How AI Can Help VARs Find Opportunities, Navigate & Win – Use Case #3

Third AI Use Case for Public Sector VARs – Winning Price or Margin

Traditional Approach: Traditionally, VARs have relied on human analysis to estimate a winning price or margin. This process is time-consuming and often based on anecdotal experiences and historical knowledge, which may not accurately reflect current market dynamics.

AI-Enhanced Approach: AI can significantly enhance the pricing strategy process by analyzing vast datasets of past award information, current market trends, competitor pricing, and more. This approach allows for the identification of best pricing strategies that maximize the likelihood of winning bids at the best possible margins. Machine learning algorithms use factors such as time of year, buying history of the agency, and competitor behavior to predict winning prices with higher accuracy.

Precision in Predictive Pricing

AI’s analysis of historical bid data, incorporating variables like the purchasing patterns of specific agencies and seasonal budget fluctuations, enables more effective strategies. For instance, analysis might reveal that bids within 2-5% of the historical average are a certain percentage more likely to win, helping VARs to fine-tune their bids.

Cost Optimization

AI can pinpoint cost-saving opportunities within the supply chain, improving margins without compromising competitiveness. For example, optimizing logistics based on AI insights could reduce delivery costs by a certain percentage. AI can also analyze opportunity data to gain access to bigger OEM discounts by finding promising OEM partner program candidates for a given VAR based on the VAR’s existing capabilities, complementary OEM programs, and award trends.

Competitive Gaming Insight

Understanding competitive trends becomes powerful with AI. If a competing VAR is seen to be on a losing streak, AI can predict their likelihood to price aggressively in future bids. Conversely, winning streaks may show a competitor’s pricing confidence, guiding strategic pricing adjustments. For instance, AI might suggest pricing strategies to outmaneuver a competitor expected to bid low to break a losing streak.

Opportunity Identification Based on Relationship Affinities

AI can effectively highlight opportunities historically awarded to specific bidders, hinting at possible relationship-driven decisions. For example, if a product has consistently been awarded to the same bidder, this might indicate a special relationship. AI can advise whether to compete aggressively for such bids or focus efforts elsewhere, based on patterns that suggest whether these trends are based on performance, relationships, or other factors.

 

**Scott Keough brings over 30 years of leadership experience in B2B SaaS, working with firms serving VARs, integrators, and OEMs. His expertise spans planning, business development, sales, and service delivery for cloud, IT infrastructure, and AI offerings. With a proven record of bootstrapping startups to profitability and positioning them for rapid growth, Scott excels at setting strategy, architecting solutions, building teams, and providing governance and guidance—qualities that uniquely position him to contribute significantly as a board member to firms in the reselling industry. Scott co-founded a procurement SaaS firm where he most recently was instrumental in the acquisition of his firm by a JMI Equity private equity-owned platform company, Unanet.