Driving the Future of Sales Operations
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Machine learning is all around us. It is driving familiar B2C brands like Amazon and Google. And it is now driving our Quote-to-Cash process. Learn how to streamline and run a global sales operations organization leveraging machine learning to increase sales efficiency and to increase margins and revenue.
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Driving the Future of Sales Operations
- 1. #AccelerateQTC Jack Borland, Wolters Kluwer Elliott Yama, Apttus May 3, 2017 Driving the Future of Sales Operations with Artificial Intelligence and Machine Learning
- 2. AVP Quote-To-Cash Intelligence, Apttus Elliott Yama Sales Operations Manager, Wolters Kluwer Jack Borland
- 3. We are a global company that provides information, software, and services. Our customers are legal, business, tax, accounting, finance, audit, risk, compliance, and healthcare professionals.
- 4. Machine Learning is All Around Us Self Driving Car Personal Assistant Page Rank, Search Results Product Recommendations Movie Recommendations
- 5. You Have to Move Beyond Legacy Analytics The Apttus Intelligence Capability Pyramid™ Cognitive Prescriptive Predictive Descriptive What will happen and auto-adjust/inform What will happen and how to handle it Anticipating what will happen and assigning probability Understanding what happened using historical data
- 6. Why Machine Learning Now? 6 *IDC Digital Universe report, 2014 http://www.emc.com/infographics/digital-universe-2014.htm **Data Scientist: The Sexiest Job of the 21st Century, Oct 2012 https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century
- 7. Use Apttus CPQ with Machine Learning to Drive Quote-to-Cash Outcomes Quote Scoring Predicts the probability of winning the deal and gives sales reps recommendations to improve the quote and increase the win probability. Cross Sell / Up Sell Cross-sell and up-sell recommendations for products and services that customers are most likely to purchase based on analysis of past purchases and similar customers. Pricing Intelligence Recommends optimal price or discount levels for each deal based on machine learning analysis of the deal characteristics and sales history.
- 8. 8 Ripe for Machine Learning: • Very large product catalog • Large amount of historical contract data • Primarily a subscription business Objectives: Help sales rep find appropriate products to cross-sell for: • Top line growth – more sales • Rep efficiency gains – automate account research Wolters Kluwer Legal & Regulatory US Business Case for Machine Learning
- 9. 6 Month Development Effort – June-September 2016 – On Hold for Q4 – January-March 2017 Launched April 2017 • Initial Feedback – saves time, provides valid recommendations • Recommendations refreshing monthly • Winner of the Wolters Kluwer Legal & Regulatory the Enterprise Achievement and Excellence in Action Award for Q1 2017 How Machine Learning is Working at Wolters Kluwer Now
- 10. Demonstration
- 11. Machine Learning in the Future Immediate Enhance Whitespace Price Optimization Near Term Territory Coverage Recommendations Price Optimization + Usage Mid Term Conversational User Interface Best Quote Recommendations
- 12. • Define a clear business case for Machine Learning • Set clear expectations – Value increases as system learns • Socialize the value of Machine Learning to identify additional stakeholders & use cases • Plan for the future – Extend incrementally Best Practice Recommendations
- 13. Appendix – Screen Captures 13
- 14. Account Planning
- 15. Makes Cross Sell Recommendations
- 16. Makes Cross Sell Recommendations
- 17. Creates A Quote Shopping Cart
- 18. Creates A Quote
Editor's Notes
- Many enterprises think they are using machine learning, but in fact, they are thinking of legacy analytics or what we call descriptive analytics (graphs, charts, dashboards that look at what happened in the past). Today, the vast majority of enterprises have needs for descriptive analytics, which are necessary for effective management, but are not sufficient to accelerate business performance. So let’s formally define each of these categories in what we term as the ”Intelligence Capability Pyramid”: Descriptive analytics. Descriptive analytics is the foundation level that helps users understand what has already occurred by laying out relevant summaries and supporting data in formats that are easy to consume both by end-user staff and management. How can you use this type of analytics? You can start with a big picture view of your metrics like booking, revenue, recurring revenue, margins, or revenue velocity and then drilldown for more granularity. For example, when it comes to pricing, your Quote-to-Cash system can analyze all of your sell-side contracts and provide a report on which pricing has been agreed to by different customer segments. Knowing this will allow you to decide which pricing to include in future deals of the same type. The system can also show the contracting terms and clauses that have been most successful in past deals. This allows you to decide which terms to include in the next authoring phase of a contract or which clauses need to be revisited by your team. Predictive analytics. Predictive analytics helps users recognize patterns and detect meaningful trends. More significantly, you’ll be able to generate projections of different developments for different time horizons based on the output of the analysis. How can you use this type of analytics? Your Quote-to-Cash system can analyze successful deals, along with attributes mined from your lead database. When this is matched with an internal product rank, you’ll be able to predict which of your prospects will not only lead to a sale, but what products they will likely buy. This allows you to plan accordingly and prioritize specific accounts. Prescriptive analytics. Prescriptive analytics on the other hand delivers granular insights and forecasts showcasing what is likely to occur, accompanied by relevant, system-driven recommendations on next best actions and tactics to adopt. How can you use this type of analytics? If your Quote-to-Cash system measures selling trends over an extended period of time, it will discern a spike in a particular product and provide certain recommendations. For instance, your system may tell you to allocate 25,000 extra units for a specific region, as opposed to the normal 15,000 you’ve been committed to in prior cases. This is a perfect example of proactively reacting to the rapidly changing needs of the customer. Cognitive analytics (machine driven). Cognitive analytics exploits machine learning to refine trend and pattern analyses on an on-going, unsupervised basis in order to constantly evaluate processes and associate data. It also leads to automatically initiating specific, suitable policies, actions and workflows. How can you use this type of analytics? Your Quote-to-Cash system notices an influx of demand for a product in a certain region and automatically adjusts the price slightly to match demand, generating a greater profit. No more manual work to derive valuable insights. No more missed opportunities. Instead, your company can take action at the speed needed to be competitive. The ultimate goal is to move up the Intelligence Capability Pyramid, from reacting based on an understanding of historical data to orchestrating quotes and contracts by automatically adjusting terms based on an understanding of what will happen as a result. So what is preventing you from starting the journey?
- A natural question to ask is – why now? Machine learning is not exactly new. People have been dealing with it in one form or another for decades. There are a number of factors making this timely that inclue – a change in the economics of cloud computing, cloud storage, along with the proliferation of sensors driving Internet of Things (IoT) connected devices growth, pervasive use of mobile devices that consume gigabytes of data in minutes, freely available algorithms coupled with the fact that data science is now one of the sexiest career paths are a few of the important factors accelerating machine learning adoption. Add to these the many challenges of creating context in search engines and the complex problems companies face in optimizing operations while predicting most likely outcomes, and the perfect environment is in place for machine learning to dramatically proliferate.
- For example, we currently offer our Apttus CPQ solution with machine learning to help an organization’s sales force navigate to the most optimal deal. It utilizes the data flowing through our Quote-to-Cash applications, as well as data from any source including ERP systems, websites, social data, usage data, and sensor data. It’s an easy to deploy, quick-start package that includes everything a business needs from machine learning infrastructure, to data science managed services, to implementation assistance for all three of following capabilities: Quote scoring - Predicts the probability of quote conversion based on the current sales stage of the quote. This solution also recommends next steps to improve the quality of the quote to increase conversion probability. All available quote information is leveraged to identify similar historical quotes to recommend the next step so that all sales reps can sell like your top sales representatives. Cross sell / Up sell - Recommends products and services that your customers are most likely to purchase next, based on analysis of past purchases by similar customers. Cross-sell and up-sell recommendations help your reps increase deal size and increase account penetration based on machine learning insights. Pricing intelligence - Recommendations for optimal price or discount levels for each quoted product based on past purchase history for the customer segment. This solution inspects related contracts to ensure that the contractual pricing terms are honored to provide the most appropriate renewal pricing. Pricing intelligence helps your sales reps and sales operations deal desk in the quoting and negotiation process with guidance for initial, target, and walk-away price levels for every deal. With pricing intelligence, integrated with intelligence approval workflow you can now eliminate rogue discounting, curtail margin erosion and increase overall profitability.
- Elliott to put some additional bullets here Plug Apttus vision on how ML plays in QTC