Bundled pricing, a simple concept with many applications

Retail pricing of cuts of meat is complex

In retail, one common use of bundled pricing is in grocery, where primals (simply put an entire side of beef) are comprised of multiple cuts of meat and could be divided different ways. Each cut of meat is priced separately and can vary by current market conditions, competitive pricing, seasonality and other factors. All the components added together represent the overall primal price, which can be used as the cost of goods to the grocery store. As you can imagine, optimizing the overall yield by analyzing different cut and pricing options based on market conditions is complex and a unique discipline of itself. For cuts of pork that include the lead time of animal maturation, curing and large peaks in seasonal demand the complexity of pricing optimally is even higher. This is the rare case where the industrial goods equivalent, even drivetrains, CAT Scan machines, and other complex equipment with deep Bills of Material, may be simpler to model and price.

Consumer device data collection is catching up to industrial IoT

Another retail example of bundled pricing is with white goods, for example refrigerators or dishwashers. The kit includes the product being sold, the cost of installation, and different warranty and service options. This creates different versions of the same item where only the length and quality level of the service contract varies. The Original Goods Manufacturer (OEM) will provide the warranty pricing, since they will have the failure rate data over the life of the good for statistical analysis, optimizing profitability over the life of the good, and the associated recommended service contract options. As telematics, embedded sensor devices, and Internet of Things (IoT) data collection becomes more common component failure predictions improve, and earlier failure notifications can trigger service events before they even happen. The advent of IoT data collection and analytics in highly complex industrial goods like multi-million dollar medical, industrial, construction, and agriculture equipment may be the predecessor to monitoring and 100% availability of everyday household goods as well.

Do-It-Yourself retailers have another need for bundled pricing. Products are often sold in customer-centric bundles like a paint project. This might comprise all the things necessary for different sized projects, including paint, brushes, ladders, drop cloths, and other items. Any of these items may be priced individually, but together the bundled project items may need a competitive aggregate price, particularly for a seasonally promoted standard paint project. This requires optimizing separate item prices separately, the rolling them from the bottom up to an overall price. Once localized, competitive price positioning is done then a top-down approach must be applied to ensure minimum margins are maintained. When you add in tiering for larger and smaller paint projects, localization, individual item optimization, and bundled project pricing you get a complex problem to solve. Fortunately, this is still an easier problem to solve then bundled pricing for expiring goods (like airline or concert tickets) using yield pricing approaches!

Common repairs and projects can be priced as a bundle

There is an industrial goods equivalent here as well. Automotive repair and Independent Aftermarket Retailers face a similar pricing challenge. In this case brake jobs and common repairs need to be priced. These projects are comprised of the individual service items priced separately, such as brake pads, rotors, and other components, along with the price of labor. Once these are rolled up to an overall job price, caps must be considered to ensure the overall price is below competitive equivalents, as well as below consumer budgetary constraints similar to psychological price points used heavily by retailers. In this case the overall job needs to be less than $250, $500, $1,000, or other perceived consumer budget points rather than ending numbers like $.49, $.99, and other ending number rulesets. Labor hours and cost per hour may then be adjusted to achieve budget break points, but only to a point. As more and more technology is used in cars, trucks, aircraft, and other industrial goods even the simplest common repairs include many small electrical components and labor hours to repair and replace them. Telematics and other IoT data collection devices are only adding to the complexity of bundled Bill of Materials and how they are priced.

Modelling and aggregating the pricing of complex bills of material that include items and labor has been done for a long time in the aerospace and defense industries to optimize service contract pricing with defined equipment uptime or Service Level Agreement (SLA). These are often referred to as “power by the hour”, and is being adopted in other industrial goods verticals. Cadillac’s BOOK and Porsche’s Passport programs offer similar flexible ownership models that guarantee a certain level of product availability. Product IoT monitoring and predictive maintenance forecasting become even more important to the OEMs to maintain margins.

It is not too hard to imagine manufacturers offering similar ownership models for refrigerators, dishwashers, and other common household appliances as their monitoring and understanding of equipment usage improves over time. Demand shaping to optimize inventory, supply chain, pricing, and forecasting together will not be enough in the future. At some point IoT data collection, analysis, and optimization will need to be included to understand failure rates, proactively predict failures and provide alerts and service maintenance, and influence better inventory and pricing decisions.

In conclusion, bundling of items together for pricing seems like a simple concept, but it has many variations and uses across industries. There are also different pricing processes that can leverage these bundles, commonly referred to as complex bills of material or kits. The key is using them to achieve financial goals as you move beyond simple pricing methodologies. Their value is increasing as different products are being offered as a service and require service contract pricing, rather than discrete product pricing.

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