# Patent

## Title
Meeting effectiveness program optimization

## Abstract

A method and system of developing an event designed to appeal to prospective attendees associated with an event-owner. First and second different events are defined as a function of modeled values and defined attributes having values corresponding to collected preferred values. The event-owner compares the first reward program to the second reward program as a function of the weighted prospective attendee preferred values and selects and implements one of the events based on the provided comparison.

## Background
BACKGROUND
Businesses spend hundreds of thousands of dollars marketing their products every year. As part of this marketing budget, many companies choose to host events or meetings that center around their products and services. Some of these meetings are targeted to customers, such as user's conferences or product launches. Others are targeted toward sales professionals, such as sales conferences. For those in charge of meeting and event programs, there is continuing pressure to justify the expense of such events by demonstrating how effective the meeting or event was in relation to the overall corporate goals.



BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a flow diagram for an exemplary nine-step method for maximizing the effective outcome of an event according to one embodiment of the invention.
FIG. 2 is a block diagram illustrating a scorecard according to an aspect of the invention.
FIG. 3 is a flow diagram of a method for developing an event for prospective attendees according to an aspect of the invention.
FIG. 4 is a block diagram illustrating a decision support tool according to an aspect of the invention.
FIG. 5A, 5B are a block diagrams illustrating a breakout session simulation tool according to aspects of the invention.



Corresponding reference characters indicate corresponding parts throughout the drawings.
DETAILED DESCRIPTION
FIG. 1 is a flow diagram for an exemplary nine-step method embodying aspects of the invention for maximizing the effective outcome of an event, meeting or conference hosted by a corporation or company. Step 102, aligning the objectives, involves performing background research to gain an understanding of the corporation and how the event fits into a corporation's marketing strategy. Additionally, information is gathered to understand the corporation's position in the industry (including competitors) and how the event fits into the industry. The compiled research is used to formulate assumptions regarding the corporation and the event. These assumptions are then discussed in detail with the event stakeholders during a discovery session to ensure that the event is aligned with the goals of the corporation. During the discovery session, (1) the corporation's corporate and marketing strategies are identified; (2) the event strategy and goals are defined; (3) the event audience expectations are identified; and (4) past communication, event promotion, content and messaging are discussed.
At step 104, the objectives identified during step 1 are used to develop the decision support tool and to create an event measurement scorecard. The event measurement scorecard defines, for example, the objectives and goals of the event in a quantitative way. The scorecard is used to measure the meeting's or event's effectiveness as a function of the event's objectives. For example, one such objective could be to influence the intent to purchase product A. The goal could be a 5 percent increase in the intent to purchase product A attributed to the event. Thus, the event is considered effective if 5 percent more people would intend to purchase product A after the event than before the event. Pre-event scorecard survey questions are then formulated to obtain baseline pre-event measurements of the scorecard objectives from prospective attendees.
To develop the decision support tool, attributes and values are defined to create further pre-event survey event choice questions. These questions are designed to gather information on the type of event prospective attendees would prefer to attend. Examples of attributes include: Time of year, Type of attendees, Type of Content, Type of Exhibitors, Amount of time to visit exhibits, General Session Speakers, Type of Info at general session, Frequency of general session, Meeting track format, Breakout Content, Breakout Length, Special Events, and Length of Event Day. Values are possible preferences of the event attendees. For example, the values for General Session Speakers may include executives from corporation, entertainers, industry experts, thought leaders and motivational speakers while the values for Breakout Length include, for example, 45 minute presentations and 3 hour “deep dive” sessions. Appendix A contains an example of possible attributes and their corresponding values.
Additional direct question pre-event survey questions are also defined. These questions are used to gather other information regarding possible attendees to the event including industry information and demographic information such as geographic location, age, and job function. Once the pre-event survey is defined, it is executed. The decision support tool is built upon the results and the pre-event metrics for the scorecard are established. Appendix B contains an example of a pre-event survey.
At step 106 of the exemplary flow diagram, an optimized event strategy is developed using the pre-event survey data and the decision support tool. Messages are aligned to corporate objectives and communicating the event value proposition (a clear and specific statement about the tangible benefits of the event). The environment for the event is optimized using the decision support tool. The decision support tool allows the customer to view many event scenarios and see how the scenario impacts the attendees' decision to attend the event. From these scenarios, the event can be designed to appeal to specific target attendee groups (e.g. IT managers and Europeans). Once the environment is established, strategies to generate and invite potential attendees are established. Furthermore, once the initial invitation is issued, strategies to sustain attendee interest in the event are developed.
At step 108, the strategies to generate and invite potential attendees are implemented. The information gathered from the discovery session and the pre-event survey are used to drive event attendance. The strategy includes promotion of the event and setting a tone for the event. Furthermore, real-time measurements of registrations by audience segments are monitored to achieve attendance goals expectations.
At step 110, the event is held. During the event, on site real-time measurements are obtained to track sessions and to aid exhibitor lead management. Session tracking includes attendance, tracking attendee types (i.e. senior management, IT support) as well as the attendees' responses to the sessions. For example, immediately after each session, a number of attendees are asked questions about the session to measure the attendee type (i.e. senior management, IT support) as well as the attendees' response to the session.
Exhibitor lead management includes tracking the type (i.e. senior management, IT support) and number of attendees that visited each exhibitor and if any of these visits lead to a sale. This information can then be used to select exhibitors for the next event or be shared with the exhibitors to add value to their sponsorship of the event.
At step 112, post-event evaluation and analysis is conducted. First, a post-event comparative survey is executed. The post-event survey is similar to the pre-event survey consisting of the direct question pre-event survey questions and the pre-event scorecard survey questions. The post-event survey is used to gather measurements used to populate event measurement scorecard and to evaluate any business opportunities identified during the event. Appendix D contains an example of a post-event survey.
At step 114, the momentum gained from the event is sustained by continued communication with attendees and other event stakeholders. In particular, attendees receive communications designed to reinforce the event message and communication that contain a call for action (next steps). Communications to other event stakeholders are designed to reinforce event value as well as to gather anecdotal feedback. These communications can be delivered via email, direct mail, telephone, or other methods known to one skilled in the art.
At step 116, comprehensive outcomes analysis is conducted. The pre and post-event research findings plus sustainment results are carefully evaluated to identify areas of success as well as areas of improvement. From this analysis, at step 118, an improvement plan is designed. In one embodiment, the improvement plan contains analysis recommendations and updated event objectives, with the goal of continuous improvement. Appendix C contains an example analysis report with recommendations.
FIG. 2 is a block diagram illustrating a scorecard according to an aspect of the invention. The metrics box 208 provides a pre-event/post-event comparison of the objectives being measured. The intent to purchase box 202 displays the percentage bump gain in the intent to purchase product A. The pre-event survey is used to obtain an accurate measure of the purchase intent before the event. The survey captures intent by self-report by attendees. The pre-event measure is compared to self-report again in a post-event survey to generate a percentage bump gain. The generated percentage bump gain is then compared to the goal set for the objective during Step 104 described above. The survey also captures data by audience segment whether they consider themselves an influencer or decision-maker on product purchases.
The acquire Product A attendees 206 box displays the acceptable gain in product A attendees. This may be expressed as a pure number or as a percentage improvement over last year's numbers. The pre-event survey will capture demographics on the product A segment due to the lack of known information on the segment. This additional demographic data can be used by decision-makers and others to improve the event and refine product marketing efforts. Data from the post-event survey results will provide a comparative report.
The brand awareness box 204 displays the gain on awareness of existing brand image and tagline. The initial metric will be derived from the pre-event survey on awareness of existing brand image and tagline. Data from the post-event survey results will provide a comparative report.
The event revenue track box 212 displays a breakeven measure. Additionally, revenue is tracked by attendee type for use in future plans for attendee acquisition and event fee strategy planning.
The cross-sell Product A and Product B box 210 displays the gain percentage increase of intent to purchase the converged product set. An accurate measure of current cross-sell intent is generated from the pre-event survey by self-report. Data from the post-event survey results will provide a comparative self-report.
Although described in the context of a relatively large event for promoting a product, it is to be understood that aspects of the invention may be applied to meetings of any size and for the purpose of accomplishing any objective.
FIG. 3 is a flow diagram of one embodiment of a method for developing an event, conference, or meeting for prospective attendees related to an event-owner. The event-owner develops the event to educate the prospective attendees about the products and solutions of the event-owner. For example, a company (as an event-owner) may wish to develop an event to educate its salespeople (as prospective attendees) regarding new products. In another example, a company may wish to sponsor a user conference to provide a forum for new and existing customers to learn how to effectively use the company's products. The event-owner may include, but is not limited to, one or more of the following: a company, consulting firm, an employer and a manufacturer. The prospective attendees may include, but are not limited to, employees, customers, potential customers, salespersons, dealers and distributors related to the event-owner. The event may include, but is not limited to, at least one of the following: a user's conference, a sales meeting, a customer event, and a product launch.
FIG. 3 illustrates one embodiment of a method for developing the event. In this instance, the method may be accomplished by the event-owner, by a third party, such as a program developer, on behalf of the event-owner or by the owner working in conjunction with the developer. Once the event has been developed, a program manager (which may be the event-owner or the program developer) implements the event. For example, the program manager administers the event by registering prospective attendees, data, communicating with the prospective attendees regarding the event, and other related administrative functions. For convenience, it is assumed herein that the program manager is the program developer.
Referring further to FIG. 3, the event-owner initially identifies a list of attributes for the event and a list of potential values for each of the attributes at 301. Appendix A contains an exemplary list of attributes and potential values.
Once the attributes are defined, a list of potential values for each attribute is specified. Potential values for the attributes are the possible values for the attribute. Subgroups of prospective attendees are optionally identified at 302 by the event-owner. By identifying subgroups of prospective attendees, the event-owner is able to target the event to a particular group of prospective attendees. For example, if the event-owner identifies the prospective attendees by geographic region, then the event-owner can develop an event targeted to the prospective attendees within the particular geographic region. Subgroups of prospective attendees may be related to demographic data, firmographic data, and past event data.
Demographic data may include, but are not limited to, the following information regarding the prospective attendee: geographic location, age, gender, ethnicity, marital status, number of children, education and household income. Firmographic data may include, but not limited to, the following information regarding the prospective attendee's employment: job function, level of responsibility and years of service. And, past event experience data may include, but not limited to, regarding the prospective attendee's past experiences with events.
At 304, the prospective attendees preferred values of the attributes are collected by the event manager. In one embodiment, a survey 305 is provided to a subset of potential prospective attendees to collect prospective attendees preferred values. The survey 305 may be offered to all prospective attendees, all potential prospective attendees to the event, or a subset of the potential prospective attendees. The survey 305 may be conducted online, conducted through paper surveys, or conducted through any other known surveying techniques.
The prospective attendees preferred values are gathered at 304 through a choice-based conjoint experiment 306 in the survey 305 that measures the impact of attributes that make up the event through utility estimates. In the experiment 306, prospective attendees are asked questions that require them to choose among pairs of events that always differ on a small number of attribute values. If the event-owner decides not to offer a particular value for one of attributes, then that value will not be part of the experiment 306. For example, if the event-owner decides that the event will not include motivational speakers, then the motivational focus value for meeting emphasis attribute will be eliminated from the experiment 306.
In one embodiment, prospective attendees are shown the survey 305 as including two different events 307 side by side and are asked to choose the event that provides the most value to the attendee to collect the prospective attendees preferred values.
The survey 305 includes enough questions per prospective attendee to allow the prospective attendee preferred values to be analyzed via hierarchical Bayesian multinomial logit (HB-MNL). Appendix B contains the survey questions used to create the choice-based conjoint experiment to collect prospective attendees preferred values in one embodiment of the invention.
In embodiments where subgroups of prospective attendees have been identified at 302, subgroup data is collected by asking the prospective attendees direct questions about demographics, firmographics, past event participation in the survey 305. The collected subgroup data allows prospective attendees preferred values for subgroups of prospective attendees to be identified. Appendix B contains questions used to create an online survey to collect the subgroup data in one embodiment of the invention. In other embodiments, the event-owner may have access to data regarding prospective attendees subgroups such that it may not be necessary to survey the prospective attendees for all or a portion of the subgroup data.
After the survey 305 is completed, at 308 the collected prospective attendees preferred values are analyzed via HB-MNL to generate a set of weighted prospective attendees preferred values 309. The weighted prospective attendees preferred values 309 quantify the prospective attendees' preference for a value of an attribute. In one embodiment, the weighted prospective attendees preferred values 309 are used to build a user interface decision support tool 400, as shown in FIG. 4, to simulate the prospective attendees' preference to events with different attribute values. The HB-MNL analysis generates a statistical model that predicts each respondent's choices as a function of the attributes and levels in the experimental design (i.e., as shown in the versions of the survey). The tool 400 is used by the event-owner to simulate the prospective attendees' preferences to potential events. The decision support tool 400 is a conjoint simulation that may be implemented in a spreadsheet or other computer program. The HB-MNL analysis produces utilities which are the raw materials for the decision support tool 400.
In FIG. 3, at 310, a first event is defined. To define the first event, each of the attributes identified at 301 are initialized to a default value. The default value selected for each of the attributes is from the set of potential values identified at 101. In FIG. 4, event A1 404A is one example of a defined first event.
Referring again to FIG. 3, a second event is defined at 311. The second event also includes each of the attributes identified at 301 and the value for attribute is selected from the set of potential values identified at 301. However, at least one of the values selected for one of the attributes of the second event is different then the value of the attribute selected for the first event. Event B1 204B shown in FIG. 2 is one example of a defined second event.
In FIG. 3, at 432, the first and second events are displayed to the event-owner. In one embodiment, the event-owner views the first and second events in the decision support tool 400 shown in FIG. 4. In FIG. 3, at 314, the event-owner is provided with a comparison of the first and second events. The comparison communicates to the event-owner how many of the prospective attendees find more value in the first event compared to the second event. The comparison is calculated as a function of the weighted prospective attendees preferred values 309. In one embodiment, as shown in FIG. 4, the comparison of the first 404A and second event 404B is expressed as a percentage 406. For example, in the comparison shown in FIG. 4, 56% 406A of the prospective attendees find more value in event A1 404A compared to 44% 406B of the prospective attendees who find more value in event B1 404B.
Referring again to FIG. 3, in one embodiment, a first choice rule is utilized for HB-MNL weighted prospective attendees preferred values 309 or utilities. The total prospective attendees preferred values 309 is calculated for each event 404 for each respondent by summing the prospective attendees preferred values 309 for each attribute of the event 404. Using the first choice rule, the simulation predicts that a given respondent would choose the event 404 with the higher total prospective attendees preferred values.
In another embodiment, a logit choice rule is utilized with aggregate prospective attendees preferred values 309 or utilities. Prospective attendees preferred values 309 are summed for a total for each event 404. A prediction of preference of the respondent is calculated as a percentage of the exponential function of each total utility.
At 315, the event-owner is given the opportunity to refine the event comparisons by modifying the values of the attributes and selecting subgroups of prospective attendees. This allows the event-owner to compare various events and select the event that will best motivate the prospective attendees of the event.
For example, if sales have been sluggish in the Midwest sales district, then the event-owner would want to implement a sales event that specifically appeals to prospective attendees within the Midwest. In this example, the event-owner may select a subset of prospective attendees who belong to the Midwest sales district and view the comparison of the first and second events for the selected subset of prospective attendees. In this case, the comparison would communicate to the event-owner how many of the Midwest prospective attendees find more value in the first event compared to the second event.
The event-owner can continue to modify attributes to determine what type of an event will appeal to the prospective attendees at 315.
Referring again to FIG. 3, at 318 the event-owner selects the event to implement. In one embodiment, the event-owner selects the event prospective attendees find the most value in. Once the event has been selected, the program manager implements the event.
The decision support tool 400 is a conjoint simulation that may be implemented in a spreadsheet or other computer program. The decision support tool 400 includes a user interface, such as the embodiment illustrated in FIG. The decision support tool 400 is used by the event-owner to simulate the preferences of prospective attendees to a variety of possible events 404.
Through the decision support tool 400, the event-owner is provided with a comparison of the first 404A and second 404B events. The comparison communicates to the event-owner how many of the prospective attendees find more value in the first event 404A compared to the second event 404B. The comparison is generated as a function of the weighted prospective attendee preferred values 309. In one embodiment, the comparison is shown as a percentage 406. For example, as shown in FIG. 4, 56% of all prospective attendees 410 find more value in the first event 404A compared to 44% of all prospective attendees 410 who find more value in the second event 404B.
The decision support tool 400 also allows the event-owner to refine the event comparisons by modifying the values of the attributes 408 and selecting subgroups of prospective attendees 410. This allows the event-owner to compare various events and select the event 404 that the prospective attendees will most likely attend.
The event-owner may select a new subgroup 410 from the subgroups that were identified during the survey 305. Subgroups 410 help the event-owner to create events that appeal specifically to subgroups of prospective attendees. Once the new subgroup 410 is selected, the decision support tool 400 will recalculate the comparison 406 for the selected subgroup 410.
The event-owner may also modify attributes 408 to determine what type of an event will appeal to the prospective attendees. For example, the event-owner may modify the second event's type of content attribute to “ROI (return on investment)” while leaving the first event's type of content attribute to “Tips and Tricks.” The comparison 406 would communicate to the event-owner how many of the prospective attendees find more value in the first event 404A with a ROI content compared to the second event 404B with a tips and tricks content. The event-owner may continue to refine the event comparisons 406 by modifying the values of the attributes 408 until he determines that one of events 404 will best achieve the objectives set in step 104 described above.
In an embodiment, the survey defines Q-sort formatted questions to collect breakout session preferences from a plurality of the prospective attendees. Q-sort is a method of scaling responses in survey research. Q-sort forces participants to rank the items (e.g., reward types) to conform to a quasi-normal distribution. That is, it requires only a very small number of items to receive the highest rating and the lowest rating. It requires larger, but still small, numbers of items to receive the next highest and next lowest rating. It forces the respondent to rate most items in a middle category, so that the resulting distribution of ratings follows the familiar bell-shaped normal curve. For example, for a Q-sort rating of 15 items, the distribution into 5 groups, lowest to highest might be: 1:3:7:3:1. In an embodiment, the breakout session preferences relate to one or more of the following: the content of the breakout session, the format of the breakout session and the speaker type of the breakout session. An exemplary survey template developed in accordance to aspects of the invention is shown in Appendix E.
The collected breakout session preferences are analyzed to determine one or more of the following for potential breakout sessions: the reach of the breakout sessions, frequency of the breakout sessions, and overlap of the breakout sessions. In an embodiment, TURF (Total Unduplicated Reach & Frequency) analysis is conducted on the breakout session preferences to determine one or more of the following for potential breakout sessions: the reach of the breakout sessions, frequency of the breakout sessions, and overlap of the breakout sessions. The TURF analysis calculates optimal configurations for breakout session to maximizing reach. Reach or coverage is defined as the proportion of the audience (e.g., prospective attendees) that chooses a particular option for a breakout session (e.g., roundtable breakout session, scripted presentation breakout session).
And, in an alternative embodiment, a breakout session simulation tool is generated as a function of the analyzed breakout session preferences. The breakout session simulation tool includes a user interface, such as the embodiment illustrated in FIGS. 5A, 5B. The breakout session simulator tool may be used for testing the reach, frequency and overlap of potential breakout sessions. For example, in FIG. 5A, the event-owner has selected the breakout session format “scripted presentation” and selected all content. The display shows 79.9% of prospective attendee chose the “Generating innovative ideas from your people” in a scripted presentation format as a first or second preference. By adding an “Aligning recognition systems and practices to reinforce corporate goals” breakout session in a scripted presentation format reaches an additional 15.25% of the prospective attendees. Additionally, 95.16% of the prospective attendee chose one these two types of breakout sessions as a first or second preference.
And, in FIG. 5B, the event owner selects the format “Roundtable” and speaker “Third Party Industry Expert” in addition format “scripted presentation” and all content. The display indicates shows 99.03% of prospective attendees chose any one of the breakout sessions in a scripted presentation format or in a roundtable format with a third party industry expert as a first or second preference. Additionally, the display shows 81.27% of the prospective attendees chose the “Generating innovative ideas from your people” in a scripted presentation format or in a roundtable format with a third party industry expert as a first or second preference. By adding an “Aligning recognition systems and practices to reinforce corporate goals” breakout session in a scripted presentation format reaches an additional 12.81% of the prospective attendees.
The order of execution or performance of the operations in embodiments of the invention illustrated and described herein is not essential, unless otherwise specified. That is, the operations may be performed in any order, unless otherwise specified, and embodiments of the invention may include additional or fewer operations than those disclosed herein. For example, it is contemplated that executing or performing a particular operation before, contemporaneously with, or after another operation is within the scope of aspects of the invention.
Embodiments of the invention may be implemented with computer-executable instructions. The computer-executable instructions may be organized into one or more computer-executable components or modules. Aspects of the invention may be implemented with any number and organization of such components or modules. For example, aspects of the invention are not limited to the specific computer-executable instructions or the specific components or modules illustrated in the figures and described herein. Other embodiments of the invention may include different computer-executable instructions or components having more or less functionality than illustrated and described herein.
When introducing elements of aspects of the invention or the embodiments thereof, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements.
As various changes could be made in the above constructions, products, and methods without departing from the scope of aspects of the invention, it is intended that all matter contained in the above description and shown in the accompanying drawings shall be interpreted as illustrative and not in a limiting sense.

## Claims

1. A computer implemented method of developing an event designed to appeal to prospective attendees associated with an event-owner, said method executed by at least one processor, said method comprising:
receiving by the processor one or more attributes of an event to be developed and preferred values of the attributes, wherein said preferred values of the attributes are collected from a survey completed by a plurality of the prospective attendees;
modeling by the processor the collected prospective attendee preferred values to generate weighted prospective attendee preferred values for the attributes;
building by the processor a user interface decision support tool from the weighted prospective attendee preferred values, wherein said user interface decision support tool is configured to simulate the prospective attendees' preference for subsequently developed events;
defining by the processor a first event as a function of a set of attributes and a first set of attribute values received from the event-owner that correspond to the set of attributes;
defining a second event as a function of the set of attributes and a second set of attribute values received from the event-owner that correspond to the set of attributes, wherein for at least one attribute the corresponding attribute value of the first set is different from the corresponding attribute value of the second set;
displaying by the processor for the event-owner the first and second events; and
providing by the processor via the user interface decision support tool a numeric comparison for the event-owner of the first event to the second event as a function of the weighted prospective attendees preferred values, said numeric comparison quantifying the prospective attendees' preference for the first event to that of second event;
receiving by the processor breakout session preferences from a plurality of the prospective attendees utilizing Q-sort surveying;
analyzing by the processor the collected breakout session preferences to determine one or more of the following for potential breakout sessions: the reach of the breakout sessions, frequency of the breakout sessions, and overlap of the breakout sessions.

2. The method of claim 1, further comprising:
generating by the processor a survey that is a choice-based conjoint experiment;
presenting by the processor the survey to the plurality of prospective attendees to gather data from the prospective attendees; and
analyzing the data through hierarchical Bayesian multinomial logit.

3. The method of claim 1, wherein the user interface decision support tool is a conjoint simulation tool which simulates choices among multiple event designs.

4. The method of claim 1, wherein TURF (Total Unduplicated Reach & Frequency) analysis is conducted on the breakout session preferences to determine one or more of the following for potential breakout sessions: the reach of the breakout sessions, frequency of the breakout sessions, and overlap of the breakout sessions.

5. The method of claim 1, wherein the break session preferences relate to one or more of the following: the content of the breakout session, the format of the breakout session and the speaker type of the breakout session.