pricing proposals may include volume purchase incentives or commitments (and non-compliance penalty formulas) based on individual lane, regional or overall contract buy commitments. Add alternative fuel surcharge formulas to the mix and the combinations to analyze and optimize across multiple bidders can be overwhelming and too time-consuming to keep pace with fast-paced negotiations, without powerful data analytics tools.
Simulation modeling. Useful for highly complex situations or when the future state of the world can only be depicted with a set of educated guesses or scenarios. In a recent acquisition project, crafting a probabilistic distribution of DCF outcomes for both parties proved a valuable real-time decision-making tool as the negotiations unfolded.

This real-time data analysis capability also facilitates timely in-process negotiation team progress reports to senior management.

Implications for the negotiation team

Embedding powerful data analytics into your negotiations playbook requires rethinking the skill sets of your negotiating team. The most successful teams are cross-functional, in our experience: Lead negotiators joined by business subject matter experts, finance, and data analytics specialists. Including analytical resources early-on yields improved results, starting with a solid business case.

With the increasing complexity and strategic nature of today’s business-to-business relationships, negotiation teams and playbooks need to be even more dynamic and intelligent. Data analytics is an under-tapped game-day strategic resource that should be added to your playbook.

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Dave Frentzel is a Partner at New Harbor Consultants. Dave brings 25 years of management consulting and hands-on executive leadership experience to improve business outcomes. Prior to joining New Harbor, he held senior management positions at 3PL and supply chain technology companies. Dave has extensive global management expertise hav living and working internationally, helping companies with their global go-to-market, organizational, sourcing, manufacturing and supply chain strategies and operations