How Do Retailers Get Their Promotion Operations AI-Ready

The current retail landscape has been more complicated than ever: more channels for discovery, shorter trend cycles, and rising economic anxiety. Retailers are finding that the demands are rising quicker than ever, while the buying journey have been more multi-dimensional as well.

On the flip side, challenges come along with opportunities. The constant demand fluctuations bring large amounts of valuable intent signals and transactional data that can be fed into advanced analytical systems for comprehensive predictions. With the rise of AI-powered applications, retailers now have advanced tools that consume large amounts of data and provide reliable insights regarding category growth and operational decision impacts.

Everything seems promising, but there is a catch: now with the increasing demand and market complexity, the bar for retail promotion operation standards also reaches a new high.

Previously, category, purchasing, and pricing teams would operate individually based on individually controlled historical data and align their strategies, which are often repetitive from previous year. For post performance evaluation, each department leverage simple BI tools and excel models for data analytics. This is still true for many small-scale retailers today, but for middle to large scale retailers, the combo of "decentralized operation + manual effort + primitive tools" is no longer sustainable for further growth and profitability.

Now, middle to large scale retailers are now expected to

• capture the data

• remove errors and redundancies

• feed the clean data to the AI tools

• make decisions as fast as possible

Simultaneously, they also need to

• collect previous performance data

• store the data in an organized, structured manner

• share it with major external partners (optional)

All the processes above aren't linear either. They are concurrent, interdependent, and frequently in conflict with each other. If the solution is simply adding headcount, the time and effort needed for alignment will further prevent maximum efficiency.

So what is the solution then?

The silent error that AI can't detect: missing / inaccurate data

Because of the combination of manual work, primitive tools, and decentralized operations, many retailers:

• Lack traceability of deal and vendor funding negotiation details

• Can't automatically link contracts, purchase order records, and transactional data for accrual calculations

All of the limitations result in direct loss of significant decision-making context for AI tools, such as price optimization engine, demand forecasting engines, and contract negotiation tools.

Without the fully-documented negotiation details, AI can't effectively identify:

• Vendors' commercial logic behind the promotion selection, time or type

• The vendor-purposed what-if scenarios, which serve as expectation and priority context

• The benchmark triggers: what it takes to make deal progress or terminated, to change price or funding amount, etc.

Without accurate accrual calculations and timely reconciled rebates, AI can't correctly identify:

• The effective net price of each SKU included in the promotions

• The promo lift and the difference between the lift and the baseline

• The true revenue of promotion activities (promo-lifted sales + vendor fundings)

By feeding AI only the end result with incomplete context, retailers are generating results from a fragmented decision-making process at scale. Companies that invest in both a robust data foundation and the AI-powered tools as a single, integrated investment are seeing the strongest ROIs across operational profitability, efficiency, and scalability, such as reduced reconciliation time and increased margin recovery.

Steps to Get Your Deal Contract Data AI-Ready

1. Document Deal/Promotion Negotiation Details

In order to feed the data to AI, retailers first need to start creating and maintaining deal/promotion negotiation details in an organized, comprehensive manner. Category managers should negotiate deals and promotions with vendors through one centralized digital channel. If any changes are negotiated and agreed upon outside the digital channel, category managers should be able to easily find the contract record, key in negotiation details, and leave a clear paper trail.

On top of specific deal/promotion details such as SKUs and deal types, retailers also need to document vendor-related behavior such as negotiation length, attached conditions, what was conceded, and negotiation leverages used. By doing this, retailers will build reliable knowledge profiles of each vendor negotiation behaviors that are transferable to new category managers or AI agents.

2. Automate accrual calculations and create an integrated rebate reconciliation workflow

Before asking AI to evaluate promotion performance and provide optimization insights, retailers need to have an accurate, up-to-date estimate of outstanding vendor funding and expected collection date. Purchasing and accounting team members each need to provide an accurate record of the finalized contract and aggregated data from POS, PO and SCM systems through automation.

To minimize disputes and shorten reconciliations, retailers need to establish one structured workflow with both granular and holistic visibility: each users can only access and manage data related to their tasks, while aware of which steps of the process they are acting upon. For example, when a vendor requires specific data, both the purchasing user and accounting users should be informed about the requirement and understand which user will be responsible for gather the data, share the data with vendors, and finalize the dispute. During the audit period, retailers should have full access to identify the time and data needed to collect different types of vendor funding, thus feeding those data points to the AI tools for better promotion predictions.

3. Establish system connectivity, strive for data standardization

To maximize prediction accuracy, retailers need to aim to unify existing data, standardize individual input fields, and remove duplicate/errors before feeding the data to AI tools, as well as establishing a stable connection between the refined data and AI engines. The data gathering and refining process should be treated as a continuous operational discipline. However, establishing and maintaining system connectivity between your AI tools and your existing tech stack is a non-negotiable requirement.

The less individual integration needed, the less risks the retailer IT team face and will encounter. When choosing any data management tool, connection flexibility and function comprehensiveness need to be prioritized to ensure implementation success.

Choosing the tool that helps you with all 3 steps -- Simplain StreamCollab

The trade funds management bundle, which includes Deals & Promotions Management and Rebate Management, is designed for FMCG retailers to streamline their deal/promotion negotiations, vendor funding management, and rebate reconciliation process. By implementing both modules, retailers and wholesalers can gain:

• Real-time visibility into promotion negotiation details & promotion-caused margin impact

• Faster vendor funding collections and fewer disputes

• Accurate accruals and reliable vendor funding estimate

• Single, integrated promotion negotiation & rebate reconciliation workflow

• High-level connectivity with major ERPs and AI-powered analytic tools

Don't want to leave your AI tools context blind? Explore our trade funds management solutions.