Our client currently manages more than $50 billion in assets and is in the top 30 mutual fund families.
Their shareholders enjoy a diverse array of investment opportunities to take advantage of our portfolio management expertise. Individuals, groups and trusts can take advantage of their retail channel for their investment needs. The Institutional Client Services area offers investment expertise for institutional investors. Enhanced services are available to individual investors.
Our client has made increased investments in data-driven marketing and has the intent to build world class capability in leveraging customer knowledge to meet the wealth management needs of its customers. The purpose of this new role is to lead a team in developing actionable segmentation strategy and descriptive and predictive marketing models that will be applied in customer segmentation, targeting and mass customization of tailored offers across the customer life-cycle from acquisition, customer development, retention and win-back, and including bundled product offering based on customers needs. This start-up role will be very hands on and will initially lead 4 analysts in developing and utilizing a broad range of marketing analysis, datamining and model development to be applied at both a strategic and tactical level. This role will also coordinate closely with external partners in the development and application of customer marketing models as required.
Key Result Areas Include:
1. Development of Annual Customer Analysis and Targeting Plan, and related Analytic Team Development (25%).
2. Segmentation Strategy Development (20%).
3. Target Marketing Modeling Development, Application & Validation ( 30 % ) Campaign Targeting & Back-end Analysis (20%).
4. Define Enhancements to Datamart (5%).
Candidate Profile Required:
- A MS or Ph.D. in market research, statistics, econometrics, operations research or related quantitative field.
- A minimum of 7 years experience in working with larger data sets for predictive / descriptive / risk modeling purposes.
- A demonstrated ability to build relationships with marketing team members and identify opportunities where decision sciences has had significant financial impact on the business.
- Strong experience with multiple linear and logistic regression, and CHAID techniques with at predicting response, attrition, next likely product, risk etc.
- Experience in exploratory data analysis to identify outlier data, non-linear data relationships, variable interaction, missing data, and data transformation to improve modeling outcomes.
- A minimum of 3 years experience in direct marketing with a solid grasp of all aspects of targeting, testing, list specification, campaign tracking and performance, control with exposure to at least two direct channels i.e. mail and telemarketing.
- Demonstrated experience with experimental design and the development of statistically valid test and control matrices to test different offers, channel preferences, and creative treatment.
- Strong computer skills for statistical modeling using SAS, Oracle, Cognos and SQL; good general PC Skills using spreadsheets, word processing and presentation software. experience with both flat file and relational database structures.
- A demonstrated ability to interpret and translate modeling results into implications for customer acquisition, value proposition development, and campaign management for marketers.
- Strong presentation skills in developing and communicating a compelling business case where micro-targeting and segmentation at both strategic and tactical levels would have impact improving business results and marketing efficiencies.
- A strong team builder who sets clear goals, provides effective direction, personal learning and coaching to team members; a drive and sense of urgency to in leading the team to achieve stretch client objectives under pressure; a track record in developing and coaching a team of at least 3 analytic professionals.
- Ideally experienced in the application of append data for target marketing or risk purposes including (complied consumer, credit bureau, and self-reported).
- Ideally knowledge and experience with other modeling techniques such as discriminant analysis, cluster analysis, factor analysis applied in identifying transactional customer segmentation, share of wallet, life-stage triggered marketing opportunities, etc.
- Ideally strong financial analysis and modeling skills (spreadsheet, NPV, IIR, ROI ) from which to evaluate the economic value of customers and customer segments.
- Ideally experience in modeling and database marketing application in a sophisticated data rich transactional environment (telecom, financial services, courier, hospitality, and or related database marketing supplier).
|A proud member of many regional and national direct marketing associations, Minnesota Association of Personnel Services, Database Marketing Institute and Advisory Board of Data University.|