The power of optimizing parts pricing
March 25, 2021
The spare parts segment of aftersales is typically highly profitable. But full success is only an offer to those who really understand the power of optimizing parts pricing. There are numerous challenges in achieving optimization. Central to these is a lack of technological sophistication. To win, companies must push beyond their traditional capabilities. By using contextualized and analytics-based pricing methods, they bring the profit driver spare parts segment to the next level.
By Maxence Tilliette, Patric Kirchner, Kathrin Schwan, Marcus Demmelmair, Sona Kochkanyan, and Claude-Henry Pignon, Accenture
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Companies can bring the profit driver spare parts segment to the next level by using contextualized and analytics-based pricing methods. | Image: CHUTTERSNAP on Unsplash
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Aftersales is becoming an increasingly important contributor to top and bottom line. Today, aftersales can drive up to 60 percent of a company’s profits. Companies that successfully optimize prices typically see a profit increase of more than 20 percent. But there are many challenges to overcome when optimizing parts pricing. They range from complex longtails of parts portfolios to diverse and evolving markets. To succeed, companies will need to push AI-based image reading, advanced analytics, and cloud technologies to maximize rewards from the parts business.
Costly challenges
While there is plenty of profit to drive through parts pricing optimization, many companies simply forget to pursue this segment of aftersales. Those that do may pay too much for optimization, leaving margins on the table. Even then, their efforts could trigger grey markets in response to price changes. And outside of these immediate issues, companies face five major challenges that limit their success with optimization:
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- Pricing is driven by so-called “value-based mark-ups” that rely on consistent and accurate cost bases
- Prices are calculated in typically anachronic Excel tools
- Little competitive monitoring of A-parts, highly automated approaches such as web-scraping only in an infancy stage
- Long-tail of parts portfolio is typically neglected as too hard to manage and maintain
- The complexity of parts pricing is multiplied by the heterogeneity of markets (size, regulation, own sales entities vs. importers, etc.)
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On top of this, clients are often under the impression that strong optimization is in place when this isn’t really the case. Most use manual processes and a simplistic tech stack. And they’re unsure of how to improve the maturity of their capabilities. This is partly due to confusion over vendor choice and the solution landscape. As a result of low maturity in pricing optimization capabilities, they’re unable to maximize aftersales profits.
Getting to know next-gen pricing
Many companies already have a progressive level of technology sophistication for their aftersales activities. Price strategy is typically determined by the value products create for different customer segments. Price optimization tools are in place and there is typically some use of price elasticity data, forecasting, and simulations, in addition to price governance.
However, this is not enough to stay ahead. Top leaders are now harnessing new technologies and analytics to bring parts pricing to the next level. Their optimization processes help push beyond traditional and basic optimization capabilities into more sophisticated approaches. And their efforts go all the way up to contextualized and analytics-based pricing methods. These are now feasible thanks to advances in machine learning.
Leaders’ next-gen pricing capabilities include:
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- Image recognition and automated reading of part information from PDF or technical drawing
- Increasingly automated competitive research, e.g. by web scraping
- Advanced analytics based portfolio clustering relying on value-based criteria
- Price optimization based on self-learning algorithms leveraging part characteristics (e.g., technical, physical, marketing, finished goods)
- Highly automated pricing operations based on cloud architecture feeding internal and external data in near-time into management dashboards
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However, while applying these capabilities will boost efforts to drive profit, companies must develop a cohesive plan on how to approach optimization to maximize reward.
Accenture’s three-part plan
At Accenture, we created a three-part framework to ensure optimization and next-gen pricing are achieved. First, we focus on Parts and Product Pricing Setting. Doing so generates a consistent, value-based, and centralized price point engineering of entire parts portfolios. We determine product value, competitive positioning, and the perception of a fair price image. We also determine the impact of price increases and decreases on volume for profit maximization. AI-based data enrichment is made possible through text and image recognition, and AI-based clustering targets thousands of parts and SKUs. From there, automated optimization of prices is introduced via a micro-cluster pivot approach.
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Next is Country Price Setting. This enables steerable local price lists for spare parts linked to a central parts pricing system. It’s done by identifying if the setting reflects local market price levels while on central price guidance. We also consider if there are local structure or level adjustments required and if the local market price level can be balanced with grey market risks. Key to this is price recommendations based on central prices and instant access to market-specific parts data via analytics. Also important is the use of analytics-based dashboards in near-time, including automated KPIs such as grey market indices.
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Parts pricing approach in the New: Technologies such as Advanced Analytics and Artificial Intelligence are applied in every step of the parts pricing process.
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Finally, we determine Customer Price Setting. This powers a discount/net price system that considers customer value and business type systematically. For this, we identify the price differentiation across sales channels. We determine customer value and develop value-based discounting. And we review the automated quotation process. Automated conditions monitoring (discounts and rebates) is collated in a central dashboard. And analytics-based optimal product bundling (e.g. repair sets for machines) is implemented.
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UP TO60%
of companies profit can be driven by aftersales.
MORE THAN20%
profit increase can be seen, if companies successfully optimize their prices.
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Examples from the field
A multination appliance manufacturer sought parts pricing optimization. The client was under pressure to cut costs and boost profitability. It had used a traditional cost-plus price model. Parts pricing was identified as a key element of its long-term profitability. Accenture started with the classification of parts into parts families. Following this, we derived new list prices based on pivot parts and price rules. Captive parts were then realigned to reflect perceived customer value. Our efforts resulted in a profitability increase of $13 million. And it enabled the harmonization and streamlining of prices across all markets.
A commercial truck OEM sought profit growth through parts pricing. The client also wanted to improve service parts pricing for both the OEM and dealers. And it needed a way to combat customer perception of non-competitive pricing for service parts. Accenture deployed a non-traditional approach to pricing and portfolio classification. A structured pricing feedback monitoring methodology was implemented. Attributes and high-definition images for the client’s e-commerce platform were provided. The program was expanded to other segments to boost impact. The results include 18,000 service parts prices optimized, $800 million revenue in scope, and +$79 million annual benefits.
Conclusion
Parts pricing optimization can help you embark on the road to true value-based pricing. But companies must implement new technologies and a cohesive optimization plan if they are to make the most of this opportunity. With the right approach, they can find the optimal price point for every SKU in their portfolio.
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About the authors
Maxence Tilliette, Patric Kirchner, Kathrin Schwan, Marcus Demmelmair, Sona Kochkanyan, and Claude-Henry Pignon
Maxence is a Managing Director at Accenture and the Global Lead for the Product-as-a-Service Enablement offering in Industry X. Meet him on LinkedIn.
Patric is a Managing Director at Accenture. He is an expert for Commercial Excellence with a focus on Monetization Strategies, innovative Pricing, and Sales digitization. Meet him on LinkedIn.
Kathrin is a Managing Director at Accenture's Applied Intelligence business. With the Data Science and Machine Learning Engineering capability, she focuses on industrializing PoCs and MVPs to make the value promise of AI consistently come true. Follow her on LinkedIn.
Marcus is a Senior Manager at Accenture and part of the Strategy, CEO & Enterprise Strategy entity. He is an expert in AI-powered pricing and has designed and implemented parts pricing programs using the latest methodologies and technologies to achieve both top-line growths as well as cost-effective pricing organizations. Get in touch with him via LinkedIn.
Sona is part of the Applied Intelligence business at Accenture. She is focusing on Parts Pricing Analytics & Cloud-based Operations. Get in contact with her via LinkedIn.
Claude-Henry is a Manager at Accenture and part of Industry X. Here he focuses on Parts Pricing implementation. Have a look at his profile on LinkedIn.
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