Predictive Modeling for Acquisition

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OVERVIEW

Acquiring new donors can be difficult and expensive for any annual giving program. Fortunately, predictive modeling can make the challenge of converting non-donors into donors easier and more efficient. Creating and applying a simple scoring system can help you to determine which non-donors are most likely to respond to direct appeals and which marketing channels will yield the highest return for various segments.

WHAT YOU’LL DISCOVER

  • Methods for building low cost donor acquisition models in-house using Excel
  • Guidelines for applying model scores to prioritize prospects and non-donor segments
  • Advice for allocating your resources to each of your marketing channels based on model outcomes
  • Tips for assessing the strength and effectiveness of your models and your acquisition efforts
  • Examples from other institutions, and more!

Thanks to the Annual Giving Network (AGN) team for inviting me to present on predictive modeling for donor acquisition.

Relationship Management Metrics

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Thanks to APRA for inviting me to present on relationship management for the 30th annual conference on 7/28/17 in Anaheim, CA.

The topic of my session is Relationship Management and Metrics: Key Fundraising Tools to Drive Acceptance of Analytics, Models and Services.

Here’s a brief description of my presentation:

Effective relationship management is critical to successful fundraising within any organization. Relationship management and metrics describe a system of information tracking tools, reports, policies, processes and measurable actions that organizations can use to 1) move prospects through the continuous fundraising lifecycle 2) chart progress to goal and 3) provide data-driven insights and strategic recommendations to key stakeholders. In this session, attendees will learn how to use relationship management and metrics to improve the acceptance of data analytics, models and services as evidence-based tools to provide context, content and competitive advantage.

Looking forward to sharing insights, expertise and decision support recommendations with the APRA community.

Analytics Adoption Factors

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The DRIVE/ Conference is the preeminent event for professionals who are determined to be subject matter experts in the fields of data, reporting, information, analytics, modeling, visualization and beyond. This thought-provoking experience is held annually and is a must-attend event for those looking to leverage the latest innovative thinking and network with thought-leaders from the world of data. DRIVE/ participants learn new skills, participate in technical deep dives and be inspired by world-class keynoters while interacting with professionals from high tech, higher education, philanthropy, government, professional sports and beyond.

This year I am presenting on Analytics Adoption: Key Factors to Drive Organizational Acceptance, Transformation and Value:

  • The underlying goal of any data-driven project is to overcome a challenge, gain insight or seize a critical opportunity to add business value.
  • Analytics projects of any size require leadership support as they face adoption issues, resource constraints and cultural challenges.
  • Learn how proven change management strategies and continuous improvement frameworks can help you, as a leader, analyst and/or stakeholder, light a fire to cultivate support and creatively motivate the action required for your success.

Thanks to the CASE team for providing the opportunity to share insights, expertise and practical solutions on how to build a data analytics organizational culture and performance-driven team.

Predictive Analytics Innovation Summit

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Thanks again to the Innovation Enterprise team for inviting me to chair the Predictive Analytics Innovation Summit on 2/14/17 and 2/15/17 in San Diego, CA.

I enjoyed connecting with creative thought leaders and diverse innovators across a wide spectrum of industries, sectors and business domains.

After two days of inspiring presentations, I left energized and ready to tackle the common challenges we continue to face in the field of applied predictive analytics and machine learning — identifying context, clarifying business requirements, linking project specification to ROI, fostering analytics culture within the organization, establishing data governance, promoting buy-in from key stakeholders, and translating results into actionable insights.

It is an exciting time to be involved with data analytics as we shift from 1) predictive to prescriptive analytics and 2) further explore the potential of deep learning and artificial intelligence to create solutions using structured and unstructured data evolving rapidly in terms of volume, velocity and variety.

I look forward to incorporating some of these themes and takeaways in my upcoming presentation at DRIVE/ in May 2017.

Hello Data-Driven World!

After years of using various platforms, I recently decided to transition and consolidate my project workspaces into a knowledge-focused blog to build community, support and thought leadership around the identification, collection and distribution of useful tips and resources, which often lead to follow-up research, discovery, insight and (eventually) knowledge.

Most projects begin with a simple business question: “Where is the untapped potential in these markets?” or “Which prospects are generating the most revenue relative to my portfolio?” or “Which accounts should I transfer out of my portfolio?” or “What are the next recommended actions to meet my goals?” or “Where should I spend my time, focus and energy?”

While the path to transforming data and insights into knowledge often begins with these types of linear, “top-down” lines of inquiry, the actual process of generating useful recommendations and decision support often requires a circuitous and sometimes recursive (nested) approach to analyzing multiple types of factors or dimensions before identifying an emergent pattern or theme:

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This site will be dedicated to documenting and sharing various examples of data analytics workflow, automation, scripts, code snippets, as well as any other types of business intelligence tools that promote efficiency, data-driven insights, and decision support to support the underlying goal of adding organizational value.