Guide to Digital Transformation Through Dynamic Pricing Strategiesby Valerie Howard
The digital economy has evolved much faster than predicted. Buyer expectations have shifted, non-traditional competitors have entered the market, and costs are constantly fluctuating. For many B2B businesses, the truth is that margins are eroding, and the legacy way of doing things is no longer competitive. Disruption is occurring as online transparency, new sales channels, low switching costs, and the Amazon experience is shaping the expectations of the modern B2B buying experience.
Today’s customers have access to more information than ever before, making them savvier and more reactive to price than their predecessors. The rise of online marketplaces has allowed manufacturers to compete with distributors directly, changing their strategies to go directly to the customer for higher margins. The buying experience has changed forever, leaving the market in flux. To stay competitive in the market, forward-thinking companies will need to embrace a digital pricing strategy.
What is Dynamic Pricing?
Dynamic pricing is the practice of dynamically calculating the price of a product or service in order to incorporate real-time market conditions, input costs, and/or competitive perspectives. For example, the price may increase in response to a surge in demand or decrease in a market that has become more competitive. Incorporating dynamic pricing practices into digital strategy allows companies to stay ahead of the curve and seamlessly keep up with fluctuations in the market.
Challenges of Developing a Winning Price Strategy
Despite the ever-evolving market, most companies still plan for higher annual revenue growth–up to 5%-10% per year. But the inescapable reality is that traditional, low-tech, relationship-based processes are not equipped to meet the expectations of today’s buyers. Customers are coming to the table with more information and less loyalty than ever before.
When costs fluctuate, businesses tend to react to these fluctuations rather than predicting them. In the midst of these changes, organizations must find a way to normalize the process and come up with pricing strategies that are proactive rather than reactive. To stay competitive in the market, many companies are turning to AI-driven solutions to ensure customers are getting the right product offering, at the right price, at the right time. In the following section, we’ll discuss the top three challenges of developing a winning pricing strategy and how to navigate the process.
Inconsistent Pricing and Reactive Discounting
A sound pricing strategy demands accuracy and consistency, yet many companies fail to adequately pass on their cost increases. According to Simon-Kucher & Partners’ Global Pricing Study 2016, 30% of participating companies failed to enforce pre-planned price increases, and 87% identified a need to improve their overall pricing strategies. A primary driver of these problems is reactive discounting. In the study, 49% of the companies engaged in price wars, and 82% complained of increased price pressures due to competition with low-cost providers, increased pricing transparency because of digitalization, and the greater negotiating power of today’s customers. The result? Pricing weaknesses were expected to cost companies .7% points in terms of profit margins.
Lack of Control, Visibility, Governance, and Relying on Manual Processes
A drive toward consolidation among wholesale distributors has been a key feature across industries in the last two decades. For example, in the wine industry, there were approximately 1,800 wineries and 3,000 wholesale distributors in 1995. Today, there are closer to 9,200 wineries and only 1,200 wholesale distributors in the U.S. Consolidation has occurred across industries, including the telecom, domestic airline, gas station, and medical device manufacturing sectors. Smaller companies consolidate with larger companies in a drive to gain better profit margins, have access to greater resources, tame competition, and secure a greater market share. This consolidation trend poses issues for both their customers and for the companies themselves.
Mergers and acquisitions are often completed hastily and are not well-researched. This can lead to the creation of larger companies that have ineffective controls and poor governance. Some of the smaller consolidated companies bring antiquated manual processes with them, leading the newly formed consolidated companies to expend substantial sums trying to incorporate their data. Ultimately, this can reduce profit margins.
You can imagine the impact a rigid legacy IT structure can have on a larger, more complex organization, or how a lack of pricing transparency can limit options trying to find a wholesale distributor for their products. These problems can be readily seen by examining data of the year-over-year top-line revenue over the past few decades. According to Deloitte, year-over-year top-line revenue growth fell from an average of 16% in 2006 to an anemic 3% in 2015. Part of this problem comes from the disruptions posed by antiquated computer technology and poor controls when companies consolidate.
Dealing with manual processes and antiquated systems means that many companies replace them with static systems that are ineffective. At the same time, larger companies are increasingly engaging in centralized buying to save money. In order for distributors to keep up with the changes, implementing a strong pricing strategy is necessary.
Poor Sales Experience
Many larger companies with greater resources have implemented digital transformation programs or have plans to do so in the near future. In fact, 34% of companies report that they have digital transformation programs in place, and 31% indicate that they plan to implement them in the next few years. However, 35% report that they do not have plans to implement a digital transformation program. According to the MIT Center for Information Systems Research, companies that have completed digital transformations have enjoyed a return on investment averaging 16%. However, the digital transformation of wholesale distributors will need to address the dynamic factors that impact the industry. Companies must anticipate what might happen next in order to reach their full potential and profitability.
In the case of wholesale distributors that have implemented some form of digital transformation, some stopped at producing online catalogs with electronic instead of manual ordering processes. These types of digital transformations have been ineffective because of a lack of price scaling, poor online customer service, long waiting periods, shipping problems, and inconsistent pricing. As distributors attempt to incorporate technology, they must do so with the future in mind.
Year-over-year top-line revenue growth fell from an average of 16% in 2006 to an anemic 3% in 2015. Part of this problem comes from the disruptions posed by antiquated computer technology and poor controls when companies consolidate.
How Dynamic Pricing Algorithms Have Changed the Way We Sell
Distributors understand that pricing is a journey rather than a destination. However, most struggle to provide a consistent, personalized experience across all channels. A large part of the problem is that many companies focus their investments on low-return initiatives while failing to invest in high-return initiatives. For example, 48% of companies invested in increasing the efficiency of their sales processes, which only impacted the topline pricing of 33% of the companies. At the same time, only 11% of companies invested in optimizing prices with big data. The data doesn’t lie—it is more lucrative to try to stay ahead of the curve than to try to keep up with it.
In order to remain competitive and to increase profits, businesses must be willing to adapt from manual processes and relation-based selling to technologically focused and price-based solutions. As the distribution industry continues to evolve, companies must integrate technology and data-driven strategy into all aspects of their operations.
From selling to pricing and everything in between, companies must be willing to focus on the future and invest in Artificial Intelligence (AI) and other modern commerce advancements to stay ahead of the curve. In the following three sections, we will discuss how a dynamic pricing strategy and algorithms change the way you should sell.
Using AI to Stay Ahead of the Competition
The modern approach to commerce involves moving away from traditional practices. Selling based on gut instinct and guesswork, manual processes, and inconsistent practices is no longer effective. Embracing true digital transformation means adopting a shift toward AI and dynamic pricing. Today’s companies should base selling on science, machine learning, algorithms, and comprehensive analytics that can pinpoint sources of revenue and margin changes using real-time data and personalization. Adopting AI-driven pricing science can allow companies to transform their pricing strategies into competitive advantages and maintain relevance and viability in an ever-changing market.
Having a Dynamic, Single Source of Truth
Pricing is key, and the payout and leverage tied to price improvements are high. According to Harvard Business Review, a 1% price improvement increases operating profits by 11.1%, assuming that there is no loss in volume. Price improvements have from three to four times the impact on profitability as proportional volume increases.
This demonstrates the importance of moving beyond a transactional sales approach, and instead embracing a customer profitability model where the focus is on real-time pricing and profit margins. The traditional idea of multiple sources of “truth” for a company is no longer viable. While customers are important, a good pricing strategy will increase profit margins and attract new clients.
Improving Wallet Share from Existing Customers
Improving wallet share from existing customers simply refers to the process of identifying current customers who could spend more money with a particular company than they already do. Increasing revenue from existing customers is easier and less expensive than finding new customers and should be a primary focus of any pricing strategy.
Companies can rely on data science and machine learning to pinpoint when and where Amazon or other competitors are a threat to their customer base. Once such threats are identified, companies can leverage this information for an even more custom and personalized customer experience –this time with retention in mind.
Collecting and analyzing data are the first two steps in the process. To make the data analysis as effective as possible, branch-level salespeople and managers must have the power to leverage the information. This can help them make effective sales based on precise data science rather than on gut instincts or competitive pressure. Smart, focused sales strategies are more valuable than high-volume sales. The way to harness this principle within a company is to make it available and usable for employees up and down the chain of command.
Being Prepared to React Faster and Compete with Frequent Price Movements
Companies should never wait for events to happen before they begin preparing to respond to price changes. In today’s wholesale distribution market, demand pricing is a necessity. Companies should model, interpret, and discuss potential situations before they occur. When they do, they will then feel prepared to handle them.
Changes in the market are inevitable, requiring companies to be confident, anticipate them, and move quickly when they occur. They should feel prepared to compete with the frequent price movements that happen within the wholesale distribution industry. Wholesale distribution companies need to develop an overall pricing strategy that will prepare them to react as quickly as possible. This can help companies compete with others regardless of their resources or size.
Empowering Sales Channels to Make Informed Decisions
Effective pricing algorithms and training can empower employees across different sales channels to make informed, evidence-based decisions. Incorporating pricing algorithms and evidence-based training can ensure that online sales, branch sales, and corporate sales will have guidance from data science. This will provide salespeople and managers from each channel the ability to make informed decisions. Empowering salespeople and managers across all sales channels to make smart decisions creates more agile companies that make effective decisions quickly regardless of which channel the customers use.
Watch this video to learn how AI-powered sales growth software can drive increased revenue in just 30 days.
Dynamic pricing can also be used to reward customers for increased purchasing consistency, which can lead to increased brand loyalty and less risk of customer churn. Customer churn refers to declining buying patterns among customers that may indicate AI-driven technologies use machine learning to create forecasting algorithms that help predict when customer sales begin to decline. This gives the seller the opportunity to take action to retain their business by meeting the customer with the right offer, at the right price, at the right time.
Why Should You Adopt a Dynamic Pricing Strategy?
A dynamic pricing strategy helps ensure price improvements and greater effectiveness with list and negotiated prices. When a dynamic pricing strategy is in place, the team will feel empowered to execute sales without the fear of making mistakes or underselling or overselling customers. With price consistency and greater accuracy, the sales volume will grow and adapt to an ever-changing market.
Wholesale distributors exist in an industry in which most of their competitors view increased sales as the key to increased profitability, but adopting a dynamic pricing strategy can yield several benefits that work in tandem to increase the bottom line.
Improved Sales Growth
For many companies, the biggest benefit of adopting a dynamic pricing strategy is increased sales growth. By analyzing the buying patterns of existing customers and using an outlier algorithm to produce insight into their preferences, AI-powered opportunity detection software can lead to increased revenue in as little as 30 days. Existing customers are 50% more likely to try new products and spend more compared to new customers, and Hanover research shows that 92% of B2B buyers desire personalized recommendations. Ultimately, companies who are not leveraging this data to better anticipate product or purchasing needs are losing revenue potential.
Increased Win Rates
Today’s B2B and B2C buyers are increasingly savvy with more choices than ever, and sellers know this all too well. According to Hanover Research, 54% of vendors say that price and competition are the reason they lose deals. This is why more companies than ever are turning to automated technologies like Smart CPQ software to accelerate the sales cycle while providing customers with the customized and efficient sales process that they demand.
The first benefit of CPQ is automated configurations. Simply put, CPQ allows for tailored configurations with real-time product and pricing information, dramatically saving sales reps time. A 2017 report by Constellation found that organizations had an average time savings of 300% by using CPQ solutions compared to manual quoting processes. And sales organizations know all too well that time is money. Automation and algorithmic pricing enable companies to provide quotes that are fast and accurate, which improves the customer experience and increases win rates.
Influence and Manage Non-Negotiated Prices
Inconsistent pricing and reactive discounting are two of the largest detractors from profit margins. Companies that lack good governance, visibility, and control often suffer from these problems. Inconsistent pricing also frustrates sales staff, employees, and customers while negatively impacting the bottom line.
The term for the list price found in catalogs, on websites, and in retail stores is non-negotiated pricing. It is often the starting point for negotiated pricing. If a company struggles with non-negotiated pricing, it is likely because it does not have a process established for selecting the non-negotiated prices. In many companies, people make non-negotiated pricing decisions in a reactionary way, which increases the potential for price inaccuracy. Instead, companies should analyze the data and use it to determine the non-negotiated prices to put into place.
Simplify this pricing data analysis by using dynamic pricing management software to replace manual updates and spreadsheets with a single source of pricing information. This means having a centralized pricing solution that will be key to every price decision made. Pricing is not static, and using software that incorporates real-time information to make recommendations helps increase pricing accuracy. The software should be scalable so that it can handle all projects.
Security and feature availability are equally important for dynamic pricing management software. A good solution should use a SaaS model to deliver the cloud-hosted by the platform. The software should be able to perform millions of pricing requests with real-time pricing each day. In the near future, most wholesale distributor sales will likely be via e-commerce, making a comprehensive e-commerce solution with the capabilities of handling large quantities of data with a high degree of accuracy a necessity.
See how a single, centralized platform for all of your pricing strategy and execution needs contributes to growth in this short video about PROS Control.
Optimizing Negotiated Pricing
Another potential benefit of a dynamic pricing strategy comes from utilizing price optimization software. This type of software based on AI technology helps take the guesswork out of negotiations. Poor price negotiations are a big source of profit leakage in bulk wholesale distributions. Negotiations should begin with the list price and end at the right price that preserves a product’s value to the customer while earning profits for the company.
Growing sales without sacrificing profitability can be difficult. Value-based pricing can make it a reality. Value-based pricing occurs when the customers’ willingness to pay coincides with the company’s strategic objectives. Since both parties want to obtain the best deal possible from the transaction, it can be difficult to arrive at an ideal price. When customers believe that they are getting a good deal, they will be more likely to return. Using price optimization software can help companies negotiate with greater confidence while knowing that they are offering the best prices to their customers.
Understanding the science behind the pricing can help companies feel more comfortable with the prices that they offer. Companies should take the time to review the metrics and data behind pricing decisions, allowing them to have all of the information that they will need during the negotiation process.
Price optimization software helps remove the emotions from the sales process by delivering price-envelope recommendations. This enables companies to deliver customer-specific pricing, depending on the overall value of each customer to the business. By basing decisions on data science, companies can prevent leakage and pursue their most valuable clients first.
What Makes PROS Different
With over 30 years of expertise in data science and analytics tools, PROS is able to create personalized pricing solutions that are scalable and yield immediate increases in win rates and revenue. As the leading AI pioneer, we are setting the standard for the future of machine learning and data analytics. By utilizing AI solutions and machine learning, our dynamic pricing and price optimization software is built to help companies price, configure, and sell their products with speed and precision.
Are you ready to embrace digital transformation? Learn how global companies are driving big revenue impact through dynamic pricing strategies.