Trade Promotion Management Software

Trade promotion software can streamline TPM management and reduce marketing expenses by identifying trade deal non-performance, chargeback, and excessive deduction errors.

Trade promotion management (TPM) is critical in consumer packaged goods (CPG), where manufacturers compensate retailers for driving sales through various marketing and promotional activities. However, managing trade promotions can be challenging, especially when tracking and reconciling retailer bill back deductions, especially the errors resulting in significant manufacturer losses. To help with this challenge, many companies are turning to automation to streamline their trade promotion management processes and reduce the costs of erroneous deductions.

A key challenge in trade promotion management is complexity. Trade promotions involve various marketing spending, including discounts, coupons, coop advertising, rebates, free products, in-store promotions, and other incentives that are difficult to track and manage manually. How they are “settled” (that is, paid to the retailer)  is by either “off-invoice” allowances reducing the cost right on the invoice or by “Bill-back” deductions which are deducted from accounts receivable remittances and must be researched and reconciled by the manufacturer. Retailers also have differing requirements, time parameters, and processes for submitting and resolving billback deductions, further complicating the process. As a result, suppliers struggle to keep track of their trade promotions, leading to errors and inaccuracies that can result in significant losses due to errors. Post-Audit Deductions, deducted one or more years after the fact, make the research and resolution extremely difficult and have error rates as high as 50%.

Depending on the CPG segment,  these “trade deals” are budgeted for 5 – 20% of total revenues, and the error rate can be as high as 3% of revenues. This is a consequential expense for any company; addressing it is an opportunity to make a remarkable difference in profits.

If integrated with Deduction Software and Accounts Receivable,  trade promotion software can help to address these challenges by streamlining the TPM process and reducing the risk of errors. For example, companies can use automated systems to capture data on their trade promotions, such as the types of promotions, the dates and locations, and the associated costs and revenues. This data can then be used to generate reports and analytics, which can help companies to identify trends, optimize their promotions, and make more informed decisions.

A combined AR, TPM, and Deduction software solution will deliver maximum productivity to this function and also ensure a reduction in the “profit and revenue leakage” due to deductions that plague consumer goods companies.

Incorporating machine learning or intelligent, configurable rules into the software can also play a valuable role by helping companies validate billback deductions to prevent erroneous deductions. Machine learning and algorithms can be trained to analyze large volumes of data, such as historical sales, promotional, and retailer deduction data, to identify patterns and anomalies. For example, machine learning algorithms can identify instances where retailers claim deductions for products that were not promoted or where retailers claim excessive deductions for advertised products. By detecting these errors early, companies can take corrective action and prevent losses.

Another benefit of using machine learning in trade promotion management is that it can help companies to optimize their promotions for maximum effectiveness. By analyzing past promotions and sales data, machine learning algorithms can identify which promotions are most effective in driving sales and which are not. This information can then be used to refine and optimize future promotions, leading to higher sales and more significant ROI.

Of course, implementing automation in trade promotion management is not without its challenges. One of the critical challenges is data quality. To be effective, machine learning algorithms require high-quality data that is accurate, complete, and consistent. Unfortunately, this isn’t easy to achieve, especially when dealing with data from multiple sources and retailers. As a result, companies may need to invest in data cleansing and normalization processes to ensure that data is of sufficient quality for machine learning.

Another challenge is the need for specialized skills and expertise. Implementing automation and machine learning in trade promotion management requires a team of skilled data scientists, analysts, and IT professionals with experience in data management, analytics, and machine learning. Companies may need to invest in training and development programs to build these capabilities internally, or they may need to work with third-party providers who can provide these skills and expertise on a project basis.

Automating trade promotion deduction management can bring several benefits to a business. Here are eight of the most important ones:

1. Improved accuracy: Automation can significantly reduce errors, such as incorrect deductions, overpayments, or underpayments. This can lead to better accuracy in managing trade promotions and deductions and ultimately improve the bottom line.
2. Time and cost savings: TPM deduction management software can free up time and resources that would otherwise be spent on manual tasks, such as data entry and reconciliation. This can lead to significant cost savings and allow employees to focus on more value-added tasks.
3. Increased visibility: TPM and A/R software can provide real-time visibility into promotions, claims, and deductions, allowing businesses to make better-informed decisions about promotions and discounts. This can also help identify trends and areas for improvement.
4. Validation: A solid TPM system can offer automated validation of bill backs by comparing them against sales data and accrual balances by customer, product, and trade deal.
5. Matching: System matching against credit memos and charging back erroneous or excessive bill-back deductions to the retailer.
6. Integrated workflow for all exceptions requiring human actions or decisions will ensure that the proper departments handle them without offline processes.
7. Workflow: A workflow process that enables the recapture of excess and incorrect deductions from the retailer.
8. Streamlining: Automating trade promotion deduction management can optimize the process from claims submission to settlement. This will reduce processing times and increase efficiencies.
9. Better customer relationships: With comprehensive trade promotion deduction management, businesses can ensure timely and accurate deduction resolution, which can help improve relationships with customers. 

CONCLUSION

Manufacturers can improve their “accrual spend” liability through specialized software solutions. Accrual spend refers to the liability for expenses that have been pre-approved but conditioned on the retailer’s performance according to the trade promotion deal parameters but not yet paid and can include items such as rebates, promotions, and discounts. Manufacturers can improve their accrual spend liability through Carixa’s TPM features that can automate the accrual calculation process, track and manage accrual balances and provide real-time visibility into accrual spend. This can help manufacturers to reduce errors and inaccuracies in their accruals and to ensure that they are accurately accounting for future liabilities. Carixa is an advanced “bolt-on” SaaS software for receivables and trade promotion management that can be integrated with manufacturers’ existing ERP systems to provide seamless functionality and improve overall efficiency.

There are challenges, to be sure, but the benefits of automating and integrating TPM with Deduction and Accounts Receivable are clear. By streamlining processes and workflow and reducing errors, companies can improve their bottom line and gain a competitive advantage in the marketplace. In addition, as the CPG industry becomes increasingly complex and competitive, companies that invest in automation and machine learning will be better positioned to succeed in the long run.