Remember when Coca-Cola came out with mini cans? Everyone noticed. It was a smart move that helped turn things around when people weren’t buying as much soda. The Washington Post called it “Coca-Cola’s clever new trick,” and health-conscious consumers loved the smaller size. They didn’t mind paying a little more for less, which boosted sales and profits.
Fast forwarding, Coca-Cola once again leaned on Price Pack Architecture (PPA) to navigate inflation, shifting consumer preferences, and rising commodity costs. With many shoppers looking for value, Coca-Cola found ways to offer both affordable and premium options. By matching the right product to the right package, price, and sales channel, they made sure they met customers’ needs wherever they were. This approach led to happier customers and higher sales.
Just like Coca-Cola, we all need to think smart to handle today’s challenges. Price Pack Architecture could be the solution – but it’s more than just new sizes or cutting products that don’t sell well. It’s about finding ways to give customers what they need while staying profitable.
Defining Price Pack Architecture in brief
Price Pack Architecture (PPA) is an analytical methodology or technique that helps businesses with the right mix of products at prices customers are willing to pay. It ensures that consumers find products that meet their needs while staying within their budgets. By understanding what features and benefits people value most, PPA helps companies create innovative products that align with customer expectations.
How can brands understand the impact of Price Pack Architecture in CPG context
Revenue managers typically have access to data on past pack and price changes in the market. However, this data only offers a look at what happened in the past, not what might happen in the future. As a result, the impact of upcoming PPA changes remains uncertain and needs to be estimated. These estimates may vary in accuracy depending on the quality and type of data available for the category.
Relying only on past data to make future pricing decisions can be risky and restrictive. While it might seem convenient to base decisions on historical sales or scanner panel data, it’s important to remember that this data reflects a different time when consumer behavior and mindsets were not the same. Plus, sticking to historical data forces you to predict future scenarios based on outdated price points. This is where stated preference experiments come in—they provide valuable insights to complement the trends seen in past data. Especially in times of inflation, getting pricing right is crucial for achieving the best outcomes.
To better understand the impact of PPA (Price Pack Architecture) changes, brands can use experimental methods like discrete choice modeling or dynamic competitive modeling. These approaches help identify the best pricing and packaging strategies to boost portfolio growth and deliver more value to consumers. Check out the table below for details.
| Discrete Choice Modeling | Dynamic Competitive Modeling |
| Uses survey research data | Uses Point of sale (POS) data |
| Respondents evaluate product choice sets representing variations in product attributes and indicate the most likely to buy products. | Identifies competition structure among SKUs in a category |
| Data used to derive utility scores for different product attributes | Estimates drivers of market share based on product attributes of all competing SKUs |
| This drives price pack innovation and architecture | Identifies other marketing and environmental drivers |
| Simulation predicts market outcome for new product bundles | Simulation helps price pack architecture scenario planning across entire competitive set |
Broader impacts of Price Pack Architecture
- Tailor product packs for specific sales channels to boost market share, attract loyal shoppers, and significantly increase in-store purchases.
- Streamline your product pack sizes to focus on the most profitable options, eliminating those that don’t add value.
- Combat market cannibalization by ensuring each product pack works to maximize overall sales and profits for the entire brand.
- Capitalize your investment strategies by using innovative packaging to save resources and get better returns.
How can Polestar help?
Creating the right price and pack combinations is key to succeeding in any market. By blending AI & Analytics you can craft a robust Price Pack Architecture (PPA) solution. This helps you set prices that boost profits, strengthen customer loyalty, and support long-term growth. PPA-based pack options are designed to solve pricing challenges, enhance product appeal, and unlock new growth opportunities—whether you’re targeting fresh markets or deepening your presence in established ones.
To learn more about RGM solution and how consulting practice can help you drive revenue growth, get in touch with them today.
