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Getting the Most Out of Your Sales Data

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If you are a CPG brand, you are likely subscribing to syndicated sales data, an aggregated picture of product retail sales activity across a category set. The idea is to compare your actual performance against an entire category with the added benefit of being able to track industry trends and manage the product lifecycle.

The problem is all too often, the data can be too much, or not detailed enough, or too narrowly defined, so a multi-million-dollar syndicated sales data subscription ends up having limited impact. In addition, the data may not be used properly or requires an army to manage it, which is often not very feasible given the shortage of data scientists, machine learning experts, and analysts.

But it’s too important to ignore. As a Key Performance Indicator, sales data has a very important role in benchmarking strengths and weaknesses, recognizing key opportunities for growth, determining sales drivers and impacts and identifying competitive advantages and threats. The best way to get there is to combine sales data with many other sources of data like voice of the consumer, key opinion leaders, patents, product reviews, competitive brand analysis, and more to extract good market intelligence that tells a multi-dimensional story. This of course adds to the complexity of the data environment, but first let’s discuss the benefits of augmenting sales data with other sources and then how to achieve this using advanced analytics platforms that are able to ingest and classify a myriad of external data sources.

First, here are 4 reasons why sales data should be augmented with other data sources:

  1. Make More Accurate and Granular Predictions – Accuracy rates nearly double when combining contextualized external data from product reviews, product listings, social media, blogs, forums, news sites and others, going from 36% to over 70%.

    Social vs. Holistic Data

  2. Assess the Competition More Holistically – Syndicated sales data can break down flavors, size, promotional strategies/discounts, pricing and packaging and shelf space of competitors, but because they are limited to retail sales, the brands that are being represented in the analytics are also limited and must be pre-defined. They will also likely not reflect what is happening online since those are extremely difficult to track given the lack of a Universal Product Code and many different ways of labeling and naming products across e-commerce channels. (Side note: See our related blog post on this subject). The other issue is that many competitors are actually emerging brands that are purposely low-key so would not show up in syndicated sales reports. This is no small matter. According to Vision Critical, 18 out of 25 consumer categories are growing due to companies that have less than $1B in revenue and more than $17 billion in CPG sales in the US have shifted from large players to upstart brands since 2013. These companies still file patents, have social media pages, are often mentioned by key influencers; in short, they have many digital crumbs associated with them that can be picked up and analyzed to fill in the blind spots that retail sales data leaves behind.

    Brand Score Model
    Combining different data sources allows you to compare and assess your brand equity over time across standard industry parameters: awareness, usage, volume, satisfaction, and alignment with consumer needs.

  3. Measure Consumer Sentiment Relative to Sales – Sales data gives absolute numbers on what was purchased but doesn’t tell the story of ‘why’, which is only possible by analyzing the buyer perspective. Sentiment analysis surrounding key product attributes or benefits can point to whether a sales trend is sustainable or likely to be short-lived. For example, over the COVID period, consumers were willing to shift brands because of price, lack of availability (a result of supply chain interruptions) and specific product attributes that became more important (those associated with immune health, for example). Conducting sentiment analysis on consumer discussions yields important indicators as to whether there was widespread satisfaction with these new purchases to help assess whether the loyalty shift is permanent.

    Trend Opportunity Model
    Augmenting sales data with e-commerce reviews, patent filings, social listening, key influencer posts, and more gives you the ability to assess the volume and velocity of market trends and determine whether they are long-lasting.

  4. Optimize Promotions by Channel – What works in retail may or may not work online, but it is important to understand the dynamics that drive each channel and how to enhance results by tailoring messaging and offers and having insight into exactly what is driving sales performance. The e-commerce channel provides unique visibility into consumer sentiment along the path to purchase, which can be used to optimize conversion by retailer, as opposed to a catch-all approach.

In short, by combining sales data with an ecosystem of other data sources –– the value of that sales data grows exponentially.

For the second part of our discussion on how to integrate all these data sources given the complexity and sheer volume, we turn to the latest advancements in Natural Language Processing and AI which manages the collection and classification of data across a broad spectrum.

Configurable platforms like Signals Analytics allow companies to stipulate the data sources to be ingested, allowing for a wide range of unstructured sources, as well as structured data sets like sales data and internal data. They will allow taxonomies to be created and adjusted regularly to adapt to the changing needs of the business and new questions that need to be asked. They will provide a series of analytical models and business-ready apps so the analytics are easily accessed, but more importantly, they will allow the data to be integrated into other business intelligence environments or data science platforms for native analysis. Having all the components built into a flexible architecture framework makes it possible to incorporate sales data into a useful data-driven approach that is used by winning brands to get ahead and stay ahead.

To learn more about how Signals Analytics can help, schedule a demo with our solutions team.

 

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Chris Thatcher
5W Public Relations

chthatcher@5wpr.com 646-430-5161
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