Mastering Descriptive Analytics to Empower the Revenue Team to Succeed
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The document discusses the importance of descriptive analytics in optimizing revenue management processes at BNSF, focusing on challenges faced due to manual handling of contracts and the lack of a formal workflow. It highlights the need for collaboration among teams and outlines best practices for implementing analytics tools to improve efficiency and insights. The key takeaways included a reduction in contract cycle times and better tracking of key contract dates, emphasizing the significance of aligning goals before adopting technology solutions.
Mastering Descriptive Analytics to Empower the Revenue Team to Succeed
- 1. #AccelerateQTC Brian Goldberg & Marilyn Hardy / May 4th, 2017 Mastering Descriptive Analytics to Empower the Revenue Team to Succeed
- 2. Customer Success Manager Brian Goldberg “I'm a sucker for new ideas and passionate about solving big problems.” @briandgoldberg • Sales cycle technologist and amateur data scientist. • 10 years selling, implementing, and training enterprise software.
- 3. Director, Revenue Management Marilyn Hardy “Data analytics has the power to transform companies if the company is insightful enough to see the value and courageous enough to take action.” • Marilyn is the business lead in implementing APTTUS at BNSF. She has been on this journey for four years starting with presenting a case for the necessity of a Contract Lifecyle Management system, contract metadata gathering, and implementation.
- 4. Cognitive Prescriptive Predictive Descriptive Summarize Business Activities Common data set for further analysis Catch Red Flags Early Prove OutcomesFormulate hypothesis Iterate on Processes Determine Actions Find hidden insights or trends Why Does it Matter? 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
- 5. Descriptive Analytics Tools CRM BI EmbeddedNL
- 6. Revenue Cycle in the Cloud
- 7. Quote-to-Cash Example Analytics Quoting/Contracting Insight Term Exceptions Can we make more contracts standard? Cycle Times Where are the bottlenecks? Attach Rates When are premium options being included? Discount Rates Can we reach the optimal discount rate? Win Rate Can we learn from the best sales reps? Revenue Recognition Insight Churn Rate Are we loosing customers? Days Sales Outstanding Have our collections processes kept up with sales? Gross Margin Are our sales tactics in line with business goals?
- 8. Out-of-the-Box Dashboards Ready to install and provide insights on Quote-to-Cash processes Best Practices Measure adoption and value with ease Advanced Analytics Arm yourself with new tools and tricks to solve problems Introducing Our Analytics Collaboration Space
- 9. • A Berkshire Hathaway company • 32,500 route miles in 28 U.S. states and three Canadian provinces • 41,000 employees • Approximately 8,000 locomotives • 13,000 bridges and 89 tunnels • Operates over 1,400 freight trains per day • Serves over 40 ports • Carloads shipped in 2016: 9.7 million • Leads rail industry in technological innovation BNSF is a Leading North American Railroad
- 10. Problem Statement • 265,000 active contracts representing $7B in spend, all handled manually • 24 different groups with their own process to create and execute • No formal workflow to ensure Legal and Risk have reviewed contracts • Contract terms and conditions are not being monitored. • No notification process for key contract dates. • No way to monitor contracts to ensure parties are fulfilling terms of agreement • Limited search capability with existing tools
- 11. Key Takeaways 11 Challenges Lessons Learned Results • Contract clean-up • Contract cycle time reduced • Can easily track key dates • Obtain data more rapidly • Implementation takes longer than anticipated • Ensure you have the correct key stakeholders • Identify business process changes prior to development • Used phased approach to implementation • Resistance to change • Decentralized ownership • Business case not enterprise supported • Meta-data is a pre-requisite
- 12. In Conclusion Descriptive Analytics are essential to any analytics strategy Align on Goals before designing a technology solution Move the Needle with focused reports and dashboards
Editor's Notes
- #5: 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?