Baselines: The Foundation for Analysis and Planning

When we sit down with prospective clients, we learn about their frustrations that led them to seek out a solution. These CPG manufacturers want better post-event analysis, need to quantify promotional ROI, and find a more controlled and profitable planning processes. However, no frustration resonates with CPG sales and marketing professionals as much as having confidence in their baselines.

The problem: Most CPG companies rely on syndicated baselines to measure promotional performance.

Baselines are often inaccurate because:

  • In many cases, syndicated providers baselines go up dramatically during a promotion – projecting what a loyal consumer would have purchased at full retail. This is a great measure to depict pantry load by a loyal consumer, but an extremely inaccurate way to measure the true incremental volume and profit generated by the promotion. In this instance, most promotions would generate no incremental profit due to the increased baseline.
  • Syndicated data can be contaminated by various data anomalies that impact the accuracy of a baseline for an inordinate amount of time before being detected by the syndicated supplier or the CPG manufacturer.

The definition of a baseline is what the consumer would purchase in the absence of a promotion.


To maintain accurate baselines, it’s essential to have a monitoring system that can identify data anomalies that contaminate baseline integrity. Contaminated baselines result in inaccurate post-promotion analysis and future planning. As a result, many trade marketing and sales planning departments approach post-event analysis with skepticism. Most have resorted to planning under the assumption that these baselines are inaccurate and therefore their forecasted plans are incorrect. The cycle of unreliable and ineffective trade promotion tactics goes on.

Why Do We Continue to Rely on Baselines that We Don’t Trust?

With greater scrutiny on organizational spending and demand for data-driven competitive strategy, accepting a “close enough” understanding of trade promotion performance is a considerable risk to the organization and places you at a significant competitive disadvantage.

Today’s CPG leaders are no longer shrugging their shoulders when it comes to quantifying their base business. Instead, they’re taking control of their data by automating the harmonization of consumption, spending and shipment data to build their baselines as an accurate reflection of in-store activity.

With quantified baselines, companies are not just prioritizing accuracy, but also intelligence. Using a comprehensive trade promotion optimization solution directly impacts your business in 3 ways:


1) Quantify ROI and KPIs


When you know where you started, calculating how far you have come is much easier. With a baseline built with a holistic picture of your business, calculating the incremental volume, revenue, profit, and ROI of promotional activity is both automated and repeatable for all customers.



2) Analyze Seasonality and New Products

shopping basket

Controlling your baseline view relative to time periods allows for a more thorough post-event analysis during more active, competitive or tenuous times. For example, a 26-week baseline may be acceptable for mature brands with little volatility. However, a 4-week baseline will indicate more significant shifts during a holiday. The ability to focus on these intricacies through a trusted baseline will better inform planning decisions during these times and when launching new products.


 3) Ushering in the Future


For too long, historical inaccuracies of baseline volume set the foundation for next year’s plans. Today, you can apply predictive analytics to plans for a more definite forecast of promotional lift. These predictive outcomes are built on the actual performance. Furthermore, organizations now can use features of their trade promotion optimization solution to simulate a future baseline based upon current trends, anticipated lost and new distribution. The TPO application’s predictive capability provides you with a dynamic future baseline versus (the all too common) static baseline for systematic future planning.


As trade marketers and sales, if you’re ready to ignite change in baselines at your organization, you need the data intelligence to see where you started as a catalyst to create better results. To date, the elusiveness of accurate baselines has hindered understanding and opportunity. This is why, if the consumer goods industry is going to evolve to address such opportunities presented with shifting retailer demands, rising e-commerce presence, and changing consumer preferences, perceived loyalty cannot be the only underpinning of our business understanding.


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