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In an AI-dominated era of the retail landscape, the current deal management scene is governed by spreadsheets, duplicative efforts, and disjointed collaboration attempts. In order to benefit from the AI-powered transformation, the traditional deal handshake between retailers and vendors must undergo a pivotal shift, with data-driven practices replacing outdated ones.
In this blog, we will delve into the world of data-driven deal management, powered by the transformative potential of Artificial Intelligence and Machine Learning, to showcase the risks associated with adopting a manually-driven deal management system and the opportunities accompanying its automation.
In short, we will be answering these questions:
In retail, deal management is the process of executing agreements between retailers and vendors after going through a few rounds of negotiations. It involves a cross-collaboration that includes overseeing and coordinating all aspects of the sales opportunity, from initial negotiation or contact with vendors and suppliers to translating the agreed-upon deal into a promotion.
It directly influences the retailer’s promotional activities, ensuring a heavy impact on sales growth, profitability, and customer satisfaction. Such a strategic process aims to maximize value and mitigate risks associated with these agreements, which requires organizing, tracking, maintaining, and carefully analyzing them through the sales pipeline.
Effective deal management requires orchestrating collaboration that involves multiple departments (marketing, merchandising, logistics, and store operations) to improve the efficiency, performance, and conversions of the entire promotional campaign they are working on. Additionally, it contributes to creating a more accurate sales forecast that allows for mitigating the risk of overstock, helps retailers prioritize the most important deals to maximize revenue, and automates sales tasks for a more efficient sales cycle and improved collaboration and communication.
However, effective communication and seamless collaboration are often tackled by the limitations of the outdated and manual deal management systems utilized by most retailers, leaving some parties unaware of most updates due to data fragmentation, inefficient workflows, and siloed negotiation approaches that lead to effort duplication. The lack of a centralized and automated deal management system often poses security concerns amidst other limitations we are about to explore.
Collaborators need a holistic view of all available deals as they make promotional decisions, capturing all negotiation details and following up on vendor offerings and approvals. Additionally, a vendor needs to see how his deals are used in promotions or events, to effectively monitor it and adjust accordingly. An atomistic and fragmented view of data can make identifying any occurring issues and resolving them quite complex. Even a small update or modification to this kind of data structure can significantly increase the difficulty of troubleshooting problems.
For example, when a managed location group is updated, deals and offers referencing that location group are not updated automatically and require manual input from vendors, which creates a soft spot for errors that affect budget, deadlines, and the overall outcome of the entire promotional campaign.
These limitations highlight the urgent need for a more reliable approach that boosts efficiency and ensures security. Thankfully, the retail landscape is undergoing an AI-led revolution, offering a comprehensive set of functionalities that fill in the gaps between a retailer’s current deal-management process and their designated business goals.
Data provides a clearer picture of past deals, customer behavioral patterns, competitor activity, customer demographics, weather, and more, establishing a suitable foundation to initiate promotion planning.
For example, a big box retailer is using an AI-powered model to generate demand forecasts based on historical sales data, market trends, and promotional calendars. By deciphering these patterns, a retailer can negotiate better terms with vendors, make better decisions about the right deals to pursue, how to price them, and even define the resource allocation plans to execute the promotion.
A data-driven deal management approach helps retailers establish an objective evaluation of vendor performance, focusing on metrics like responsiveness, quality, and delivery timeliness. This allows for vendor performance assessment; recognizing high-performing vendors that are eligible for rewards, and identifying underperformers to investigate the reasons for the downturn.
Retailers can leverage data to identify vendors with whom they can form strategic partnerships, which involves joint planning and shared goals, leading to a mutually beneficial relationship. Data-driven insights can serve as a common language between vendors and retailers, bridging the gap between them and facilitating clear communication, building trust, and ensuring faster adjustments. For example, a retailer can back up his decision to change order volumes or payment terms based on data, which can be understandable and lead to transforming transactional interactions between a retailer and a vendor into a collaborative and more strategic one.
The secret resides in using predictive analysis and real-time monitoring. AI & ML trained models predict potential disruptions in the supply chain by analyzing its patterns to address risks before materializing, whereas continuous monitoring of data streams allows the detection of risks at an early stage, and creates room to minimize the impact of these risks on the ongoing operations.
Retailers can rely on AI & Analytics to enhance their current deal-management process in terms of scenario planning, using the available data to simulate different scenarios and ensure preparedness for any inconvenience. Data analytics can highlight the risks of depending on single suppliers or following a single pricing plan, which allows retailers to rethink their strategies, spread risks across multiple sources (suppliers), and review their pricing plans with vendors.
Data Analytics can identify bottlenecks, risks, and inefficiencies in the deal-management process, leading retailers and vendors to know what to focus on to streamline operations. Such a feat can result in labor cost savings and enhanced sales growth, as retailers can now improve value-based opportunity prioritization and frontline delivery.
With a clear deal management strategy, retailers can create optimized promotions for better ROI, focusing on the most effective tactics to target the right consumer with the most suitable deal at the right time. Despite the market challenges, this AI-powered strategy directly impacts the retailers’ business outcomes positively.
Spreadsheets simply can’t handle the fast-paced changes and updates inside a dynamic retail deal management environment, whereas outdated methods and measures may result in missed opportunities and therefore; a significant decline in potential promotional ROI.
The key to avoiding such an inconvenience? A dedicated AI-powered promotion management solution like Cognira’s PromoAI, with built-in features that automate operations and accelerate the whole deal-management process to save labor costs. Imagine a single platform to Incorporate vendor deals into AI-driven promotion and event recommendations, simplify communication and invite the participation of all parties involved, and maximize vendor funding with forecasted and historical performance.
Such a feat would Increase promotion effectiveness and event decisions supported by deals during the planning process while simplifying end-user decision-making with recommendations, accurate projections, and automation. It can also automate the alignment of products and locations across the life of the deal, and deliver comprehensive and useful promotion recommendations based on deal terms.
It is worth noting that retailers can utilize dedicated solutions to collaborate with vendors and merchants and do more than deal negotiations and execution, as the deal-management feature falls into a more comprehensive set of features that allow them to manage these deals from A to Z. A dedicated promotion management solution gives retailers the tools to optimize their entire promotion lifecycle, capturing the largest financial opportunity and maximizing vendor funding, transforming their promotion strategies and revolutionizing the way promotions are planned, executed, and optimized.
Based on that, a retailer can boost their chances to create and execute a promotion that aligns with their business objectives by leveraging AI & analytics during the whole promotion lifecycle, and not just the deal-management process. Such a smart process won’t be enough to revolutionize promotions and boost profit margins. AI-powered optimization must be there during all phases of the promotion lifecycle to drive smarter and more effective promotions.
Transitioning to a data-driven deal management process powered by AI-driven tools is no longer a luxury, but a necessity for retailers seeking more efficient ways to handle all operations that lead up to executing deals and ensuring smooth negotiations between vendors and retailers for the sake of more relevant and profitable promotions
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