Have you ever walked into a store, seen a creative product display, thought to yourself “that’s a clever promotion”, and wondered what went on behind the scenes that led to that display? Hint: it was probably a collaborative data-driven insights effort between the product manufacturer and the retailer. The leading consumer packaged goods (CPG) companies of today are known for their analytical capabilities. They are able to understand what’s going on across their businesses and at their retailers using Point of Sale (POS) and consumer behavior data, making appropriate recommendations that drive sales in retail channels. This is why retailers rely on CPG companies to be category advisors, and why media companies partner with them to create compelling consumer campaigns.
Even so, CPG companies today struggle to keep pace with the increasingly large amounts of data coming at them from every angle: weekly, daily or even hourly POS data, scan reports, panel-based information, ecommerce trends, social listening word “clouds”, weather reports, macro economic statistics, consumer studies, website analytic metrics, and more. Not to mention increasingly complex internal data related to raw materials, logistics, financials…the list goes on. All in different formats and from different sources.
So what is keeping CPG companies from being able to get more out of the vast amount of data they have? It’s no secret that having data sets in disparate formats makes it difficult to conduct nuanced, well-rounded analysis. For starters, data often sits across multiple IT systems that don’t work together very well. A patchwork of solutions is created across these systems, but it is often rigid and inefficient, limiting analytic possibilities and tying up precious hours. For example, account teams want to match the retailers’ POS data to the shipments data they get from their own supply chain teams but they can’t do this easily because the retailer’s products have different names or different hierarchies. As a result, CPG account teams find themselves having to manually extract and organize the POS information they receive from their retail accounts. We’re talking about the practice of consolidating data from multiple Excel spreadsheets into a centralized one every week! A time-consuming affair. This limits the ability of account teams to generate useful insights and make category-driving recommendations for their retail customers.
On the marketing side, brand and consumer insights teams are handicapped by having scan data that gives them one dimension of performance but panel data that gives them a different one; and then there is external data such as digital transactions, ecommerce sales, gas prices, census data and more, that offer an entirely different perspective. As such, teams end up reporting at an incomplete or superficial level: sales and share for all channels excluding ecommerce; sales and share for ecommerce only; estimates of aggregate performance based on macro trends but no ability to drill down by demographic or geography. A brand manager, for example, might be able to see that the total skincare category is growing nationally thanks to a national ad campaign. But without access to all the data together, the brand manager might not be able to see that facial moisturizer penetration specifically among millennials on the West Coast is declining—all because the data on regional trends and consumer behavior by segment are not linked together.
But there is a better way to manage data. Deriving stronger insights from multiple sets of granular data – and doing it in minutes rather than days – is not impossible. In fact, the solution lies in leveraging a fully integrated system that not only has the ability to handle data at its most granular level, but also brings different data sets together seamlessly and quickly. With the right solution, there’s no reason why retailer POS data can’t be easily mapped against a manufacturer’s shipment data without forcing either party to change their hierarchies or taking all day. By being able to quickly access all data sets in one place, leveraging tools they already know like Excel, CPG managers can save time and energy otherwise spent gathering, organizing and cleansing data. With an end-to-end solution like the 1010data platform that easily unifies data, account managers can free up time to conduct more analysis, which leads to richer insights that can mean the difference between being a trusted category advisor and being just another brand on the shelf. Likewise, brand managers and consumer insights managers can leverage the power of data unification – without having to learn to code – to get a more complete view of the market, make more strategic business decisions, and drive winning results.
For deeper insights into the challenges and, more importantly, how data unification can be addressed, check out the white paper written by CGT – Consumer Goods Technology, sponsored by 1010data – The Promise of Data Unification for CPG Companies.