Trade Promotion Forecasting
Trade promotion forecasting challenges
Trade Promotion spending is one of the consumer goods industry’s
largest expenses with costs for major manufacturers ranging from 10 percent to
20 percent of gross sales. Understandably, 67 percent of respondents to a
recent survey said they were concerned about the return on investment (ROI)
gained from such spending. Quantifying ROI depends heavily on the ability to
accurately identify the “baseline” demand (the demand that would exist without
the impact of the trade promotion) and the uplift.[1]
In fact,
forecast accuracy plays a critical role in the success of consumer goods
companies. Aberdeen Group research
found that best-in-class companies (with an average forecast accuracy of 72
percent) have an average promotion gross margin uplift of 28 percent, while
laggard companies (with an average forecasting accuracy of only 42 percent)
have a gross margin uplift of less than 7 percent.[2]
A
bottom-up sales forecast at the SKU-account/POS level requires taking into
account product attributes, historical sales levels and store specifics. A
complicating factor is that the large number of different variables which
describe the product, the store and the promotion attributes, both quantitative
and qualitative, could potentially have many different values. Selecting the
most important variables and incorporating them into a prediction model is a
challenging task.[3]
Despite
these challenges, two-thirds of companies in the consumer supply chain consider
forecast accuracy a high business priority. 74 percent said it would be helpful
to develop a bottom-up forecast based on stock-keeping
unit (SkU) by key customer.[4]
Traditional trade promotion forecasting methods
Many
companies forecast the impact of trade promotions primarily through a human
expert approach. Human experts are unable to take into account all the
variables involved and also cannot provide an analytic prediction of campaign
behavior and trends. A recent survey by Aberdeen Group showed that 78 percent
of companies used Microsoft Excel
spreadsheets as their primary trade promotion technology tool. The limitations
of relying upon spreadsheets for trade promotion planning and forecasting
include lack of visibility, ineffectiveness and difficulty in tracking
deductions.[5]
Specialized
applications have been developed and become more common. 35 percent of
companies now use legacy systems, 30 percent use Sales and Operations Planning (S&OP) applications, 26
percent use integrated Enterprise Resource Planning (ERP) modules and 17percent use
home grown trade promotion solutions. These applications support the planning
process, while still primarily relying on human knowledge and intuition for
forecasting. One problem with this approach is that humans tend to make
optimistic assumptions when forecasting and planning. The result is that
forecasts most commonly err on the optimistic side and that human forecasters
also tend to underestimate the amount of uncertainty in their forecasts.[6]
A further
issue is that the majority of manufacturers use legacy trade promotion systems
that contribute to internal fragmentation of trade marketing data. Many of
these companies are currently using assumption-based forecasts with limited
accuracy.[7]
Analytic approaches to trade promotion forecasting
TPF is
complicated by the fact that campaigns are described by both quantitative (such
as price and discount) and qualitative (such as display space and support by
sales representatives) variables. New approaches are being developed to address
this and other challenges. Most of these approaches attempt to incorporate
large amounts of heterogeneous data in the forecasting process. One researcher
validated the ability of multivariate regression models to forecast the impact on sales
of a product of many variables including price, discount, visual merchandizing,
etc.[8]
The term Big Data
describes the increasing volume and velocity of heterogeneous data that is
coming into the enterprise. The challenge is to combine this data across all of
the silos within the organization for a single view. The data can be used to
improve trade promotion forecast accuracy because it usually contains real
connections and causation that can help to better understand what customers are
buying, where they are buying it, why they are buying and how they are buying.[9]
Traditional
methods are insufficient to assimilate and process such a large volume of data.
Therefore more sophisticated modeling and algorithms have been developed to address
the problem. Some companies have begun using machine learning methods
to utilize the massive volumes of unstructured and structured data they already
hold to better understand these connections and causality.[10]
Machine
learning can make it possible to recognize the shared characteristics of
promotional events and identify their effect on normal sales. Learning machines
use universal approximations of nonlinear functions to model complex nonlinear
phenomena. Learning machines process sets of input and output data and develop
a model of their relationship. Based on this model, learning machines forecast
outputs associated with new sets of input data.[10]
Intelligible
Machine Learning (IML) is an implementation of Switching Neural Networks that
has been applied to TPF. Starting from a collection of promotional
characteristics, IML is able to identify and present in intelligible form
existing correlations between relevant attributes and uplift. This approach is
designed to automatically select the most suitable uplift model in order to
describe the future impact of a planned promotion. In addition, new promotions
are automatically classified using the previously trained model, thus providing
a simple way of studying different what-if scenarios.[11]
TPF
systems should be capable of correlating and analyzing vast amounts of raw data
in different formats such as corporate sales histories and online data from
social media. The analysis should be able to be performed very quickly so
planners can respond quickly to demand signals.[12]
Groupe Danone used
machine learning technology for trade promotion forecasting of a range of fresh
products characterized by dynamic demand and short shelf life. The project
increased forecast accuracy to 92 percent resulting in an improvement in
service levels to 98.6 percent, a 30 percent reduction in lost sales and a 30
percent reduction in product obselecense.[13
Source: Wikipedia.
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