The argument: Backed by research
Recent research findings by McKinsey & Company provides compelling evidence for the business case of integrating Generative AI and data analytics into organisational processes, including bidding. The findings give us insight into the latest trends and evidence that organisations are nowseeing meaningful cost reductions from generative AI use in HR and revenue increases in supply chain management. Similarly, the research reveals that the use of analytical AI frequently yields cost reductions in service operations and revenue increases in marketing and sales.
The impact of LLMs on bidding processes
LLMs are sophisticated AI models trained on massive datasets, enabling them to understand and generate human-like text. However, the models are getting smarter. Their ability to grasp complex language structures, context, and nuance makes them invaluable for businesses seeking deeper insights from their data. Specific to bidding, LLMs offer several advanced analytic capabilities (please note: confidential data, such as buyer/supplier information or contract pricing should never be entered into public or free-to-use services).
Transforming bidding processes with LLMs
1. The impact of sentiment analysis (and more)
Sentiment analysis is critical in understanding stakeholder feedback and market sentiment, which can directly influence bidding strategies. LLMs can automate this process by analysing large volumes of buyer feedback data in real-time, categorising sentiments accurately. For example, analysing buyer feedback using LLMs can provide granular insights into market needs and preferences, enabling businesses to tailor their bids more effectively.
Use case: A construction firm can use LLMs to analyse feedback from previous project bids to understand sentiments towards key evaluation criteria including project method statements, pricing, and other aspects of their bid – such non-conformances. This data can reveal internal systemic patterns, and offer areas of improvement and aspects you the company is particularly strong at, guiding them to address their weaknesses and extend their strengths.
2. Understanding the ‘why’ behind your wins and losses
Understanding why a bid was successful or unsuccessful is crucial for refining future strategies. LLMs can process historical bid data to identify patterns and factors that contributed to wins or losses. This comprehensive analysis can help companies adjust their tactics and enhance their chances of success in future bids. They can potentially identify not just patterns at the customer level, but could dig deeper to understand if there are historical or other interpersonal relationship issues that need addressing.
Use case: An IT services company can leverage LLMs to analyse their past bids to identify key factors that led to either win or loss. This analysis can include various attributes such as client feedback on pricing and non-price (quality) criteria, helping the company to refine their future qualification process, solution offering, and resource matching. They could then analyse their contract KPI data to see if there are any perceptual gaps or real correlations between how they are being evaluated at the bid level, and how they performing at the contract delivery level (and are you overstating claims in your bids that could ultimately leave your company exposed)?
3. Unlocking competitive insights
Building on your win/loss analysis, sentiment analysis and then benchmarking against competitors can provide a significant edge. LLMs can analyse your combined win/loss data and competitive intelligence to tell a story; and then combine it with publicly available data, such as community profiles, population data, financial reports, press releases, and market trends, to offer insights into competitors’ strengths and weaknesses, recurring client pain points, and market gaps.
Use case: A healthcare staffing agency can utilise LLMs to continuously monitor competitors’ activities and gather insights about their strategies. This information can be used to differentiate the agency’s proposals and offer unique value propositions that close service gaps and resonate with potential clients and their clients/patients.
4. Pricing and contract intelligence
Determining the optimal pricing strategy and understanding contractual terms are critical for successful bids. LLMs can analyse historical pricing data, market conditions, and contract details to recommend competitive yet profitable pricing strategies. They can ensure that contract terms are favorable and compliant with industry standards.
Use case: An engineering company can use LLMs to analyse past contracts and pricing models to determine optimal bid prices that balance competitiveness and profitability. This analysis can highlight ideal price points / margins and contractual terms that have historically secured wins.
Continuous improvement through data analysis
Collecting and analysing data across the bid lifecycle forms the basis for a continuous improvement loop. By processing vast amounts of bid-related data, LLMs can generate actionable insights that help businesses refine their bid strategies continuously.
Identifying strengths and weaknesses: LLMs can help businesses identify which aspects of their bids are consistently strong and which areas require improvement. This continuous feedback can guide incremental improvements, leading to more compelling and competitive proposals (including solutions and contract delivery) over time.
Optimising proposal strategies: With detailed analytics, businesses can optimise various elements of their proposals. Whether it’s adjusting the tone, structure or core messaging of proposal content based on sentiment analysis or tweaking pricing strategies based on competitive intelligence, LLMs provide the data-driven insights needed to enhance proposal effectiveness.
Fostering Innovation: By continuously learning from bid outcomes, LLMs can foster innovation in bidding and proposal strategies. Businesses can experiment with new approaches, analyse the results, and systematically refine their tactics for better outcomes.
Key takeaways
Integrating LLMs into your bidding practice is fast becoming a business-as-usual approach for content generation. But the advanced capabilities of LLMs in sentiment analysis, win-loss analysis, competitive intelligence, and pricing and contract intelligence provide businesses with a new set of tools they could be using to succeed in competitive bidding environments beyond content generation alone. By leveraging these powerful models, companies can form a continuous improvement loop, generating stronger insights, more compelling proposals, and delivering more client-centric service. As LLM technology evolves, its impact on business analytics in bidding will only grow, enabling businesses to make data-driven decisions and gain a significant competitive edge.
The post Better together: The business case for generative AI and data analytics in bidding appeared first on Bidhive.
