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

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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.

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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.

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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.

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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

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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

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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

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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

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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 WPAS-Promax@wipro.com

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

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