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:
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.