What is Machine Learning?

“Artificial Intelligence” is a broad term used for software or systems that attempt to mimic biological intelligence and act with autonomy.

A subset of Artificial Intelligence, “Machine Learning” is a method used in software that can ingest large amounts of data, detect patterns, report on patterns and adjust algorithms (or learn) from changes in data.

Could You Use Faster Pricing Insights?

Traditional pricing organizations employ talented individuals to analyze historical pricing data. After gaining analytical insights, they communicate pricing recommendations to decision makers. The process is only as fast as an analyst can churn through data and as valuable as they can effectively relay the complex underlying drivers of price.

Now imagine transaction data consumed and evaluated against historical indicators of buyer behavior and demographics in near-real-time. Your top analysts are freed-up to connect qualitative intersections with company strategy and macro trends. The data is visually displayed using standard reports and dashboards. Your organization can quickly act and implement changes.

The new scenario is happening now. Using the power of AI/machine-learning-enabled optimization tools and data visualization, enterprises are quickly realizing pricing gains that fall directly to their bottom line.

Optimizing Price – A Practical Application of AI/Machine Learning

The technology can “understand” drivers of price by using customer attributes to segment customers. For example, did the average income in a ZIP code, device type of a purchaser, the time of day, or even the weather have effects on win/loss? It can “predict” effects on demand based on changes to price. If you change price to maximize margin, what is the effect on volume?

They can also:

  • Pinpoint the significant drivers of price among many variables
  • Create Floor, Stretch, and Target guidance pricing for sales teams
  • Test proposed price changes in a controlled environment and model the impact on demand
  • Understand customer price sensitivity using transactional history and supplemental 3rd-party data such as weather, competitive information, geographic, demographic, etc.
  • Create and optimize bundled pricing offerings
  • Use “solving algorithms” to optimize based on company objectives (revenue, margin, customer acquisition, etc.)
  • Detect at-risk customers

Double-digit Growth 

Companies are taking advantage of the technology. Gartner® has observed double digit market growth in Price Optimization and Management every year since 2017. Nimble SaaS vendors in the space have made Pricing Optimization more affordable than ever before. Connectivity to leading CRM tools and bundling of Price Optimization software with CPQ and Contract Lifecycle Management suites has led to integrated platforms requiring less investment than just a few years ago.

The integrated platforms show how pricing optimization has matured and become a key part of many companies’ overall strategy. Connections to your CRM, quoting platform, point of sale systems and data warehouses are part of almost all implementations. By incorporating pricing systems and discipline into all company activities, employee adoption is increased, and benefits are therefore maximized.

At right are a few companies that have reduced the time to implement and accelerated payback compared to the early versions of pricing software.

Benefit Leverage of Price Optimization

Companies are reaping the benefits of more sophisticated pricing. The benefit leverage is significant, making these pricing optimization projects attractive. Small pricing changes can have significant impacts compared to large, disruptive operational initiatives. On average, between case studies, software vendor’s claims, and our own experience, you could see:

With significant operational benefits:

  • Higher sales conversion rates
  • Faster deal approvals
  • Automated approval thresholds
  • Shorter sales cycles

“What AI and machine learning allows you to do is find the needle in the haystack.”

Colonel Robert O. Work, USMC, Ret.& Fmr. US Deputy Sec. of Defense

Summary

AI & Machine Learning are already proven and available in the Pricing Optimization software market – it is no longer the stuff of science fiction. These technologies are beginning to proliferate multiple industries as the cost of implementations and subscriptions go down. An objective analysis and software selection process often creates compelling business cases.

Five Lessons Learned 

  1. Data is key: This cannot be overstated. Lack of past transactional data is a showstopper. While messy, disorganized, underwhelming data can work, not enough data prevents the statistically significant, optimized results. If the data needs cleansing for extraction, expect longer implementations. Post-implementation, the machine continues to learn as you build your data history.
  2. Align internally on pricing strategy and incentives: Pricing Optimization is useless if no one adopts it. Holistically aligned as part of a larger pricing strategy and incentive program, success is reached by concerted efforts to align your organization on what they’re trying to accomplish – whether it be increased margin, increased revenue, or gaining market share.
  3. Do your research in selecting your Price Optimization software: Take the time to gather business requirements. Perhaps just as important, do your due diligence and compare functionality when selecting a vendor. This software selection process should last between 4-12 weeks depending on scope.
  4.  Bundle for savings and integrate: Many Price Optimization software vendors are either getting into the CPQ space or already have a CPQ offering. If you are in the market for both, expect more savings. If the solution you pick is standalone, it should be capable of quick integrations with leading CPQ and CRM software.
  5. Find the right blend of focused resources to partner with the software vendor: The best integrations mix in -house resources, technical staff and focused partners representing your business strategy and requirements. Many software solutions provide an in-house implementation team, while others have a wide array of partners. Finding the right group of people to help make the solution work in your unique situation leads to successful implementations.

About Fisher

Fisher Management Partners is a specialized consulting firm designed to address the unique needs of large and middle market businesses. With a focus on critical, cross-functional initiatives in strategy execution, finance, customer experience, value chain, and technology, Fisher partners with clients to achieve strategic and operational business objectives driven by two types of need:

  • Execution of new corporate transformation strategies
  • Rapid corporate expansion through acquisition, equity infusion, or market growth
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