Jump to content

Trade promotion management

From Wikipedia, the free encyclopedia

This is an old revision of this page, as edited by Sangdeboeuf (talk | contribs) at 05:42, 6 April 2019 (top: Adding acronym, refs). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Trade Promotion Management (TPM[1][2]) typically refers to one or more software applications that assist companies in managing their complex trade promotion activity. Trade Promotion Management is a challenge faced by most CPG/FMCG companies around the globe. Consumer goods companies spend substantial amounts of time and money—14 percent of revenue, according to an AMR Research study—on promotions with retailers designed to boost revenue or increase/protect market share (or both).

Gartner

Gartner believes that technologies related to managing trade promotions have never been more relevant, as the average revenue expended by manufacturers for promotions now exceeds 20%. More and more companies are leaving spreadsheets for automated technologies, while others are adding promotion optimization capabilities.

Gartner published the "Market Guide for Trade Promotion Management and Optimization for the Consumer Goods Industry" in February 2017.[3]

Key functions

  • Sales Forecasting
  • Promotion planning and budgeting
  • Predictive modeling/optimization
  • Promotion execution and monitoring
  • Settlement
  • Post event analysis

Business problems addressed

Historically, there have been many solutions to trade promotion management. Commonly, companies use their accounting systems or spreadsheets, but as the complexity of trade increases software solutions have been developed and implemented to fill the needs of companies in various industries including consumer goods, food manufacturing, food service and others.

Lack of accurate and timely information to support trade promotion decision-making

Trade promotion decisions are often rushed and based on sub-par data. While sales and marketing managers are surrounded by promotion information, questions on retail commitment and product forecast accuracy can hinder the process. Multiple data sources and conflicting needs from various departments further complicate the issue.

Inability to plan promotions based on analytics

Historical trade promotion data should be analyzed in order to continually improve trade promotions. If a company does not utilize processes and systems that measure trade promotion performance, future trade promotion executions could be less effective than if they’d been planned using past analytical information.

Ineffective organization and partner integration

Lack of integration both internally and with external partners can hinder trade promotion success. Key elements of organizational integration include standardized metrics, regular information sharing, cross-functional department collaboration, and collaborative processes. Integration with retail partners is important to executing promotions successfully, as well as maintain strong relationships with retailers over time.

Lack of appropriate key performance indicators (KPI)

KPIs tell manufacturers and retailers how trade promotions performed relative to their pre-determined objectives. A lack of understanding on what trade promotion data to measure and how to measure performance can hinder the overall process. Manufacturers and retailers will not know what made a promotion effective or ineffective unless they have predetermined data points to measure and analyze.

References

  1. ^ Lebreton, Baptiste; et al. (2014). "Architecture of Selected APS". In Stadtler, H.; Kilger, C.; Meyr, H. (eds.). Supply Chain Management and Advanced Planning: Concepts, Models, Software, and Case Studies. Springer. p. 355. ISBN 978-3-64-255309-7.
  2. ^ Duffy, Jim; Koudal, Peter; Pratt, Stephen (2012). "The Future of Collaborative Customer Relationship Management". In Kracklauer, A.H.; Mills, D.Q.; Seifert, D. (eds.). Collaborative Customer Relationship Management: Taking CRM to the Next Level. Springer Science & Business Media. pp. 99–100. ISBN 978-3-54-024710-4.
  3. ^ https://www.gartner.com/doc/3587241