Guided Selling

Not every rep has deep product knowledge across your entire catalog, and not every deal is straightforward enough for a one-size-fits-all recommendation. Guided Selling in SalesOS uses AI to bridge that gap -- walking reps through structured needs assessment, matching customer requirements to the right products, and surfacing bundle opportunities that increase deal value while genuinely serving the buyer's needs.

What Guided Selling Is

Guided Selling is an AI-powered workflow that activates during the quoting and deal management process to help reps select the right products and configurations for each customer. Rather than relying on tribal knowledge or static product guides, the system dynamically recommends products, bundles, and pricing strategies based on:

• The customer's stated and inferred needs.
• Their industry, company size, and use case profile.
• Historical data on what similar customers have purchased.
• Product compatibility rules and dependency logic.
• Current pricing rules, discount thresholds, and margin targets.

The result is a consultative selling experience where every rep -- from newly hired to veteran -- can present well-matched solutions that maximize both customer fit and deal value.

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Accessing Guided Selling

Guided Selling activates in two contexts:

1. During quote creation -- When a rep creates or edits a quote, the Guided Selling panel appears on the right side of the quote builder, offering recommendations in real time.
2. From the deal view -- On any open deal, the Guided Selling tab presents a needs assessment workflow and product recommendations tied to that opportunity's context.

You can also access the configuration and analytics for Guided Selling from Settings > Sales Operations > Guided Selling (admin access required).

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Needs Assessment Workflow

The needs assessment is the foundation of Guided Selling. It captures structured information about what the customer actually needs so that product recommendations are relevant rather than generic.

How It Works

When Guided Selling is triggered for a deal, the system presents a series of qualifying questions tailored to the deal's context. These questions are not hardcoded -- they are generated dynamically based on your product catalog, industry rules, and any custom logic your admin has configured.

Qualifying Questions

Questions are organized into categories that mirror common buying considerations:

Answering Questions

Reps fill in the qualifying questions based on information gathered during discovery calls, emails, and meetings. Answers can be:

• Multiple choice -- Select from predefined options.
• Numeric -- Enter quantities, user counts, or budget figures.
• Free text -- Provide detailed context.
• Auto-filled -- Some answers are pre-populated from data already in the deal record (e.g., company size from the linked account, industry from enrichment data).

Customer Requirements Score

As questions are answered, the system calculates a Requirements Completeness Score that shows how much information has been gathered versus what is needed for a high-confidence recommendation. A score of 80% or above typically produces strong product matches. Below that, the system still provides recommendations but flags them as lower confidence.

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Product Matching

Once the needs assessment has sufficient data, the AI engine matches the customer's requirements against your product catalog to surface the best-fit products.

How Matching Works

The matching algorithm considers multiple factors:

• Direct requirement mapping -- If the customer needs 500 users, products with user-count tiers are ranked by fit to that quantity.
• Industry patterns -- Historical purchasing data from similar companies in the same industry.
• Use case alignment -- Products tagged with relevant use cases receive higher scores.
• Compatibility -- Products that integrate with the customer's existing systems score higher.
• Success correlation -- Products that have high renewal rates and customer satisfaction among similar buyers are weighted favorably.

Recommendation Cards

Each recommended product is displayed as a card showing:

• Product name and SKU -- Identifying information.
• Fit Score -- A percentage indicating how well the product matches the stated needs (e.g., 94% match).
• Match Reasons -- Bullet points explaining why this product was recommended (e.g., "Supports 500+ users," "Includes compliance module," "Popular with healthcare companies").
• Price -- The list price from the applicable price book.
• Alternative -- If a better-fit product exists at a different price point, it is noted.

Adding Products to the Quote

From any recommendation card, click Add to Quote to insert the product as a line item. The system pre-fills quantity and pricing based on the needs assessment answers. You can adjust these values before confirming.

Declining Recommendations

If a recommendation does not fit, click Not Relevant and optionally provide a reason. This feedback trains the matching algorithm and improves future recommendations for similar deals.

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Bundle Recommendations

Beyond individual product matching, Guided Selling identifies opportunities to bundle products together for greater value.

Cross-Sell Suggestions

When a rep adds a product to a quote, the system checks for complementary products that are frequently purchased alongside it. These cross-sell suggestions appear as:

• "Customers who bought X also bought Y" -- Based on historical co-purchase data.
• "Recommended add-on for X" -- Based on product rules configured by your admin.
• "Complete the solution" -- Products that fill gaps in the customer's requirements that the primary product does not cover.

Upsell Suggestions

If the customer's needs profile suggests they would benefit from a higher-tier product than the one selected, the system surfaces upsell opportunities:

• Tier upgrades -- "The Enterprise tier includes the compliance module the customer requires."
• Volume upgrades -- "Moving to the 1000-user tier provides a 15% per-unit discount."
• Term upgrades -- "A 3-year commitment saves 20% versus annual billing."

Bundle Pricing

When products are added as a bundle, the system automatically applies any configured bundle discount rules. The quote builder shows:

• The individual line item prices.
• The bundle discount applied.
• The net bundle price.
• The savings versus purchasing each item separately.

This transparency helps reps communicate the value of the bundle to the buyer.

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Guided Selling in the Deal Context

When It Triggers

Guided Selling activates automatically at key deal milestones:

• Deal creation -- A lightweight needs assessment prompt appears, encouraging the rep to capture initial requirements.
• Stage advancement to Proposal -- The full Guided Selling panel activates with product recommendations.
• Quote creation or editing -- Real-time suggestions appear in the quote builder sidebar.
• Deal value change -- If the deal value changes significantly, the system re-evaluates recommendations.

Contextual Awareness

The Guided Selling engine is aware of the full deal context:

• Previous interactions and objections raised.
• Competing products or vendors mentioned in calls (via Conversation Intelligence).
• The buyer's role and decision-making authority.
• Contract renewal timelines from existing agreements.
• Budget signals from email analysis.

This context means recommendations are not purely product-catalog-driven -- they account for the real dynamics of the deal.

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Configuring Product Rules and Recommendation Logic

Admins control how Guided Selling behaves through the configuration panel at Settings > Sales Operations > Guided Selling.

Product Rules

Define relationships between products that the AI uses when making recommendations:

Qualifying Question Templates

Admins can create and customize the qualifying questions that appear during needs assessment:

1. Navigate to Settings > Guided Selling > Questions.
2. Click Add Question.
3. Define the question text, answer type (multiple choice, numeric, text), and the category it belongs to.
4. Set trigger conditions -- which products, industries, or deal sizes should cause this question to appear.
5. Map answers to product attributes so the matching engine knows how to weight responses.

Recommendation Weights

Adjust how heavily different factors influence product matching:

• Needs match weight -- How much direct requirement alignment matters (default: 40%).
• Historical pattern weight -- How much purchase history from similar companies influences scoring (default: 25%).
• Margin weight -- How much product margin contributes to recommendation ranking (default: 15%).
• Success correlation weight -- How much customer satisfaction and renewal data influence results (default: 20%).

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Rep Experience

Prompts and Suggestions During Quoting

When a rep is building a quote, the Guided Selling sidebar provides a streamlined experience:

1. Needs Summary -- A card at the top summarizes what is known about the customer's requirements, with a link to complete any unanswered questions.
2. Top Recommendations -- The three highest-fit products are shown with their match scores.
3. Bundle Opportunity -- If a bundle applies, it is highlighted with the potential savings.
4. Warnings -- If the rep adds a product that conflicts with a rule (e.g., adding an excluded combination), a warning appears with an explanation.
5. Confidence Indicator -- Shows how confident the system is in its recommendations based on available data.

One-Click Actions

Reps can take immediate action from any recommendation:

• Add to Quote -- Inserts the product with pre-filled quantity and pricing.
• Compare -- Opens a side-by-side comparison of the recommended product against alternatives.
• Learn More -- Displays detailed product information, use cases, and customer references.
• Dismiss -- Removes the recommendation from view for this deal.

Guided Selling Notifications

When new information becomes available that changes recommendations (e.g., a call reveals a new requirement, or the customer mentions a budget constraint in an email), the rep receives a notification:

• In-app notification badge on the deal.
• Optional email or Slack alert (configurable per user).

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Customer Fit Scoring

Beyond individual product matching, Guided Selling calculates an overall Customer Fit Score for the deal that indicates how well the proposed solution addresses the buyer's total needs.

Score Components

Interpreting the Score

• 90-100% -- Excellent fit. The solution strongly addresses the customer's needs.
• 70-89% -- Good fit with minor gaps. The rep should consider whether gaps are acceptable or if additional products fill them.
• 50-69% -- Moderate fit. Significant requirements are unmet. The rep should revisit the needs assessment and explore alternative configurations.
• Below 50% -- Poor fit. The proposed solution does not align with the customer's stated needs. Proceeding without adjustment risks post-sale churn.

The Customer Fit Score appears on the quote summary, in the deal sidebar, and in pipeline reports to help managers identify deals where solution fit may be a risk factor.

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Dynamic Pricing Rules Integration

Guided Selling works in conjunction with the CPQ pricing engine to ensure that recommended products are presented at the correct price for each customer.

How Pricing Flows

1. The product recommendation includes the list price from the applicable price book.
2. When added to a quote, any volume discount rules, customer-specific pricing agreements, or promotional rates are automatically applied.
3. Bundle discounts layer on top of individual pricing when products are part of a named bundle.
4. The system alerts the rep if the resulting price requires approval (e.g., discount exceeds the rep's authority threshold).

Margin Guardrails

Guided Selling respects margin floors configured in your pricing rules. If a recommendation combined with applicable discounts would bring the deal below the minimum margin threshold, the system:

• Flags the line item with a margin warning.
• Suggests alternative products or configurations that achieve similar customer outcomes at healthier margins.
• Notes when a price exception approval would be required.

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Analytics

The Guided Selling analytics dashboard helps sales leadership understand how the feature impacts revenue and rep behavior.

Recommendation Acceptance Rate

Track what percentage of AI recommendations reps actually add to quotes. This metric indicates whether the matching algorithm is surfacing relevant products. A healthy acceptance rate is typically 40-60%. Rates below 30% suggest the recommendation logic needs tuning.

Revenue Uplift

Compare the average deal size for quotes where Guided Selling recommendations were accepted versus quotes where reps selected products manually. The uplift percentage shows the incremental revenue attributed to AI-driven recommendations.

Bundle Attach Rate

The percentage of deals where at least one bundle or cross-sell recommendation was accepted. Higher attach rates indicate that the bundling logic is effective and that reps trust the suggestions.

Needs Assessment Completion

Track how thoroughly reps complete the qualifying questions. Deals with high completeness scores should correlate with higher win rates and better fit scores -- if they do not, the questions may need refinement.

Time to Quote

Measure whether Guided Selling reduces the time from deal creation to first quote. Faster quoting typically correlates with higher win rates because it demonstrates responsiveness to the buyer.

Filtering Analytics

All analytics views can be filtered by:

• Time period.
• Individual rep or team.
• Product category.
• Deal size segment.
• Industry vertical.

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Best Practices

• Complete the needs assessment before recommending products. Reps are tempted to jump straight to product selection, but recommendations are only as good as the input. Encourage your team to gather at least 80% completeness before relying on match scores.

• Keep qualifying questions concise and deal-relevant. Too many questions create friction and slow down the process. Aim for 6-10 high-impact questions per deal type rather than an exhaustive survey. Each question should directly influence which products are recommended.

• Review and update product rules quarterly. As your catalog evolves -- new products launch, old ones are deprecated, pricing changes -- the rules that govern Guided Selling must stay current. Stale rules lead to irrelevant recommendations that erode rep trust in the system.

• Use the Customer Fit Score as a pipeline health indicator. Deals with low fit scores are at higher risk of stalling or churning post-sale. Managers should review deals below 70% fit in pipeline reviews and ensure the solution is genuinely aligned before advancing to negotiation.

• Encourage reps to provide feedback on recommendations. When a rep dismisses a recommendation, the optional reason they provide trains the algorithm. Make it a team habit to click "Not Relevant" with a one-line explanation rather than simply ignoring suggestions.

• Start with high-value deals and expand. If your team is new to Guided Selling, enable it initially for deals above a certain threshold. This lets reps build familiarity and trust with the tool on deals where the investment in needs assessment pays the highest dividend.

• Leverage bundle recommendations during renewal conversations. Guided Selling is not only for new business. During renewals, the system can identify add-on products the customer has grown into since their original purchase, creating natural expansion opportunities.

• Align recommendation weights with your sales strategy. If your organization prioritizes margin over volume, increase the margin weight in configuration. If customer retention is the top priority, increase the success correlation weight. The defaults work for most teams, but tuning produces better alignment with your specific goals.

• Monitor the acceptance rate trend, not just the absolute number. A declining acceptance rate over time suggests that the product catalog or customer base is shifting faster than the rules are being updated. Treat it as an early warning signal for configuration review.

• Combine Guided Selling insights with coaching. When analytics show that a rep consistently ignores high-fit recommendations in favor of manual selections with lower win rates, it becomes a coaching opportunity -- not a performance issue. Use the data to have constructive conversations about selling approach.