Bottom Line: AI-based deal intelligence, pricing and predictive analytics are defining the future of CPQ selling today by providing real-time insights applicable to every sales cycle, from initial quote through contracts and renewals.
AI and machine learning are making immediate contributions to driving more revenue by improving deal price guidance, deal intelligence, dynamic pricing and improving rebate & incentive management. Every CPQ vendor knows that pricing is the catalyst they need in their applications to attract and keep new customers. They’ve redefined their product road maps to enable customers to create pricing segmentation models, provide price optimization guidance to sales teams, and optimize pricing for each product and customer. Salesforce is providing Einstein Pricing and Einstein Analytics Templates to optimize margins, improve close rates, improve quoting efficiency and improve subscription metrics.
What CROs Are Looking For
Chief Revenue Officers (CROs), Sales and Sales Operations VPs are looking for CPQ solutions to step up and deliver improved pricing guidance across sales cycles, ideally through intuitive, easily understood visualizations. A CRO of a medical device manufacturer who is generating over 40% of all revenue from CPQ sales strategies told me recently that for CPQ to make a difference in their company, it needs to do the following:
- CPQ applications need to improve how discounts, price agreements, markups and subscriptions get reflected in the final price and margin for each deal.
- Visualizing pricing optimizations, workflows, guardrails for specific deals and margins is a must-have with pricing waterfalls being a favorite area the CRO would like to see more work done on.
- Better integration with SAP Variant Configuration logic including rules, product data, definitions and product hierarchies.
- Having the flexibility of creating 2-D, 3-D and AR models in the quoting workflow and providing an optional Bill of Materials (BOM), priced out, so manufacturing, accounting, finance and sales can immediately have what they need without slowing down the quote reaching the prospect.
- CPQ selling strategies are succeeding beyond the CRO’s expectations which is driving the need for real-time integration to compensation & financials, contract & document management, tax & payment and commerce & pricing applications.
AI’s Four Cornerstones Of CPQ Growth
Pricing agility, intelligence and speed win more deals by enabling organizations to complete quotes faster and more completely than competitors. AI’s four cornerstones of CPQ growth are intelligent pricing, embedding sales insights into more sales cycles, improving deal workflows with greater intelligence and improving enterprise-wide CPQ integration. User experience is now table stakes to deliver any CPQ solution, and it’s the glue that keeps all four cornerstones aligned. Several vendors including Vendavo are defining their product strategies based on these trends and are well-positioned for 2020. Expanding on each of these four cornerstones provides insights into how AI can drive more CPQ revenue in 2020:
- Simplifying product, pricing, workflow and maintenance user experiences is enabling AI to make greater contributions to CPQ sales growth. Applying supervised and unsupervised machine learning to product, pricing and margin trade-offs is delivering solid revenue gains for those manufacturers using these techniques today based on conversations with CROs and Sales VPs. Administrator screens need to be more role-based to reduce the amount of wasted time previous-generation CPQ applications required for product and pricing management. AI and machine learning are also streamlining role-based dashboards and alerts, making better use of sales executives’ time. CROs say that SAP CPQ is keeping up with how quickly their pricing, product configurations and workflows are changing while also providing customized views of data to several different groups of users, all leading to more closed deals.
- Another usability gain sales and sales engineering teams need is for auto-complete to become more standard across product and service configurators. The goal of improving user experiences and helping to make them more productive is motivating CPQ and product configuration software providers define products in as little as 5 to 10 product attributes. They’re relying on auto-complete logic to make this happen. Having a mobile CPQ app capable of auto-completing a configuration in seconds will increase quoting accuracy and improve sales close rates.
- Integrating pricing optimization into CPQ applications is going to become more commonplace as organizations look to create continuously learning price ecosystems. Realizing the potential of machine learning in CPQ starts by creating a closed-loop learning ecosystem where pricing decisions are continually fine-tuned based on previous pricing results. Machine learning needs to be the catalyst that enables a pricing ecosystem to keep improving by continually learning from transaction data. Target and stretch pricing can be improved using this technique, while also discovering if deal thresholds are set at the optimal levels to automatically approve quotes.
- To drive more CPQ sales using AI, integrating pricing, CPQ and analytics in a common architecture so together they can benefit every sales cycle. Getting the most value out of AI needs to start with the goal of winning more deals with intelligent pricing. To accomplish that, selling organizations are using AI to drive pricing guidance that includes creating and managing segmentation models. They’re also using AI to optimize pricing guidance on every sales quote. Integrating optimized pricing applications with CPQ improves guided selling results by recommending the best possible product, pricing and configuration for a given customer’s needs. When pricing and CPQ are integrated with analytics, it’s possible to better track pricing and margin performance and create a pricing ecosystem that is always learning.
- Using AI & machine learning insights to fine-tune quoting workflows that frequently change for new products, subscriptions and usage-based pricing strategies. Sales Operations VPs and their teams spend a lot of time manually modifying quoting workflows based on new subscription and usage-based pricing available in leading CPQ applications. Using the insights gained from AI and machine learning to provide workflow recommendations would save thousands of hours per year for Sales Operations teams.
- The greater the percentage of total sales from CPQ, the more urgent the need for real-time enterprise integration. Once CPQ begins to deliver 25% or more of total company revenue, CROs tell me that they need to have real-time integration to compensation & financials, contract & document management, tax & payment and commerce & pricing applications. CROs most mention the need for compensation and financial integration with Jitterbit, FinancialForce and Xactly. For contract & document management, DocuSign is the standard along with a few mentioning Icertis and SpringCM, Avalara, Authorize.Net, PayPal and Vertex are most often mentioned for tax & payment. For integration, commerce and pricing, Enosix, Tacton, Vendavo and Zilliant are the most top of mind from CROs.
CPQ selling strategies’ effectiveness is improving thanks to AI and machine learning. Based on visits with medical device and discrete manufacturers who rely on CPQ for a large percentage of sales, four cornerstones of why CPQ is improving based on AI emerge. The first is how important intelligent pricing is becoming. In addition, embedding sales insights into more sales cycles, improving deal workflows with greater intelligence and improving enterprise-wide CPQ integration are cornerstones of CPQ in 2020. AI and machine learning are today improving revenue lifecycles by providing sales teams guidance on which deals to quote, at which price, for which specific products.
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