SAP Trade Promotion Optimization Experience and learning’s
SAP Trade Promotion Optimization (TPO)
Recently I was involved in SAP TPO Proof of Concept (PoC) for a TOP FMCG company in US region. This project I believe may be one of its kinds for exploring SAP TPO capabilities and predict accurate volume and lift which involved Modern Trade POS data. We received last 3 years POS data with Account planning, promotion and sales data. I wanted to share learnings & highlighting few features of SAP TPO.
Background: Research trends indicate trade promotion related spends accounts for 8-12 % of overall turnover of a CPG company and up to 60% of CPG marketing budgets for stimulating channel demand. While trade promotion spending as a percentage of marketing budgets has increased dramatically, the inefficiency of trade promotion represents the “number-one concern” among manufacturers. Yet, there is little visibility into where this spending actually goes, or how effectively it increases revenues, expands market share, or creates brand awareness among consumers. With millions of dollars being spent to stimulate demand a marginal improvement in the fund allocation and recalibration of promotion processes could have a disproportionate impact on sales uplift and promotion ROI. SAP TPO uses advanced analytical constructs like optimization, predictive analytics; What-if analysis can provide significant visibility into the effectiveness of this trade promotion spends. The information attained can provide insights in terms of sales uplift contributions and can help in optimizing the same in the face of many real world constraints during the fund allocation process.
What is Trade Promotion Optimization?
TPO assists CPG manufacturer strategically to optimize the trade spending across their total product portfolio. Trade Promotion Optimization is an approach that uses business rules, constraints, and goals to mathematically create a trade calendar that can meet all of these requirements. Optimization is helpful for strategic questions, such as “what combination of promotional events (feature price, frequency, timing & depth of deal allowances) will meet or beat my revenue and/or profit goals and still stay within my trade promotion budget?” Right TPO models can also solve for ratio mix of revenue, volume and/or profitability, as well as profit contribution for both the manufacturer and retailer. SAP TPO enables trade marketing and sales teams to leverage advanced predictive modeling to suggest optimal price and merchandising decisions based on goals and objectives, or to assess revenue, volume and profitability.
SAP TPO: It’s a SAPCRM Add-on, which comprises a forecasting and modeling engine. The TPO science is dependent on DMF. SAP TPO enables users to understand the demand baseline (Sell out baseline) prediction. SAP TPO predicts the regular volume, revenue, profit margin etc. for manufacturer and planning account for agreed duration.
SAP CRM: Supports all processes involving direct customer contact throughout the entire customer relationship life cycle- from market segmentation, sales lead generation and opportunities to post sales and customer service. Includes business scenarios such as account and trade promotion management.
SAP DSiM: Demand data is loaded into DSiM system which harmonizes the data as per the original master data system (ERP). SAP delivers few methods to harmonize the Syndicated (Market Research) / POS / external data.
SAP BW: Receives harmonized data from DSiM and send it to DMF system for demand modelling.
SAP DMF: Demand Management Foundation provides predictive demand driven forecasts and optimization simulations for all promotion planning across channels and customer segments. In DMF you can do model and forecast for set of customers, channels and markets. By using demand data, DMF systems help to forecast and optimize the predictions as per the requirement. It’s a science engine, which transforms historical demand data into models for generating forecasting & optimization. SAP TPO uses ‘Bayesian ‘science techniques. A forecast run is created for each call of science system (DMF). The forecast run can be used to see the parameters and results of each prediction that adds to the what-if scenario.
Data: Historical data plays a major role in TPO. Prediction / forecasting results of SAP TPO depend on historical data. SAP TPO supports mainly POS, Syndicated (market research) or internal data that can be uploaded into DMF directly or through DSiM. DSiM harmonizes the data based on your primary data (product hierarchies in ERP).
Analytics: Historical sales and promotion data is used for building predictive models which is used for planning future promotions. Bayesian Hierarchical Modeling (BHM) techniques are used for building these models. BHM not only consider the individual product and markets behavior while modeling instead it also considers the learning from category or brand sales trend. The main advantage with BHM is that it provides better accuracy even with small data sets and the accuracy can be further improvised by correctly specifying settings for priors factors like price, promotional lift etc.,
Accurate promotional uplift could be derived by correctly specifying the demand patterns of promotional sales in different days of the week.
Predictive models not only captures the impact of factors like price, holiday, distribution and sales trend but it also provides a flexibility to capture the impact of dynamic demand behavior of the product by classifying them into various homogeneous groups based on their demand pattern.
SAP TPO has inbuilt analytics which is visible from CRM TPO screen.
User Interface / Integration options:
- TPO integrated in Trade promotion planning without additional assignment block
- TPO integration assignment block
- Promotion optimization can be created independently of any trade promotion (prediction & simulation are also available)
TPO Forecast types controls whether to predict, SAP TPO has 2 types of forecasts
What-if Analysis forecast types
- Prediction: It analyses past promotions performance for a given price and promotional vehicles (like displays, features, price reduction, and multi-buys) and predicts one outcome in line with trend.
- Simulation: Most of times, the challenge is not just getting results but getting them with in constraints, what can be a best option in such case. Simulation, in addition to price and promotional vehicles can also consider objectives like profit optimization & sales volume optimization and more importantly constraints like trade spending limits and forecast multiple optimal scenarios. The best suitable one can be chosen after analyzing all scenarios.
What-if Analysis results: SAP TPO presents forecasted results in intuitive graphical dashboards which makes it easy to view and compare different forecast outcomes in a single view. As of version TPO 2.0, it will depict forecasted results in 5 dashboards with a different perspective in each. On one dashboard the user has the option to change ‘trade spends’ and see impact instantly. More dashboards can be added through enhancements. These dashboards not only present data but also the insights. This can reduce the strain of going through various details on each forecast scenario to make a choice.
Dashboards: SAP TPO screen has got few dashboards like Basic analysis( provides with key figures like Volume uplift, non -Promo Revenue, Promo revenue, retailer revenue) , Volume decomposition (Provides volume uplift with respect to base demand, tactic lift, price lift, seasonality, holiday, cannibalization) , win-win assessment(Promo margin and promo profit). SAP TPO Agreement screen has got few dashboards like weekly review (base line & total volume) Price & Volume decomposition, Profit and loss.
Integration with SAP TPM: SAP TPO is tightly integrated with SAP TPM. Few additional assignment blocks, fields and buttons are provided. Assignment blocks like Promotion causals, What-if analysis, and optimization scenario etc.,
Learnings: Data quality is the most important and critical element of any forecast as it will influence the forecast results. It is essential to have complete and accurate data without gaps. When external data like syndicated or research data is used it is crucial to check if they are true or close representative of retailers being used in all required locations.
One important lesson learnt from experience is do not underestimate how much effort it will take to source, clean, format and load the data.
Within SAP TPO, each forecast has a forecast confidence indicator, which represents system model confidence in the forecast and is based on past data.
Suggest having an exercise called “KNOW BUSINESS INSIGHTS” which will generate business insights for any organizations. SAP DSiM on HANA can help you here.
Conclusion: SAP TPO can be implemented on its own as a standalone tool but implementing in conjunction with SAP TPM can realize the true potency of each other. SAP TPO can plan promotion strategy and TPM can execute it smoothly with its integration to other processes i.e. funds management & claims management.
SAP TPO requires consultants with DMF knowledge getting experienced people is a challenge. It would be really helpful to have statisticians to model and improve the models based on external factors.