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SaaS Pricing Practices Typology: A Case Study

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• Andrey Saltan 8 , 9 &
• Kari Smolander 8  

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Software-as-a-Service (SaaS) pricing addresses decisions of monetary compensation and the conditions for the SaaS solution to the customer. Efficient SaaS pricing requires sophisticated decision-making and analytics, as well as coordination and compromises between the many business functions involved. The decision-making includes integrated analysis of different perspectives and streams of information. Like in many other product management areas, there is no silver-bullet solution for pricing. We conducted a multiple case study using fifteen SaaS companies with data collection primarily through semi-structured interviews to assess SaaS pricing practices and identify major factors that affect the way pricing is done. We identified four distinct types of SaaS pricing patterns and detailed their main characteristics.

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1 Introduction

Software-as-a-Service (SaaS) pricing refers to the entire scope of decisions, practices, underlying conditions, and processes that determine the monetary compensation for using SaaS solutions. It is an essential and challenging element of SaaS product management, with a significant impact on business success. Incorrect pricing can lead to market failure, even for a technologically advanced SaaS solution. Pricing serves as an essential bridge between different business functions (e.g., product planning and development, revenue and cost management, and customer acquisition and retention) and business units (e.g., R&D, product management, sales, and marketing). Recent studies and reviews indicate the progress and sophistication in SaaS pricing and the growing attention from practitioners. Multiple challenges for companies can still be identified that require support from the research community [ 1 ].

Overwhelming and complex pricing-related processes and structures, the unclear segregation of responsibilities for pricing between managers involved, premature decision-making practices, and constantly changing objectives are often prime challenges. Efficient pricing requires developing sophisticated multi-layered structures with many different mechanisms and options, considering the trade-offs, objectives, and outcomes that pricing must meet. Informed SaaS pricing decision-making requires the involvement of different stakeholders and the consideration of many factors that include market characteristics, product and technology specifications, customers, and customer needs and expectations. Taking into account these factors requires collecting a vast amount of data and advanced analysis, tasks that are not trivial.

Existing publications by scholars and practitioners reveal the variety and complexity of mechanisms available for SaaS companies while pricing their solutions [ 2 , 3 , 4 , 5 ]. They also provide an overwhelming number of recommendations concerning different pricing aspects [ 1 ]. However, the repeated enumeration of possible pricing options and fragmented recommendations does not bring the required clarity to SaaS companies, and pricing remains one of the most under-managed functions in many of them. Little evidence exists about the interconnection of different components of SaaS pricing, typologies of overall pricing practices, or decision-making organization principles.

This paper aims to identify and evaluate patterns in SaaS pricing, identify the major factors that affect it, and propose a typology of SaaS pricing practices. This study continues our inquiry into how SaaS companies design and deploy their pricing practices and processes.

2 Background

2.1 related studies.

SaaS pricing is a maturing and prominent area of research. Existing SaaS pricing studies indicate the progress and sophistication in SaaS pricing practices and offer solutions that can carry SaaS pricing state-of-the-practice to a higher level. Our recent multivocal literature review [ 1 ] identified multiple challenges that require further support from the research community.

Some studies have already adopted the case-study method to evaluate various pricing aspects in SaaS and software companies. For example, based on interviews with software professionals from multiple case companies, Ojala [ 6 , 7 ] identified and assessed factors that affect selecting revenue and pricing models in software companies. In another study [ 8 ], Ojala and Laatikainen investigated the interrelation between SaaS architecture and SaaS pricing practices.

2.2 SaaS Pricing

Existing pricing state-of-the-art and state-of-the-practice suggest distinguishing between four main pricing strategies: value-based pricing, market-based pricing, competitor-based pricing, and cost-based pricing. In short, they can be explained as follows in the SaaS context. Value-based pricing assumes aligning prices with the value perceived by the customer. Market-based pricing is grounded in an analysis of the market equilibrium of all customers and SaaS providers. Competitor-based pricing assumes aligning prices with the prices offered by competitors with the premium or discount depending on the circumstances. Finally, Cost-based Pricing suggests setting prices based on the cost structure of SaaS providers. In application to SaaS, researchers and practitioners have repeatedly emphasized the advantages and importance of value-based pricing. However, all four pricing strategies might exist in practice, and in many cases, the actual strategy is a hybrid combination of these strategies.

Several frameworks and structures exist to organize and systematize pricing in application to SaaS and cloud solutions in general [ 1 ]. However, in our study, we adopted a more generic, widely accepted, and comprehensive one called the Strategic pricing pyramid [ 9 , 10 ]. The framework has the following levels from the bottom up:

Value Creation: The logic of value generation for customers from using the SaaS solution, including the metrics of impact of specific parameters on value.

Price Structure: The logic of structuring prices for a given SaaS solution, including principles of price variability depending on the customer-specific parameters.

Price and Value Communication: The principles of price and value communication to customers.

Pricing Policy: The principles of how prices may be altered, by whom, under what circumstances, and to what degree.

Price Level: The actual charge within the price structure according to the pricing policy.

3 Research Method

The following research question drove our study: What types of SaaS pricing practices can be identified in a real-life context? To address this question, we used a multiple case study research design to compare existing SaaS pricing practices and processes [ 11 ]. The case sampling strategy was guided by the diverse case approach with its primary objective to achieve variance along the relevant dimensions. Our scope of companies includes two major types of SaaS providers, “born-in-the-cloud” companies that usually have just one flagship SaaS solution and large IT vendors or traditional enterprise software vendors looking to expand into SaaS software markets. Other dimensions, including company size and maturity, target market type, maturity, and location, were considered while selecting case companies.

We selected a set of fifteen primary and secondary cases. Our primary cases include companies whose pricing managers we interviewed. Most of them are “born-in-the-cloud” small and medium-sized companies that usually have just one flagship SaaS solution. We could not involve large US-based SaaS companies in our study, although their presence is essential to understand and develop a comprehensive SaaS pricing typology.

To remedy this situation, we decided to include cases that we did not interact directly with. We assessed their pricing practices through available information and teaching cases on their business strategies and operations. We referred to these cases as secondary and found them in the Case Center Footnote 1 , the largest repository of teaching cases. This allowed us also to make assessments of pricing in large SaaS and digital companies as well as in enterprise software vendors with SaaS solutions in their product portfolio. An overview of the primary and secondary case companies is summarized in Table 1 .

The goal is to identify decision-making practices and processes and understand the logic behind them. A within-case analysis was conducted with the analytical strategy of explanation-building based on case descriptions. The case analysis can be classified as exploratory. We developed patterns and categories and identified similarities and differences in the data. The logical sequence followed the research goals, starting with within-case analysis to establish themes and then continued by a cross-case comparison to identify similarities and differences.

For primary cases, the data collection consisted of interviews with SaaS managers responsible for pricing. The length of interviews varied from 1 to 2 h. The goal of the interviews was to identify the pressure points of decision-making in SaaS pricing, motivate companies to participate in the longitudinal study, and assess both the current status quo and product managers’ perceptions of existing processes and practices. The data we obtained covered the following topics:

General information about the company and SaaS solution: name, industry, market, number of employees, number of customers, maturity level, business model, number of SaaS solutions, SaaS solution type, maturity level, etc.

SaaS pricing practices and processes: Pricing frameworks used, product activities allocation across business units, collaboration principles between business units, pricing tools used, SPM performance assessment principle, etc.

SaaS pricing decision-making principles: formal regulation and written policies on SaaS pricing activities, risks, and uncertainty identified, types of data collected for pricing decision-making, models and tools used to process provided data, information system support for pricing processes, etc.

For secondary cases, the data collection consisted of content analysis of the documented teaching cases and teaching notes to extract similar information.

4 A Typology of SaaS Pricing Practices

The qualitative research approach with semi-structured interviews allowed us to identify four major factors that affected SaaS pricing. The factors were the following:

Factor 1: types of customers and market segments targeted. We can distinguish between B2B, B2G, and B2C customers, as well as the size of targeted customers (especially in the B2B market).

Factor 2: delivered value and willingness to pay (WTP) for the SaaS solution. Specific estimates based on a limited number of cases are difficult to make; still, conventionally, we can distinguish between SaaS solutions with an average monthly usage fee of up to 100 USD, SaaS solutions with an average fee of more than 5000 USD, and those in between these two price levels.

Factor 3: the complexity of SaaS purchase and usage. We can distinguish between self-service SaaS solutions, SaaS solutions that might require human assistance in the purchase, customization, and maintenance, and SaaS solutions that require intensive human involvement, including offering additional professional and training services.

Factor 4: the level of nicheness of the SaaS solution. We can distinguish between mass-market SaaS solutions focused on solving problems typical for a wide range of customers and SaaS solutions focused on solving issues specific for customers from the same industry, country, or facing similar regulatory constraints.

Based on the analysis of these four factors, we developed a typology of four generic SaaS pricing approaches that we labeled Mass-market SaaS pricing , Generalist SaaS pricing , Specialist SaaS pricing , and High-rise SaaS pricing . While typology was based on our investigation of SaaS company pricing, it also appears reasonable from a general business model perspective as it represents different business models and pricing practices. These four pricing approaches are presented in Table 2 and described below.

Mass-market SaaS pricing refers to pricing practices often implemented in SaaS companies that offer mass-market solutions and operate in the B2C market and B2B market, focusing on small-sized companies. Such SaaS solutions might also be used in large companies as a part of private initiatives by small teams and individuals. The main pricing objectives for this type of pricing are customer acquisition, market share maximization, and winning the competition. A value-based pricing approach, to a large extent, is supplemented with market-based pricing. Companies of this type also often adopt the freemium model and a free model with monetization other than charging customers (i.e., advertisement). Adjusting for the level of company and SaaS solution maturity, the pricing-related processes can be highly formalized, driven by data analytics, and even automated.

Generalist SaaS pricing is often implemented in SaaS companies that offer mass-market services for customers on the B2B market, serving both small, mid-sized, and large companies. The main pricing objective for this type of pricing is customer acquisition, monetization and retention and winning the competition. Companies with this type of pricing employ a hybrid pricing approach based on a combination of value-based pricing and competitor-based pricing. While competing companies might evaluate and structure perceived value differently, the average amount of money charged per customer or account are quite similar. Instead of freemium in the case of mass-market SaaS pricing , companies with generalist SaaS pricing often use penetration pricing and sophisticated usage-based tiered pricing with multiple available options. Pricing-related processes are often formalized and driven by data analytics. Pricing automation may be employed; however, a sales team exists, and large companies can negotiate pricing individually.

Specialist SaaS pricing refers to pricing practices implemented by B2B SaaS companies that have a niche SaaS solution. The limited market requires more focusing on monetization and retention of existing customers with a high-quality service rather than acquiring new customers. Companies with this type of pricing implement value-based pricing in its canonical understanding with a fair match of prices to the value perceived. As a result, defining value metrics and assessing perceived value is crucial. However, most pricing-related processes are not usually formalized. Decision-making data can consist of direct feedback from customers. The basic pricing information might be publicly available; however, purchase processes typically involve interaction with the sales team.

High-rise SaaS pricing is implemented in companies aiming to serve large organizations with their SaaS solution. The main pricing objectives are customer monetization and retention along with sustainable business development. This type of SaaS pricing involves combining value-based pricing with cost-based pricing. The complexity of these SaaS solutions and the requirements for reliability and security means the associated costs might be quite high. Therefore, it is essential for companies with this type of pricing to ensure that revenue from a reasonably limited number of customers with high charges per account will cover these costs. Most of the pricing-related processes are not formalized, pricing contract terms are discussed individually with all customers, and the required supplementary services define the final price to a large extent. Pricing information is not publicly available.

The literature discusses and proposes many factors that should be considered while designing and implementing pricing. As part of the multivocal study, we revealed 24 factors and classified them into four categories: Market, Company, Consumers, Product [ 1 ]. However, the impact of these factors and the aspects of pricing they affect remained unclear. Factors 1–4 correspond with the most cited factors as specified in [ 1 ]. While Factors 1 and 2 have a direct match, Factors 3 and 4 can be considered subfactors of a broader factor “functions and features” in the Product category.

Besides these four factors, product/company maturity, cost structure, and type of solution might affect and explain pricing practices in SaaS companies. However, our qualitative analysis suggests that maturity and costs could explain pricing practices ex-post rather than define them ex-ante. These factors set certain constraints and limitations on companies and managers; however, various companies overcome these constraints and limitations differently. As for the type of the solution, it was not clear how this could be determined and generalized from the case study as we covered only several categories of SaaS solutions from the extensive hierarchy (i.e., G2 software category hierarchy Footnote 2 ). As a result, we decided not to incorporate these three factors in the typology.

5 Discussion and Practical Implications

The results of our study contribute to the understanding of pricing practices. We aimed to answer the research question of what types of SaaS pricing practices can be identified in a real-life context. To answer this question, we adopted a case-study research approach to explore pricing in fifteen SaaS companies. As a result, we developed a taxonomy of pricing practices. This typology can serve as a foundation for designing and establishing pricing practices in SaaS companies.

Our findings suggest that major factors of pricing in SaaS companies are the following: the targeted types of customers and market segments, the perceived value and willingness to pay for the SaaS solution, the complexity of the SaaS solution and its adoption by customers, and the level of nicheness of the SaaS solution. While the typology was based on an assessment of SaaS pricing practices, it can also be interpreted from the perspective of SaaS companies’ business models.

Several implications for SaaS companies can be derived from our study. Gaining a clear understanding of pricing complexity for a given SaaS business model is essential to its long-term viability. While certain types of SaaS pricing practices can be identified, there is still no silver bullet. Within each recognized type, practices may vary depending on many different factors (i.e., product/company maturity) and circumstances (i.e., regulatory constraints). Constant evolution and analytical-based experimentation with pricing might help to find the unique combination of pricing parameters that will allow the company to reach its objectives and ensure its long-turn market success.

The findings should be considered in light of limitations that may have an impact on generalizability. Our sample of SaaS companies was reasonably limited and not randomly selected. Within our study, we felt that we reached a saturation point where the same patterns started recurring, and no new insights were obtained by performing additional interviews. We included several secondary cases to have large, mostly B2C SaaS companies in our sample for analysis. However, a more extensive and more diverse selection of cases may have yielded different findings.

Although this study provides valuable insights into SaaS pricing, we call for further research probing the question of designing and implementing SaaS pricing. Our qualitative study offered a taxonomy of SaaS pricing, but its generalizability is limited. With our previous industry survey [ 12 ], this study provides some solid ground for further research that could employ quantitative analyses based on a large industry survey.

https://www.thecasecentre.org/ .

https://www.g2.com/categories .

Saltan, A., Smolander, K.: Bridging the state-of-the-art and the state-of-the-practice of SaaS pricing: a multivocal literature review. Inf. Softw. Technol. 133 , 106510 (2021)

Article   Google Scholar  

Laatikainen, G., Ojala, A., Mazhelis, O.: Cloud services pricing models. In: Herzwurm, G., Margaria, T. (eds.) ICSOB 2013. LNBIP, vol. 150, pp. 117–129. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39336-5_12

Chapter   Google Scholar  

Lehmann, S., Buxmann, P.: Pricing strategies of software vendors. Bus. Inf. Syst. Eng. 1 , 452–462 (2009)

Kullar, P.: 25 Companies Show You Their Best SaaS Pricing Models as Examples. https://blog.upscope.io/25-companies-show-you-their-best-saas-pricing-models/ . Accessed 03 Oct 2020

Campbell, P.: The Anatomy of SaaS Pricing Strategy (2017)

Google Scholar  

Ojala, A.: Adjusting software revenue and pricing strategies in the era of cloud computing. J. Syst. Softw. 122 , 40–51 (2016). https://doi.org/10.1016/j.jss.2016.08.070

Ojala, A.: Selection of the proper revenue and pricing model for SaaS. In: IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Proceedings, pp. 863–868 (2014)

Laatikainen, G., Ojala, A.: SaaS architecture and pricing models. In: IEEE International Conference on Services Computing (SCC) Proceedings, pp. 597–604 (2014)

Hogan, J., Nagle, T., Hogan, B.J., Nagle, T.: What is strategic pricing? (2005)

Kittlaus, H.-B., Fricker, S.A.: Software Product Management: The ISPMA-Compliant Study Guide and Handbook. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-642-55140-6

Book   Google Scholar  

Yin, R.K.: Case Study Research: Design and Methods. Sage, Thousand Oaks (2009). https://doi.org/10.1097/FCH.0b013e31822dda9e

Saltan, A., Smolander, K.: How SaaS companies price their products: insights from an industry study. In: Klotins, E., Wnuk, K. (eds.) ICSOB 2020. LNBIP, vol. 407, pp. 1–13. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67292-8_1

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Saltan, A., Smolander, K. (2021). SaaS Pricing Practices Typology: A Case Study. In: Gregory, P., Kruchten, P. (eds) Agile Processes in Software Engineering and Extreme Programming – Workshops. XP 2021. Lecture Notes in Business Information Processing, vol 426. Springer, Cham. https://doi.org/10.1007/978-3-030-88583-0_8

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Mastering the Pricing Case Study: A Comprehensive Guide

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Setting the optimal pricing for products or services is important for a company as it directly impacts profitability. So every management consulting firm helps its clients with pricing strategies. The primary goal of a pricing case is to recommend a price that maximizes profit, taking costs for product/service and market considerations into account.

A typical pricing case interview would start something like this –

A manufacturer of kitchen knives sells a range of products, from low-end to professional, to customers at different price points. They’ve developed a new line of knives in collaboration with a celebrity chef and would like help setting the prices for these products.

Pricing cases might not seem straightforward initially, but with the right frameworks and practice cases, we will help you prepare for it.

In this article, we’ll discuss:

• Examples of pricing cases.
• The alternative pricing methods.
• How to approach a pricing case interview.
• An end-to-end pricing case example.

Let’s get started!

Pricing Case Examples

How to approach a pricing case interview, what are the most common pricing strategies, an end-to-end pricing case example, the relevant pricing strategy for our pricing case examples, 6 tips for solving a pricing case interview.

As companies mature, pricing becomes more complex because:

• Companies develop multiple products with different cost structures.
• Clients have different product/service needs and price sensitivities.

Pricing can also be a source for driving revenue growth if you can identify opportunities to price based on value to the customer or in a way that optimizes the tradeoff between revenue and costs. Let’s explore a few situations where consultants can help with pricing:

New Art Museum

A new modern art museum is scheduled to open next year in a major European city. The project lead has requested your help with the pricing of the admission tickets. He has two questions: How would you approach selecting a pricing method for the museum? What price would you recommend and why?

Nail the case & fit interview with strategies from former MBB Interviewers that have helped 89.6% of our clients pass the case interview.

Animal Healthcare

Our client provides healthcare services for animals and develops veterinary drugs. The client has recently developed a product that enables cows to increase milk production by 20%. They have turned to you to figure out how to price this new product in order to maximize profits.

California Municipality

Your client is a local municipality in California. The town recently built a complex of six parking lots, encircling a nearby community center and outdoor mall, which features shopping, restaurants, and some light attractions. In total there are 20,000 parking spots in these lots. Our client wants to maximize the profit it generates from the parking lots with a focus on revenue generation. How would you think about different types of pricing structures and revenue models for the parking lots?

In most pricing case questions, you’ll have to work through one or a combination of the following pricing strategies:

Most companies use a combination of these alternative pricing strategies to maximize profitability. For example, a manufacturer of diet pills that costs $10 to produce may be able to charge $100 per bottle if the target customers have low-price sensitivity and high perceived value (a savings of many hours working out in the gym and/or eliminating the negative health effects of being overweight).

Note that for these examples, multiple correct solutions are possible. The important thing in pricing case interviews is to back up your answer well with analysis and logic.

Relevant pricing strategies + sample approach:

• Since the costs of running the museum are mostly fixed (e.g., staff, maintenance, and utilities), a cost-based pricing strategy will not provide much insight. 
• The new museum should therefore use a combination of demand-pull and market-based pricing.
• Since the museum is new, they should set their pricing below the average market price in order to draw in early customers to check out the museum and spread awareness to their friends.
• Do a check that the proposed price point will cover a good portion of the museum’s costs with expected attendance numbers.
• Note that for a museum, ticket sales are probably not expected to fully cover costs. Exhibit sponsors, grants, and donations will be additional sources of funding.

Our client provides healthcare services for animals and develops veterinary drugs . The client has recently developed a product that enables cows to increase milk production by 20%. They have turned to you to figure out how to price this new product in order to maximize profits.

• The cost of making a dose is $30.
• The competition charges $300 per dose.
• Clients are willing to pay $300 per dose because the 20% increase in their revenues will more than offset the product’s price.
• Therefore, the client should set its price based on a combination of market-based pricing and demand-pull.
• The recommendation of whether to price above or below the competitor’s $300 price depends upon how our product compares to theirs. If the client’s product is superior in any way, we may be able to command a higher price. If we are entering the market late and with a comparable product, we’ll need to set our price lower in order to provide an incentive for customers to try our product.
• At $300, this will provide a very attractive gross margin of close to 90%.

Your client is a local municipality in California. The town recently built a complex of six parking lots, encircling a nearby community center and outdoor mall, which features shopping, restaurants, and some light attractions. In total there are 20,000 parking spots in these lots. Our client wants to maximize the profit it generates from the parking lots with a focus on generating additional revenue. How would you think about different types of pricing structures and revenue models for the parking lots?

• Costs are mostly fixed (staff salaries and bond payments for garage construction), so a cost-based strategy doesn’t provide much insight. 
• Competitive garages are priced similarly, but less convenient for shoppers.
• Excess capacity provides the opportunity to identify additional revenue sources by driving higher utilization of the parking spaces and offering customers additional services while parked in the garage. 
• Brainstorming options to increase revenue identifies a variety of options (offer monthly passes, charge stores monthly fees to validate customer parking, provide valet parking and car washing, use excess capacity for concerts, fairs, or other large events that require parking spaces.)
• Do a check that the proposed price point, including value-added services, will cover salary and bond payments.

Like any other case interview, you want to spend the first few moments thinking through all the elements of the problem. Also, there is no one right way to approach a pricing case study but it should include the following:

• Cost-based: What is the cost of making the product or delivering the service?
• Market-based: What is the pricing of a comparable product or service? Can we price above what the competition is charging?
• Value-based: What is the customer’s willingness to pay? Will we lose customers if we charge higher prices? Are there incremental services we could provide that customers would value?  
• What is the volume impact of the alternative pricing model? What is the incremental revenue expected?
• What significant costs will be incurred if the pricing model changes?
• What revenues and costs will be realized if value-added services are launched?
• It can be hard to raise prices once customers are used to a low price. Price anchoring (establishing a higher price but discounting it) may be needed for some time to transition customer expectations.
• What is the expected response from the competition?
• What is the impact on the brand if we reduce prices?
• What is the impact on volume if we increase prices?

Let’s go through the pricing case for the California municipality with 6 parking lots. Remember that the instructions said to focus on incremental revenue. As you develop your structure for the case, remember the key components of our pricing issue tree approach:

• Pricing strategies including offering value-added services
• Financial Impact

Tailor Your Pricing Case Approach for this Client

The first thing you will need to do in a pricing case study, as well as any other consulting case, is to ensure you understand the problem you need to solve by repeating it back to your interviewer. If you need a refresher on the 4 Steps to Solving a Consulting Case Interview , check out our guide.

Second, you’ll structure your approach to the case. Stop reading for a moment and consider how you’d structure your analysis of this case. We gave you some hints in our sample cases section. After you’ve outlined your approach, read on and see what issues you addressed, and which you missed. Remember that you want your structure to be MECE and to have a couple of levels in your Issue Tree . 

• What is the municipality’s cost structure? 
• How much revenue is required to cover costs?
• How much of a profit expectation does the municipality have? Do they want to generate as much revenue as possible or cover their costs and provide a service to their community at an attractive price?
• What are the municipality’s current pricing structure and prices?
• How do the municipality’s current prices compare to alternative parking options?
• What alternative pricing structures could the municipality use (hourly rates, daily rates, monthly rates, store validation, etc.)
• What non-price considerations are there? (Proximity to popular destinations, roof vs. no roof, lighting/safety, cleanliness)
• What services could the parking lots provide in addition to the parking spot?
• How much space would providing additional services require?
• How much revenue would they generate?
• What are the expected revenues of alternative pricing models?
• What are the expected revenues of value-based services?
• Would additional costs be incurred?
• How might customers react to alternative pricing models?
• To value-based services?
• How might competitors react?

Pricing Case Brainstorming Exercise

After you structure your approach, the interviewer asks you to brainstorm some revenue growth opportunities for the California municipality. Again, stop reading for a moment to do this exercise yourself because you’ll learn more if you do. When you’re done, note the ideas you didn’t consider. Few candidates hit every possibility, but to move on to the next round of interviews, you’ll definitely want to go beyond the straightforward responses. 

• Charge store owners for parking spots to offer free parking for visitors.
• Charge higher pricing for spots closest to the stores.
• Offer annual/monthly parking passes.
• Valet parking
• Car washing
• Quick car servicing (e.g., oil change)
• Locate public transportation/bus stops adjacent to the lots and provide parking to commuters at a monthly rate.
• Rent space to event attendees (e.g., sporting events, concerts, fairs).

When you ask about the municipality’s current pricing and parking space utilization rates, your interviewer provides you with the following exhibit and asks you to calculate the daily revenue. Note that the parking lot has two sources of revenue:

• Tourists/shoppers buying parking tickets for an hourly rate.
• Store owners buying monthly parking permits for their staff.

Calculation of current daily revenue:

• 3 hour parking: Revenue = (20,000 parking spots) * (30% of total lot occupancy for tourists/shoppers) * (75% of tourists/shoppers occupancy for 3hr parking) * ($2/hr) * (*3hrs) = $27,000
• 5 hour parking: Revenue = (20,000 parking spots) * (30% of total lot occupancy for tourists/shoppers) * (25% of tourists/shoppers occupancy for 5hr parking) * ($10 flat fee) * = $15,000
• Total tourist/shopper revenue – $42,000
• Store owners: Revenue = (20,000 parking spots) (5% lot occupancy for owners) *($240/month) (1/30 to convert to daily revenue) = $8,000
• Total tourist/shopper + store owner daily revenues= $50,000

Alternative Pricing Model: Store Validation

If we move to a store validation model, in which a store validates the ticket of any customer who buys something, the spots taken would increase to 10,000, or 50% of available capacity. This is because the cost of parking is currently a deterrent to customers shopping at this mall. More shoppers at the mall would be a significant benefit to store owners.

The cost per validation would be $5 to the store. Assume every person parking a car purchases something. The number of store owner permits would drop to 750 since store owners will likely decide to save money on permits to pay for visitor parking spots.

What would be the impact on daily revenue?

• Increase in the tourist/shopper revenues = $8,000 = (10,000 spots) * ($5 per spot) – $42,000
• Store owner revenues would decrease by $2,000= (750 permits) * ($240 per permit/30 days per month) – $8,000 
• Change in total daily revenue = + $6,000
• A good candidate will recognize that the increase of 12% in daily revenues is a positive move forward.

Risks to the Change to a Validation Pricing Model

Do you see any risks to a validation pricing model? Do you think you’re likely to run into any resistance? From which types of stores and why?

• Stores with low price per transaction (such as ice cream shops) will likely lose money if they pay for the $5 validation fee, therefore 100% of stores will not be willing to participate.
• An alternative validation model would be to charge stores based on a percentage of transactions or profits. This would get less pushback.
• Under the percentage model, there would need to be a cap on the price charged to stores. A 5% charge on an ice cream may be reasonable but a 5% charge on a $1000 handbag would not be.
• You could note that while the focus of this case is on revenue generation, the costs to run a validation model might be slightly higher because the municipality will need to process the validation numbers and bill the stores.

Recommendation

Lastly, provide your recommendation for the client. Try coming up with your own before reading our sample.

The California Municipality should proceed with the transition to the validation pricing model because it provides an incremental $6000 per day or an increase in revenue of 12%. While doing this, it should study the risk of pushback from store owners with low transaction value and the possibility of charging based on a percentage of the transaction. Additionally, the municipality should roll out revenue growth opportunities such as renting out excess capacity and offering value-added services (e.g., valet parking) over time.

Determine the relevant pricing strategy to apply (e.g., cost-based vs. market-based or demand pull).

Brainstorm all possible changes in pricing methodologies that might bring in additional revenue (e.g., hourly, daily, or monthly pricing, a store-validation model for our parking lot case), don’t forget that charging for value-added services could be part of a broader pricing & revenue generation strategy (e.g., oil changes, car wash for our parking lot case)., calculate the incremental revenues from suggested changes in pricing., always have an answer to whether to proceed or not., detail the risks associated with the pricing changes..

– – – – –

In this article, we’ve covered:

• Examples of pricing cases
• Approaches for solving a pricing case
• Different pricing strategies 
• Tips for solving a pricing case

Still have questions?

If you have more questions about pricing case study interviews, leave them in the comments below. One of My Consulting Offer’s case coaches will answer them.

Other people prepping for pricing case studies found the following pages helpful:

• Our Ultimate Guide to Case Interview Prep .
• Types of Case Interviews .
• Consulting Case Interview Examples .
• M&A Case Study Interview.
• Market Sizing Case Questions .

Help with Case Study Interview Prep

Thanks for turning to My Consulting Offer for advice on pricing case study interviews. My Consulting Offer has helped almost 85% of the people we’ve worked with to get a job in management consulting. We want you to be successful in your consulting interviews too. For example, here is how David  was able to get his offer from  Deloitte.

2 thoughts on “Mastering the Pricing Case Interview: A Comprehensive Guide”

In the Alternative Pricing Model: Store Validation

The original tourist/shopper revenue is $42,000 Under the alternative pricing model, (10,000 spots) * ($5 per spot) = $50,000, an $8,000 increase

The original store owner revenue is $8,000 Under the alternative pricing model, (750 permits) * ($240 per permit/30 days per month) = $6,000, a $2000 decrease.

The new total daily revenue = $56,000 Original daily revenue = $50,000

Shouldn’t the change in total daily revenue be = $56,000 – $50,000 = $6,000, a 12% increase?

I’m confused about the change in total daily revenue $2,000 and 4% numbers.

Yes, good catch! We’ll make the change. Sorry for the confusion.

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The ghosts of ‘Wintel’: What leaders can learn from the diverging paths that made Microsoft a $3 trillion powerhouse and flatlined Intel

Steve Jobs wasn’t accustomed to hearing “no.” But that was the answer from Paul Otellini, CEO of Intel . 

It was 2006, and Intel, the global king of computer chips, was bringing in record revenue and profits by dominating the kinds of chips in hottest demand—for personal computers and data centers. Now Jobs wanted Intel to make a different type of chip for a product that didn’t even exist, which would be called the iPhone.

Otellini knew chips for phones and tablets were the next big thing, but Intel had to devote substantial capital and its best minds to the fabulously profitable business it already possessed. Besides, “no one knew what the iPhone would do,” he told The Atlantic seven years later, just before he stepped down as CEO. “There was a chip that they were interested in, that they wanted to pay a certain price for and not a nickel more, and that price was below our forecasted cost. I couldn’t see it.”

Otellini, who died in 2017, was a highly successful CEO by many measures. But if that decision had gone the other way, Intel might have become a chip titan of the post-PC era.  Instead, it gave up on phone chips in 2016 after losing billions trying to become a significant player. As he left the company, Otellini seemed to grasp the magnitude of his decision: “The world would have been a lot different if we’d done it.”

Meantime, some 800 miles north, in Seattle, Microsoft was struggling to find its role in a tech world dominated by the internet, mobile devices, social media, and search. Investors were not impressed by its efforts. No one could have foreseen that years later, a few key decisions would set the company up as an AI powerhouse and send its stock soaring. There was a time not so long ago that Microsoft and Intel were both atop the tech world. They were neither competitors nor significant customers of each other, but what New York University’s Adam Brandenburger and Yale’s Barry Nalebuff deemed “complementors.” Microsoft built its hugely profitable Windows operating system over the years to work on computers that used Intel’s chips, and Intel designed new chips to run Windows (hence “Wintel”). The system fueled the leading tech product of the 1990s, the personal computer. Microsoft’s Bill Gates became a celebrity wonk billionaire, and Intel CEO Andy Grove was Time ’s 1997 Man of the Year.

Since then their paths have diverged sharply. Microsoft in 2000 was the world’s most valuable company, and after losing that distinction for many years, it’s No. 1 again. Intel was the world’s sixth most valuable company in 2000 and the largest maker of semiconductors; today it’s No. 69 by value and No. 2 in semiconductors by revenue, far behind No. 1 TSMC (and in some years also behind Samsung ).

A Fortune 500 CEO makes thousands of decisions in a career, a few of which will turn out to be momentous. What’s easy to explain in hindsight—that Microsoft would be at the forefront of AI, that Google would become a behemoth, that Blockbuster would fade into obscurity—is never preordained. Often the fateful decisions are identifiable only in retrospect. Nothing more vividly illustrates this than the parallel stories of Microsoft and Intel. The case study of what went right and wrong at those two giant corporations offers a master class in business strategy not just for today’s front-runners at the likes of Google, Open AI, Amazon , and elsewhere—but also for any Fortune 500 leader hoping to survive and thrive in the coming decade.

Wintel’s origin story

The two companies were founded a mere seven years apart. Intel’s founders in 1968 included Robert Noyce, coinventor of the computer chip, and Gordon Moore, who had written the seminal article observing that the number of transistors on a chip doubled every year, which he later revised to two years—Moore’s law, as others later called it. Andy Grove was employee No. 3. All three are still regarded as giants of the industry.

Bill Gates famously dropped out of Harvard to cofound Microsoft with Paul Allen, a childhood friend. They were excited by the prospects of creating software for a new concept, the personal computer, also called a microcomputer. They launched Microsoft in 1975. 

The two companies’ paths crossed when IBM decided in 1980 to produce a PC and wanted to move fast by using existing chips and an existing operating system developed by others. It chose Intel’s chips and Microsoft’s operating system, profoundly transforming both companies and the people who ran them. IBM’s size and prestige made its design the industry standard, so that virtually all PCs, regardless of manufacturer, used the same Intel chips and Microsoft operating system for decades thereafter. As PCs swept America and the world, Intel and Microsoft became symbols of technology triumphant, glamour, success, and the historic bull market of 1982 to 2000.

Then everything changed.

The reign of Gates and Grove peters out

In October, 2000, Fortune ran an article with an illustration depicting Gates and Grove as monumental Egyptian sphinxes. The headline: “Their Reign Is Over.”

The reasoning: “Gates and Grove attained hegemony by exploiting a couple of key choke points in computer architecture—the operating system and the PC microprocessor,” the article explained. “But in the new, more diverse IT world wired together by universal internet protocols, there are no such obvious choke points to commandeer.”

Thus began a multiyear identity crisis for both companies. Intel’s PC chips and Microsoft’s PC operating system and applications remained bountifully profitable businesses, but both companies and their investors knew those were not the future. So what was? And who would lead this new era?

In January of 2000, Gates stepped down as CEO after 25 years, and Steve Ballmer, Microsoft’s president and a college friend of Gates, took his place; Gates remained chairman. Two days later, Microsoft’s stock rocket ran out of fuel. On that day the company’s market value hit $619 billion, a level it would not reach again for almost 18 years.

Grove was no longer Intel’s CEO in 2000, having handed the job to Craig Barrett, a longtime company executive, in 1998. But as Intel’s visionary and most successful CEO, Grove remained an important presence as chairman of the board. His health was becoming an issue; he had been diagnosed with prostate cancer in 1995, and in 2000 he was diagnosed with Parkinson’s disease. Intel’s stock roared until August, when the company’s market value peaked at $500 billion. It has never reached that level since.

But most significantly, 2000 was the year that the internet began to seem like it just might make Wintel irrelevant. 

At Intel, Barrett responded with acquisitions, many of which were in telecommunications and wireless technology. In concept, that made great sense. Cell phones were going mainstream, and they required new kinds of chips. “Craig tried to very aggressively diversify Intel by acquiring his way into new businesses,” says David Yoffie, a Harvard Business School professor who was on Intel’s board of directors at the time. “I would say that was not his skill set, and 100% of those acquisitions failed. We spent $12 billion, and the return was zero or negative.”

In the lean years after the dotcom balloon popped, Barrett continued to invest billions in new chip factories, known as fabs, and in new production technologies, so Intel would be well positioned when demand rebounded. That is a hint to one of the most important lessons of the Wintel saga and beyond: Protecting the incumbent business, even in a time of transition, is almost impossible to resist. That course usually sounds reasonable, but it holds the danger of starving the company’s future. As the great management writer Peter Drucker said: “If leaders are unable to slough off yesterday, to abandon yesterday, they simply will not be able to create tomorrow.” 

‘We screwed it up’

At Microsoft in the 2000s, “it was not at all obvious what would happen with the shape and volume of PCs, with operating system margins, or the future of applications like Word or Excel,” says Ray Ozzie, a top-level Microsoft executive from 2005 to 2010. “There was significant internal debate at Microsoft and in the industry on whether, in the future, the PC was dead, or if it would continue to grow and thrive.” Maybe Word, Excel, and those other applications that resided on your hard drive would move to the internet, like Google Docs, introduced in early 2006. In that case Microsoft would need a new business model. Should it develop one? Some executives thought so. But no one knew for sure.

During this period, Microsoft was hardly a model of corporate innovation, and succumbed to what often happens when successful companies are disrupted. Ozzie explains: “When you are rolling in resources and there are multiple existential threats, the most natural action to protect the business is to create parallel efforts. It’s more difficult to make a hard opinionated choice and go all in. Unfortunately, by creating parallel efforts, you create silos and internal conflict, which can be dysfunctional.”

As competing teams fought for primacy, Microsoft missed the two most supremely profitable businesses since the PC era: search and cell phones. Those misses were not fatal because Microsoft still had two reliable, highly profitable businesses: the Windows operating system and the Office suite of apps. But in Drucker’s terms, those were yesterday businesses. Investors didn’t see substantial tomorrow businesses, which is why the stock price went essentially nowhere for years. Missing search and cell phones didn’t threaten Microsoft’s existence, but it threatened Microsoft’s relevance and importance in a changing world, which could eventually damage the company’s appeal among investors and the world’s best employees. The reasons for those crucial misses are instructive.

In 2000 Google was an insignificant internet search startup with no clear business model, but it had an inkling that selling advertising could be profitable. We know how that turned out: Google’s 2023 ad revenue was $238 billion. The model was entirely foreign to Microsoft, which made tons of money by creating software and selling it at high prices. Charging users nothing? Selling ads? Microsoft had never run a business at all like Google’s. By the time Google’s model had proved itself, Microsoft was hopelessly far behind. Today its Bing search engine has a 3% market share across all platforms worldwide, says the StatCounter web-traffic analysis firm. Google’s share is 92%.

Microsoft’s failure in cell phones was, in a large sense, similar—the company didn’t fully grasp the structure of the business until it was too late. The company assumed the cell phone industry would develop much like the PC industry, in which sellers like Dell combined Intel’s chips and Microsoft’s software in a final product. But Apple’s starkly different iPhone business model, in which it designs its own chips and writes its own software, was an enormous hit. The other big winner in the industry, Google’s Android smartphone operating system, likewise ignored the PC model. Instead of selling its operating system, Google gives it away to phone makers like Samsung and Motorola. Google makes money by putting its search engine on every phone and by charging app makers a fee when users buy apps.

Bill Gates acknowledges that Microsoft’s miss in cell phones was life-changing for the company. Looking back on his career in 2020, he said: “It’s the biggest mistake I made in terms of something that was clearly within our skill set.”

Intel also lost the mammoth cell phone opportunity, and in a similar way. It couldn’t adapt. Intel understood the opportunity and was supplying chips for the highly popular BlackBerry phone in the early 2000s. The trouble was, Intel hadn’t designed the chips. They were designed by Arm, a British firm that designs chips but doesn’t manufacture them. Arm had developed a chip architecture that used less power than other chips, a critical feature in a cell phone. Intel was manufacturing the chips and paying a royalty to Arm.

Understandably, Intel preferred to make phone chips with its own architecture, known as x86. Paul Otellini decided to stop making Arm chips and to create an x86 chip for cell phones—in retrospect, “a major strategic error,” says Yoffie. “The plan was that we would have a competitive product within a year, and we ended up not having a competitive product within a decade,” he recalls. “It wasn’t that we missed it. It was that we screwed it up.”

Groping for a megatrend

Just as 2000 was a turning point for Intel and Microsoft, so was 2013. Broadly they were in the same fix: still raking in money from the businesses that made them great; getting into the next big opportunities too late or unsuccessfully; groping for a megatrend they could dominate. Their stock prices had more or less flatlined for at least a decade. Then, in May 2013, Paul Otellini stepped down as Intel’s CEO. In August, Steve Ballmer announced he would step down as Microsoft’s CEO.

Succession is the board of directors’ No. 1 job, more important than all its other jobs combined. The stakes are always high. How the Intel and Microsoft boards handled their successions, nine months apart, largely explains why the two companies’ storylines have diverged so dramatically.

Under Otellini’s successor, Brian Krzanich, Intel kept missing new-chip deadlines—ironically failing to keep up with Moore’s law even as competitors did so—and lost market share. The company gave up on smartphone chips. After five years as CEO, Krzanich resigned abruptly when an investigation found he had had a consensual relationship with an employee. CFO Bob Swan stepped in as CEO, and the production troubles continued until, by 2021, for the first time in Intel’s existence, its chips were two generations behind competitors’. Those competitors were Taiwan’s TSMC and South Korea’s Samsung.

In crisis mode, Intel’s board brought back Pat Gelsinger, an engineer who had spent 30 years at Intel before leaving for 11 years to be a high-level executive at EMC and then CEO of VMware . As Intel’s CEO he has announced an extraordinarily ambitious and expensive plan to reclaim the company’s stature as the world leader in chip technology.

Microsoft’s board spent almost six months finding Ballmer’s successor under worldwide scrutiny. At least 17 candidates were publicly speculated upon. British and Las Vegas bookies offered odds on the eventual winner; Satya Nadella, who recently marked 10 years as CEO, was a 14-to-1 long shot. 

Nadella has arguably been the best corporate succession choice, regardless of industry, in years or perhaps decades. Under his leadership the stock finally broke out of its 14-year trading range and shot upward, rising over 1,000%. Microsoft again became the world’s most valuable company, recently worth $3.1 trillion. Gelsinger, with just over three years in the job, can’t be fully evaluated; industry experts wonder if he’ll be Intel’s Nadella. But both CEOs offer useful examples of how to move a company from the past to the future.

Nadella orchestrated Microsoft’s dramatic turnaround by taking an outsider’s look at the company and making big changes with little drama. He began by making Office apps (Word, Excel) compatible with Apple iPhones and iPads—heresy at Microsoft, which regarded Apple as an archenemy. But Nadella realized the two companies competed very little, and why not let millions more people rely on Office apps? The move sent a message to the company and the world: The Microsoft culture’s endemic arrogance would be dialed down considerably. Interoperating with other companies could now be okay.

That was largely a new business model at the company, with many more to follow. For example, Nadella bought LinkedIn , a player in social media, which Microsoft had entirely missed, and later bought GitHub, a repository of open-source code, which Microsoft had previously despised. Both deals and several others have been standout successes. 

More broadly, Nadella brought a new leadership style for a new environment. In a company known for vicious infighting that could paralyze action, he settled long-running debates over major projects. For example, in 2016 he sold the Nokia cell phone business that Microsoft had bought a year before he became CEO, acknowledging that the company had lost the battle for phones. “People don’t quite grok why things have blossomed under Satya,” says a former executive. “His superpower is to make a choice, eliminate conflict, and let the business blossom.”

At Intel, Gelsinger also introduced culture-defying changes. The company had risen to dominance by designing leading-edge chips and manufacturing them with industry-leading skill. Amid that intense pride, the idea of creating a separate foundry business—manufacturing chips designed by others—was anathema. Yet under Gelsinger, Intel has created a new foundry business while also relying more on other foundries, including TSMC, the world’s largest chipmaker, for some of its own chips—a double shock to the culture. 

Getting a long-established company with a titanium-strength culture to adopt seemingly strange business models as Nadella and Gelsinger did can be painfully hard. Often only a new CEO can bring the openness necessary to make it happen. The same problem arises when a company needs to update its corporate strategy. Microsoft had been seeking and debating the next big thing for years, but Nadella saw that the company didn’t need to find a potentially huge new future-facing business. It already had one: Azure, its cloud computing service. Amazon Web Services was and is the industry leader, but Azure has grown to a strong No. 2 because Nadella has given it abundant capital and some of the company’s brightest workers. He also made an unorthodox investment in OpenAI, creator of ChatGPT, commiting $13 billion to the company starting before it was famous. Now Azure offers its customers OpenAI technology. In Drucker’s terms, it’s a big, thriving tomorrow business. 

Gelsinger changed Intel’s strategy even more radically. He bet heavily and successfully on billions of dollars from the U.S. government. Via the CHIPS and Science Act, Intel could receive up to $44 billion in aid for new U.S. chip factories the company is building in coming years. “As I like to joke, no one has spent more shoe leather on the CHIPS Act than yours truly,” he tells Fortune. “I saw an awful lot of senators, House members, caucuses in the different states. It’s a lot to bring it across the line.” 

A key insight is that for a major company with a history of success, like Microsoft and Intel, moving beyond an outmoded strategy and fully embracing a new one is traumatically difficult and sometimes impossible. For years both companies tried and failed to do it. A related insight: Doing it is easier for Nadella and Gelsinger because they have the advantage of being “insider outsiders,” leaders with deep knowledge of their organization but without heavy investment in its strategy; Nadella was working on Azure, not the Windows operating system or Office apps, long before he became CEO, and Gelsinger’s 11-year absence from Intel gave him license to rethink everything.

A larger lesson is that, in the stories of these two great companies, succession is the most important factor. Considering that Microsoft on the whole has fared better than Intel over the past 24 years, it’s significant that over that period, Microsoft has had only two CEOs and Intel has had five. Most people study the CEO when explaining a company’s performance, but they should first examine those who choose the CEO, the board of directors.

Looking back at these stories, asking “what if” is irresistible. What if Paul Otellini had said yes to Steve Jobs? What if any of Intel’s or Microsoft’s CEOs had been someone else? What if Intel, under a different CEO, had developed a successful GPU, the kind of chip that powers today’s AI engines (it tried)—would you ever have heard of Nvidia? Bill Gates said in 2019, “We missed being the dominant mobile operating system by a very tiny amount.” What if that tiny amount had shifted slightly? Whose phone would you be using today? 

It’s all endlessly tantalizing but of course unknowable. The value of looking back and asking “what if,” is to remind us that every day leaders are creating the future—and neglecting their duty if they don’t learn from the past.

5 lessons from the Wintel case study:

1. Success can be a company’s worst enemy. The great management writer Peter Drucker said every company must “abandon yesterday” before it can “create tomorrow.” But in a successful company, every incentive pushes leaders to protect yesterday. Intel and Microsoft struggled for years to create their tomorrows. 2. Leaders must be open to business models that seem strange. Whether giving away software or manufacturing chips designed by others as a separate business, both Microsoft and Intel faced competitors doing things differently.  3. Get everyone on the same page. Debate is healthy up to a point, but at Microsoft it continued far too long until Nadella became CEO and set clear priorities. At Intel a series of CEOs backed differing solutions to its declining business, which prolonged a muddled strategy.  4. Succession is the board’s No. 1 job, more important than all its other jobs combined. Everyone knows it, but some boards still do their job poorly. If they make a mistake, none of the other lessons matter. Considering that Microsoft has come through the past 24 years better than Intel, it may be significant that Microsoft has had only two CEOs in that period while Intel has had five. 5. Failure isn’t fatal. The Wintel story is a pointed reminder that all companies, including the best, suffer failures and fall into crises. There are no exceptions. The leaders of any company, even the grandest, must always be ready to engage the skills of organizational rescue, and know that even that can be part of greatness.

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