Accelerating Momentum in CPG Data and Analytics Capabilities
Many of the latest trends in the consumer-packaged goods (CPG) industry have centered around the supply chain, manufacturing, and customer execution aspect of solutions. Foundational to these solutions is data and analytics capabilities that enable innovation, advancement, and speed. Let’s explore how data and analytics have evolved for CPG companies, where we’re experiencing new patterns and growth, and what we can expect for the future.
A look back to look forward
Attend nearly any talk on the topic of CPG companies and data analytics and you’ll surely hear stories about the challenges of implementing modern integrated solutions. In fact, data is at the heart of every challenge and opportunity that our team is addressing today – whether in consumer and shopper insights, connected factory and digital manufacturing, upstream procurement and sourcing, or retail execution and revenue growth management.
Fifteen years ago, when I was working for Coca-Cola, we faced data analysis struggles just like many large CPG manufacturers. In response, we developed a powerful platform to collect, analyze, and act on our operational data. We operated with 72 different Coca-Cola bottlers in North America, some of whom manufactured product and all of whom distributed product to retailers, wholesalers, and other outlets. Each had their own back-office “ERP.” For the smaller bottlers, it was Access and Excel. For larger bottlers, various versions of SAP and other operating systems supported the business. Coca-Cola Enterprises – the largest bottler at the time – represented 80%+ of the North America business and the top seven bottlers comprised approximately 90%+ of North America volume, leaving the remaining volume to the other 60+ bottlers.
Coca-Cola made a strategic decision to make data and information a competitive advantage for the Coca-Cola system. We invested in the technology, tools, data, people, and processes to power what would essentially be a two-to-three-year effort to collect, harmonize, and act on insights derived from operational data. The big idea was to create a “single face to the customer” across all of the Coca-Cola lines of business. We wanted to enable our customer teams to better serve, deliver, and win with retail customers. This strategy contrasted with the legacy-siloed approach to the “system”, which often engaged with customers without a clear view of operational performance (e.g., SKU performance, outlet performance, On-Time-In-Full compliance, and volume and price performance).
Initially, the focus was to acquire the volume and financial information on case sales daily from each bottler and other operating companies (e.g., Foodservice). That would give the customer (aka retailer) account teams a cleaner view of operational performance, as well as put them in a better position to improve customer-enabling capabilities and trade programs. Prior to this solution, there was often tension when it came time for customer planning and trade execution discussions. The retailer had a set of figures that didn’t sync with our own.
Over time, we established a set of master data management hierarchy routines and a team to manage them. The team handled changes such as customer structure changes, store openings and closings, alignment of account teams in the field to customers, and more. Then, we built daily feeds from each back office “ERP” into our data store. We managed data outliers manually as exceptions, while the same hierarchy team mapped them to the correct place daily. Trade promotion calculations were run at night based on updated daily sales data. Two Business Intelligence solutions sat on top – a business-friendly rows/columns-type solution, and another tactical outlet-level reporting solution.
In the end, we had a single book of record for North America operational and performance insights by customer and products. But there’s much more to this story (enough for a book!) if you add up the challenges that came from security, data validation, business alignment, capex/opex funding, and more.
This initial solution provided us with a daily view of North America sales at a volume and price level, thereby enabling field sales to better steward the customer relationship and for our trade promotion process to operate more efficiently.
Next, we integrated and aligned retailer point-of-sale (POS) data from the top five to seven retailers (measured by sales volume) in the same data platform. We needed to test the use cases for reducing out-of-stocks, enabling predictive ordering, delivering insights to route drivers, merchandisers, and field sales with data on outlet SKU performance, trade promotion execution performance, product recommendations, etc. We were able to leverage our data COE to fast-forward the data integration and harmonization processes, since we learned many lessons from setting up the shipment data environment. Then, we quickly realized significant quantitative savings based on the insights, yielding improved sales performance for us and increased sales for the retailer. In addition, we transformed the customer planning process based on new insights and analytics not available just a few years before.
Fast forward to 2022. Many major CPG manufacturers still struggle with similar data challenges. This trend is especially true as manufacturers have grown through acquisition without consolidating, shifting, or migrating to an environment that enables the types of insights and analytics described above. In addition to the shipment and POS challenges, it’s challenging to integrate these data sets with syndicated data from Nielsen, IRI, and Spins, as the hierarchies and data timing do not match. Many tech companies have tried to solve this issue, but no clear leader has emerged in the market today. This could change as artificial intelligence (AI) and machine learning (ML) advance, thus enabling non-manual routines in data analysis to happen quickly and to gather new insights with faster decisions. However, Digital Native CPG brands don’t have this data challenge, since they originated on cloud technology and continue to evolve in line with their volume and geographic needs.
Consumer first-party data
Across the CPG landscape, there are multiple approaches being deployed to managing consumer data, whether in-house or with an agency’s help. But today’s data challenges only get more complex when we consider consumer data, supply chain and manufacturing data, and outlet execution data. CPG brands don’t typically have access to first party data unless they have a branded website for sale of product or a loyalty program (e.g., MyCokeRewards during my time at Coca-Cola). As CPG brands launched more Direct-to-Consumer (DTC) platforms over the last two years, they faced new challenges, such as understanding the consumer data and deciding what to do with it as opposed to the legacy data sets.
Manufacturing and supply chain data
Manufacturing and Supply Chain innovation continues to be the leading area of “need” for our customers seeking solutions since early 2021. CPG manufacturers are eager to find help managing their data and analytics in Sourcing and Procurement, especially as they attempt to better assess supplier risk and track raw materials. In manufacturing, CPG brands are investigating new ways of using IoT sensors and digital twin capabilities to model performance, predict downtime and outages, better utilize assets, and improve quality. On the supply chain side, the buzz around “supply chain control tower” and “connected factory” is growing louder, as manufacturers look for better ways to “see” into facilities, benchmark across facilities, leverage performance best practices across locations, and better utilize people assets. Finally, on the supply chain side, new ways of route planning are being considered using AI/ML that pull together disparate data types to plan deliveries. What do all of these activities have as a common thread? Data. They all rely on and leverage vast amounts of data.
As manufacturers work to put the right product in the right store and in the right geography based on shopper insights, innovative capabilities for shopper and consumer insights are powering the channel price-package planning process. For example, in-store image recognition is as popular as ever. New tech providers are enabling photo capture in-store, translating those images into data to show shelf prices, in-stock condition, competitor placement, and more, all based on AI. Several manufacturers have developed and deployed a “connected front office” solution to deliver data in real-time to field sales, store reps, or merchandisers to engage with the customer at the store level.
Technology has fast-forwarded data and analytics capabilities
Technology goes hand-in-hand with data and analytics innovations. On-premises, expensive, and inflexible hardware and software used to be the go-to solution, but it took weeks or months to procure, install, support, test, and finally use it. Once up and running, the manufacturer often found that it wasn’t enough to meet storage and processing demand.
Now, with cloud capabilities, we can spin up (and down) storage and compute resources in minutes. Manufacturers can test and learn quickly with on-demand capacity access. They can go global in minutes with new solutions, and they can lower their capex costs (no on-premises equipment) simultaneously. Most importantly, they can innovate faster and free up “on-premises resource time” to focus on new advancements in spaces like AI, ML, and Blockchain.
Want to learn more? Listen to Part 1 – Data and Analytics for CPGs and Part 2—Data and Analytics for CPGs of our podcast with The Consumer Goods Forum to catch up on current analytics trends in the CPG industry.