Like many of its peers, a medical-technology company was struggling to adapt to changing market dynamics. Its traditional relationship-based sales model was coming under threat from increased competition and scrutiny from hospitals, while end users, especially doctors, were still expecting highly customized solutions.
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Company leaders quickly saw that deploying a system that enabled them to reset price targets in real time at a customer/product level, based on actual facts, could help improve pricing. In other words, they realized that they needed dynamic pricing.
Leadership ran into a few issues with these tools, however: historic data was not clean, no competitive data were available, and sales teams didn’t trust the pricing recommendations and so wouldn’t use them, since previous pricing tools had earned a reputation for making “theoretical” recommendations that bore little resemblance to realities in the field.
This situation is all too familiar to many companies. Developing a dynamic-pricing capability needs as much emphasis on people and processes as it does on analytics and technology (Exhibit 1).
When the medical-technology company shifted to this approach, it started to make real progress. It addressed the people side of the equation by explaining to the sales force, in simple, understandable terms, how dynamic pricing works, taking into account real-world constraints and treating the sales team as partners in development from the get-go. Combining advanced analytics with human-defined rules helped them overcome the data-scarcity challenge.
This careful approach led to enthusiastic adoption, a 4 to 8 percent margin uptick, and an increase in revenue growth of more than 5 percent. Other companies following a similar approach have raised prices by as much as 60 percent on certain products and services without seeing any significant loss in volume.
That level of impact comes only when sales teams and their managers are willing—and have the confidence—to incorporate new processes into their daily work. Most importantly, they need to shift their mind-set. Dynamic-pricing organizations think less about volume and more about value.
The real benefits of dynamic pricing
Digital technologies and platforms are disrupting B2B sales models and forcing players to fundamentally rethink their pricing strategies. Fortunately, these same technologies are also enabling this new dynamic-pricing approach, which brings substantial benefits for those companies that can embed it successfully.
Although interest in dynamic pricing continues to grow, we find that leaders tend to have a vague understanding of what it actually is and what its true benefits are. Dynamic pricing allows companies to better understand and predict when to push prices higher to quickly capture the upside, or lower, to avoid volume losses. And it helps to improve and speed up the decision-making process while providing more granular insights, for example by scoring deals against peer groups and factoring multiple criteria into price recommendations, such as strategy, deal size, customer type, and product type and mix.
Dynamic pricing is also self-reinforcing: as sales teams test new pricing approaches, they can feed win and loss information back into the system to steadily improve its accuracy and uncover new insights. The most effective teams also use the insights generated to tailor their offerings more closely to customers’ needs, for example by making relevant cross-sell recommendations, which significantly improves loyalty.
One chemicals-distribution company learned that its highly manual contracting and approval process was so slow that it irritated reps and customers alike. By introducing a suite of integrated dynamic-pricing and sales tools, including an iPad app that sales reps could use while negotiating with customers, the company reduced deal-processing times from several weeks to just one or two days.
Building out your dynamic-pricing engine
Before building anything, companies need clarity on what their goals are. The choice of tool and approach will depend on the nature of the offerings and on the company’s strategy, such as margin or profit-share targets.
Analytics—the essential foundation to any dynamic-pricing capability
There are many flavors of analytics, and the choices depend largely on the business’s goals and the complexity of its transactions. This is critical: there is no one algorithm to rule them all; rather, each business needs to develop its own dynamic-pricing engine to achieve its specified goals based on the complexity of its pricing transactions (Exhibit 2).
Companies need to take a segmented approach to their pricing to harness the full power of analytics. Statistical clustering techniques that create groups of products with similar pricing behavior (based on recent sales data, product lifecycle, level of competitiveness, etc.) can be used to price large assortments of products, for example in distribution or spare parts.
For other products that have higher value, companies might choose a value-pricing approach based on a mapping of customer’s buying factors, and a quantification of them using a combination of calculations (How much does my customer save by using my product in their production versus an alternative?) and customer interviews (How do you rate the reliability of our offerings versus that of our three main competitors?).
The algorithms that drive the pricing engine itself are based on advanced analytics techniques—including AI, statistical modeling, and machine learning—that can deliver insights on relatively small datasets. The best B2B players are dipping into smaller data pools such as market indices, news articles, and other online sources to understand target segments, competitors, and price boundaries. They then combine this with all their internal deal data and invest in people who have deep market experience.
A critical final component of the analytics engine is the self-learning algorithms that incorporate each customer segment’s willingness to pay and update prices based on this information. Proxies for willingness to pay include volume evolution, offer win rate, and click conversion. Where there is sufficient data, companies can even consider splitting customer segments into subgroups to run differentiated price tests.
To ensure pricing engines achieve the expected results, top companies include a performance management layer that provides easy-to-read visual outputs that synthesize key metrics so that managers understand what’s going on. Managers should also be able to track the number of price changes made, their magnitude, and the system’s impact on revenue in a given period. The pricing engine needs to be seamlessly integrated with the tools that sales teams use on a day-to-day basis to quote.
People—changing mind-sets and building capabilities
Having a good dynamic-pricing engine in place will not necessarily change the way salespeople think or act. If they believe that lower prices lead to more deals, for example, they may resist recommended price hikes for fear of losing volume.
Leading companies use a range of approaches to get salespeople to buy into dynamic pricing. Perhaps most importantly, the team building the new dynamic-pricing approach and new dynamic-pricing tools needs to incorporate sales teams’ knowledge into the system from the beginning. By being part of the process rather than passive recipients of an ever-changing price list, salespeople will understand that their experience is actually a key part of the new model.
The pricing team also needs to set the right expectations regarding the subtleties of the price changes: some may rise, others fall, and still others remain flat, depending on the situation. The best pricing tools offer pricing suggestions rather than formal directions and provide the rationale behind them. This helps teams prepare for negotiations by providing the underlying rationale, while still giving salespeople control over the ultimate negotiated price.
A strong argument for dynamic pricing is that it helps salespeople stay on top of what are often incredibly complex product portfolios. Advanced analytics can offer rich, fact-based guidance on product and customer-specific pricing and show what the best salesperson would do in each situation. If those recommendations are easy to understand and use in negotiations, they make each salesperson more effective.
Incentive structures also need to be realigned so that sales people are rewarded for following the recommendations. This means compensating reps based on the results of recommendations generated by the pricing tool.
Demonstrating quickly that the model works can help to convince salespeople that the approach and the tools have enormous value. It can also help secure additional investments for dynamic pricing initiatives. A pilot on a limited, yet representative scope is the best way to achieve this. The whole exercise can even be gamified: most salespeople are naturally competitive, so explicit information about which teams are delivering more value can spur everyone to do better.
A new dynamic-pricing system should also be used as an opportunity to reset the coaching setup between sales managers and their teams. New deal-scoring processes, for example, mean that deals that require deeper discussion get escalated, creating a coaching opportunity. The sales manager can pull up similar deals to help the rep understand what has worked in similar environments, and then discuss and model alternative strategies.
Senior leaders also need to think and act in new ways. If a salesperson relies on the pricing engine and holds firm on a price but loses the deal, then the manager should be curious rather than angry. Every deal, whether it’s won or lost, can provide valuable new data.
One chemical company identified a potential $60 million in additional annual EBITDA, or 8 percent return on sales, through dynamic pricing. In addition to building a new pricing system, the company undertook an intensive salesforce training program involving hundreds of people. This program helped sales teams to become comfortable with using new insights to prepare for negotiations, including building a case for price increases and rehearsing for meetings with senior management. This quickly led to multiple successes, and within a year, the majority of the commercial organization was following the new dynamic-pricing approach.
Process—organizational and procedural changes to support the new way of pricing
The type of digital and advanced analytical skills that are needed to develop and maintain a sophisticated pricing approach require a dedicated pricing unit. Whether it will be local, regional, or global depends on the industry structure and dynamics. Regardless of the geographical setup, it will need to include the following core elements:
- A local/regional/global pricing office that maintains and develops the pricing engines, constantly monitors price and sales performance, tracks deviation from models, develops suggestions, and advises management on price evolution. Such an office needs to combine more-traditional roles, such as industry ones, with new roles, such as data scientists, who bring relevant analytic skills.
- Standardized pricing processes that start with market intelligence, raw material forecasts, and other pricing inputs and end with granular, dynamic price recommendations, actions, and monitoring of execution. A clear pricing calendar with milestones helps ensure flawless execution and that pricing happens proactively—based on foresight—not reactively, based on insights.
- Pricing performance management that defines targets on pricing, margins, and profitable growth, not only on volume acquisition; develops a simple but effective dashboard; includes regular performance dialogues; and gives feedback about successful and not-so-successful approaches.
How to get started
While approaches to adopting a dynamic-pricing approach vary, we have identified five important steps to begin:
- Identify potential impact in order to create a strong business case and ensure the top team is fully aligned with and committed to capturing that potential.
- The impact of dynamic pricing is comparable to a sizable new business idea and therefore merits the involvement of your best people. It is a business solution, not just an IT tool. It should be owned by a dedicated business team that has the advanced analytics capabilities to drive the project forward in conjunction with sales and marketing.
- Focus on capability building and mind-set change from the very beginning. Legacy systems will not be the reason you fail; legacy thinking will. Identify likely promoters and detractors across the organization. Develop a strategy for amplifying the former and converting the latter. Review incentives to better align them with value to help change mind-sets. Showing people that they are partners on the journey by involving them in decision making and incorporating their feedback, for example, is also critical.
- Our experience indicates that companies with a bias for action do better. Fail fast and learn fast. It is better to deploy a minimum viable product fast and get valuable insights to the front line in a matter of weeks, while setting the stage for long-term impact. Companies that follow that approach typically achieve the first bottom-line impact within three months and full run rate in 12 to 18 months; they also keep innovating and refining their approach over time, making their systems increasingly autonomous and self-learning.
- Success requires a broad mix of talent, some of which can be in short supply. Start early hiring people to fill roles that are less likely to be sourced in-house, like data scientists. Combine these new roles with more-traditional talent who understand the industry, the company, customers, and competitors.
Dynamic-pricing skills are fundamental to stay ahead of the competition. But in our experience, tools and algorithms are not enough to capture and sustain significant impact. Companies need to put equal focus on people and processes.