Sensitivity Analysis Framework for Improving Profitability and Valuation

Some of the ideas we have been promoting in the last few weeks – appear to have caused some buzz within the blogosphere- more specifically, our sustainable valuation formula, definitely has caught the attention of few readers (http://strategywithapurpose.blogspot.com/2010/11/purpose-profit-balanced-sustainable.html). One of our fellow readers made a casual observation - “our simplified valuation formula [i.e. V= NOPAT x (1- g/ROIC)/ (WCCP-g)] does not seem to take in to account the impact of all key value drivers (e.g. pricing, margins, volume, COGS etc) –and, hence it trivializes the whole valuation exercise”.

While we agree that it is a simple formula, it is an accurate one from financial algorithm standpoint. The reason we took McKinsey’s “Zen of Corporate Finance” as the base in our article last week was -to build a simple yet compelling case for discounting the free cash flow with a purpose value driver (WACP) very much like how WACC is used to discount the free cash flow. At the same time, we definitely empathize with our readers –who, otherwise perform the detailed valuation exercise with multiple spreadsheet pages of DCF cash flow projections and value driver assumptions. We still recommend our readers to go through the similar DCF process and validate the result with our summarized formula. In other words, our formula is more of a validation formula than a working formula.

The question however is – does that mean our sustainable valuation formula do not take in to account the impact of those granular value drivers? Before answering that question, let us first dissect ROIC within the valuation formula

ROIC = NOPAT/Sales x Sales/Capital => Operating Margin x Operating Velocity

As we further inspect the Operating margin and its sub components– some of the key value drivers that contribute towards the operating margin are – Price, COGS and Volume - which are further dependent upon promotion scenarios (including their depth and frequency), competitor responses, elasticity and boundary conditions to name a few. We sure can expand our formula mathematically to explain how these value drivers are indirectly accounted for within our valuation formula. However, it does not communicate the causal chain relationship effectively, and so we thought that it is a worthwhile exercise to come up with a sensitivity analysis framework to show the impact of these value drivers on margin using a real world case study – clearly showing the impact of price changes on volume (& hence on margin and the overall NOPAT/enterprise value).

Our goal for this case study is to develop a flexible framework model from the methodology we had used for one of our client CPGR-Co Inc. (our fictitious CPG/Retail Corporation pronounced as ZipGyarCo) who had asked us to help them to increase the profit by 10% (& hence enterprise value) by increasing (or decreasing) price by a certain percentage, yet without substantially impacting the volume.

The Working Framework Model

As part of that exercise, we decided to develop a working framework model (and a mathematical formula) of linking all the input and output variables that contribute towards increasing the operating margin, profit and valuation as outlined in the schematic at the top of the page. With all things being equal (consumer purchase criteria, brand perceptions, trade promotion rates and efficiency etc), we quickly realized that there still exists few cost differences (e.g. supply chain/distribution costs) across the market regions and hence we decided to develop a region specific pricing model (as opposed to one mammoth corporate model) to better simulate the real world business conditions. Within that approach, we then divided the CPGR-Co’s markets in to 5 RMA’s (East, West, Central, South and North) selling all of their top 5 Product models or package sizes (P1, P2, P3, P4, P5) – resulting in 25 model cells (i.e. one model each for each cell). Please note that the more granular we divide the markets the more accurate our models will be.

The objective of our case study was to develop an analytics based pricing model with a regression based mathematical formula producing the following projected results.

• Recommendation to price up or down in each cell.
• Projected volume, profit, share and valuation outcomes from price changes.
• Optimization recommendations for competitive response scenarios.

To achieve these objectives - we went through a systematic problem solving process with the following steps.

1. Calculate VCOGS matrix – at the product model level for each RMA.
2. Arrive at the optimum adjusted trade price (from retail price) to better negotiate with retailers/vendors.
3. Load the Product/RMA matrix with historical volume and price data from IRI/Nielson.
4. Develop the analytics based CORE Pricing model with all the inputs, outputs and their interdependent relationship.
5. Develop the equivalent log-log form mathematical equation simulating the core model- more specifically, develop a formula to arrive at the unit volume – which is a function of
   • Current price (e.g., P1)
   • Previous period’s price (e.g., P1_PriorPeriod)
   • Price of our other pack and/or model sizes (e.g., P2, P3)
   • Price of our competitors (e.g., CPCp1, CPcp2,C Pcp3, CPcp4)
   • Brand Equity impact
   • Other ancillary variables (e.g., Holiday, Trend etc)
   

The corresponding equation format is

New Volume = e0 + b1*ln(PP1) + e2*ln(PP1_Prior_Period) + e3*ln(PP2) + b4*ln(PP3) + …
e5*ln(CPCP1) + e6*ln(CPCP2) + e7*ln(CPCP3) + e8*ln(CPCP4)…
e9*Brand Equity + 110*Holiday …

Where

• e1 - own price elasticity (negative) - “If we raise P1 price by 1%, P1 volume will change by e1%”.
• e2 - lagged price impact (positive) - “If we raise P1 price by 1% this period, next period’s P1 volume will change by e2%.
• e3 and e4 - own model size cannibalistic -elasticities (positive) - If we raise P2 price by 1%, P1 volume will change by e3%.
• e5-e8 - competitive elasticities (positive) - “If competitor raises CPcp1 price by 1%, P1 volume will change by e5%.
• e9 - Brand Equity impact (could be negative depending upon the depth and frequency of the promotion and where the firm is on the innovation maturity curve) - Every additional period we move forward in time, we expect P1 volume to change by e9 * 100%.
• e10 Holiday impact (positive) - For holiday periods, we expect P1 volume to be e10*100% higher than for non-holiday periods”.

6. Calculate the price elasticities for three scenarios (SELF, SELF CANNIBALISTIC and COMPETITIVE) based on IRI/Nielson data.

7. Adjust the results based on competitor responses.

8. Adjust the results based on boundary condition analysis.

9. Use economic model to further optimize the results across various regions/accounts.

10. Finalize the projected volume, profit, share and valuation changes from price changes.

Value Driver Strategy Execution Considerations

As we can recognize from the steps above, we can easily extend this working model and mathematical formula for other value drivers like COGS containment, inventory cycle reduction, idle capacity containment (& few more) as well for arriving at a similar set of recommendation/results for improving profitability and valuation. However, one of the big challenges in implementing those recommendations within large organizations like “CPGR-Co” is building the consensus across various stake holders – from the standpoint of - first identifying the right set of appropriate value drivers, developing the right framework, arriving at the the action plan and finally implementing them.

For example, although there is a substantial opportunity to achieve profit/valuation goals by making the pricing changes as identified by our analysis - executing those price changes in a timely manner, is not an easy thing to do- given the fact pricing is embedded in a wide range of broader business decisions like strategy development (e.g., capacity utilization, new product pricing), price optimization (e.g., average price targets) ,trade strategy and sales execution (e.g., EDLP vs. HiLow) scenarios. In addition, the optimal pricing decisions require the input and expertise of a wide range of domain “experts” from both business units and front line sales strategy teams.

To circumvent these execution challenges, we made a recommendation to our client “CPGR-Co” to form few cross functional Value Driver Councils (VDC’s) to coordinate and proactively shape these value driver decisions (e.g. pricing decisions) across accounts, channels and brands. More specifically, within the context of our value driver council for pricing, the VDC will be chartered with the following

• Define pricing strategies/moves across the portfolio.
• Identify other value drivers that compliment pricing.
• Communicate approved pricing strategies to the field.
• Ensure region/account-level trade policies fit with strategy.

In addition, create a forum or platform for regular review of pricing trends, and other value driver needs by analyzing

• Market trends, competitor behavior
• Performance of new products
• Performance of competitor products

Finally, also act as the steering committee or governance layer for long-term pricing improvement

• Identifying “next level” pricing challenges
• Pricing pilots/tests—spreading best practices
• Upgrading analytics foundation (tools, processes, techniques, policies and organization models)

“Winning by Analytics” Considerations

As we can clearly recognize from the framework model steps and VDC’s governance layer requirements, Analytics is critical for successfully executing the tasks in every step of the way. We cannot stress the importance of analytics enough - that we encourage organizations like CPGR-CO to develop a solid analytics foundation with a right set of tools, techniques and templates to reach the winning level of "Level 5 analytics maturity" as promoted by Davenport & Harris and as explained in some of our articles published by Academy of Business Strategy (http://theacademyofbusinessstrategy-businessanalytics.com/). Within the context of that winning spirit, we specifically encourage our client CPGR-Co to develop the following set of tools.

• RMA/Product Matrix level optimization tool, techniques and templates to develop tailored pricing decisions across pack and model sizes and RMAs.
• IRI /Nielson price elasticity tool, technique and templates to estimate profit and volume impact of these decisions across competitive scenarios.
• Average price to trade calendar tool, techniques and templates to estimate impact of trade calendar scenarios on average price/unit.
• Category management tool to develop compelling cases to support field execution of pricing decisions.
• Forecasting and sales management tool to provide input to financial management system and to track progress against pricing targets in the form of “what if analysis based” drill down dashboards.

Bottom Line:

At the end of the day – developing winning strategies is definitely a great thing to do – but, institutionalizing them is all the more important - as strategy in its "execution form" is what brings the tangible outcome to stakeholders. Let us face it - fully empowered price strategy decisions (or for that matter, any other value driver strategy decisions) requires extensive consensus building in most organizations- and this is where- our Value Driver Councils (VDC) come in to the picture. So, it is a call to action for our client “CPGR-Co” and other similar clients-not only to develop winning strategies to improve profitability (and valuation), but also, institutionalize them using VDC’s.