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Net revenue management helps turn data into profits

Cameron Turner
Airplane Hero

In December of 2022, severe winter weather that dominated much of the United States combined with point flu/COVID-created staff absences put Southwest’s data strategy to a rigorous test. As airports shut down, the cascading effect played out, with displaced planes and crews and a digital management system that couldn’t process the data needed to correct the situation. Thousands of flights were canceled over several days, stranding travelers and flight crews. It will take some time to fully understand how the airline’s digital strategy failed.

One of the root causes of this disaster is the company’s approach to net revenue management (NRM). NRM is a data-driven process aimed at maximizing sales and profits. In its simplest sense, it is about using data to make decisions that lead to selling the right product to the right customer at the right time for the right price.

It first took root in the airline industry, starting with early-bird discounts to stimulate demand, and was further refined through yield management, maximizing revenue through analytics-based inventory control. The impressive increase in sales that resulted from the strategy caught the attention of other industries, notably the hotel industry. NRM continued to yield higher sales, and the strategy took hold in more sectors like manufacturing, retail, and the food and beverage industry.

NRM and Airlines

An airline manages the availability of seats, lead time, and price. It tries to maximize revenue, through sales, to maximize profits. In one scenario, the shopper can buy a cheaper seat by booking well in advance and pay more if they book last minute. In another scenario, if the airline has many open seats at the last minute, it sells them cheaply.

But there’s more to it than that. In addition to dynamic pricing, the airline must plan its inventory—the planes, staff, and flight schedules—to optimize revenues. A complex network of flight plans requires having the right planes in the right locations. There is often little room for error, and when errors do happen, the result can approach catastrophic.

In the case of Southwest Airlines, the company operates on a “point-to-point” route system: A plane will fly consecutive routes and swap out crews along the way. The advantage is that it allows Southwest to operate more flights over a given 24-hour period than other carriers, which is normally a sound NRM strategy. But if an airport goes offline because of weather and a flight can’t make its destination, the point-to-point system has a cascading cancellation effect, which is what occurred in 2022.

NRM, the CPG Version

Consumer packaged goods (CPG) companies are under pressure to achieve profitable growth in a tough market:

  • New opportunities for sustainable in-market growth are needed.
  • Different capabilities are needed to exploit new opportunities.
  • Digital purchasing has changed how consumers and retailers relate to brands.
  • Because of the size and complexity of global CPG organizations, there can be a chasm between strategy and execution.
  • The regulatory landscape is becoming more rigorous in some countries and product categories.
  • Trade spend has become one of the biggest and fastest-growing lines on the P&L, but it is also often the least understood.

These and other challenges have made NRM top of mind for many CPG leaders. However, many enterprises have found it difficult to implement. The requirements for a granular data-driven approach and improved collaboration between sales and marketing can spotlight cultural and operational cracks in the organization that present roadblocks. Still, given the benefits NRM can provide CPG companies, it can be worth the effort and investment.

Working with NRM

Whether for an airline optimizing pricing and routing or a CPG enterprise boosting sales through promotions, NRM puts decision-making squarely on data to allow a business to get smarter. On the surface NRM is straightforward: it uses predictive analytics, leveraged across pricing, promotion, product, and marketing strategy to uncover untapped opportunities for expanding margins, lowering risks, and optimizing spend.

Going deeper into NRM reveals complexities. For a CPG enterprise as an example, it is an interconnected set of levers—data collection, segmentation, forecasting, and optimization—manipulated and optimized in unison.

Activities such as promotional planning, pricing strategy and price–pack architecture, portfolio and assortment strategy, marketing mix, and trade spend management are tweaked in concert over hundreds, perhaps thousands, of SKUs. Demand forecasting, based on customer buying behaviors, includes factors such as the ability to substitute, complementary products, and seasonal items.

Kin + Carta helps a consumer packaged goods (CPG) enterprise start the NRM journey

Machine learning and optimization—key parts of NRM—are complex, but the interface doesn’t have to be. Kin + Carta specializes in making complexities accessible, and we are working with clients to manage their processes themselves without ongoing support from the data and acquisitions team.

For example, we were engaged by a CPG company to begin its NRM journey. The company had huge amounts of data derived from the retailer experience and the consumer end-user experience. Still, the company could not fully use this data due to siloed information, organizational changes, and other roadblocks.

When we started, there was no structure to use the data siloed across multiple brands. The company needed to free this locked data, unify data sources to predict users’ needs, and turn intelligence into insights to drive measurable results.

Drawing on Kin + Carta thought leadership in product development, engineering, and design, we:

  • Leveraged machine learning to increase accuracy demand forecasts at the SKU, Geography, Store, and SKU levels
  • Deployed models to production to affect all areas of NRM, including pricing and promotion calendaring.
  • Scaled and integrated this platform globally across markets, to take advantage of the new possibilities data and data tools have to offer

Within eight months, the company had a robust set of machine learning models that extracted over 40k elasticity insights from five years of training data. They also improved forecasting by 12% or $2.7B in product value of error reduction. A roadmap for further performance improvements and expansion to other markets was laid out, and the NRM journey was underway.

While there may always be a human element to the optimization activities related to NRM, machine learning makes the complex job easier, more accurate, efficient, and collaborative. Through a human+AI approach, Kin+Carta helps clients reach ROI and time-to-value that are impossible to achieve through prior rules-based approaches.

With Kin + Carta as a partner, companies are reducing the complexities of executing on their NRM strategies and unlocking data and insights for better decision-making and automation. Together we can accelerate the NRM journey to create competitive breakthroughs, higher sales, and improved margins.

Ready to start your own NRM journey?

Revenue Management is complex but with the right support, complex is achievable.

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