Begin with the Base: Building the foundation for accurate post-event analysis and planning

Topics: Trade Marketing, Sales, Baselines

When we sit down with prospective clients, there are common frustrations that lead them to seek out a solution – better post-event analysis, need to quantify promotional ROI, more controlled and profitable planning processes, etc. However, none resonate 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.

These can often be inaccurate for various reasons:

  • Syndicated providers baselines in many cases go up dramatically during a promotion projecting what a loyal consumer baselines_lgwould 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 investment. 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 is essential to have a monitoring system that can identify data anomalies contaminating baseline integrity.
This results 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 promotions performance is not only a significant risk to the organization but will place the company 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 are taking control of their data by automating the harmonization of consumption, spending and shipment data to build their baselines as a more accurate reflection of in-store activity.

With this, companies are not just prioritizing accuracy, but also intelligence as part of a comprehensive Trade Promotion Optimization Solution to directly impact their business in these ways:

1) Quantify ROI and KPIs

When you know where you start from, 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 a promotional activity is both automated and repeatable for all customers.

2) Analyze seasonality and new products

The ability to control 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 hone in 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, companies are applying predictive analytics to their plans for a more accurate forecast of promotional lift. These predictive outcomes are built on the actual performance and as a result, rely on accurate history. 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. This predictive capability provides the user a dynamic future baseline vs. the common static baseline for more accurate future planning.

If trade marketers and sales teams are going to ignite change in their organizations, they will need the data intelligence to see where they have been as a catalyst to create better results. To date, the elusiveness of accurate baselines has hindered understanding and as a result, 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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