TABS Analytics Blog

Maximizing the Summer Season

Summer is not only a time for sunshine and relaxation, it's also a festival of feasting, socializing, family get-togethers, picnics, and major holidays like Memorial Day, 4th of July, and Labor Day.

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Chaos to Cohesion - How Data Harmonization Unlocks Omnichannel Data

As the retailer landscape continues to shift between online and in-store, it is becoming increasingly important to get a solid understanding of consumer sales within the omnichannel universe. 

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How Data Harmonization Can Help CPG Companies Win Both Online and In-Store

Retail eCommerce growth continues post-pandemic, with sales projected to continue to rise at 8 percent globally through 2024. Food and beverage categories have one of the highest projected eCommerce growth rates through to 2026, with health and beauty second.

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Thoughts on the State of Grocery Retail

As we enter 2020, let’s take a look at how some key grocery retailers have performed in 2019 through their first three fiscal quarters. I want to share my thoughts on what I think has worked – and hasn’t worked – for several prominent players. Below are sales and profit results from the top 4 grocery chains in the US plus Walmart Global for comparison (60% of their sales are grocery/consumables) for 2018 and the first three quarters of 2019.

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4 Ways to Test if Your CPG Sales Baselines Are Accurate

For some strange reason we are fielding lots of inbound traffic from managers seeking a better model for baseline sales. This has been a persistent issue in the industry for at least 20 years, but only now are the calls coming…odd. For those of you not familiar with the concept for the CPG industry, a sales baseline is an estimate of sales in the absence of retailer price promotion. You can learn more about the concept in this sales baselines blog post and trade promotion optimization white paper.

Just know that without an accurate baseline, trade promotion optimization analysis will be inaccurate, and more importantly many of the decisions made from the analysis will be wrong. Some in the industry will say there are many ways to calculate a baseline (true), and given that, there is no way to tell which model or estimates are the best (false). There actually is a method for measuring which baseline estimates are the most accurate, and if you are interested in the technical details you can find them in this academic paper.

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Using Force For More Effective Selling Presentations | TABS Analytics

Few forces have shaped the consumer packaged goods (CPG) industry over the past 20 years like category management. Originally, the domain of large firms with massive budgets, sev­eral improvements in information technology and the emergence of sophisticated, less expensive outsourcing options have largely erased the huge advantage these firms once enjoyed. Just about every company over $50 million nowadays has some kind of category management infrastructure and the largest manufacturers continue to increase their investment with headcount, software, data and hardware at retailer locations (on-sites).

But, as we argued in our recent webinar, the process of presenting category management findings in fact-based selling presentations to retailers more often than not remains mired in arcane procedures and complexities that churn out the wrong conclusions. There's a better way to deliver fact-based presentations to the retail trade: using the FORCE process.

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How Consumer Surveys Can Reveal Hidden Opportunities | TABS Analytics





Consumer Surveys and Their Metrics

In the previous parts of this series, we have covered both syndicated data as well as your own panel data. In this final report, we’ll conclude with quality metrics that you can obtain by surveying your customers.

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The Most Useful Household Panel Metrics You've Never Heard Of | TABS Analytics




Household Panel Data Metrics  

In the last several posts, we surveyed the innovative things we can do with the Nielsen, IRI, SPINS and other syndicated data that you’ve been buying for years. Now it’s time to turn inward and look at household panel data.

Few CPG companies extensively mine their own panel data, instead relying on syndicated data almost exclusively. These companies focus on the fact that syndicated data gives them a view of the entire market, and not just their own shoppers. But, when panel data is used, it’s often too generic (buyer count, purchase frequency, etc.), to be useful. There’s rarely a story or strategic information on which they can act.

Successfully wielding panel data requires a core understanding of the basics and a certain amount of creativity. Let’s begin with loyalty shoppers.

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Useful Sales & Marketing Metrics That Are Unknown - Part 3 | TABS Analytics

Innovation and Promotion Metrics

 In our last post, we concluded our discussion of distribution-based metrics. In this post, we’ll continue to make use of the syndicated data you already have, but this time, we’ll focus on the innovation of new products and the promotion of existing ones. 

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Useful Sales & Marketing Metrics That Are Unknown - Part 2 | TABS Analytics

Distribution Metrics: Velocity Change and Organic Growth

In our last post, we began this series on the new metrics that are delivering major results for those in the CPG industry who are leveraging them. Just like last time, the metrics we’ll be discussing can be found in the syndicated data you already have.

In this post, we’ll conclude our discussion of distribution-based metrics. The two measures described in our last post, Equivalized SKU and Sales Productivity, have built a solid distribution-based analytics foundation. The first metric gives you an accurate measure of your distribution, the average number of items of a particular brand in every store. The second metric takes your sales and divides by the Equivalized SKU, letting you take distribution into account when weighing up the relative strength of any brand.

The two metrics we’ll be talking about today are Velocity Change, a simple measure that’s remarkably good at predicting sales trends, and Organic Growth, which augments that velocity change score by taking price elasticity into account.

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