The Beginner’s Guide To CPG Data Analytics
As with many industries in the 21st Century, the CPG market is facing major change. Factors ranging from slow to negative retail growth, increased competition from e-commerce platforms, and rapidly changing demographics have left the CPG industry open for disruption. In response, organizations are turning to data analytics to increase productivity and efficiency.
Yet despite their proven track record for success, many companies struggle to make use of CPG data analytics tools. One study from Accenture found that only 9% of businesses have fully implemented an analytics operating model in its entirety, while 40% state their models are only partially defined. Only 9% have made predictive analytics a priority, focussing instead on hindsight descriptions of what has already occurred.
For these reasons, we’ve put together a brief introductory guide on CPG data analytics, along with its benefits, data sources, and other helpful considerations that can make your daily operations more effective.
So what is CPG data analytics, exactly?
While data analytics can seem daunting at first, it’s not so frightening in reality.
At its core, analytics refers to insights and predictions generated using predetermined datasets. Within the CPG data analytics context, that addresses any product or consumer behavior data that is relevant to producers and retailers.
For example, some data points relating to products include the following:
- Sales trends of an individual product
- Competitive analysis of similar products within a category
- Price changes during sales and promotions
- Distribution statistics
Meanwhile, data points relating to customer behavior include the following:
- Customer demographics
- Store and brand loyalty
- Purchase frequency
- Completed transactions and abandoned products/carts
Using this information, CPG companies can study long-term trends to conduct a comparative analysis of any particular data points. Sales and marketing teams then use these CPG data analytics outputs to highlight strengths, weaknesses, opportunities, and inefficiencies of categories within an organization.
Most importantly, a sufficiently comprehensive data analytics model allows for predictive insights that increase the likelihood of successful product launches. In turn, CPG data analytics encourage growth for individual businesses, product categories, and the industry overall.
What are some useful CPG data analytics KPIs?
While each of these metrics can be useful, they are not equally useful to all organizations. Certain KPIs will be more relevant depending on a given store’s CPG channel, size, location, or customer demographics served. Given the high volume of information covered by syndicated data sources, individual businesses will need to narrow their scope to focus on specific CPG data analytics categories.
As a starting point, product startup specialist Ed Soehnel created a KPI breakdown based on CPG channels. For example, here are the top metrics for products sold under retail channels:
- Units sold per retailer
- Sales order change over 52 weeks
- Weeks of inventory on hand
- Retailer weeks of inventory target number
- Value of outstanding purchase orders
- Product returns
- Retailer ROI, trade spend, and penalties
- PO delivery on-time rate
- Out of stock percentage
- Logistics costs-to-revenue ratio
By comparison, here are the preferred metrics for direct-to-consumer channels:
- Marketing spend
- Message engagement rates (email, social views, social shares, hashtags, etc)
- Click throughs
- Average Order Value
- Cost per click
- Cost per visitor
- Cost per order
- Cart abandon rate
- Cancels/Declines/Returns rates
- Return on advertising spend (ROAS) or marketing efficiency ratio (MER)
- Lifetime value (typically calculated over 12-months)
What sources allow for comprehensive CPG data analytics models?
While each of these product metrics should be well-understood within an individual company, CPG manufacturers should also be aware of product metrics from other retail stores, channels, and even categories. This is where syndicated data — a retail sales data category that measures aggregated product activity — comes into play. Syndicated data is made available by third-party data sources that collect KPIs from a broad range of retailers and offer access to the aggregated results.
Your can learn more about syndicated data from our previous post on the subject, but in the interest of context here, it’s worth mentioning the three major data providers for CPG manufacturers. These organizations offer comprehensive product metrics on the largest scale, making them essential references for any CPG data analytics team.
The Nielsen Company is a global information, data, and management company that aggregates data on consumer goods, consumer behavior, and media. It provides a comprehensive overview of products and purchasing behavior in over 100 countries using syndicated data.
SPINS is a syndicated data and retail measurement platform specializing in cross-channel point of sale reporting alongside data-based services and solutions. It covers both the Natural and Conventional goods categories.
IRI is a market research company and digital data analysis company. Its datasets cover purchasing, media, social, causal, and loyalty channels.
While each of these companies are important data sources, businesses will likely need to invest in other CPG data analytics tools. Nielsen in particular offers the most comprehensive range of aggregated product KPIs, but lacks the visualization and analysis tools that allow for rich insights of each category. This issue is especially prominent for smaller retail businesses and product lines that don’t have the resources to invest in dedicated business intelligence teams.
What CPG data analytics tools generate the most useful insights?
Thankfully, companies like Bedrock Analytics can make the difference. Founded by experts in management, marketing, and analytics, Bedrock is dedicated to making CPG data analytics easy for anyone to understand and master. Our analytics platform aggregates data from syndicated and retail data sources, allowing clients to generate actionable insights without technical knowledge or an IT department. We also support easy-to-create visualizations that explore your data storyline, improving reports and product presentations.
If you want to make CPG data analytics work for you, click here for details on Bedrock products and services that will help achieve your goal.