June 13 by Eric Dreshfield
Quote-to-Cash application solutions emerged over the past decade to improve the productivity, agility, and selling effectiveness of enterprises of all sizes. This trend resulted from companies needing to better integrate functions around product configuration, quoting, pricing, contract management, and ordering. Efforts focused on reducing errors and accelerating processes by eliminating manual steps, providing rules for product combinations, standardizing pricing, enforcing workflows, and better managing document repositories.
As a result, integrated, single-vendor, single data model Quote-to-Cash solutions displaced standalone point solutions. Enterprises discovered that they gain far greater value from such technologies when they are consolidated in a single solution that supports the full Quote-to-Cash business process.
Continuing innovation in Quote-to-Cash opens enormous opportunities for applying technology to improve practices, processes, and overall business outcomes. Automation now extends to encompass ordering, billing and subscription management, and revenue recognition to enable touch-less ordering that drastically reduces manual work. Similarly, support for E-Commerce and Partner Commerce requirements lets enterprises orchestrate sales activities across multiple sales channels.
Moreover, enterprises benefit from vastly improved process visibility, which creates a superior understanding of market dynamics and can increase agility in translating insight into action. Enterprises can better influence behaviors in sales cycles by employing promotions, compensation, and rebates. Similarly, analytic applications deliver prescriptive advice on key decision points in the Quote-to-Cash process, such as recommending the right products and the right pricing and discounts for deals.
For comprehensive Quote-to-Cash applications to meet today’s business challenges, three layers of capabilities are required to generate optimal business outcomes.
Automating Processes, End-to-End
End-to-end automation provides the foundation for Quote-to-Cash by making it possible to efficiently execute – as one coordinated process – all of the tasks required by an enterprise to cultivate and close sales. Through comprehensive automation, dramatic productivity gains are realized from better management of processes, better communication and collaboration among stakeholders, and eliminating manual steps in processes.
Quote-to-Cash automates three core applications: Configure Price Quote, Contract Management, and Revenue Management. Each application flows naturally into the next, creating a seamless Quote-to-Cash process.
Configure Price Quote (CPQ) empowers salespeople by providing up-to-date product and pricing information. The CPQ application ensures sales people provide prospects with valid and complete proposals, no matter the complexity of bundling rules or size of product catalog. The application also enforces the company’s pricing rules to prevent inappropriate discounting. With CPQ, salespeople get accurate proposals out more quickly and accurately, enabling them to close more deals.
Contract Management (CLM) enables sales and legal teams to generate, negotiate, store, and comply with all sales contracts, along with related legal documents such as NDAs. The Contract Management application ensures that deal terms can be created quickly, following all company policies, and that the company has total visibility to every step of the negotiation process. Once deal documents are signed, Contract Management tools ensure that all the company’s new obligations are tracked correctly.
Revenue Management ensures correct, timely control of all revenue-related processes, including order management, billing, and revenue recognition. With the Revenue Management application, these critical back-office functions work in sync with each other and in accordance with the terms of the deal. Revenue Management reduces the risk of errors in the ongoing customer relationship and makes sure that the business captures the revenue opportunities, such as renewals, that otherwise may slip through the cracks. Revenue Management handles the diversity of business models a growing enterprise may offer clients: physical goods, professional services, subscriptions, usage-base fees, and one-time fees.
Quote-to-Cash automation provides rich data for systematically understanding the motivations of buyers and sellers and the business impact of their actions. Applications such as promotions management, rebate management, and variable compensation can be used to encourage the desired behaviors in sales and buying cycles.
Influencing behaviors represents a practical application of Big Data to real-world concerns, turning incentives and promotional programs into formidable tools for securing better business outcomes. By gathering data on all selling and buying activities throughout the Quote-to-Cash process, companies can identify the inducements most likely to influence target audiences. The success of these efforts can be continually tracked and analyzed.
An example would be commissions for salespeople. When offered an attractive payment to sell a product, they will strive to sell it. So when salespeople see the compensation implications of deals, they will act according to the information provided. For instance, commission calculators integrated. with quoting processes show how commissions decrease as larger discounts are given or increase as pricing is successfully defended.
Enterprises gain substantial business advantages when they design and synchronize incentive programs across sales channels to align and drive behaviors that increase demand, revenue, and profit. Promotions stimulate demand attracting prospects to different channels. Rebates drive partners to focus on selling specific products on behalf of suppliers and can be helpful for working with partners that may lack knowledge and awareness of product portfolios. Commissions and rewards guide sales priorities to include specific products or services in deals.
To fundamentally improve business performance, enterprises need to be able to use incentives intelligently to further corporate objectives. The self-interest of customers, prospects, partners and salespeople must be systematically harnessed, requiring applications that manage variable compensation, rebates and promotions programs in the Quote-to-Cash process.
Embedding Intelligence into Processes
The saying “forewarned is forearmed” is very applicable to competing in business today. Possessing a solid grasp of market demand, evolving customer preferences, and overall trends in buying patterns represents a powerful advantage for any enterprise. Even more potent is the ability to translate insight into timely action that capitalizes on market opportunities.
That means providing intelligence at the right time, to the right person or role, to complete an activity and achieve a purpose. With advances in Big Data, machine learning, and artificial intelligence (AI), predictive and prescriptive guidance can be delivered at key decision points within Quote-to-Cash.
Quote-to-Cash applications offer unprecedented access to volumes of data with context on buying and selling activities. And data are accessible from CRM systems, ERP systems, and other systems integrated with Quote-to-Cash. Enterprises have the information and means to rapidly pinpoint patterns on what is getting sold, to whom (the key customer segments), by whom (which channel), at what price, at what rate, and more.
Organizations can apply a range of analytics to Quote-to-Cash processes. Machine learning stands out as offering enormous opportunities for improving selling. Key areas include recommendations for products (cross-selling and up-selling opportunities), pricing (discounting guidance), and quoting (comparing quotes to those of the top performers and scoring).
Suppose management seeks to have its entire sales organization perform like the top 20 percent that generate the majority of revenue. Using data from similar deals closed by the top performers, machine learning can guide the 80 percent on quoting best practices. Data-driven recommendations can be given on the cross-sales of add-ons and on optimal discounting levels, with the guidance changing as buying patterns and practices of top sellers change.
With well-developed tools, libraries of algorithms and best practices, machine learning applied to Quote-to-Cash processes can yield more useful, relevant insights as more decisions are made and more data is acquired. The analytic models based on these technologies are not static, but evolve as companies’ markets and business practices change.
Modern user interfaces (UIs) and intelligent agents have improved how insights are fed to users without requiring them to learn new tools. For example, cross-sell recommendations can be presented discreetly in shopping carts or discounting guidance in deal management applications. Intelligent agents can convey prescriptive advice in conversations via different media – voice, chat, text messages, and even in augmented reality.
Efforts to apply intelligence to Quote-to-Cash are unfolding rapidly, notably with machine learning. Embedding data driven insights in processes empower sales teams of all sizes to sell more intelligently raising their performance. And machine learning technologies are being deployed across multiple channels to enable partner networks and provide guidance to end-customers utilizing E-Commerce sites.