Digital Pricing Strategy
From quantum technology to advancement in smart and rational AI, to digital platform, and connected ecosystems, the sky is the limit.
Critical areas of digital transformation: pricing of data, subscription-based pricing, pricing in platforms and marketplaces, AI pricing, and digital pricing.
John Porter: The Essential Ingredient for More Effective Digital Pricing
Cost-based pricing is almost never informed by your product’s true market potential.
A value-based sales strategy is key to sustaining a stronger digital price point and ensuring that discounting doesn’t take over during the renewal process.
Your customers’ priorities, problems, and areas of focus are value drivers.
Identify the problem. Enumerate the value drivers of solving that problem. Test across a variety of situations to quantify the return and impact based on those value drivers. And then assess the market potential for the value delivered.
CVM is customer value management. Is the ability to quantify the value that products or services provide to a customer. Whatever that customer’s value drivers are should be what’s measured.
The major complication of a value-based pricing strategy is that value itself changes over time. So, it is important to have strong value management foundation.
The greatest goal of value-based selling is to justify and extract a price premium.
Digital transformation has made value data more accessible than ever.
Value-based pricing and value selling build on the same input – customer value models that quantify value realization.
Kyle T. Westra – Publish Your Prices
Price and pricing transparency are largely issues of competitive differentiation, not ethics.
- Price transparency: when customer knows what they will pay at the onset of the transaction.
- Pricing transparency: when a customer knows how a company will arrive at the final price.
Several imperatives of a successful SaaS company that benefit from increased transparency:
- Practicing value-based pricing.
- Lowering barriers to buy.
- Enabling customer self-selection.
- Demonstrating customer-centricity.
In all industries, cost-plus pricing is problematic for one simple reason: customers are not responsible for a company’s cost structure. If a company justifies having high prices because of its high costs and not because of its high value, customers will simply walk away.
A company must demonstrate its value creation, showing how customers are better off buying its offering than the competitive alternatives.
A successful sale requires not only that a prospect becomes a customer but that the customer purchase the right products and services for its goals.
CAC (customer acquisition cost) measure is important, because it indicates the value of investment that must be overcome for a customer to become profitable. Churn is also important, because it dictates whether customers are staying with the offering long enough to turn profitable.
Customer-centricity is, according to Gartner, the ability of people in an organization to understand customers’ situation, perceptions, and expectations. But that is not enough. The customer centricity demands that the customer is the focal point of all decisions related to delivering products, services, and experiences to create customer satisfaction, loyalty, and advocacy.
Lack of transparency is competitor-centric.
If executives want better value-based pricing, faster sales-cycles, more accurate customer segmentation, and increased customer-centricity, there is a simple solution: publish your prices.
Arnd Vomberg: Dynamic Pricing Process; How to Transition from Fixed to Dynamic Pricing
Depending on the industry, studies indicate a revenue-increasing potential of dynamic pricing between 2% and 8% and potential profit increase between 3% and 25%.
Two forms of dynamic pricing are: time-based and personalized pricing. Two key dimensions that can describe dynamic pricing are frequency and range of price.
Personalized pricing forms are: personalized baseline prices, personalized coupons, location-based pricing and price steering.
The dynamic pricing process:
- Dynamic pricing strategy.
- Decision about dynamic price setting.
- Dynamic pricing implementation.
- Dynamic price auditing.
Repricing software is one way to implement time-based dynamic pricing.
Overall, the effectiveness of the dynamic pricing approach depends on which determinants companies include in their algorithm:
- Market-related factors.
- Customer-related factors.
- Company-related factors.
It is also important to think about which product to include in dynamic pricing. This decision depends on the comparability of the products, the brand, and whether the product constitutes a key-value item. First, the ease with which consumers can obtain prices for different products determines how competitor prices should inform the dynamic pricing approach. Second, dynamic pricing likely differs between private-label brands, national brands, and luxury brands. Third, a final important consideration is the role of key-value items (sometimes referred to as product heroes). Key-value items pull customers into store or initiate a purchase.
The implementation of dynamic pricing requires that companies delegate pricing decisions from human to algorithms.
Successful dynamic pricing needs ongoing auditing.
Mrinal (MG) Gurbaxani and Alex Smith: Realizing Your Monetization Potential Needs Customer Value Management
In industrial companies, to steer clear of the commodity trap, savvy commercial excellence organizations are looking to transform from an SKU orientation to a solution orientation by packaging their product and wrapping in services, maintenance, support, upgrades, financing, monitoring, replenishment, and other value-add services.
While selling on value is an age-old concept, the need for consistent and scalable value management is amplified in the B2B subscription world.
SaaS is 75% of market in 2018, up from 6% in 2010. 71 % of companies are willing to check for alternatives for IT solutions they already have.
Previously, most software companies were “hunter-seller” focused. In XaaS world with fewer practical barriers to customer churn, companies must proactively cultivate customer contract renewals. Without a CVM strategy, service providers are blind to the real health of the account. With a good CVM strategy, service providers gain visibility into which customers are getting value from their solutions.
Sales teams need to discover value and sell on outcomes. Needs, business objectives, and KPIs create version 1 of the “’living’ customer success plan. Delivery and customer success teams need to ensure that they deliver on value for shared success. The solution delivery must focus on business outcomes. Product and marketing teams need to continually optimize the proposition based on customer insight.
While discover is part of every sales methodology, few sales teams do it correctly. 92 % of buyers want to hear a value proposition early on in the sales cycle, and 76% of customers state that they expect companies to understand their needs and expectations. Many larger B2B XaaS companies have invested in the ‘value’ professional: a strategic resource to help the sales team deliver a personal business case. SVP of business value engineering, and a chief value officer has been a rare new addition to the C-Suite.
CRM must evolve to become a platform focused on the value the customer is getting and on digital and virtual collaboration with the customer.
Customer value must be discovered through the eyes of the customer.
Stephan M. Liozu: Measure and Quantify the Value of Your Digital Solution
Think about competitive intelligence and dollarizing your digital offer’s differentiation.
Competition in digitally enabled products and services is not only intense and multifaceted but also structurally different from competition in traditional industries. We have:
- Direct competition.
- Indirect competition.
- Customer internal solutions.
- And option that customer does nothing.
Author groups the potential areas of advantage in digital world into five categories:
- Data-based differentiators.
- Analytics-based differentiators.
- Business model differentiators.
- Value constellation differentiators.
- Pricing-based differentiators.
The last two differentiators can be part of business model differentiator.
Dollarization is about translating your competitive advantage into financial benefits for customer. Some dollarization techniques are:
- TCO – total cost of ownership.
- TBO – total benefit of ownership.
- EVE – economic value estimation (CVM model).
- LCC – life cycle costing analysis.
- LTV – customer lifetime value analysis.
- ROI – customer return on investment calculation.
The objective of EVE is to begin with a reference value and then add and subtract the values of your significant differentiators until you arrive at the net differentiation value your solution generates.
Reference value is the value you and competitor deliver. Positive differentiation is your unique additional value. You subtract your unique costs. The difference is the unique value you provide the customer.
Value modeling is where words and concepts and high-level estimates are transformed into numbers, such as ratios, fractions, or percentage differences. These are then expressed in terms of hard money. Approach to CVM is: translate differentiators into customer benefits, prioritize customer benefits based on segment, turn those benefits into compelling facts & ratios, and create your value story or value script.
You can do CVM with:
- Theoretical calculations based on technical assumptions.
- Academic research in the end-use application.
- Comparison from other similar contexts.
- Make assumptions to be evaluated with customers.
During the dollarization process you need to list all your assumptions, document all your calculations, and document all your data sources. Documentation and validation are key as you iterate until you have defined your value pool, the aggregate value of your dollarized differentiation.
Software and Subscription-Based Pricing
Maciej Wilczynski: Price Increase for Discounted Customers in SaaS; Pricing Research Description and Success Story
Hermann Simon: “Price is what you pay. Value is what you get.”
The subjective perception of the value created by the company differs from the client’s perspective (perceived value). The unseen value is effectively where marketing and communication efforts fail. Next step is when customers decide how much money they can pay for the value (willingness to pay – WTP). As a pricing principle, clients’ WTP should get as close to perceived value as possible. One of the most common pricing pitfalls is setting the price too low versus what customers can or want to pay (target price). Price realization of a previously set price is as usual quick win in any pricing initiative. Transactional discounts, negotiations, promotions, and price execution of a salesforce create a substantial optimization opportunity for most companies (realized price). Price realization is part of the value versus price waterfall.
From 1980s until the early 2000s, ‘per user’ license keys were the most popular, focusing heavily on headcount billing. The model has evolved with the cloud revolution, become more complicated, and relied on more usage-based approaches.
Pricing initiatives generate the fastest and highest return on investment for revenue optimizing efforts. They require solid knowledge and organizational confidence.
Andreas Hinterhuber: SaaS Pricing; From Subscription to Usage-Based Pricing Models
Key tasks in pricing are price setting and price getting. The combination of these tasks leads to the pricing capability grid.
Price setting refers to the different approaches that companies use to determine selling prices: cost-based pricing, competition-based pricing, and customer value-based pricing.
Price getting refers to different abilities to actual obtain the price set in the first place: some companies are very good at realizing their list prices, via, for example value communication, customer value quantification, or price controlling.
Pricing is the moment of truth for a new product.
Goldszmidt in his 2004 Siebel case study claimed that an average discount is 39% and the average discount, weighted by deal size, is a staggering 86%, and the average weighted discount excluding outliers (5%) is 66%.
Software pricing models are changing. Perpetual licensing is gradually being replaced by subscription-based pricing models. These are in turn being replaced by consumption-based pricing models. Some companies are developing pricing models that link prices to business outcomes, leading to performance-based and risk-sharing models.
Perpetual licensing, subscription, and usage-based pricing models are all cases of non-linear pricing models. A two-part tariff contains an access fee plus a per-unit usage fee. Three-part tariffs contain an access fee, an allowance for free units, and a fee for overage use based on consumption.
A shift from subscription to usage-or consumption-based pricing is a true Copernican shift. Usage is hard to predict, which causes problems for both suppliers (revenue forecasting) and customers (expense budgeting). Monitoring of usage becomes critical. In addition, suppliers need to define pricing metrics that are aligned with quantified customers benefits.
The value quantification capability refers to the ability to translate a firm’s competitive advantages into quantified, monetary customer benefits. The value quantification capability requires that the sales manager translates both quantitative customer benefits – revenue/gross margin increase, cost reduction, risk reductions, and capital expense savings – and qualitative customer benefits – such as ease of doing business, customer relationships, industry experience, brand value, emotional benefits or other process-based benefits – into one monetary value equating total customer benefits received.
The following criteria for effective pricing metrics suggested by Nagle and Muller in 2018:
- Alignment with customer value across segments.
- Alignment with cost-to-serve.
- Easy measurement and enforcement.
- Advantage over competition.
- Alignment with customer value within a given segment.
Many of the pricing metrics currently applied by SaaS companies are input and not output metrics.
Four areas where managers need to develop individual and organizational capabilities further in order to implement usage-based pricing:
- Value quantification.
- Customer experience management.
- Customer access management.
- New KPIs.
Scott Miller: The Digital Pricing Framework; Best Practices in B2B Pricing and Offer Design
Successfully developing a value-based segmented pricing approach involves using the right design inputs, the right company-wide price-value training, and the right approaches to executing pricing within B2B sales opportunities.
Continous cycle of pricing improvement:
- Offer design
- Price value analysis
- Commercial structuring (packages, price metrics, tiers)
- Financial analysis (price stress testing, cash flow predictions)
- Train & enable
- Support & reinforce
- Go-live launch
- Deal & bid process
- Monitor, review & adjust
At the center of the frameworks is the pricing ecosystem (PECO); this includes all other people, processes, systems, incentive structures, and policies within an organization that are impacted by changes to revenue models, pricing, and offer structures.
Value-based pricing is defined as setting prices primarily to the perceived or estimated value of a product/service to the customer.
Monetization strategy and tactics are most definitely a key consideration for digital products and pricing teams.
Linking price with value can be achieved using two useful software pricing tools: economic value analysis and price-value trade-off analysis.
A software metric is a standard unit of measure that links a fee structure to six possible software dimensions:
- Access-based (who is accessing).
- Architecture-based (resources being accessed).
- Content-based (what is being accessed).
- Usage-based (how much and how often it is being accessed).
During metric evaluation phase ask those questions: Do the metrics represent value delivered? Are they scalable and provide incremental monetization opportunities? Is it sellable and well understood by the client? Can the metrics be measured, tracked, and billed?
Tiering is a methodology that creates variation in pricing and/or packaging value that targets a defined client segment. We have: volume discount matrices, good-better-best bundles and price levels.
Gaurav Sanpar and Michael Mansard: Tapping into the Subscriber Psychology with Good/Better/Best; Is There an Optimal Ratio Between Tiers?
3D pricing and packaging (P&P) framework helps companies launch and refine a GBB-based packaging model.
Buffet: “The single most important decision in evaluating a business is pricing power. If you have the power to raise prices without losing business to a competitor, you have a very good business.”
In a traditional transactional business, making a pricing mistake is unfavorable, but it’s not the end of the world. But in a recurring business, it’s a catastrophe.
Packaging is simply the bundling of features and functionalities to formulate a unique value proposition for the target audience.
GBB is driven by a well-studied behavioral mechanism observed in decision theory: asymmetric dominance. People’s psychological inclination to avoid extreme options in favor of intermediate or middle options.
Considerations when adopting GBB pricing:
- Expand market reach.
- Grow revenue pools.
- Predictable revenue.
- Diversified pricing strategies.
- Clear upgrade path.
- Maximize CLV.
- Name the game.
- Paradox of choice.
- Packaging and optimization.
- Apple-to-pear comparison.
- Managing subscriber expectations.
- Suboptimal value capture in some context.
GBB is an effective strategy for addressing multiple customer segments because it offers differentiated, progressive service tiers at increasing price points.
GBB tiers must incorporate meaningful trade-offs between price and perceived value. This means finding the right balance of features, services, quantity offered, usage, customization, and so forth.
The stages for implementation are define, design and deploy. Define target audience, willingness to pay and value metrics. Design tiers to address the need of your customer segments. Deploy in-sync with your go-to-market strategy.
Outcome-based metrics are the Holy Grail, but they are often impractical and hard to measure. Outcome-based metrics should be reserved until the offerings – and the customers – mature.
The subscriber personas, WTP, and value metrics become the foundation for the GBB tiers. A feature with high perceived value but low WTP will be table stakes (good), whereas a high value feature with a high WTP will represent a differentiated offering (better/best). A low-value feature with a high WTP represents a premium add-on. Low value and low WTP features are throwaways, suitable for a freemium, or free trial offer.
Deploy pricing strategy based on your organizational maturity and market positioning.
If you are going for penetration or revenue monetization play (WIN), the ratio of packages is 70:20:10 as you offer more value-added features at a lower price point.
If you are going for skimming or profit maximization play (GROW), the ratio is 20:65:15.
If you play the retention play (RETAIN), the ratio is 35:60:5.
Tobias Leiting, Calvin Rix, Regina Schrank and Lennard Holst: Value-Based Pricing of Smart-Product-Service Offerings in the Manufacturing Industry
The established manufacturing industry is strongly focused on the product-oriented business around physical products and services. In these businesses, the cost-oriented pricing approach dominates. However, this model does not align with smart-product-service offerings, as the cost structure is different because of high fixed development costs and low scaling costs.
A systematic framework for pricing smart-product-service-offerings: design the smart-product-service-system, determine benefits and value, select and design the appropriate pricing model, details the selected price models according to the pricing metric design.
The four different archetypes of smart-product-service offerings can be identified:
- Data product.
- Smart product.
- Digital product.
- X-as-a-Service offering.
Four customer-oriented price models can be distinguished with a focus on customer value:
- The availability-oriented model (flat rate).
- The use-oriented model (pay-per-use).
- The result-oriented model (pay-per-part).
- The success-oriented model (pay-per-cost-decrease or pay-per-profit-increase).
A differentiation between three types of price components can be made: a fixed one-off payment, a fixed subscription fee, and a variable subscription. For both types of subscription fees, the payment intervals must be defined first.
In the context of pricing, four central challenges hinder the establishment of customer-oriented data-driven product-service offerings: selling value propositions, data-driven quantification of value, design of value-driven pricing models and definition of subscription-based metrics.
Maciej Wilczynski and Matt Johnston: Price Sensitivity Meter and Conjoint Analysis as Tools for Setting Your Industrial Subscription Pricing
Van Westendorp’s price sensitivity meter (PSM) has been with us since 1976. 100 survey responses are enough to drive meaningful results in B2B industries.
Plotting the data in PSM. On the x-axis, you need to plot the price point, ideally in thresholds, closest to your hypothetical price. On the y-axis, it’s critical to plot the cumulative number of responders.
The intersections are called PMC – point of marginal cheapness, PME – the point of marginal expensiveness, OPP – optimum price and IPP – the indifference price point.
You can expand PSM with two additional questions (Miller-Smith). At the not expensive price and at not cheap price – how likely you will buy this product in next six months.
How you determine willingness to pay is critical.
Conjoint analysis presents products in a way that simulates real-world product comparisons and asks respondents to make realistic trade-off decisions. What’s needed to conduct conjoint analysis:
- Who should your target?
- What should you test?
- Agree on the burning question: it can be a challenge or opportunity that needs to be addressed with all stakeholders before designing the survey.
- Never test more than eight attributes in one study.
- ‘None of these’, or a derivate of it, should feature as an option of every choice card.
- No more than 12 choice card in a study.
- Make sure user is familiarized with new features, products, or pricing models.
The Value and Pricing of Data
Lalit Wadhwa: Overcoming Real-World Challenges in B2B Digital Pricing Transformation
When organizations launch their digital transformation journey, a pricing transformation from a cost-based model to a value-based model is generally considered to be low-hanging fruit.
When it comes to who is best positioned to lead the digital pricing transformation, two key attributes need attention. The first is familiarity with an agile approach. The second is the need to understand value-creation opportunities in a digital pricing workflow to some degree of granularity.
Pricing models are usually built using some combination of algorithms. As an example, basic pricing models can be built using combination of clustering and decision trees, while complex pricing models could use reinforcement learning and neural networks.
Lusi Prato: Holistic Approach to Market Segmentation of Industrial Smart Services; What Is the True value of Data
The term industrial smart services describe a new generation of services resulting from the combination of data collected from an installed base or connected products and information from other additional field sources.
The ability to identify target market segments is critical for OEMs while selling their industrial smart services.
Defining the key triggers from a buyer’s perspective is alternative framework for segmentation. The framework has value of digital capabilities on y-axis with categories from lowest to highest: data capturing & connectivity, data transformation, data analytics; and performance orientation on x-axis with categories from lowest to highest: inputs, outputs, outcomes.
Bill Schmarzo: Three Considerations for Data Monetization and Value Creation in the Digital Age
First consideration is the four stages of data monetization. Data, like oil, has latent value. That is, data and oil have potential value that is not yet realized. To get value from data, one needs an economics-based monetization strategy where value is created in the ‘use’, not the ‘possession’, of the data.
Four stages of data monetization are:
- Data is cost.
- Data monetization exploration.
- Data monetization value realization.
- Data monetization value acceleration.
Second consideration is creating a data strategy that delivers value. Organizations do not need a Big Data strategy; they need a business strategy that incorporates Big Data. The value of data is not having it (data-driven). The value of data is using it to derive and drive new sources of ‘wealth’ (value-driven).
Third consideration is value engineering. The secret sauce for data science success. Steps are:
- Identify a key business initiative.
- Identify key business stakeholders. You can use personas to identify stakeholders.
- Identify, validate, value and prioritize the decisions. You can use prioritization matrix (business value and implementation feasibility.
- Identify supporting predictions.
- Identify potential data sources and instrumentation strategy.
- Identify supporting architecture and technologies.
Alex Fournier and Claire Gubian: The Economics of AI; How to Shift Data Projects from Cost to Revenue Center
When organizations take their first steps into the enterprise AI world, the most common technique is to begin with a finite list of select use cases, ideally optimized for a balance between difficulty in execution and potential impact.
Common sense and economics tell us not to start from nothing every time, and that is exactly the principle behind reducing costs associated with data cleaning, preparation, operationalizing, model maintenance, and even hiring woes.
Interview with Jian Pei: The Pricing of Data
Pricing is still the best mechanism to fairly evaluate the value of data and the utility of or the gain by using data.
In many situations, data pricing may be through data-based services.
Information is not technology. Technology helps to process information, but information itself should be a resource.
Pricing measures the value and the utility of products to users.
When we sell the data, the seller should exactly know what the data would be used for, what its second use is, and then anything beyond the second use should be prohibited or should be renegotiated because that is the boundary of privacy.
The data industry will be more vertical, because vertical has a few benefits.
The Pricing of Platforms and Marketplaces
Jacek Lubinski: Marketplace Monetization Methods
Methods of monetization in the marketplaces:
- Commission on all transactions.
- Commission on transactions generated through the marketplace.
- Recurring fee for tools provided (SaaS-enabled marketplace).
- Payment fee.
- Subscription fee.
- Fee for additional services provided by a marketplace or by third parties.
- Listing fee.
- Advertising revenue.
- Pay per lead.
- Promotion fee per premium listing.
- Data monetization fee.
- Asset management fee.
The commission as percentage value of gross merchandise value (GMV) is usually called take rate or rake. The size of commission is based on different factors: what value is delivered by the marketplace, how much money suppliers make, cost of providing the product/service by the supplier, set of services provided, different rakes for different products, competition. Usually, commission is paid by supplier.
All monetization methods have two dimensions: attractiveness and popularity.
Murali Saravu: The Monetization of Marketplaces and Platforms in the Context of Web 3.0
The sharing economy introduced an ecosystem-driven, multiparity world.
Web 3.0 is: verifiable, trustless, self-governing, permissionless, distributed and robust, stateful and native built-in payments.
To succeed in monetizing in Web 3.0, you need technology that can do three things.
- An agile platform – the ability to model and create rules for your services with various dimensions such as: features to monetize, charge, cost, payout, value, usage transaction definition.
- Go beyond FIAT currencies to support tokens and virtual currencies.
- Invest in instrumentation and flexible service usage tracking and processing engines.
Simone Cicero: Pricing in Platforms and Marketplaces; A Primer in Understanding All the Dimensions of the Pricing Problem and Opportunity in Marketplace Platforms
When thinking about pricing, in a platform marketplace setting, one needs to understand that there are at least three contexts where pricing is a fundamental question to be addressed.
- The transaction engine.
- The learning engine – a sort of workflow engine.
This corelates to three critical components in a platform or marketplace: the marketplace itself, the product or services, and the extensions to these products and services.
There are several approaches to frame the ‘stack’ of elements that make the transaction possible:
- Customer acquisition and attraction.
- Discovery/matchmaking refers to facilitating between product being sold and the perfect buyer.
- Trust building and risk reduction.
- Customer services and refunds.
- Ancillary services and refunds.
- Production refers to the actual execution of the core of the niche value proposition.
- Niche VP (value proposition) innovation.
According to Ben Evans, advertising essentially becomes a form of ‘taxation’ that the ecosystem owner can put in place as the ecosystem matures.
The ‘product’ side of platform value proposition can be described as a mix of a ‘learning’ engine and a ‘workflow’ engine.
The first thing one needs to understand about pricing this side of the business is the idea of the so-called value metrics. It is essentially what your charge for. Visit, CPA, GB used, transaction. Once you understand your value metric, you can pursue several approaches to building a contextual pricing strategy.
The extension platform side is the part where third-party can extend the value proposition of the product side.
Bill Gates: “A platform is when the economic value of everybody that uses it, exceeds the value of the company that creates it. Then it’s a platform.”
Robert Phillips: Online Pricing Experimentation
It is much easier and cheaper for an online seller or marketplace to change prices than it is for their offline, brick-and-mortar counterparts.
In a pricing experiment, one population of customers is temporarily exposed to a pricing policy to be tested – the treatment – while to other population is priced as usual.
A pricing policy is any approach to setting and updating prices over time.
The purpose of a pricing experiment is typically to determine how customers will respond to the treatment relative to ‘pricing as usual’.
We have three types of experiments:
- A truth-seeking experiment. Verifying a hypothesis.
- An exploratory experiment.
- A decision-support experiments. Validating a particular policy.
Then we need to choose a metric. What we want to measure. Experimental metrics can be grouped in three categories:
- Headline metrics. Small set that are calculated for every experiment and highlighted in the results.
- Experiment-specific metrics.
- Secondary metrics.
While there are different ways to evaluate the results, the most common in a business environment is difference-in-difference (DiD). But this standard approach does not necessarily establish causality.
To avoid effects of cannibalizing use clustering of items, that can be cannibalized with the ones cannibalizing them.
To avoid biases, the group designing, running and interpreting experiments should be separate from those who have a stake in the outcome of the experiment.
Pricing and Artificial Intelligence
Louis Columbus: Artificial Intelligence and Its Impact on Pricing Technology
The following are the ways AI is improving pricing and revenue management today:
- Identify and eliminate the most unproductive customer discounts and segments, freeing up more financial resources and time for those that contribute to profits.
- Automating pricing rules with AI in revenue management systems increases total revenue by 5%.
- Capitalizing on the many insights that transactional data can provide by using AI and ML to look for patterns in pricing, volume, and mix analysis is delivering measurable results today.
- AI and ML are helping in finding a given customer willingness to pay or helping pricing managers optimize prices across their customer and product mix.
- AI is making it possible to create propensity models by persona, and they are invaluable for predicting which customers will act on a bundling or pricing offer.
- Price optimization and price elasticity are growing beyond industries with limited inventories, including airlines and hotels, proliferating into manufacturing and services.
- Fine-tunning price segmentation strategies with insights gained from AI is helping to stabilize and increase margins and revenues today.
- AI is providing sales and revenue managers with more accurate deal price guidance than was available in the past, leading to more effective use of pricing discounts.
- Relying on AI to monitor risk-based metrics and KPIs to gain greater visibility into the root cause of potential risks to revenue.
- Eliminating pricing errors on orders that lose sales while providing a more precise approach to automating special pricing requests (SPRs) drives more sales.
- Combining historical selling, pricing, and buying data in a single machine learning model improves the accuracy and scale of sales forecasts.
- Knowing the propensity of a given customer to churn versus renew is invaluable to improving customer lifetime value.
- Improving demand forecasting, assortment efficiency, and pricing in retail marketing has the potential to deliver a 2% improvement in EBIT, a 20% stock reduction, and 2 million fewer product returns a year.
- AI is improving demand forecasting by reducing forecasting errors by 50% and lost sales by 65% with better product availability.
Joel Hazan, Camille Brege, Jean-Sebastien Verwaerde, and Arnaud Bassoulet: Why AI Transformations Should Start with Pricing
The success of AI pricing transformations generally hinges on the quality of three factors: data, vision and change management support. Focus on data early and sustain the effort. Have a clear target vision, but also invest in less advanced solutions. Emphasize the change effort.
Craig C. Zawada: Digitization of B2B Pricing; A Fundamental Shift Required
Prior to the 1990s, pricing could be characterized as ‘everybody does pricing, nobody does pricing’.
In the HBR article from 1992 Managing Price, Gaining Profit, Marn and Rosiello, had two big ideas. First was that profits were extremely sensitive to small changes in price. The second big idea was that the price waterfall framework.
During the pricing development, pricing increasingly became a customer experience issue in addition to a management discipline issue.
The sales experience is the new key to revenue growth.
The company’s win rate fell by 42% if they responded more than 24 hours after receiving the RFQ compared with when they responded in less than four hours.
There are several customer-centric pricing capabilities that companies need to add versus solely focusing on improving the management discipline around pricing:
- Shift more pricing to low-touch or no-touch pricing.
- Use science and algorithms to deliver market-relevant prices.
- Remove complexity when high-touch pricing is needed.
- Provide a consistent channel experience.
- Build a nimbler pricing organization.
Stella Parks and John V. Colias: Value-Based Offers Assisted by Artificial Intelligence
The combination of AI with value-based selling would produce digital innovations that would facilitate offering to customers ‘the right solution at the right price at the right time’, enabling a ‘blue ocean strategy’.
AI has been, and continues to be, a tool for accelerating and improving decision making.
Data requirements for AI offering:
- Customer-level data for solutions offered to customers cross-referenced with the outcome of the offer (purchase or no purchase).
- Multiple offers made to a statistically significant number of customers.
- Sufficient variation in features included in the solution and the offer price.
- Data engineering that automatically generates the required data.
The core of AI methodology is automation.
The quantification of feature value is essential to value-based pricing and selling.
The final deployment of the AI methodology would be seamlessly integrated into the digital sales platform and software.
One of the greatest sources of disagreement between marketing and sales is knowing the right time when customers and sellers are ready to talk.
The optimization decision tool would enable sellers to adopt the right activities today to increase future probability. It would be used to promote retention, deliver ROI from acquisition efforts, and identify profitable solutions that customer value.
The AI tool would predict the WTP a premium price with prescriptive loyalty program offers.
The AI assisted platform would find broadly desired attributes (complement attributes) across all customers, force those attributes to be included in a profit-maximized or loss-minimized solution, and pick the best initial customers to offer the solution.
Mitchell D. Lee and Darius Fekete: Digital Transformation; How to Convert a Buzzword into Real Bottom-Line Value
The roots of digital transformation date back to the 1940s when Claude Shannon laid the foundation for digitization and digitalization in his paper ‘A Mathematical Theory of Communications’.
Today’s buyers are omnichannel creatures who expect to be able to weave in and out of channels without interference or interruption.
In B2B, digital commerce empowers your sellers to shift from catalog-explainers and order-takers to innovation experts and value-drivers.
Insufficient product description, lack of transparency into product availability, and slow response times are among the most-cited reasons for buyer reluctance to purchase online.
Automation and AI have changed the pricing game for good.
By using technology that combines human understanding, experience, and contextual insight with AI’s tireless processing power, decision-makers can leverage their data to act promptly and decisively.
The goal should not be digitalization itself but the ability to future-proof your business, drive agility, and differentiate yourself from competitors.
One of the first steps in achieving commercial excellence is to understand your organization’s current estimated level of commercial excellence maturity. Four stages of progressing maturity according to Vendavo: unstructured, disciplined, adaptive and optimized. The maturity can be considered across six key business-process capabilities that make up your middle office:
- Customer accounts.
- Analytics capabilities and insights.
- Process integration.
Successful pricing transformation is not a project; it is an ongoing process relying on proven people and process expertise, and purpose-built, enterprise-ready capabilities.
To unlock the potential of digital transformation, organizations must take a comprehensive approach to commercial business processes, aligning experts and best practices with purpose-built, enterprise-ready, technology capabilities to achieve commercial excellence.
Commercial excellence is an ever-evolving practice integrating capabilities across your pricing, selling, products, channels, and customer knowledge.