How and When to Use Behavioral Nudges to Drive Quote-to-Cash Outcomes
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Business is cyclical, and revenues can ebb and flow based on several factors like seasonality and market conditions. When the business plan calls for a flow, but sales have started to ebb, what tools are at your disposal to help close the gap between expectations and reality? Attend this session to learn how to deploy targeted nudges to close the behavioral gap and drive positive business outcomes.
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How and When to Use Behavioral Nudges to Drive Quote-to-Cash Outcomes
- 1. #AccelerateQTC Jason Loh – Apttus Pablo Dominguez – AppNexus May 4, 2017 How and When to Use Behavioral Nudges to Drive Quote-to-Cash Outcomes
- 2. Senior Director and General Manager – Incentives Jason Loh
- 3. Vice President, Global Business Operations Pablo Dominguez
- 4. “In life, there are essentially no major or minor characters. To that extent, all fiction and biography, and most historiography, is a lie. Everyone is the hero of his own life story.“ John Barth, “The Remobilization of Jacob Horner”, Esquire magazine, 1958
- 5. An Illustrated Example OPPONENTS “The most failed first 100 days of any president,” a leading historian has claimed PROPONENTS "There are those that say I've done more than anybody in the first 100 days“, the President has claimed
- 6. Bias (definition) bi·as/ˈbīəs/ – noun – prejudice in favor of or against one thing, person, or group compared with another, usually in a way considered to be unfair. synonyms: prejudice, partiality, partisanship, favoritism, unfairness, one-sidedness – verb – cause to feel or show inclination or prejudice for or against someone or something. synonyms: prejudice, influence, color, sway, weight, predispose A Fundamental Component of Human Psychology
- 7. Predictive Analytics (computer technology, machine learning algorithms, big data) is most often about providing tools that correct for mental biases Behavioral Insights is about challenging and influencing “present bias” (short- term desires) that may prevent us from achieving long-term goals Mitigating Bias in Judgments and Business Decisions
- 8. Data Science Meets Behavior Science NUDGE Applying psychology and behavioral economic findings to prompt people to make ideal decisions MONEYBALL Applying data analytics to make more economically efficient decisions in business and beyond
- 9. Q1 Q2 Q3 Q4 The Behavior Gap: Expectations vs Reality Acceptable Gap Unacceptable Gap Inflection Point $
- 10. Sensitivity Analysis – a technique used to help people understand the potential underlying relationships between seemingly dissimilar factors. Understanding the Relationships Between Data Correlation vs Causation • Ice Cream Sales Spike in Summertime • Shark Attacks Peak in Summertime • Do Ice Cream Sales Cause Shark Attacks? JAN MAR MAY JUL SEP NOV ICE CREAM SALES SHARK ATTACKS
- 12. Pipeline Analysis – Sound Familiar?
- 13. Pipeline Analysis – Sound Familiar? PROSPECT 10% QUALIFY 25% PROPOSE 75% NEGOTIATE 90% CLOSE 100% $40MM Weighted: $4MM $40MM Weighted: $10MM $40MM Weighted: $30MM $40MM Weighted: $36MM $40MM Weighted: $40MM WEIGHTED TOTAL: $120MM
- 14. 14 $90 MM $165 MM $100 MM$125 MM $104 MM Views Across The Enterprise
- 15. What is Monte Carlo? 15
- 16. Deals and Close Rates Vary Regionally 16
- 17. Applying Regional Variances Provides Further Accuracy 17 $54 MM P(100%) $134 MM P(~0%) $104 MM P(80%) $115 MM P(60%)
- 18. And We Can Be Even More Precise 18 PROSPECT 10% QUALIFY 25% PROPOSE 75% NEGOTIATE 90% CLOSE 100% A
- 19. “When the ultimate goal is behavior change, predictive analytics and the science of behavioral nudges can serve as two parts of a greater, more effective whole” James Guszcza, "The Last-Mile Problem", Deloitte Review, 2015
- 21. 21
Editor's Notes
- How and When to Use Behavioral Nudges to Drive Quote-to-Cash Outcomes Business is cyclical, and revenues can ebb and flow based on several factors, including seasonality, market conditions, etc. When the business plan calls for a flow, but sales have started to ebb, what tools are at your disposal to help close the gap between expectations and reality? Attend this session to learn how to identify and deploy targeted nudges that can close the behavioral gap and how incentive solutions can drive business outcomes.
- Figuratively speaking, our “present selves” manifest different preferences than our “future selves” do.
- Slide 1: [Slide content: Show a funnel with five stages on the left, then an arrow in the middle and a picture of Wall Street on the right. Question mark on top of the arrow] So here’s a typical question that may get asked by various individuals in any organization. Do we have enough deals in the pipeline to achieve our quarterly goal? Sound familiar?
- Slide 2: [Show a pipeline with the following stages: Prospect, Qualify, Propose, Negotiate, Close. Build animation on click: show %s next to each pipeline stage 10% for Prospect, then 25%, 75%, 90%, 100%. Then show the value of deals next to each stage $25,000,000 value of each deal multiplied by the factored rate, so 10% x $25,000,000 = 2,500,000. Etc. for a total of $75,000,000] So here is a typical approach. Typical approach. Lets’ assume the following # of deals in each stage, with certain booking values, and let’s assume that the probability to close % are the following: 10%, 25%, 75%, 90%, 100%. A very common approach would be to take the deals in the pipeline, lets’ assume we have $25M in total deals at every stage and factoring them by the expected close rate. So here we know our target for the quarter is $100M in revenue, and the factored funnel says that we will come in at $75M. So we’re about $25M short.
- Slide 3: [Show four people plus the leader each with their inputs, then taking an average. The inputs are 90M, 115, 100, and 110. Use different pictures for each person, one is Ops, one is Finance, one is Sales VP, one is Regional VP In the middle have all of their inputs converging to $104M. This should be a build. We show the four people, we show their inputs building on a click, then the final click is the 104M in the center of the page]. However, let’s be more realistic. You have your sales leader, your Ops leader, and your Finance leader all in a room. They each have a slightly different view, or bias, on how the pipeline will end for the quarter. Now we are slightly more precise, however there is bias here, and we can be better. The additional input from the team suggests our revenue will finish at $104M. They also have ranges of high and low, but they are all over the place. Ok, so the standard funnel is $75M, and the team after triangulating suggests we will overachieve at 104M. But how do we know that we’re right? Or close to right, considering we can’t fully predict the outcomes?
- Slide 4. [Show picture of Monte Carlo (France). On click, show mathematical equations (stock image). Monte Carlo. No we’re not talking about the French Riviera, we’re talking about mathematical simulations. Imagine instead of getting the input from four of your leaders, you could run a thousand scenarios, with the data and assign parameters around certain variables?
- Slide 5. [TBD slide] So now, using modeling software, you are able to be more prescriptive. You know that deals are variable and that the value can change, the probability to close varies, that certain regions (e.g, EMEA – Germany) tend to close deals more quickly than other regions (APAC- Japan). By leveraging simulation analysis, we can now run a simulation 1,000 times and we end up with the following: The range of performance can be as low as 80M, as high as 112M, and on average the model is 95% confident that we will finish at 101M. [Show a distribution curve building with multiple simulations and then the output of the findings]
- Slide 5. [TBD slide] So now, using modeling technique, you are able to be more prescriptive. You know that deals are variable and that the value can change, the probability to close varies, that certain regions (e.g, EMEA – Germany) tend to close deals more quickly than other regions (APAC- Japan). By leveraging simulation analysis, we can now run a simulation 1,000 times and we end up with the following: The range of performance can be as low as 80M, as high as 112M, and on average the model is 95% confident that we will finish at 101M. [Show a distribution curve building with multiple simulations and then the output of the findings]
- Slide 6. We can make this even more precise. As we look at the data in the Negotiation stage we can look at additional data in the Quote to Cash process, and we realize that between negotiate and close it takes us on average 30 days to get a contract signed on Product A. Product A happens to be a high margin product and one of our newer products. We’ve run several SPIFs in the last two quarters and we know there is high correlation between performance and SPIFs on Product A in Germany, the US, and Australia. Thus we can now update the simulation above since we know these behavioral incentives, will move the needle on our performance. These nudges will allow us to further ensure the accuracy in the model. So we re-run the simulation and assume that we can get deals closed in 10 days when applying incentives for deals in Germany, the US, and AUS. Now our model says we are 95% confident we finish the quarter at $106M. Obviously, this can go on forever based on inputs around historical performance of the sales team, and can also help us more accurately predict our sales compensation costs as we forecast our cost of sales with Finance and the HR teams.
- Behavioral economics is the natural framework to scientifically attack the last-mile problem of going from predictive model indication to the desired action.
- This slide is intended to be used by Product Managers and other employees approved to be discussing upcoming features and conveying roadmap items. If you will be discussing future releases, product roadmaps or demoing non GA functionality this slide should be inserted at the front of your slide deck in customer facing scenarios. This slide text should not be altered, however anyone using this slide does not need to cover the text on the slide word for word. A simple statement referencing safe harbor as it pertains to any forward looking statements that might be made during the meeting and that Apttus is providing any info in accordance with Safe Harbor statements on the slide.