Summary of Rotem AR's Key Marketing and Research Tool Box
1. Advanced Market Segmentation
Market segmentation is a cornerstone of modern marketing, originating from Wendell Smith’s concept that markets are heterogeneous. It identifies subgroups within a market based on defined characteristics. Two main approaches exist: a-priori (segments are predefined, e.g., by age) and post-hoc (segments emerge from data analysis, e.g., lifestyle, and benefit sought). Segmentation helps companies tailor their marketing mix to specific consumer groups.
2. Strategic Fit Model
This model emphasizes aligning a company's market positioning with its optimal target segments, known as Strategic Marketing Fit or Product-Market Fit in startups. It maps the market and competition, guiding firms to strategically serve the most relevant customer groups for success.
3. Advertising Effectiveness Research
These studies assess if advertising meets goals such as informing, persuading, or reminding. Key metrics include brand awareness, ad recall, brand linkage, and purchase consideration. Rotem’s approach adds brand equity and hypothetical market share (using MaxDiff) for deeper insight into ad impact.
4. MaxDiff Analysis
MaxDiff is a powerful method for prioritizing consumer preferences. Respondents choose the most and least important attributes from sets of options, producing ratio-scaled results. It removes response bias, provides clear differentiation between attributes, and supports accurate segmentation based on benefit preferences.
5. Discriminant Analysis
Used to classify individuals into groups based on predictors, this technique helps explain what differentiates consumer choices or predict future behavior. It has applications in product choice analysis, voter behavior, and predicting customer churn using internal data like complaints or visits. We use discriminant analysis as a learning machine tool to predict and identify the right segments for each consumer from companies' data sets.
6. Conjoint Analysis
Conjoint analysis simulates real-life trade-offs to reveal how consumers value different product features. It helps optimize product/service mixes, predict demand, and evaluate brand equity. It reduces bias by requiring respondents to make realistic choices, offering high predictive accuracy. This technique is used by us to perform accurate pricing studies.
7. Brand Equity Testing
Often integrated with conjoint analysis, this assesses the value a brand adds to a product. It reveals how brand name influences consumer preference and can inform brand extension strategies. Accurate modeling helps businesses isolate the brand’s true contribution to success.
8. Importance-Performance Analysis (IPA)
IPA maps customer satisfaction against the importance of product/service attributes. It helps identify strengths, weaknesses, and improvement priorities by analyzing performance gaps. The results are visualized on a 2D grid guiding marketing and operational strategy.
9. Regression-Based Prediction Models
Regression models (especially multiple regression) identify which variables influence outcomes like sales, loyalty, or customer satisfaction. These models support marketing decision-making by isolating the factors with the greatest predictive power, such as ad exposure or sales rep quality.
10. Emotional Experience Index (EXI)
Developed by rotem ar, EXI measures customers' emotional journey with a brand, going beyond traditional metrics. It provides a score (0–100), identifies key emotions, and segments customers emotionally. AI-powered insights suggest actionable improvements, enhancing loyalty and customer-centric strategy.