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Michael Lieberman
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    • Data specification --> Modeling --> Testing --> Application --> RefinementOur objectives are outlined below.The business objective:Measuring the return on investment (ROI) of programs, initiatives, campaigns, etc., in order for the company to be able to:Ascertain its past successes on the basis of cost vs. the returns attributable to those costs;Develop an understanding of “what works and what doesn’t work” that can be applied to future initiatives.The analysis objective:Develop models using the data currently available to the company and/or recommend additional types of data that are not presently available, which could provide the insight required for attaining the business objectives.Explore the most likely model designs and select the ones that work best at the various levels of disaggregation e.g., market segment, population demographics, behavioral characteristic relating to segments utilization of health care delivery systems, etc.The scope of the work envisioned here applies to the following subgroups of Anaheim’s served market:Individual, small group, Medicare supplement and Medicare Advantage customers whose records are available at the individual level.The large group and national market segments whose records are available at the employer level.The object is to prepare modeling recommendations that are applicable to a variety of customer levels – from market segments all the way down to specific consumer groups defined by their demographics [url] => https://www.quirks.com/articles/deploying-existing-client-databases-cx-financial-linkage [authors] => Array ( [0] => Array ( [first_name] => Michael [last_name] => Lieberman [slug] => michael-lieberman [name] => Michael Lieberman ) ) [published_at] => 2018-02-26T15:27:53+00:00 [industries] => Array ( [0] => Array ( [name] => Consumers [slug] => consumers ) ) [specialties] => Array ( [0] => Array ( [name] => Consumer Research [slug] => consumer-research ) [1] => Array ( [name] => CX/UX-Customer/User Experience [slug] => cx-ux-customer-user-experience ) [2] => Array ( [name] => Data Analysis [slug] => data-analysis ) [3] => Array ( [name] => Database Development/M.I.S. [slug] => database-development-m-i-s ) ) [popularity] => 1108 [articleTypes] => Array ( [0] => Array ( [name] => Research Methodology [slug] => discussion-of-technique ) [1] => Array ( [name] => E-Newsletter Article [slug] => e-newsletter ) ) [sort_date] => 2018-02-26T15:27:53+00:00 [year] => 2018 [article_image] => Array ( [url] => /storage/attachments/5a8f165cd82f1c29ba240fc9/5a8f16efd82f1c2c89463c18/original/Figure1.png [width] => 718 [height] => 477 ) [content_type_display] => Array ( [type] => Article [icon] => fal fa-file-invoice ) [objectID] => deploying-existing-client-databases-cx-financial-linkage [_highlightResult] => Array ( [title] => Array ( [value] => Deploying existing client databases - CX-financial linkage [matchLevel] => none [matchedWords] => Array ( ) ) [abstract] => Array ( [value] => The author discusses how to link customer experience variables to financial outcomes. [matchLevel] => none [matchedWords] => Array ( ) ) [body] => Array ( [value] => Editor's note: Michael Lieberman is founder and president of Multivariate Solutions, a New York data science and strategy firm.Data. Companies have lots of it. So much that we have begun urging them to commoditize it. Or at the very least put it to practical use. With today’s technology not only can nearly everything be gathered, counted and measured but the information can be stored and then processed at record speeds. The result is analysis that goes beyond sums, averages and basic statistics to aggregates, benchmarks, recommendations and predictions. So what does one do with all of this game-changing data and analysis? Create a data product. They’re all around us and they’re changing the way we as consumers interact with companies and the way businesses interact with each other.There are stories to be told hidden within the databases of major corporations. Traditional marketing research projects often take data that is fielded by way of panels or other field measures. While these will continue to function, an opportunity to use existing customer databases and purchased data – for example, Nielsen PRIZM segments – to apply to marketing research techniques and produce an actionable product are now becoming more prevalent.Many companies wish to link their customer experience (CX) variables to specific financial outcomes. They wish to estimate customer retention based on data they already possess and identify variables that will predict financial performance. This article will illustrate how we would go about this. BackgroundAnaheim is a fictional insurance company with a lot of data. Anaheim finds itself at a crossroads; it has accumulated massive amounts of data and has conducted a significant number of analyses. Still, it is removed from having a simple-to-implement input/output model that management can use for making investment decisions in new customer experience initiatives.At the same time, Anaheim is confronted with a research problem: How useful are the data currently available? This data has not necessarily been collected with a view to addressing the input/output query mentioned earlier. Can it be put to use in an investment frame of reference?Anaheim wishes to link its customer experience variables to specific financial outcomes. It wants to estimate customer retention based on data it already possesses and identify variables that will predict financial performance. We have been asked to employ our analytical methods focusing on key measures, such as the Net Promoter Score and likelihood to churn.Current marketing research and practice aims at maximizing the correct classification of customer retentions and losses. Profit from targeting a customer depends on not only a customer’s propensity to retention but also on her spend or value, her probability of responding to retention offers as well as the cost of these offers. Overall profit of the firm also depends on the number of customers the firm decides to target for its retention campaign.This project is an extension of typical marketing research assignments. It requires a high level of understanding of business strategy and executive decision-making in addition to a command of statistical and modeling approaches, techniques and solutions. In other words, the new role of the marketing researcher: consultant.This linkage project requires Anaheim to advance along the continuum:Problem definition --> Data specification --> Modeling --> Testing --> Application --> RefinementOur objectives are outlined below.The business objective:Measuring the return on investment (ROI) of programs, initiatives, campaigns, etc., in order for the company to be able to:Ascertain its past successes on the basis of cost vs. the returns attributable to those costs;Develop an understanding of “what works and what doesn’t work” that can be applied to future initiatives.The analysis objective:Develop models using the data currently available to the company and/or recommend additional types of data that are not presently available, which could provide the insight required for attaining the business objectives.Explore the most likely model designs and select the ones that work best at the various levels of disaggregation e.g., market segment, population demographics, behavioral characteristic relating to segments utilization of health care delivery systems, etc.The scope of the work envisioned here applies to the following subgroups of Anaheim’s served market:Individual, small group, Medicare supplement and Medicare Advantage customers whose records are available at the individual level.The large group and national market segments whose records are available at the employer level.The object is to prepare modeling recommendations that are applicable to a variety of customer levels – from market segments all the way down to specific consumer groups defined by their demographics [matchLevel] => none [matchedWords] => Array ( ) ) [authors] => Array ( [0] => Array ( [slug] => Array ( [value] => michael-lieberman [matchLevel] => none [matchedWords] => Array ( ) ) [name] => Array ( [value] => Michael Lieberman [matchLevel] => none [matchedWords] => Array ( ) ) ) ) [industries] => Array ( [0] => Array ( [name] => Array ( [value] => Consumers [matchLevel] => none [matchedWords] => Array ( ) ) [slug] => Array ( [value] => consumers [matchLevel] => none [matchedWords] => Array ( ) ) ) ) [specialties] => Array ( [0] => Array ( [name] => Array ( [value] => Consumer Research [matchLevel] => none [matchedWords] => Array ( ) ) [slug] => Array ( [value] => consumer-research [matchLevel] => none [matchedWords] => Array ( ) ) ) [1] => Array ( [name] => Array ( [value] => CX/UX-Customer/User Experience [matchLevel] => none [matchedWords] => Array ( ) ) [slug] => Array ( [value] => cx-ux-customer-user-experience [matchLevel] => none [matchedWords] => Array ( ) ) ) [2] => Array ( [name] => Array ( [value] => Data Analysis [matchLevel] => none [matchedWords] => Array ( ) ) [slug] => Array ( [value] => data-analysis [matchLevel] => none [matchedWords] => Array ( ) ) ) [3] => Array ( [name] => Array ( [value] => Database Development/M.I.S. [matchLevel] => none [matchedWords] => Array ( ) ) [slug] => Array ( [value] => database-development-m-i-s [matchLevel] => none [matchedWords] => Array ( ) ) ) ) [articleTypes] => Array ( [0] => Array ( [name] => Array ( [value] => Research Methodology [matchLevel] => none [matchedWords] => Array ( ) ) [slug] => Array ( [value] => discussion-of-technique [matchLevel] => none [matchedWords] => Array ( ) ) ) [1] => Array ( [name] => Array ( [value] => E-Newsletter Article [matchLevel] => none [matchedWords] => Array ( ) ) [slug] => Array ( [value] => e-newsletter [matchLevel] => none [matchedWords] => Array ( ) ) ) ) [year] => Array ( [value] => 2018 [matchLevel] => none [matchedWords] => Array ( ) ) ) ) 
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