Lionel Wang

Founder and Chief Research Officer.

Lionel Wang set up Marketing and Innovation, L.L.C in June 2001 and Engage 123, Inc. in 2006. Since starting up the operation, he successfully implemented several marketing research and clickstream applications to major advertising agencies, management consulting firms, marketing research companies, and a global e-Commerce firm. His work in CRM received an honorable mention in Asia's First 1-to-1 Innovator Awards sponsored by Peppers and Rogers Group.

Prior to founding Marketing and Innovation, Lionel Wang was Vice President of Product Development at NFO Interactive from June 2000 to June 2001. In that capacity, he evaluated and implemented numerous Web-based Marketing Science applications.

Lionel Wang joined The NPD Group in 1996 as Director of Analytical Services. He was responsible for providing analytical support, research design, and consultation to both NPD clients and staff. Mr. Wang specialized in Market Structure, Consumer Segmentation, Brand Equity, Choice Modeling, New Product Forecasting, Advertising Analysis, and Point of Sales Modeling. He also developed Web-based Marketing Science applications for The NPD Online Research Group. He played a dominant analytical role in developing Forrester's Technographics and FCB's Relationship Monitor.

Before NPD, Mr. Wang was Vice President of Analytics at A.C. Nielsen where he managed an analytical modeling group on Kraft Foods/White Plains account. Before that he was the Director of Product Development at Information Resources from 1989 to 1994. In that position, he developed and implemented several household panel and store level applications. Between 1984 and 1989, Mr. Wang held two separate positions in Corporate Planning and Marketing Research at The Gillette Company.

Mr. Wang presented several research studies at the Marketing Science conferences, ESOMAR, AMA/Advanced Research Technique forums and DMA conferences. His works on media effectiveness and consumer segmentation analyses received outstanding accomplishment recognition at the Kraft Foods research conference. His visionary research on incorporating brand equity into baseline analysis broadens the scope of standard marketing mix modeling.

Mr. Wang holds a Master of Science Degree in Operations Research from Stanford University, an MBA from California State Polytechnic University, and a Bachelor of Commerce degree from Tamkang University, Taipei.

Engage your customers in creating and evaluating their ideal products and services.

Consumers "create" their ideal product on a kiosk or desktop computer. You can view demos of these products on our "Demonstrations" page.

"Drag and drop" interface creates for easy customization

One relevant attribute examined at a time, allowing for variable control

Consumers have the ability to see what they are creating in real-time

Consumers answer questions about what they have created

Consumers are then asked to similarly "create" a second choice product to evaluate.
Then they are asked to compare the two.

Analytical results are guided

Product-focused market structure is examined

Customer-focused preference segmentation develops naturally

Disaggregate relational database query is leveraged

We calculate the lifetime value for each customer

Customer Lifetime Value (CLV) is derived from margin, discount rate, and retention.
Retention is estimated from both historical pattern and future purchase intent.

We identify the relationship pyramid status that each customer establishes with your company

We developed a relationship scoring that properly places each customer to a dominant relationship segment – partnership (commitment-driven), family/friends (trust-driven), practical relationship (leadership-driven), and causal relationship (satisfaction-driven).
Relationship scoring is developed based on a variety of relationship drivers - I can’t be temped away from this brand, I trust this brand, this brand rewards me for my business, this brand is a market leader, I buy this brand because it’s less expensive, I keep my eyes open for other alternatives, etc.

We connect CLV and relationship status with a common measure – retention

Establish a predictive link between retention and relationship drivers using multinomial logit model.
Changes in relationship drivers lead to improvement in both relationship and CLV.
Different strategic options can be implemented for different combinations of relationship status and CLV.

We further identify the role of functional satisfaction (quality, service, ease, etc.,) in the relationship pyramid. Please contact us for further information.

A case of optimizing customer value from the perspective of relationship building.

Introduction

A recent study from a global market research company showed how customer needs can be applied to brand positioning in automobiles. Their study showed that both BMW and Mercedes Benz (MB) ultimately tap into the need for leadership, yet they each satisfy different customer needs. BMW satisfies the need for fun, attractiveness, and self-indulgence, while MB satisfies the need for respect, knowledge, security, and tradition. Their study, like many others, claims that understanding customer needs leads to building strong customer relationships. However, they did not discuss how to measure customer relationships nor did they discuss how improving those relationships can lead to improving customer value. We find this to be a common shortcoming or possibly oversight in studies such as the automobile brand positioning one. It is our intent to address these unanswered issues in the following automobile example.

Bridge the gap between customer understanding and customer relationship

Different customer needs lead to different relationships with brands. Many references can be found discussing ways to define a customer’s relationship with a brand. For our purposes, we think of a customer relationship as a hierarchy. We use the image of a pyramid to make clear the different levels of this hierarchy. Our relationship pyramid consists of five different layers or styles. From the top, they are: partnership, trust, leadership, cost consciousness, and causal relationship. Obviously, the higher a customer is on the pyramid, the stronger his or her relationship with a brand. For example, we found that MB customers form a strong trust relationship with Mercedes Benz, while BMW customers value their brand as a partner. In this example, partnership style is mostly driven by caring, excitement, and uniqueness among others.

We use two separate approaches – an analytical approach vs. a visual approach -- to identify the relationship that each customer establishes with his/her brand. Under the analytical approach, we ask each customer a battery of 10 attribute questions such as “I trust this brand”, “This brand cares about my needs”, “This brand is exciting”, “There is simply no other brand likes this brand”, “This brand is a market leader”, etc. Confirmatory factor analysis on these attributes detects the relative distribution of five relationship styles for each customer. The most dominant relationship style is then identified. Five relationship styles are further validated by a Flash-enabled visual approach where customers tell us which relationship style best describes their experience with their brand.

It is well documented that trust is a necessary but not a sufficient condition to build the strongest customer relationships. We did observe in our auto study that partnership style yields the highest loyalty score (90%), followed by trust (83%), leadership (75%), cost consciousness (70%), and causal relationship (55%). Our Loyalty score metric is a composite measure of brand loyalty, willingness to make recommendations, and new product/service.

Each brand has its own relationship migration pattern

Although partnership style is the most preferred relationship that a brand wants to have with its customers, it sometimes makes sense to focus on building a trust or leadership relationship. In our auto study, trust-driven attributes turn out to be more significant than partnership-driven attributes in predicting loyalty for BMW, while the reverse sequence is observed for Mercedes Benz.

Our understanding of customers’ relationships to these luxury automobile brands offers a deeper insight to RI’s consumer findings. BMW is positioned as a brand that generates fun, attractiveness, and self-indulgence. From the perspective of customer relationship, BMW should also communicate trust (I trust BMW, I can count on BMW, etc.) to its customers since improving consumers’ trust in the brand is likely to lead to a significant increase in loyalty or retention.

In contrast to BMW’s position, Mercedes Benz is positioned as a brand that generates respect, knowledge, security, and tradition. Although MB’s position fosters a trust relationship, it is important to be viewed as a true partner by its customers. This can be accomplished by improving such attributes as uniqueness and care about my needs.

Optimize customer lifetime value

In categories such as financial services and telecommunications where individual customer transactions are more frequent, marketers not only want to build strong customer relationships but also want to maximize those customers’ lifetime value (CLV) to their company or brand. CLV is a measure of the value of a customer over the lifetime of that customer’s relationship to the company or brand. It is derived from margin, discount rate, and retention rate.

Retention is the single most important measure that links customers’ relationships and their lifetime value for frequently purchased product or service categories. For automobiles, an infrequently purchased product category, we chose a composite loyalty measure since retention isn’t as meaningful a measure. Whether we predict loyalty or retention, our logic of identifying relevant relationship styles remains unchanged.

Insofar as we can develop a reasonable prediction of retention based on both a battery of 10 relationship attributes and other individual customer data, we are able to develop marketing programs that simultaneously create powerful relationships and maximize customer lifetime value. In our work to date, we have been able to develop those reasonable predictions.

Growth Potential Structure

Growth Potential Structure Web Application

We offer a three-step road map that can help you achieve organic growth for your products.

1. Usage Domain Structure

What market are we in?
A Usage Domain is a cluster of products drawn together by a similarity of end-uses by consumers.

2. Category Purchase Structure

Explain how brand, form, type and other product attributes factor into consumers’ decision to purchase. Category Purchase Structure is hierarchical in nature. The higher a brand is in its structure, the more efficient is its marketing budget.

3. Need States Structure

Define how consumers’ needs explain competition between and interaction with products in each usage domain. Properly developed need states enable marketers to make a clear product association between need states and usage domain.

Loyalty Identifier: this tool analyzes purchase data to determine the switching behavior of consumers and identify where loyalty lies.
Categories, such as flavor or health features, are compared against each other to determine which category carries greater weight in customer loyalty. Within each category, the individual attributes (such as chocolate, peanut butter, fruit, etc under flavor) are also compared against each other to identify customer tendencies to switch to other products.

Benefits & Needs Profiler: this tool analyzes survey data regarding what qualities customers attribute to what brands and creates a map highlighting the strength of these connections.
Like attributes in the Structure Identifier, brands and categories can be combined into new entities. The resulting grid below shows the correlation between attributes and brands/categories/domains. The "Cutoff" input in the toolbar allows you to dictate a percentage of the column's average, where brands higher than (100+input%) * column average will be in green and brands lower than (100-input%) * column average will be in red.

Top Attributes grid: this grid shows the top performing attributes for any given brand/category/domain. By clicking them, you get a pop up of all other brands where the selected attribute also performs well (within the "Threshold" input in the toolbar).

Management Consulting

"Lionel Wang has been an excellent partner across our consulting practice. He is exceptionally knowledgeable about all aspects of market research and has helped us with innovate thinking on how and why products compete with each other, what are the drivers of brand equity, and which consumer segments marketers should target. He has applied some very sophisticated analytic concepts to deepen our understanding of behaviors and attitudes that are highly predictive.

His depth of experience in using general consumer research, multivariate analysis, and econometric modeling makes him invaluable for the creation of a comprehensive body of knowledge to drive optimal marketing strategies. Not only does he deliver best-in-class insights but also works collaboratively with cross-functional teams to ensure the analyses drive outcomes that reflect real business world priorities."

- Managing Director, HRCP Insights, a subsidiary of McKinsey & Company

"Booz & Company engaged Lionel to design and implement an extensive modeling program on behalf of a large CPG manufacturer. Dealing with a very tight timeline and relative sparse data, Lionel employed a mix model approach that allowed us to analyze promotion and price elasticity for over 100 product groups in under three weeks.

The results were incorporated into an ROI analysis that uncovered over $50M in potential profit improvement. Throughout, Lionel's creativity, professionalism and work ethic were key success factors. I would recommend Lionel without question for any marketing science engagement, but especially where standard approaches are unlikely to crack the problem."

- Partner, Booz & Company

"Marketing and Innovation's "design your ideal product" application represents a real breakthrough in understanding what consumers want in new products. The drag-and-drop technology makes the questionnaire come to life for respondents and yields new and helpful insights. Marketing and Innovation is responsive, creative and flexible--a pleasure to work with."

- Partner, Marakon, A Charles River Associates Company