Powering the TPM Lifecycle with Predictive Analytics
Joe Bellini, CEO of AFS Technologies, shares insights into a new approach of integrating predictive analytics into a trade promotion management (TPM) system. The benefits are two-fold: 1) account managers gain access to information they need to make better decisions, and 2) the company maximizes the value from its pricing and promotion models.
How are pricing and promotion models used today?
Bellini: Pricing and promotion models are digital assets developed either by a corporate department specialized in this area or outsourced to a third party. They are strategic in the sense that these models are used to determine a company’s direction in terms of frequency, depth of deals and types of merchandising employed. Manufacturers are rightly concerned that they are missing out on near term revenue opportunities when predictive analytics are used primarily for strategic or annual planning. The mindset is rapidly shifting to applying predictive analytics tactically as part of the TPM lifecycle so it can drive greater returns for the business.
What challenges are account managers facing without having easy access to predictive analytics?
Bellini: Account managers must stretch their trade spend as part of managing their forecasts. They may have historical data on last year’s lift factors to reference as part of the forecasting exercise but they are not able to generate an accurate view because price points and merchandising are rarely consistent year over year. This tends to leave three options: 1) be creative in developing your own lift factors; 2) request pricing and promotion models from market research or brand departments, which hopefully are maintained to ensure they reflect the current market situation; or 3) access models from third-party sources such as Nielsen.
Assuming you have access to models, the next hurdle is the “swivel chair.” This refers to the situation where data that comes from a different source must be formatted in a way that can be imported into a TPM system. For instance, most syndicated data is provided in units, based on consumer behavior; but in the planning stage, account managers tend to forecast in shipping units or cases.
How do you integrate cost-effective, predictive analytics capability into the TPM system so that the account manager in the field is empowered to make business decisions?
Bellini: This is the question we are starting to hear from our customers. As more companies are trying to maximize the value of their trade spend, predictive analytics must be viewed more than just a strategic tool. Predictive analytics must be integrated into the TPM system so that account managers are able to incorporate the lift factors as they evaluate their trade events to ensure optimal performance as well as to understand the impact on their demand planning, before they execute into the market.
Recently we announced an integrated analytic alliance with Nielsen providing a single point of entry through the AFS Trade Promotion Management Retail (TPM Retail) system into Nielsen’s predictive, price and promotion models. With this alliance, AFS’ TPM clients can now easily access Nielsen’s Revenue Management and Optimization (RMO) Analytics to make information-rich, analytic-driven decisions throughout the selling process to improve the effectiveness of their brands’ everyday pricing and trade promotions.
Extending predictive analytics with optimized insights and delivering on a coordinated strategy (from headquarter guidelines to retailer-specific joint business plans) will significantly increase spending efficiency, drive incremental sales and maximize financial impact for a brand and its retail customers.
How are pricing and promotion models used today?
Bellini: Pricing and promotion models are digital assets developed either by a corporate department specialized in this area or outsourced to a third party. They are strategic in the sense that these models are used to determine a company’s direction in terms of frequency, depth of deals and types of merchandising employed. Manufacturers are rightly concerned that they are missing out on near term revenue opportunities when predictive analytics are used primarily for strategic or annual planning. The mindset is rapidly shifting to applying predictive analytics tactically as part of the TPM lifecycle so it can drive greater returns for the business.
What challenges are account managers facing without having easy access to predictive analytics?
Bellini: Account managers must stretch their trade spend as part of managing their forecasts. They may have historical data on last year’s lift factors to reference as part of the forecasting exercise but they are not able to generate an accurate view because price points and merchandising are rarely consistent year over year. This tends to leave three options: 1) be creative in developing your own lift factors; 2) request pricing and promotion models from market research or brand departments, which hopefully are maintained to ensure they reflect the current market situation; or 3) access models from third-party sources such as Nielsen.
Assuming you have access to models, the next hurdle is the “swivel chair.” This refers to the situation where data that comes from a different source must be formatted in a way that can be imported into a TPM system. For instance, most syndicated data is provided in units, based on consumer behavior; but in the planning stage, account managers tend to forecast in shipping units or cases.
How do you integrate cost-effective, predictive analytics capability into the TPM system so that the account manager in the field is empowered to make business decisions?
Bellini: This is the question we are starting to hear from our customers. As more companies are trying to maximize the value of their trade spend, predictive analytics must be viewed more than just a strategic tool. Predictive analytics must be integrated into the TPM system so that account managers are able to incorporate the lift factors as they evaluate their trade events to ensure optimal performance as well as to understand the impact on their demand planning, before they execute into the market.
Recently we announced an integrated analytic alliance with Nielsen providing a single point of entry through the AFS Trade Promotion Management Retail (TPM Retail) system into Nielsen’s predictive, price and promotion models. With this alliance, AFS’ TPM clients can now easily access Nielsen’s Revenue Management and Optimization (RMO) Analytics to make information-rich, analytic-driven decisions throughout the selling process to improve the effectiveness of their brands’ everyday pricing and trade promotions.
Extending predictive analytics with optimized insights and delivering on a coordinated strategy (from headquarter guidelines to retailer-specific joint business plans) will significantly increase spending efficiency, drive incremental sales and maximize financial impact for a brand and its retail customers.