Appendix B — Selected Relevant Research and Business Experience

Jim Idelson brings broad and deep experience in market research, analytics, strategy, and go-to-market planning that is directly relevant to this proposal. His work has included large-scale user surveys, multi-mode market research, recurring annual measurement programs, and advanced analytics used to understand segments, behavior, likely future action, and the factors most associated with stronger outcomes.

B.1 Multi-Method Market Research for Product and Messaging Decisions

For a major telecommunications company, Jim led a multi-mode market research effort combining focus groups and survey methods with professionals in the general public. The work identified specific recommendations for product direction and market messaging, and it directly informed a major television advertising campaign.

The project required turning mixed-method evidence into concrete investment and messaging decisions, the same kind of decision-support translation this ARDC work is designed to deliver.

B.2 Annual Salary and Job Satisfaction Benchmark Program

Jim designed and led a multi-year Salary and Job Satisfaction Survey for a large technology user group. The program was run annually as a longitudinal measurement effort, with a target population defined by job role. Compensation results helped respondents benchmark themselves against peers, while the satisfaction module tracked community health over time.

This model emphasized reusable baseline measures and periodic refreshes to track change over time, an approach that directly supports ARDC's need for trend-sensitive planning.

B.3 Product Leadership for a Customer Data Platform

As product leader for a Customer Data Platform, Jim led a team building and operating a big-data platform used by large consumer brands around the world to collect and process data about their customers with the goal of providing highly personalized experiences for their customers. The platform supported advanced analytics at scale, including clustering, propensity modeling, classification, loyalty analysis, and correlation/causal analysis workflows.

That experience is directly relevant to segment-based decision support in this proposal, where the goal is to identify which groups are most likely to engage, continue, or drift, and where interventions may have the highest leverage.

B.4 Large-Scale Videoconferencing User Research

As a consultant to Fortune 500 class companies, Jim ran a benchmarking service, collecting thousands of surveys of videoconferencing users to understand lived experience, perceived value, and sources of friction that reduced adoption and sustained use.

The same analytic pattern is applied in this ARDC proposal: identify friction points early, quantify where drop-off occurs, and inform practical actions that improve engagement and continuation over time.