Making the “Big Leap” from TPM to “Best-in-Class” Trade Promotion OptimizationPromax Optimize
www.wipro.com/
Rob Kaplan, Director of Business Solutions
03 ............................................................................. Introduction
04 .............................................................................Analytics-driven Tools
06 .............................................................................Data Transformation
06 .............................................................................Data Driven Business Planners
07 .............................................................................Analytics-driven Business Processes
08 .............................................................................A note on Measuring Success
09 .............................................................................Conclusion
Table of Contents
3
Many CPG companies have invested heavily in trade promotion management
applications that have delivered transactional improvements, yet fail to
provide robust trade promotion analysis. In some cases, CPG companies
either do not have the right tools or data management capabilities to
make the transition from simply managing trade promotions, and hence
trade promotion investment, to optimizing trade promotions and trade
spend ROI.
To fill this gap, many companies buy their promotional insights from
external data vendors. The problem with this approach is, these “insights”
are not embedded in the planning layer of the enterprise, and hence lack
executional value, and predictive insights.
Some companies that have invested in a traditional “black box” approach
to trade promotion optimization also have not enjoyed the expected
value from their TPO solution. Although the “black box” solutions may
be deployed, they only have the capacity for rule-based, quantitative
Introduction
To manage the complex challenges faced by the Consumer Product industry today, companies need to achieve competitive differentiation by developing
data-driven value chains. Trade promotion optimization solutions have become a core component of that strategy. In order to successfully deploy effective
trade promotion optimization capabilities, CPG companies must be able to employ the right tools, data management competencies, personnel management
practices and business processes to achieve “best-in-class” results.
analysis and do not provide for end-user qualitative knowledge
inputs as well as active data pattern and outlier reviews by skilled data
scientists. The servers from “black box” deployments will eventually end up
in the same discarded technology closet as palm pilots and pagers.
To successfully make the leap from basic trade promotion management
to advanced trade promotion optimization, CPG companies must take a
comprehensive approach to changing their current “transaction-based”
business practices. CPG companies must develop competencies in the
following 4 areas: Analytics driven tools, Data transformation, Data driven business planners and Analytics driven processes. Excellence in these 4 areas will provide the framework for successful
transformation through trade promotion optimization. The business
benefits will multiply as predictive trade promotion planning becomes
a dynamic collaborative effort across sales, finance, marketing and
demand planning.
4
Not ensuring excellence across these 4 areas will further put organizations
behind in the coming years as trade promotion strategies continue to
evolve from a “retailer-focused” promotion approach to a targeted
“shopper-focused” promotion approach. These coming changes will require
more data, more complex technology solutions, greater process interaction,
and higher demand for business planners comfortable in the use of
data analytics.
Analytics Driven Tools
Solution Capabilities: Trade Promotion Optimization must be able to support a seamless
connection between trade promotion management processes, and trade
promotion optimization. For companies that have disconnected tools
where trade promotion management is performed in an enterprise system,
and the trade promotion optimization portion is completed “elsewhere,”
they may be under the illusion that they are just one spreadsheet away
from business insights nirvana, but the reality is disconnected tools usually
make it challenging to execute the process effectively. The tool set for TPM
/ TPO should also provide a “fit-to-purpose” end-user experience where
sales people have visibility to their plans, and all the promotional tactical and
pricing inputs that drive the predicted outcomes. Sales people and planners
do not need visibility to the underlying analytics layer where algorithms
and other quantitative solution components are exposed. Minimizing the
exposure to the complex quantitative objects to just those users who
need to access the “engine” (data scientists, analysts, etc.) is an important
end-user adoption consideration.
The outputs from trade promotion optimization should provide insights
into the following areas:
1. Volume Decomposition Analysis: Utilizes historical sales
patterns to derive “base” sales volume (sales that would have been
derived in the absence of promotional activity) from “incremental” sales
volume (sales that are directly attributable to promotional activity).
This analysis should account for other causal factors such as seasonality,
competitive activity, economic factors etc. This analysis should create
the required correlation coefficients to understand how pricing, display,
advertising and other promotional inputs will affect sales results.
2. Pre and Post Promotion Effect Analysis: Which
accounts for the impact of promotional activity on baseline sales in
the time windows before, and after a promotion is executed. Many
Consumer Goods companies have observed a consistent drop in
baseline sales during the time period(s) before a promotion. In this
instance, it is quite possible that retailers and manufacturers have
trained the shopper to postpone purchases until a product goes on
sale based on a predictable pattern of previous sales activity. Another
common promotion-related phenomenon is the “pantry load” effect,
where deep discounting during a promotional window may generate
large sales volume during the promotion, but since consumers have
“stock piled” the promoted product during the promotion, they are
not in a typical “re-buy” pattern in the time periods after the promotion
runs. This phenomenon is especially common with non-perishable
The ChallengeMost CFOs in the CPG industry have the tools and processes
to track how much their business spends in trade, as well as, the
total amount of trade spend by customer. The question that most
CFOs cannot answer is, “what is the return on my trade spend
investment?”
Why Measuring Trade Spend is so important?Annual trade spend in the industry has consistently averaged around
15% to 25% of total revenue. Trade spend is typically the largest
item on a typical CPG company P&L after cost of goods sold. So
the size of the prize remains large. Even modest improvement in
trade spend efficiency can yield substantial value. Effective trade
promotion planning and execution has a direct positive impact on:
Supply Planning
As accurate, predictive demand signals inform production decisions
and inventory levels.
Cash Management
As effective and accurate planning helps the business predict and
manage trade spend accruals.
Profitability
As companies allocate resources to those promotions that provide
the best return on trade investment, remember what gets
measured typically gets improved.
Another major driver for trade promotion optimization capability
is competitive positioning. To maintain competitive standing, CPG
companies must be able to collaborate with their trade partners,
and define mutually agreed and sustainable success metrics for
promotional activity. This practice will help ensure retailer (or
e-tailer) compliance and change the perception of the CPG
Company from product vendor to solution partner status.
5
products sold in larger pack sizes. Another measurable example of
this phenomenon occurred when the US government tried to revive
the US auto industry through the “cash for clunkers” program, where
government subsidies were offered to stimulate sales of used cars in an
effort to create incremental demand for new cars. Used car sales did rise
during the program; however, once the subsidies ended, used car sales
fell well below the historical average sales rate. If one averages the sales
during the “cash for clunkers” program and afterwards, the aggregate
demand for used cars was actually constant. These “lost” sales should
be factored into the effectiveness measures of a promotion.
3. Cannibalization Effect: This is where the impact of
promoting a product may “steal” sales from other products. Consumer
Goods companies are especially interested in this analysis when they
have multiple segments in the same market. A few years ago, a major
manufacturer of laundry detergent released a “basic,” less expensive
version of the same premium labeled brand of laundry detergent.
By measuring the cannibalization impact of the “basic” sales vs. the
“premium” label, they were able to determine if the “basic” brand
generated enough incremental unit sales to compensate for the sales
lost by the premium label.
Being able to conduct this type of analysis will provide incentives for
retailers to share total category sales data. In this case, CPG manufacturers
can use trade promotion optimization to measure (cross-manufacturer,
or competitive cannibalization). This measure is very useful from a retailer
perspective to see if promotional activity is actually growing a category,
and generating truly profitable incremental sales, or if the promotion is
just incentivizing brand switching behavior.
Trade Promotion Optimization analysis must also be able to detect
“halo” effect, where the sales of one product actually increase the sales
of another complementary product. Manufacturers of hot dogs can
effectively cross promote hot dog buns. For planning purposes, effective
TPO analysis can detect, predict and quantify the correlated sales when
“halo effect” occurs.
4. Price Elasticity Analysis: Supports the ability to identify the
effect of price on demand, and thus optimize price. A truly robust
solution must be able to detect counter-intuitive patterns. For example,
a premium ice cream manufacturer discovered that by increasing price,
they were able to increase sales volume.
6
Data Transformation
The term “big data” has developed numerous meanings, of late. In the
context of trade promotion optimization, the phrase “big data” should be
seen as a management competency. In order to effectively conduct trade
promotion optimization, CPG companies must have the ability to consume
data in both structured and unstructured formats from various sources,
transform the data into a useable format, and effectively model the data in
a consolidated repository to meet business user requirements. Advances in
data processing capacity, the proliferation of data services vendors, and the
decision of most retailers to openly share their data to partner with vendors
on various analytics initiatives has made the acquisition and management of
data a “mission critical” competency to develop for the IT departments at
most CPG companies.
However, as companies develop greater competency managing data
through various tools and processes, they are still struggling to make that vital
transformation to convert large volumes of data into useful information. This
is especially true for trade promotion optimization initiatives where there is
a lack of domain-specific knowledge being applied to the transformation and
shaping of the raw data feed. For successful trade promotion optimization,
it is vitally important to incorporate a feedback loop to refine “history.”
Most trade promotion optimization tools will systematically throw out
one time outlier events (extreme weather, business closure, etc.) to avoid
skewing the predictive value in the data set. However, in order to maximize
value, the historical data set should be further scrubbed by knowledgeable
resources who can apply qualitative knowledge inputs into the data stream.
For example, if a sales person is able to identify the reason that a major
promotion did not meet expectations was because of supply problems,
retailer compliance or competitive activity, these useful data points should
be flagged, and incorporated into the analytical outputs of the process. In
addition to enhancing the predictive value of the data stream, this practice
provides the useful benefit of helping the user of the data feel more invested
into the analytical insights provided by the process.
Another important consideration for trade promotion optimization
data management is scalability. Even small to mid-size Consumer Goods
companies can generate many terabytes of enterprise master, consumer,
shopper and promotional data. The solution must be able to effectively
store, model and manage increasing large volumes of data. The best
practice is to always conduct a sizing exercise prior to deployment to
ensure that processing and storage capacities will not be overwhelmed by
the anticipated transactional data volumes.
Data-Driven Business Planners
For a number of reasons, the first generation of trade promotion management
tools suffered from challenges with end user adoption. Some of the lessons
from this initial experience are very applicable to the successful adoption of
“next generation” trade promotion optimization capabilities.
As anyone who has worked on a trade promotion management
implementation project will understand, TPM can become very complex
as the required functionality, and business processes sit at an intersection
between the functional areas of Finance, Sales, Trade Marketing, Brand
Marketing and Supply Chain Management. Often, the effort to meet the
needs of one stakeholder group is actually in direct competition with the
needs of another stakeholder group. For example, the finance group may
require a rigorous “promotion approval process control” at the expense of
the desired process flexibility from sales planners.
Many trade promotion management deployments failed to gain the desired
adoption because the “true” needs of each stakeholder were not taken
into account in the solution design. The first generation of TPM tools
would connect the promotion system of record with back office financial
transaction systems, which met the needs of the finance department to
become more efficient at trade settlement, and closing financial transactions.
However, these transactional tools did not provide any useful account
7
planning capabilities that a sales person, or trade marketing person, could
utilize to make better business decisions. Hence, sales people had absolutely
no incentive to change the promotion input process from spreadsheets to
the new “connected” enterprise tool. The lesson from this experience is
that to truly leverage new tools in the trade promotion management /
trade promotion optimization space, there must be a compelling (W.I.F.M.
– “What is in it for me”) proposition for all stakeholders. The “WIFM” allows
planners to become “data-driven” and invested in adopting new tools
and processes.
An effective trade promotion optimization solution design must be able to
provide this key business for each stakeholder group. For example, sales
people need visibility to the most effective pricing and promotion tactics
with the ability to shape, and predict demand during the planning process.
Finance Managers desire enhanced accuracy in the sales forecast (especially
during promotion cycles), which leads to better cash management decisions,
as well as the ability to truly measure and predict ROI on trade spend.
Brand Marketers desire greater insight into brand level performance, and
shopper responses to pricing and product positioning strategies. Supply
Chain Managers value enhanced accuracy in sales forecasts which lead to
higher customer fulfillment rates, and reduces the amount of inventory
needed as “safety” stock.
In addition to these business benefits, senior executives will be empowered
to make more informed strategic decisions armed with the insights gained
through effective trade promotion optimization. All stakeholders should
feel invested in process and understand the value. The days of competing
interests across functional departments to create solution designs that
do not meet the needs of all stakeholders must end for successful trade
promotion optimization deployment.
The key point is that every stakeholder involved in developing inputs to the
trade promotion optimization process has a clear, focused, and relevant
“WIFM” as an output. This is the only way to ensure end-user adoption
in the transformation from transactional trade promotion management to
trade promotion optimization.
Analytics-driven Business Processes
Effective trade promotion optimization capabilities can dramatically improve
the Sales & Operations Planning processes at CPG companies. With trade
promotion optimization capabilities, planners actually can shape and predict
promotional demand vs. simply react to demand when system capabilities
are only capable of transaction management. This new capability can allow
organizations visibility across functional departments and drive alignment
around the consensus demand plan. There are some important process
considerations that need to be taken when planning orientations switch
from reactive transactional to proactive and predictive.
1. Data Modeling, Review and ApprovalThis set of processes will be completely new when companies initially
adopt predictive planning tools. It requires the addition of data science
resources to create and continually modify the models used for volume
decomposition and predictive planning. Trying to fully automate the data
scientist function leads to highly variable models without a feedback loop to
modify and course correct forecasts when needed. This skill set will very
likely be new to the sales organization and can be always be provided as
a service.
The new data scientist resources or data science services will create the
initial data models, and it will be incumbent on the sales teams to validate
the qualitative inputs into the models. This review is necessary as the
models are the first step in the creation of account plans. This process will
culminate in the approval of the model to use in the planning process. The
modeling review will be a continuous learning process that should involve
coordinated automation, and workflow between the various stakeholder
groups.
2. Predictive PlanningA systematically driven business plan should be used to “seed” the business
planning process. After the “first pass” is created, the account owners
can make necessary refinements to the plans leveraging their qualitative
knowledge and desired strategy. As they move closer (and into) the plan
year, further adjustments are made as more “current” information becomes
available. In this scenario, the account owners are responsible for choosing
which promotions and tactics they move forward with. Optimizing trade
promotions is not a once a year activity, or even once a month; it is
continuous and constant. Providing automated predictive trade planning
8
capabilities allow the account owners to maximize their effectiveness by
reducing administrative “data collection” time, yet still maintain the overall
responsibility for effectively executing against the agreed business plan.
3. Collaborative Planning Process Impacts from Trade Promotion Optimization InsightsAs planners obtain greater proficiency in identifying the promotion tactics
and pricing strategies that provide the greatest return from a manufacturer’s
perspective, it will also be important to incorporate trade partner KPIs into
the planning and analysis cycles. Promotion strategies that only create value
for the manufacturer and not for the retailers are usually not sustainable.
Hence, the planning process needs to incorporate retailer KPIs. Trade
promotion optimization initiatives that create value for retailers reinforce
the benefits of data sharing and collaborative planning.
For example, when retailers provide category data, trade promotion
optimization analysis should be able to determine what degree promotional
activity will “grow the category” and yield incremental sales vs. incent brand
switching behavior where the total category sales are constant. Retailers
do not have an interest in promotions that do not generate incremental
category sales. The net result of incorporating retailer KPIs into the
business plans will be an enhanced, “value-driven” relationship between
manufacturers and retailers.
4. Are brand level strategies incorporated into the business plan?It is very important for brand managers and sales managers to harmonize
their planning processes as the visibility provided by trade promotion
optimization outputs could lead to sales promotions that undermine
product positioning strategies. For example, sales planners may determine
the optimal promotional price for a product to maximize unit sales during
a sales cycle. Sales planners also discovered that since this was a perishable
product, demand stayed fairly constant after frequent promotion cycles (no
measurable pantry loading effect). However, brand managers engaged in
a consumer media campaign to position this same product as a “premium
brand” that should be priced higher than competitive offerings. The net
result could lead to an erosion of brand equity when a premium brand
is over-promoted. Therefore, trade planning processes should account
for brand strategy when determining the optimal promotion plans, and
there should be organizational acceptance that there are considerations
beyond the use of predictive analytics when developing an optimal
go-to-market strategy.
5. Do sales people have the right incentives to help maximize forecast accuracy?Once CPG companies deploy trade promotion optimization capabilities,
quite often there is a desire to hold sales people more accountable to their
projections. After all, now that the business has provided them access to a
sophisticated modeling tool, their forecasts should become more accurate.
In theory, this is a logical assumption. However, quite often organizations
fail to see all the potential conflicting process incentives that impact how
effectively stakeholders will make use of analytical tools in their predictions.
For example, in a supply constrained environment, inventory is quite often
allocated based on sales forecasts. Sales people can become conditioned to
“adding buffer” to their sales projections to try to direct as much product as
possible to the accounts they call on.
Process incentives can also be in place for sales planners to provide lower
than expected sales forecasts as well. Every sales forecasting process is
subject to the desire to manage expectations by under-promising and
over-delivering as well. Senior managers should discourage this practice
as it could create a disruptive and counterproductive pattern that would
compromise the value of advanced analytical insights. In fact, senior managers
should make sure to reward employees who accept change and adapt to
new analytical tools and processes. Sales people should always have the
ability to manually adjust systematically generated forecasts. However, it is
also important to maintain visibility to the systematically generated forecasts
to continually measure system accuracy and adaptively learn from historical
sales patterns.
A Note on Measuring Trade Promotion Optimization Success:
Trade promotion optimization initiatives are usually accompanied by new
expectations that the company can immediately quantify the value creation
from trade promotion optimization. This is a very reasonable expectation,
but a foundation must be put in place before effective measurements can
be offered. The most common method for measuring the benefits from a
trade promotion management initiative would be the ability to show an
improvement in overall ROI on trade spends. However, in reality it may
take a few planning cycles before this success metric could be realized.
In the absence of a trade promotion optimization solution, CPG companies
would not have the ability to take a benchmark measure to compare
performance against.
The first success measurement should be a simple answer to the question,
“do you now have the required visibility and ability to measure ROI on
9
Trade Promotion Optimization solutions should provide substantial value
in this situation by being able to direct planners to the right pricing and
promotional tactics to maximize unit sales vs. focusing on maximizing trade
spend ROI. In summary, success should be measured by capabilities as well
as key performance indicators.
Conclusion
“Future-proof” your Capabilities
In today’s environment of the connected, “digitally enabled shopper,”
CPG companies and retailers need to know how to translate the rich
downstream data into actionable demand signals. Next generation cell
phone applications exist to inform shoppers where to get the best prices for
products that they can now scan with their digital devices. CPG companies
and their retail trade partners should strive to partner and advance trade
promotion optimization initiatives to leverage these shopper level demand
signals. Analysis of customer loyalty data can provide insights into the
buying patterns and preferences of target shoppers. Additional data analysis
could give marketers and retailers insights into which product positioning
messages and value propositions, would be most effective with target
shoppers. Retail promotions with a focused affinity marketing approach will
typically provide significantly better results than the traditional broad-based
“temporary price reductions.”
In the rapidly changing consumer goods industry, there is an emerging
consensus that the ability to transform data into useful insights is the
“mission” critical requirement. Effective trade promotion optimization
tools enable the processes and the business competencies reviewed in this
whitepaper that are required for success.
trade spends?” It is also possible that some of the challenges to effective
trade promotion execution (i.e., supply problems, competitor actions, etc.)
may inhibit trade spend ROI results regardless of the effectiveness of the
optimization solution and processes in place.
There will also be times when a conscious business decision may actually
compromise the trade spend ROI measure. For example, if a CPG company
decides to deeply discount large inventories of perishable products that
will spoil if not sold in a timely manner, the deep discount will negatively
impact the promotion ROI measure. However, in the end, the lower ROI
through promotional discount is better for the company’s finances than a
large write-off due to product spoilage.
Gain the visibility needed to answer these critical questions:
What is my real return on my trade promotion investments?
What are the most effective promotion tactics and pricing strategies?
How much does cannibalization and pantry loading effect impact my promotional outcomes?
Are my promotions growing the category for my trade partners?
To what degree are my promotions causing brand switching behavior?
The AnswerPromax Optimize from Wipro Promax Analytics Solutions
The Promax Optimize solution combines industry-leading enterprise planning and analysis
software with the insights of skilled, CPG domain expert data scientists provided as a service.
Our product stack and services offering is designed to provide a comprehensive approach to
addressing all the change management obstacles (Tools, Data, People, and Process)
to deploying an effective trade promotion optimization solution.
PROMAXOPTIMIZE
10
About the Author
Rob Kaplan is Wipro Promax’s Director of Business Solutions in The Americas. He provides both CPG industry thought leadership and TPM / TPO product
domain experience. He has helped manage numerous TPM / TPO initiatives for many of the global leaders in the CPG industry.
About Wipro Promax Analytics Solutions
Wipro Promax Analytics Solutions (WPAS), a Wipro Group Company, is a world-leading specialist in delivering a combination of Trade Promotion Management
and Optimization solutions and services designed to ensure clients achieve the most efficient and effective return on their promotion investment. With
headquarter operations based in Australia and offices in North America, United Kingdom, Central Europe, India and New Zealand, WPAS boasts an impressive
stable of global consumer goods companies. Promax solutions are the result of more than 25 years experience working in close collaboration with leading
consumer goods manufacturers, retailers and distributors.
For more information, please visit www.wipro.com/promax or email us at [email protected]
About Wipro Ltd.
Wipro Ltd. (NYSE:WIT) is a leading information technology, consulting and business process services company that delivers solutions to enable its clients do
business better. Wipro delivers winning business outcomes through its deep industry experience and a 360 degree view of “Business through Technology.” By
combining digital strategy, customer centric design, advanced analytics and product engineering approach, Wipro helps its clients create successful and adaptive
businesses. A company recognized globally for its comprehensive portfolio of services, strong commitment to sustainability and good corporate citizenship,
Wipro has a dedicated workforce of over 160,000, serving clients in 175+ cities across 6 continents.
For more information, please visit www.wipro.com
DO BUSINESS BETTERCONSULTING | SYSTEM INTEGRATION | BUSINESS PROCESS SERVICES
Wipro Limited, Doddakannelli, Sarjapur Road, Bangalore - 560 035, India. Tel : +91 (80) 2844 0011, Fax : +91 (80) 2844 0256
Wipro Promax Analytics Solutions, 1/7 Sky Close, Shearwater Business Park, Taylors Beach Nsw 2316, Australia. Tel: +61 (2) 4982 2262, Fax: +61 (2) 4982 2286.
Wipro Promax Analytics Solutions, 3575 Piedmont Road Ne, Bldg 15 Suite 600, Atlanta Ga 30305, Usa. Tel: +1 (404) 495 3215, Fax: +1 (404) 949 0869.
Wipro Promax Analytics Solutions, West Wing, Level 2, 3 Sheldon Square, London W2 6ps, Uk. Tel: +44 (207) 432 8500, Fax: +44(207) 286 5703.
IND/PMCS/WIPRO/FEB 2016 - APR 2016