BPO Thought Process

BPO Thought Process

Opinions expressed on this blog reflect the writer’s views and not the position of the Capgemini Group

The Role of Big Data in CPG to Capture Revenue Leakage


Most every global company that sells into the retail channel knows the challenges of deductions management and the consistent burden of averting revenue leakage. Despite ongoing dissatisfaction and viable alternatives, many Consumer Packaged Goods (CPG) companies continue to rely upon manual processes and desktop spreadsheets to track trade promotions, manage deductions and forecast sales planning and production.

Many people talk about the “velocity” aspect of Big Data and how it helps businesses to grow, sustain and manage working capital.  A number of CPG companies have jumped on the opportunity to use Big Data and analytics tools to accelerate the speed of their business. Now, they need to do the same – with Big Data – to capture revenue leakage.

The problem and the impact is sizeable – approximately 4 to 7 percent of revenues in some cases. I always ask myself, why would a CPG company concede to this type of revenue leakage?

How Big Data Supports the CPG Lifecycle

Being able to align execution with strategic intent is essential to CPG manufacturers and by having real-time visibility into order fulfillment, returns, VMI, trade fund management and invoicing and payment processes. Business intelligence (BI) and plays a significant factor in delivering the critical infrastructure needed to manage finance and accounting functions efficiently, while streamlining process costs and automating manual tasks. 

Amount of Data

A huge driver of success is the amount of historical data that is available for analysis.  If you have the right data and it is accurate, your model should do a very good job at describing how a particular promotion or activity impacted sales and profits.  But companies interested in optimizing trade spend and generating ‘what if’ scenarios that is reliable will need YEARS of history.  Two, three or four years is a prerequisite.  The phrase ‘the more the better’ really does apply to predictive modeling.  Those ‘what if scenarios’ will be more accurate and will truly have an impact on improving trade effectiveness and stretching the trade spend dollar.  But managing historical data has its own challenges.

Changing History

Look at your business over the last three or four years.  Has it stayed exactly the same?  Have you changed packaging, messaging or quantity size? Do you have more SKUs or less?  Is the sales team comprised of more people?  How about brand awareness and advertising spend? The better you are at capturing all these changes in your system, the stronger your results are going to be.  And don’t forget about shopper insights data.  Including external sources of information can really give you an edge over the competition.

Analytics (demand signal repository, portfolio optimization, marketing spend effectiveness, trade spend effectiveness, sales force effectiveness, cost to serve and operational reporting) are critical to the success of addressing revenue leakage.

I believe that by leveraging technology and improving processes, you can drive significant cost reductions while improving the time to visibility across the finance ecosystem.  Imagine being able to capture information from multiple parties and sources (customers, sales, distributors) with greater control. Imagine real time follow up with all disputes being adjudicated between 5-60 days versus 120 days when it’s simply too late.



About the author

Rob Sherman
Rob Sherman
As leading various solutions, delivery, and marketing initiatives over 18+ years, I have built relationships with clients, analyst, and advisors providing excellence within Finance and Accounting BPO. Automation and reduction of manual processes through innovative technology are my core ambitions for each of our client partners.

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