Dynamic Deal Scoring

What is Dynamic Deal Scoring?

Dynamic Deal Scoring (DDS) is a function of McKinsey’s Periscope platform. It is an AI-powered deal-scoring system that combines machine learning, incentives, and governance to assess deal quality in real-time.

DDS aims to uncover the true value of any given deal and ensure sales team decisions are based on data-driven insights. By evaluating specific deal situations, DDS identifies opportunities for increased profitability and helps companies to inject dynamic pricing into their sales processes.

Synonyms

• DDS
• Dynamic deal pricing
• Predictive deal scoring
• Sales deal scoring

How Dynamic Deal Scoring Works

DDS builds on traditional deal scoring models, which use customer, product, and pricing information to evaluate commercial deals. McKinsey adds advanced analytics to this approach, allowing companies to set up scoring objectives tailored to their specific needs.

Briefly, Dynamic Deal Scoring provides pricing guidance in four key steps:

1. Identification. The system identifies determining variables like deal size, deal stage, product configuration, and which sales channels the deal runs through.
2. Segmentation. The backend groups similar deals using advanced analytics models like K-means clustering and Chi-square Automatic Interaction Detector (CHAID) decision trees.
3. Deal scoring. After comparison, the deal scoring mechanism returns a score for each deal based on quality and value, along with color-coded price optimization insights.
4. Workflow integration. The deal scores are incorporated into essential sales processes, such as adjusting incentives based on deal quality and directing the deal to the appropriate level for approval.

Business Use Cases for Dynamic Deal Scoring

Dynamic Deal Scoring is especially crucial in highly competitive industries, where small margins can make a huge difference in profitability.

Here are some of the most critical use cases for Dynamic Deal Scoring in modern businesses:

1. Sales Onboarding

DDS makes the sales onboarding process easier by automating the deal evaluation and guiding new reps through sales workflows, so new sales reps don’t have to learn from scratch.

A standardized deal scoring system dramatically reduces sales ramp time, giving new reps vital support and guidance.

2. B2B Dynamic and Real-Time Pricing

Periscope supports real-time pricing (RTP) and other dynamic pricing models, which makes DDS’s AI-driven backend a critical tool for utilities, energy companies, hotels, airlines, and other B2B companies with pricing subject to continuous change.

3. Discount Management

Dynamic Deal Scoring accounts for factors causing variability in discounting (e.g., buyers’ perceived value, new market entrants differentiating on pricing, elasticity, forecasted demand), which takes some of the difficulty out of discount management and ensures company profitability.

4. Sales Management

Since Dynamic Deal Scoring (and the rest of Periscope’s suite of sales AI tools) is directly embedded into core selling processes, its function extends to the sales management role.

Sales leaders can use DDS to guide their teams through selling, delivering price estimates, and escalating deals. Meanwhile, they can track metrics like average deal score and sales success rate to develop incentive processes and drive long-term adoption.

5. Bid Desk and Proposal Management

Bid desk managers use Dynamic Deal Scoring to optimize bid submissions. With a real-time score, the bid desk team can quickly decide which deals are worth pursuing and understand which channels should receive their bids.

In this way, DDS streamlines (and partially automates) the proposal management process for the bid desk team while maximizing sales efficiency by only passing along potential deals that offer the highest return on investment.

6. Frontline Sales Operations

Arguably the most important user of Dynamic Deal Scoring is the sales representative.

DDS helps reps identify profitable deals quickly, which leads to increased efficiency and better-informed decision-making for those delivering sales demos, qualifying leads, and conducting cold outreach.

The end result is higher customer satisfaction and improved overall sales performance — all while maintaining (or even improving) the company’s bottom line.

Advantages and Disadvantages of Dynamic Deal Scoring

Advantages of Dynamic Deal Scoring

• Data-driven decision-making. With AI-powered analytics and machine learning algorithms, DDS enables companies to make informed decisions based on data, reducing guesswork and human bias in deal evaluation.
• Increased profitability. When sellers can identify high-quality deals and optimize their pricing strategies, they become more productive, efficient, and profitable.
• Streamlined sales processes. DDS automates certain aspects of the sales process, such as deal approval, helping sales teams focus on more important tasks.
• Improved sales team performance. Aligning incentives with deal quality and incorporating deal quality into performance evaluations encourages sales teams to prioritize high-quality deals.

Disadvantages of Dynamic Deal Scoring

• Implementation complexity. Integrating DDS into existing sales operations and systems can be complex and time-consuming, especially if they don’t have significant IT infrastructure.
• Risk of low adoption. Some sales team members may resist change, especially when it involves new technology and performance evaluation criteria.
• Dependency on data quality. The effectiveness of DDS relies heavily on the quality of the data being fed into the system. Inaccurate or incomplete data can lead to suboptimal deal evaluations and pricing decisions, potentially negating the benefits of DDS.
• Implementation cost. DDS implementation can be costly, requiring investment in technology, training, and potentially hiring specialized IT personnel.

Dynamic Scoring Technology

Dynamic scoring technology uses machine learning and predictive analytics to produce real-time evaluations. It stands out from traditional deal-scoring methods for its unique features and adaptability.

A data-driven dynamic deal guidance model goes beyond historical data analysis — it accounts for real-time discounting variables and micro-market trends like competitive pricing, perceived value, and price elasticity to produce contextually relevant insights for sales reps and bid desk managers.

It also integrates seamlessly with pricing, deal escalation, sales onboarding and training, and incentive management processes.

In short: The AI that drives Dynamic Deal Scoring makes it more agile compared to traditional deal scoring methods, giving companies a competitive edge in today’s quickly-changing business landscape.

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