Sales Activity Recommender

Boost Sales Performance with AI-Powered Recommendations

Empower your sales teams to recommend the right product at the right time, aligned with margin targets, inventory goals, and customer needs.

Sales Activity Recommender
Overview

Align Every Sales Activity with Your Margin and Revenue Targets

Adastra’s Sales Activity Recommender helps frontline teams make smarter, faster product suggestions by combining real-time sales performance data, margin targets, seller behaviour, and customer insights. We deliver through a proven path: a 6-12 week Proof of Concept to validate recommendation logic and financial models; a 2-6 month Pilot to integrate with live systems and tune based on real-world feedback; and 6-12 months to Deploy enterprise-wide with controls, observability, and continuous learning, so you get measurable ROI and strategic alignment between field performance and financial outcomes.

The Challenge: Sellers Don’t Know What to Prioritize

Sellers Struggle with Product Prioritization

Changing business goals, static playbooks, and high turnover create inconsistent recommendations.

Incentives Don’t Always Align with Profitability

Best-selling products aren’t always the most profitable or strategically important.

Promotions Aren’t Surfaced Effectively

Campaign compliance and attach rates underperform despite strong product portfolios.

Corporate Goals are Hard to Enforce in the Field

Clearing inventory, promoting strategic SKUs, and hitting margin targets require real-time guidance, not guesswork.

How Adastra’s AI Sales Activity Recommender Works

1

Connect Your Data

We integrate with your CRM (Salesforce, etc.), inventory systems, and sales performance tools to ingest historical and real-time data.

2

Score Every Product

Our AI calculates Sales Advocacy and Performance Impact scores at the product, category, region, and account level.

3

Surface Recommendations

Sellers receive actionable suggestions, directly in their workflow, showing which products to recommend and why.

4

Generate Sales Pitches

Optional AI-powered pitch generation provides tailored talking points based on account context and product fit.

5

Learn and Improve

The model continuously tunes recommendations based on seller engagement, conversion data, and financial outcomes.

Key Benefits of AI-Powered Sales Recommendations

For Sales Leadership

  • Align frontline behaviour with margin targets and strategic priorities
  • Improve attach rates, cross-sell conversion, and campaign compliance
  • Gain visibility into which products are over- or under-promoted

For Frontline Sellers

  • Know exactly what to recommend without guessing
  • Understand why[EC4.1] each product is suggested (explainable AI)
  • Spend less time on research, more time selling

For the Business

  • Increase revenue and contribution margin per transaction
  • Clear inventory faster by surfacing the right products at the right time
  • Adapt quickly to market changes with real-time recommendation updates

Get Your Roadmap to Smarter Selling

Stand up a focused sales recommender for your top product categories, connected to your sales systems, with clear margin and revenue goals.

Why Adastra? Get It Right from the Start

Multiple production deployments of sales recommender solutions across retail, distribution, and services industries. Multi-cloud delivery with cloud-agnostic, portable architectures.

Microsoft Delivery Partner | Chat with Your Data in a Day
Adastra Achieves Gold Partner Status with Databricks 2026
25+

years delivering enterprise sales and analytics solutions across data, AI, and digital transformation

2200+

professionals in 40+ countries; proven track record operating production-grade AI solutions with governance and quality

100+

Data Scientists & AI specialists

Our Framework

1. Proof of Concept
  • Identify a focused sales channel or product category to prototype the recommender
  • Ingest historical sales data and simulate impact scenarios
  • Test recommendation logic, validate financial models, and build frontline trust
6-12 Weeks

Outcome: A working PoC with quantified impact and a clear plan to scale

2. Pilot
  • Roll out in a limited geography, product line, or team with real-time integration
  • Monitor seller engagement, financial uplift, and customer experience
  • Iteratively tune the model based on real-world feedback
2-6 Months

Outcome: An integrated pilot with measurable productivity gains and a rollout plan

3. Deploy and Scale
  • Deploy the recommender enterprise-wide with controls, observability, and continuous learning
  • Ensure scalability, explainability, and measurable ROI
  • Drive strategic alignment between field performance and financial outcomes
  6-12 months

Outcome: A scalable, reliable production solution with continuous improvement

Outcomes You Can Expect

The Sales Activity Recommender is designed to deliver tangible improvements across sales execution metrics:

90% reduction

in time spent consolidating sales-related information

Faster Deal Cycles

AI-driven suggestions help reps prioritize high-impact actions

Greater Quota Attainment

Actionable guidance translates to improved rep performance

More Accurate Forecasts

Integrated predictive signals refine pipeline visibility

Higher Rep Satisfaction

Tools that reduce manual analytics workloads free sellers to focus on relationships and strategy

Ready to Align Your Sales Team with Your Business Goals?

Stop leaving margin on the table. Adastra’s AI Sales Activity Recommender, helps your frontline teams recommend the right products, backed by real-time data, explainable logic, and proven results.

Sales Activity Recommender FAQ

How is this different from our existing CRM or sales enablement tools?

Most CRMs track activity but don’t provide prescriptive guidance on what to sell. Sales enablement platforms offer content but not real-time, financially aligned recommendations. Adastra’s solution fills this gap by combining Sales Advocacy Scores with Performance Impact Scores, telling sellers what to recommend and why, directly within their existing workflow.

The solution integrates with your existing CRM (such as Salesforce), sales enablement tools, inventory management systems, and performance dashboards. During the Pilot phase, we establish real-time data connections to ensure recommendations reflect current margin targets, inventory goals, campaign priorities, and customer behaviour. The architecture is cloud-agnostic and works across AWS, Azure, and Google Cloud environments.

Implementation follows a phased approach: a 6-12 week Proof of Concept to validate the recommendation logic with a focused product category or sales channel; a 2-6 month Pilot to integrate with live systems and tune based on real-world feedback; and 6-12 months for full enterprise deployment. This phased approach lets you prove value quickly before scaling, minimizing risk while building organizational confidence.

As a recognized & premier partner of leading cloud providers, including AWS, Microsoft Azure, and Google Cloud, Adastra has access to funding programs that can help offset costs during each phase. We’ll work with you to identify applicable programs and maximize available incentives to reduce your overall investment.

We establish clear KPIs aligned with your business objectives from day one. Common metrics include improvement in attach rates and cross-sell conversion, increase in contribution margin per transaction, campaign compliance rates, seller adoption and engagement with recommendations, and overall revenue lift attributed to recommended products. During the Pilot phase, we track these metrics in production-like conditions to quantify impact before full deployment.

Seller adoption is a core design principle. The recommender provides explainable AI; every recommendation comes with transparent reasoning that sellers can understand and trust. Rather than replacing seller expertise, it augments their judgment with real-time data and strategic context they wouldn’t otherwise have. Our client, Mark Anthony Group, saw 100% user adoption rate and has seen significant increases in product performance transparency and seller confidence when recommendations are backed by clear logic.

The Sales Activity Recommender works well for any organization with a diverse product portfolio where sellers need guidance on what to prioritize, including consumer packaged goods (CPG), retail, distribution, manufacturing, financial services, and B2B sales teams. It’s particularly effective when you have multiple products or solutions with varying margins, strategic importance, or inventory considerations, and when aligning frontline behaviour with corporate goals (like clearing inventory or promoting specific SKUs) has historically been challenging.

Let’s Talk!

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