CivixAI – Predictive Behavioral Intelligence for Donors, Voters and Legislators

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

How will CivixAI benefit your organization?

CivixAI is not another statistical model or predictive analytics platform 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 actually a secondary benefit. Our real super power lies in our prescriptive approach, which is a proprietary model rooted in the principles of cognitive neuroscience. CivixAI tells you exactly when your target audience actually wants to be engaged or influenced.

CivixAI can be used to achieve one or more of the following goals: 1. Identify and replicate the decision-making bias of legislators at the federal and state levels.

This allows organizations to know exactly when to engage specific legislators, based on each legislator’s own cognitive neurological processes, in order to have the highest probability of getting them to support or oppose their legislative agenda. Simply put, CivixAI puts you inside the mind of any legislator you choose.

2. Acquire new market share from within a targeted donor audience. For example, if currently 70/300 donors or members donate to your organization, use CivixAI to identify the most opportune time to engage the other 230 donors or members not currently giving and begin converting them.

3. Take current donors or members to the next level of giving, or earn additional gifts, by identifying when their sentiment for your mission or vision is at its highest level based on their own cognitive patterns.

4. Mobilize voters in hard-to-reach or underserved segments by pinpointing when their sentiment for your candidate or issue is at its most positive point during your campaign timeline – and earn their vote or support.

Each of the following corresponding case studies contain graphical readouts.

IMPORTANT: Look at each graph like a reading of an electroencephalogram (EEG) of a person’s brain, because what you are seeing is a readout of the audience’s brain activity as it relates to the cognitive process of decision making.

Frequently Asked Questions 1. How will CivixAI benefit my organization/company?

CivixAI will show you when your audience is most susceptible and wants to be persuaded.

Need to raise more money?

CivixAI shows you when your target audience wants you to ask them.

Need to influence legislation?

CivixAI shows you when legislators want you to approach them.

Need to earn more votes in a key demographic?

CivixAI shows you when voters want to hear from you.

2. We know our audience fairly well. How will CivixAI’s platform add value?

The purpose of CivixAI is not to change what you are currently doing or to tell you what you already know about your audience. Its primary purpose is to identify new intelligence about your current and prospective audiences that will allow fundraising and/or political staff to unlock additional opportunities for growth – whether that be through new sources of fundraising revenue, more legislative wins, or mobilizing voters with greater speed and effectiveness.

3. How is CivixAI different from other donor-centric software?

CivixAI is a proprietary, deep-learning neural network designed to identify and perfectly mimic the behavioral and decision-making patterns of your audience. This is different than many of the machine learning platforms that you encounter today that simply automate statistical models to be applied across any general audience. CivixAI’s neural network can learn and mimic your audience’s brain activity, allowing you to see how their brain works, relative to their behavioral and decision-making tendencies.

4. Once we engage, how does this work? Will it require extra hours from our staff?

In most cases, you need only to sign up in order to begin receiving donor, legislator, and/or voter intelligence reports in less than a week to incorporate into your existing operational strategy.

In donor environments outside of political fundraising, it works best if you have a record of every contribution given to your organization, and the date on which it was given, compiled in an Excel or CSV document. That’s it. After we receive your data, you will begin receiving donor intelligence reports in less than a week so you can begin applying the intelligence to your existing strategy.

5. How does CivixAI account for world events that can quickly change a target audience’s sentiment about giving, voting or passing legislation?

CivixAI exists in the same world that we do. In fact, CivixAI’s neural network is more in tune with external events than we are because it is constantly taking in external, non-objective data like stock market prices, news headlines, weather data by zip code, etc. In other words, CivixAI’s neural network is not thinking in a vacuum of “best case scenarios.”

6. Does CivixAI replace work that our own professionals are already doing?

No! 21st century success inevitably requires 21st century innovation, but technology is not a standalone solution, and it certainly cannot replace your people and relationships.

Opportunities for new growth can only be maximized when technology works with your people and your relationships as one formula. None of these three inputs are mutually exclusive and therefore all three are needed. When used properly, AI makes people much more successful by giving them more opportunities, rather than replacing them.

7. Does CivixAI make financial sense for our organization?

CivixAI, like all technology investments should do, makes your life easier, not more burdensome. CivixAI will give your team(s) more opportunities to raise money from target audiences, pass or defeat more legislation by directly influencing legislators, and win more campaigns by effectively persuading voters without increasing their workload. As a technology investment, CivixAI directly supports revenue growth in the first year and leads to increased efficiency by staff, which increases your long-term profitability.

8. How have other organizations benefited from using your technology?

Organizations incorporating CivixAI into their existing operational strategies are experiencing an average ROI between 908% - 2,400%. To put this into a monetary perspective, a nationally focused organization leveraging CivixAI could realize a financial net gain of $2.25M - $6M per year.

CASE STUDY:

DONOR PREFERENCE CHALLENGE: Find a solution to increase giving levels among existing targeted donors and increase market share of households in targeted communities that have the financial capacity to give but have not yet given to a specific non-profit organization.

SOLUTION: Leverage CivixAI’s predictive behavioral intelligence platform. In the case of this non-profit organization, 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 of giving. Our goal was to indicate when donors’ preferences for giving will increase or decrease over the next year in order to guide the timing of the organization’s engagement strategy. By doing so, this allowed the organization to increase donor revenue, both during and after traditional giving periods.

RESULTS: Within the six donor segments for this campaign, CivixAI correctly identified the increases and decreases in donor preference more than 80% of the time – as benchmarked against increases and decreases in donor contributions on our specified dates.

This non-profit organization is achieving an annualized ROI of 908%. Assuming a similar outcome, a contract for nationally focused industry trade groups and non-profit organizations would yield a bottom-line impact of more than $2.2M per year in additional fundraising .

CASE STUDY:

DONOR PREFERENCE PROSPECTIVE DONOR PREFERENCE Graph displays a readout of the audience’s brain activity as it relates to the cognitive process of decision making. It represents exactly when the target audience wants to be engaged or influenced.

DONOR/MEMBER PREFERENCE POSITIVE PERIOD WHEN AUDIENCE WANTS TO BE APPROACHED NEGATIVE PERIOD WHEN AUDIENCE IS LESS LIKELY TO RESPOND

3

2

1

0

-1

-2

-3

-4 0.18 .20.18 1.20.18 .20.18 1.20.19 .20.19 .20.19 .20.19 .20.19 .20.19 7.20.19 .20.19 .20.19 5 3 9.2 2 9 6 8 4 1 12 10

KEY INSIGHTS DATES Positive Audience Sentiment

Negative Audience Sentiment

10/27/18 – 11/18/2018

9/17/ 2018 – 10/27/2018

1/15/2019 – 3/6/2019

11/19/2018 – 1/14/2019

4/29/2019 – 5/15/2019

3/7/2019 – 4/28/2019

CASE STUDY:

US SENATOR (D-MA) MONTHLY BRIEF CHALLENGE: Improve government relations/lobbying outcomes with any selected federal legislators (i.e. US Senators and US Representatives) throughout the year.

SOLUTION: Leverage CivixAI’s proprietary predictive behavioral intelligence algorithm to model and predict the behavioral sentiment and decision-making bias of every legislator in US Congress. This allows any government relations professional(s) to know exactly when to engage any legislator they choose to target and have the highest probability of getting them to support or oppose their objective (e.g. supporting or defeating a particular piece of legislation).

RESULTS: As a result of CivixAI’s identification of this senator’s decision-making bias and behavioral sentiment, we were able to successfully guide the precise timing of our client’s direct engagement with this legislator. CivixAI’s six-month forecast of when exactly this senator was most likely to support legislation was accurate 75% of the time..

CASE STUDY:

US SENATOR (D-MA) MONTHLY BRIEF LEGISLATOR SENTIMENT Graph displays a readout of the audience’s brain activity as it relates to the cognitive process of decision making. It represents exactly when the target audience wants to be engaged or influenced.

LEGISLATOR SENTIMENT POSITIVE PERIOD WHEN AUDIENCE WANTS TO BE APPROACHED NEGATIVE PERIOD WHEN AUDIENCE IS LESS LIKELY TO RESPOND

1.6

1.4

1.2

1

.8

.6

.4

.2 0 18

1.

5.3

0.17 6.3

18

.

7.31

18

1.

8.3

0.18 9.3

18

1.

10.3

0.18

11.3

1.18

12.3

KEY INSIGHTS DATES Positive Audience Sentiment

Negative Audience Sentiment

6/14/2018 – 7/12/2018

5/31/2018 – 6/14/2018

8/9/2018 – 12/13/2018

7/19/2018 – 7/26/2018

CASE STUDY:

US SENATOR (R-GA) MONTHLY BRIEF CHALLENGE: Improve government relations/lobbying outcomes with any selected federal legislators (i.e. US Senators and US Representatives) throughout the year SOLUTION: Leverage CivixAI’s proprietary predictive behavioral intelligence algorithm to model and predict the behavioral sentiment and decision-making bias of every legislator in US Congress. This allows any government relations professional(s) to know exactly when to engage any legislator they choose to target and have the highest probability of getting them to support or oppose their objective (e.g. supporting or defeating a particular piece of legislation).

RESULTS: As a result of CivixAI’s identification of this senator’s decision-making bias and behavioral sentiment, we were able to successfully guide the precise timing of our client’s direct engagement with this legislator. CivixAI’s six-month forecast of when exactly this senator was most likely to support legislation was accurate 80% of the time.

CASE STUDY:

US SENATOR (R-GA) MONTHLY BRIEF LEGISLATOR SENTIMENT Graph displays a readout of the audience’s brain activity as it relates to the cognitive process of decision making. It represents exactly when the target audience wants to be engaged or influenced.

LEGISLATOR SENTIMENT POSITIVE PERIOD WHEN AUDIENCE WANTS TO BE APPROACHED NEGATIVE PERIOD WHEN AUDIENCE IS LESS LIKELY TO RESPOND

12

10

8

6

4

2

0

-2 -4 17 6.1.

7 7.1.1

17 8.1.

17 9.1.

.17 10.1

.17

11.1

.17

12.1

KEY INSIGHTS DATES Positive Audience Sentiment

Negative Audience Sentiment

6/8/2017 – 6/22/2017

6/2/2017 – 6/8/2017

7/21/2017 – 8/31/2017

6/23/2017 – 7/6/2017

10/12/2017 – 12/21/2017

7/13/2017 – 7/20/2017 9/21/2017 – 9/28/2017

CASE STUDY:

US SENATOR (R-IA) MONTHLY BRIEF CHALLENGE: Improve government relations/lobbying outcomes with any selected federal legislators (i.e. US Senators and US Representatives) throughout the year.

SOLUTION: Leverage CivixAI’s proprietary predictive behavioral intelligence algorithm to model and predict the behavioral sentiment and decision-making bias of every legislator in US Congress. This allows any government relations professional(s) to know exactly when to engage any legislator they choose to target and have the highest probability of getting them to support or oppose their objective (e.g supporting or defeating a particular piece of legislation).

RESULTS: As a result of CivixAI’s identification of this senator’s decision-making bias and behavioral sentiment, we were able to successfully guide the precise timing of our client’s direct engagement with this legislator. CivixAI’s six-month forecast of when exactly this senator was most likely to support legislation was accurate 100% of the time.

CASE STUDY:

US SENATOR (R-IA) MONTHLY BRIEF LEGISLATOR SENTIMENT Graph displays a readout of the audience’s brain activity as it relates to the cognitive process of decision making. It represents exactly when the target audience wants to be engaged or influenced.

LEGISLATOR SENTIMENT POSITIVE PERIOD WHEN AUDIENCE WANTS TO BE APPROACHED NEGATIVE PERIOD WHEN AUDIENCE IS LESS LIKELY TO RESPOND

1.6

1.4

1.2

1

.8

.6

.4

.2 0 18

1.

5.3

0.17 6.3

18

.

7.31

18

1.

8.3

0.18 9.3

18

1.

10.3

0.18

11.3

1.18

12.3

KEY INSIGHTS DATES Positive Audience Sentiment

Negative Audience Sentiment

6/8/2018 – 7/26/2018

5/31/2018 – 6/7/2018

9/6/2018 – 12/13/2018

8/10/2018 – 8/23/2018

CASE STUDY:

US SENATOR (D-CA) MONTHLY BRIEF CHALLENGE: Improve government relations/lobbying outcomes with any selected federal legislators (i.e. US Senators and US Representatives) throughout the year.

SOLUTION: Leverage CivixAI’s proprietary predictive behavioral intelligence algorithm to model and predict the behavioral sentiment and decision-making bias of every legislator in US Congress. This allows any government relations professional(s) to know exactly when to engage any legislator they choose to target and have the highest probability of getting them to support or oppose their objective (e.g supporting or defeating a particular piece of legislation).

RESULTS: As a result of CivixAI’s identification of this senator’s decision-making bias and behavioral sentiment, we were able to successfully guide the precise timing of our client’s direct engagement with this legislator. CivixAI’s six-month forecast of when exactly this senator was most likely to support legislation was accurate 77% of the time.

CASE STUDY:

US SENATOR (D-CA) MONTHLY BRIEF LEGISLATOR SENTIMENT Graph displays a readout of the audience’s brain activity as it relates to the cognitive process of decision making. It represents exactly when the target audience wants to be engaged or influenced.

LEGISLATOR SENTIMENT POSITIVE PERIOD WHEN AUDIENCE WANTS TO BE APPROACHED NEGATIVE PERIOD WHEN AUDIENCE IS LESS LIKELY TO RESPOND

31

20

27

25

23

21

19

17 15 18

1.

5.3

0.17 6.3

18

.

7.31

18

1.

8.3

0.18 9.3

18

1.

10.3

0.18

11.3

1.18

12.3

KEY INSIGHTS DATES

KEY INSIGHTS DATES

Positive Audience Sentiment

Negative Audience Sentiment

6/8/2018 – 7/26/2018

5/31/2018 – 6/7/2018

Negative Audience Sentiment Positive Audience Sentiment 9/6/2018 – 12/13/2018 8/10/2018 – 8/23/2018 5/31/2018 – 6/7/2018

6/7/2018 – 6/21/2018

6/21/2018 – 7/12/2018

7/12/2018 – 7/26/2018

7/26/2018 – 8/16/2018

8/16/2018 – 8/23/2018

9/6/2018 – 10/4/2018

10/11/2018 – 10/18/2018

10/25/2018 – 11/1/2018

11/8/2018 – 11/15/2018

11/22/2018 – 12/27/2018

CASE STUDY:

2018 MIDTERM ELECTION CHALLENGE: CivixAI achieved an 87.5% correct prediction of voter sentiment in the 2018 US Midterm Elections.

SOLUTION: The unique ability of CivixAI to forecast voter preferences (i.e., sentiment) and event outcomes originates from our deployment of a proprietary deep-learning neural network, 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.

RESULTS: 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) US Senate elections and three (3) gubernatorial elections.

The first table 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%.

The second table 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.

The graphs following the two tables display our platform’s election predictions as graphical readouts and display the date on which they were published.

CASE STUDY:

2018 MIDTERM ELECTION

2018 MIDTERM ELECTION RESULTS

CASE STUDY:

2018 MIDTERM ELECTION

2018 VOTER SENTIMENT

TEXAS :: US SENATE

CRUZ :: TX

O’ROURKE :: TX

31

20

27

25

23

21

19

17 15 5.18 9.2

8

.1 10.2

8

.1 10.9

18

6.

10.1

3.18 10.2

ARIZONA :: US SENATE

0.18 10.3

SINEMA :: AZ

.18 11.6

MCSALLY :: AZ

31

20

27

25

23

21

19

17 15 8

.1 10.2

.18 10.9

6.18 10.1

3.18 10.2

0.18 10.3

.18 11.6

FLORIDA :: US SENATE

SCOTT :: FL

NELSON :: FL

31

20

27

25

23

21

19

17 15 8

.1 10.2

.18 10.9

6.18 10.1

3.18 10.2

NEW JERSEY :: US SENATE

0.18 10.3

HUNGIN :: NJ

.18 11.6

MENENDEZ :: NJ

31

20

27

25

23

21

19

17 15 8

.1 10.9

18

6.

10.1

3.18 10.2

0.18 10.3

.18 11.6

MISSOURI :: US SENATE

HAWLEY :: MO

McCASKILL :: MO

31

20

27

25

23

21

19

17 15 0.18 10.3

3.18 10.2

18

8

6.

10.1

.1 10.9

TENNESSEE :: US SENATE

BLACKBURN:: TN

.18 11.6

BREDESEN :: TN

31

20

27

25

23

21

19

17 15 5.18 9.2

8

.1 10.2

8

.1 10.9

18

6.

10.1

3.18 10.2

0.18 10.3

.18 11.6

FLORIDA :: GUBERNATORIAL

DeSANTIS :: FL

GILLUM :: FL

31

20

27

25

23

21

19

17 15 .18 10.9

6.18 10.1

3.18 10.2

GEORGIA :: GUBERNATORIAL

0.18 10.3

.18 11.6

KEMPS :: GA

ABRAMS :: GA

0.18 10.3

.18 11.6

31

20

27

25

23

21

19

17 15 .18 10.9

6.18 10.1

3.18 10.2

NORTH DAKOTA:: US SENATE

CRAMER :: ND

HEITKAMP :: ND

31

20

27

25

23

21

19

17 15 .18 10.9

6.18 10.1

3.18 10.2

OHIO :: US SENATE

0.18 10.3

DeWINE :: OH

.18 11.6

CORDRAY :: OH

31

20

27

25

23

21

19

17 15 .18 10.9

6.18 10.1

3.18 10.2

0.18 10.3

.18 11.6

NEVADA :: US SENATE

HELLER :: OH

ROSEN :: OH

31

20

27

25

23

21

19

17 15 8

.1 10.9

18

6.

10.1

3.18 10.2

PENNSYLVANIA :: US SENATE

0.18 10.3

BARLETTA :: PA

.18 11.6

CASEY :: PA

31

20

27

25

23

21

19

17 15 8

.1 10.9

18

6.

10.1

3.18 10.2

0.18 10.3

.18 11.6

WEST VIRGINIA:: US SENATE

MORRISEY :: WV

MANCHIN :: WV

31

20

27

25

23

21

19

17 15 .18 10.9

6.18 10.1

3.18 10.2

WISCONSIN :: US SENATE

0.18 10.3

VUKMIR :: WI

.18 11.6

BALDWIN :: WI

31

20

27

25

23

21

19

17 15 8

.1 10.9

6.18 10.1

3.18 10.2

0.18 10.3

.18 11.6

INDIANA :: US SENATE

BRAUN :: IN

DONNELLY :: IN

31

20

27

25

23

21

19

17 15 8

.1 10.2

.18 10.9

6.18 10.1

OHIO :: US SENATE

3.18 10.2

0.18 10.3

RENACCI :: OH

.18 11.6

BROWN :: OH

31

20

27

25

23

21

19

17 15 8

.1 10.2

.18 10.9

6.18 10.1

3.18 10.2

0.18 10.3

.18 11.6