B E H AV I O R A L P R E D I C T I O N S T H AT C O U N T
CIVIXAI.COM
CivixAI is a predictive, behavioral intelligence company focused on behavioral analysis and event prediction in multiple applications, including: donors, public policy, government relations, public health, and economic/consumer behavior. How will CivixAI benefit your organization? CivixAI identifies and subsequently emulates your audience’s sentiment and decision-making patterns as far as 12 months into the future to pinpoint exact timeframes in which you should engage them to achieve your call-to-action, helping your organization grow your market share and/or increase revenue. Our research and data can be used to achieve one or more of the following goals: 1. Acquire new market share from within a targeted segment. For example, if currently 70/300 members from a specific community give to your organization, use CivixAI to identify the most opportune time to engage the other 230 members not currently giving and begin converting them to new donors. 2. Similarly, in consumer environments, leverage CivixAI’s findings to identify exactly when to approach new and previous consumers of your product or service to convert them to your desired call-to-action. 3. Take current donors to the next level of giving by identifying when their personal preference for your mission or vision is at its highest level. 4. Earn second gifts from current donors by using CivixAI to identify the best time to approach them outside of their traditional giving period. 5. Mobilize voters in hard-to-reach or underserved segments by pinpointing when their sentiment on your candidate or topic is at its most positive point during your campaign timeline and earn their vote. What differentiates CivixAI from other AI platforms? CivixAI is not another statistical model or predictive analytic designed to simply forecast outcomes in your market. While it’s true that our platform can do that with a high degree of accuracy, it’s a secondary benefit. Our real super power lies in our prescriptive approach. We measure your audience’s sentiment, a.k.a preference, as far as 12 months into the future and tell you exactly when to engage them to achieve your goals in greater volume during your operational timeline. Our customers no longer meet their forecasts, they beat them. The following pages contain seven case studies from our work in multiple markets which highlight CivixAI’s versatility and applicability to any human audience. 5 0 0 W . F O U R T H S T R E E T, S U I T E 2 0 1 - A | W I N S T O N - S A L E M , N C 2 7 10 1 | P H O N E : 7 0 4 . 6 51. 9 9 2 8
Public Transit System Case Study Challenge: Increase ridership on a public transit system’s 15 bus routes and mitigate the risk of declining ridership in certain zones across the service area. Solution: Leverage CivixAI’s proprietary, predictive behavioral intelligence model. For the public transit system, CivixAI measured the “Relative Preference” (i.e sentiment) of riders based on ridership statistics across the system’s 15 routes. The purpose was to indicate when the sentiment among riders in each zone would increase or decrease over the next 3-4 months. Our behavioral intelligence model also allowed the public transit system to plan for resource needs and/or develop targeted campaigns to address significant changes in ridership, including improving ridership on low-performing routes. Key Insights: Dates of Positive Audience Sentiment: 9/15/2018 – 9/29/2018 Dates of Negative Audience Sentiment: None reported during the measurement timeline. Results: As a result of CivixAI’s identification of a sharp increase in preference on Routes 1 & 4, the transit system was able to deploy a communications campaign to highlight the benefits of using their buses, as well as events and resources that can be found within walking distance of each stop on both routes. Over the defined period that CivixAI identified, ridership increased by more than 30% in September. Ridership along Routes 1 & 4 have continued to sustain weekly ridership levels at an annual high through November 2018. *Note: In some cases, the increase or decrease being predicted occurs as early as a week before the prescribed date or it may run longer than the prescribed date by up to two weeks.
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United Way Donor Preference Case Study Challenge: Find a way to increase giving levels among targeted donors in the chapter’s database as well as increase market share of households in targeted communities that have the financial capacity to give but have not yet given to United Way. Solution: Leverage CivixAI’s proprietary, predictive behavioral intelligence model. In the case of this United Way chapter, CivixAI measured the personal preference (i.e sentiment) of various donor audiences (e.g donors within different communities) based on their reported contribution levels and frequency. Our goal is to indicate when donors’ preferences for giving will increase or decrease over the next year. Measuring donor preference in this way will allow United Way to know the most opportune dates on which to engage each of their audience segments, both during and after typical giving periods, to increase donor revenue. We also segmented the chapter’s donor audiences by homogenous community groups, which allows us to target first-time donors within those communities at exactly the right moment to increase the chapter’s market share of donors. The attached graphical readout is for the period measured from August 13, 2018 - September 17, 2019. Key Insights: Dates of Positive Audience Sentiment: 10/27/18 – 11/18/2018 1/15/2018 – 3/6/2018 4/29/2018 – 5/15/2018 Dates of Negative Audience Sentiment: 9/17/ 2018 – 10/27/2018 11/19/2018 – 1/14/2018 3/7/2018 – 4/28/2018 Results: To date, CivixAI’s predictive behavioral intelligence has correctly identified the increases and decreases in donor preference 80% of the time, when measured against the dates on which contribution levels were at amounts classified as “substantial” by the chapter’s contribution history. This is an active campaign in which we are predicting on a timeline of 12 months, instead of 3-4. Five other donor audience segments are actively being managed along with this one for the United Way chapter. *Note: The increase or decrease in donor preference being predicted can occur as much as two weeks before the prescribed date range and it may run longer by as much as two weeks after. 5 0 0 W . F O U R T H S T R E E T, S U I T E 2 0 1 - A | W I N S T O N - S A L E M , N C 2 7 10 1 | P H O N E : 7 0 4 . 6 51. 9 9 2 8
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Ford Escape Buyer Preference Case Study Challenge: Accurately predict future car buyers’ preferences (i.e sentiment) for specific makes and models based on using our predictive behavioral intelligence model. The purpose of predicting future buyer preference is not just to predict sales increases or decreases, but also to provide sales and marketing teams with predictive intelligence on when a particular customer segment (e.g Ford Explorer buyers) is most receptive to engagement for making a purchase or trading up to the latest model. Solution: Using CivixAI, take numerical data that is tied to specific human decision making, like retail car sales data, to identify and subsequently emulate that audience’s decision-making process as far as three months into the future. This allows an organization with access to CivixAI’s predictive analysis to know the optimum time in which to engage a specific audience segment (e.g Ford Explorer buyers) via marketing and sales strategies to increase sales among new and repeat buyers – or to hedge against declines in sales and lack of movement in inventory. Key Insights: Dates of Positive Audience Sentiment: 9/1/2017 – 9/30/2017 Dates of Negative Audience Sentiment: 8/1/2017 – 8/31/2017 10/1/2017 – 11/2/2017 Results: We reported our predictive behavioral intelligence results to our client in June of 2017, to be benchmarked alongside actual sales of Ford Escapes. As reported in actual industry sales data, sales of the Ford Escape declined 23% in the month of August before rising again in September by 7.6% and then dipping in October by 10.8%.
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Ford Explorer Buyer Preference Case Study Challenge: Accurately predict future car buyers’ preferences (i.e sentiment) for specific makes and models based on using our predictive behavioral intelligence model. The purpose of predicting future buyer preference is not just to predict sales increases or decreases, but also to provide sales and marketing teams with predictive intelligence on when a particular customer segment (e.g Ford Explorer buyers) is most receptive to engagement for making a purchase or trading up to the latest model. Solution: Take numerical data that is tied to specific human decision making, like retail car sales data, to identify and subsequently emulate that audience’s cognitive state and decision-making process as far as three months into the future. This allows an organization with access to CivixAI’s predictive analysis to know the optimum time in which to engage one of their specific audience segments (e.g Ford Explorer buyers) via marketing and sales strategies to increase sales among new and repeat buyers. Key Readout Insights: Dates of Positive Audience Sentiment: 8/1/2018 – 8/31/2018 Dates of Negative Audience Sentiment: No dates of significantly negative sentiment among this audience during the measurement timeline. Results: We reported our predictive behavioral intelligence results to our client in April of 2018, to be benchmarked alongside actual sales of Ford Explorers during the months of July and August 2018. As reported in actual industry sales data, sales of the Ford Explorer remained flat during the month of July but increased in August by 5.2%.
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Netflix (NFLX) Shareholder Preference Case Study Challenge: Accurately predict the sentiment among shareholders of a publicly traded firm in an effort to create more targeted risk management and investor relations strategies for strategic initiatives like share buybacks or mitigating risk of investor buyout attempts. Solution: Leverage CivixAI’s unique ability to forecast target audience preferences (i.e sentiment) and events via their proprietary, predictive behavioral intelligence model. In the case of publicly traded companies on the three major U.S stock exchanges, CivixAI measures the sentiment of the company’s shareholders by processing daily trading volume through our platform. Our goal is to indicate when shareholders’ preferences will increase or decrease over the next 6-12 months to allow corporations, and investment firms, to have the highest probability of achieving their goal, from shareholder buybacks, to buyouts, to takeovers, and more. We refer to this as AI-powered predictive behavioral intelligence. The reports are updated on a weekly basis. The reports are updated on a weekly basis. Key Insights: Dates of Positive Audience Sentiment: 1/26/2018 – 2/9/2018 4/13/2018 – 4/27/2018 5/11/2018 – 6/29/2018 Dates of Negative Audience Sentiment: 1/5/2018 – 1/19/2018 2/9/2018 – 2/16/2018 5/4/2018 – 5/11/2018 7/13/2018 – 10/26/2018 Results: The first attached graphical readout is for the period predicted from December 2017 – November 2, 2018. The second graphical readout is the actual performance of Netflix’s trading volume. The dates correctly predicting trading volume are in black, while the dates incorrectly predicted are in red. The accuracy rate of CivixAI’s shareholder sentiment prediction benchmarked against trading volume in advance was 85.7%.
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Canopy Growth Corp. (CGC) Shareholder Preference Case Study Challenge: Accurately predict the sentiment among shareholders of a publicly traded firm in an effort to create more targeted risk management and investor relations strategies for strategic initiatives like share buybacks or mitigating risk of investor buyout attempts. Solution: Leverage CivixAI’s unique ability to forecast target audience preferences (i.e sentiment) and events via their proprietary, predictive behavioral intelligence model. In the case of publicly traded companies on the three major U.S stock exchanges, CivixAI measures the sentiment of the company’s shareholders by processing daily trading volume through our platform. Our goal is to indicate when shareholders’ preferences will increase or decrease over the next 6-12 months to allow corporations, and investment firms, to have the highest probability of achieving their goal, from shareholder buybacks, to buyouts, to takeovers, and more. We refer to this as AI-powered predictive behavioral intelligence. The reports are updated on a weekly basis. Key Insights: Dates of Positive Audience Sentiment: 1/19/2018 – 2/9/2018 4/27/2018 – 6/1/2018 6/22/2018 – 7/13/2018 8/10/2018 – 10/19/2018 Dates of Negative Audience Sentiment: 2/9/2018 – 3/2/2018 3/16/2018 – 3/30/2018 4/13/2018 – 4/27/2018 6/2/2018 – 6/22/2018 7/20/2018 – 8/10/2018 10/19/2018 – 11/21/2018 Results: The first attached graphical readout is for the period predicted from December 2017 – October 31, 2018. The second graphical readout is the actual performance of Canopy Growth Corporation’s trading volume. The dates correctly predicting trading volume are in black, while the dates incorrectly predicted are in red. The accuracy rate of CivixAI’s shareholder sentiment prediction benchmarked against trading volume in advance was 80%.
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2018 Midterm Election Case Study CivixAI achieved an 87.5% correct prediction of voter sentiment in the 2018 U.S. Midterm Elections. The unique ability of CivixAI to forecast voter preferences (i.e, sentiment) and event outcomes originates from our deployment of a proprietary artificial intelligence analytic, Intuality.AI, which leverages numerical data tied to human behavior and decision-making and combines it with an exclusive framework developed by our analysts for each audience covered. For political elections, CivixAI measures the sentiment of voters for specific candidates in a race by processing polling data in real-time. Our AI platform indicates when voters’ preferences will increase or decrease over the course of a campaign for each candidate in the race. Using this proprietary sentiment analysis approach, we are also able to predict the outcome of an election with a high degree of accuracy. The following two tables report the performance of CivixAI’s sentiment and outcome predictions against the outcome in each race measured. In the 2018 midterm election cycle, CivixAI measured voter sentiment and made predictions of outcomes in 16 races including thirteen (13) U.S Senate elections and three (3) gubernatorial elections. Page 2 displays CivixAI’s performance as reported by predicted outcome on Election Day compared to the actual winner of that race. CivixAI made its final predictions in September and correctly predicted the outcome in 13 of the 16 elections covered, representing a prediction accuracy rate of 81.25%. Page 3 displays CivixAI’s performance as reported by predicted sentiment of voters on Election Day compared to a margin of victory in races where the technology incorrectly predicted the winner. In order to qualify as being an accurate sentiment prediction, where the predicted winner did not win his/her election, the final outcome must be within a margin of victory of 3% or less. In the three elections that CivixAI incorrectly predicted the winner, one election result was correctly measured with respect to voter sentiment, as it fell within the margin of victory requirement. This brings CivixAI’s sentiment prediction accuracy rate to 14 out of 16 races covered or an 87.5% correct prediction of voter sentiment. Pages 4 -19 display our platform’s election predictions as graphical readouts and display the date on which they were published.
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2018 Election Results
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2018 Voter Sentiment
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