In this glossary, the most important market research terms from this database and the textbooks are simply explained
CAPI stands for Computer-Assisted Personal Interviewing. It is a method of data collection in empirical social research in which interviews are conducted by trained interviewers, using a computer for assistance. The interviewer asks the questions using a computerized interface that displays the questions and records the responses directly into the system. CAPI offers the advantage that data are captured and stored digitally immediately, which facilitates data analysis. The choice between CAPI and PAPI depends on several factors, such as: 1. Resources: CAPI usually requires the provision of computers and related software for interviewers, while PAPI requires only paper and pens. 2. Complexity of questions: for complex questions that use complex response options or scales, CAPI may facilitate data collection because interviewers may need assistance navigating the questions and entering responses. 3. Time Frame: CAPI allows for more efficient data collection and faster data availability because responses are entered digitally directly. PAPI may require additional steps such as manually transferring the data into a digital form. 4. Privacy and data security: CAPI offers the ability to encrypt and secure the data directly, whereas with PAPI, the physical paper forms could potentially be stolen or lost.
CATI stands for Computer Assisted Telephone Interviewing. As the name suggests, this uses a computer-assisted system to conduct surveys by telephone. Computer assistance allows easier retrieval of more complex questionnaires with filter guidance. Telephone interviews are often used for surveys when regional control of random selection is possible via pre-selection, telephone numbers are available or the online population is not representative. Opinion research institutes in particular rely on a method mix of CAWI and CATI to create a representative sample.
CAVI, or Computer Assisted Visual Interviews, are a relatively new method of capturing immediate feedback from customers at the POS. Most commonly, customers can rate their satisfaction with a product or service by pressing a smileys on a touchscreen, or, after scanning a QR code, on their cell phone. This data can be monitored and analyzed in real time by staff to improve the customer experience. In addition to this function, CAVIs also have a customer engagement effect.
CAWI stands for computer assisted web interviewing. Here, surveys are conducted online and participants can answer the questions on their computer or mobile device. Computer assistance allows easier querying of more complex questionnaires with filter guidance. CAWI is a fast and cost-effective method for conducting surveys with a large sample.
Charting refers to the graphical representation of data in the form of charts, line graphs, bar graphs, or pie charts. They are used to identify and visualize trends and patterns in data.
Cluster analysis groups similar cases based on their characteristics. It answers questions such as: Are there natural groupings of cases in the data? Which cases are most similar and which are most different? How can cases be grouped into different categories or clusters?
Coding involves analysing qualitative data (e.g. interview transcripts, open answers in surveys) and sorting them into previously defined codes or categories. Many coding providers listed in The MR Directory use AI tools that can summarise and code texts.
Conjoint analysis is a statistical technique used to study consumer preferences. It makes it possible to determine the relative importance of different features of a product and to deduce how consumers make their purchasing decisions. The results of conjoint analysis can help companies target their products and services, develop marketing strategies, set prices, and predict the success of new products. Typically, participants in the survey are presented with a series of product profiles that differ in their characteristics. Each profile consists of a combination of characteristic attributes that are combined to create different product variants. Participants are then asked to rate or rank the presented product profiles. Depending on the specific method of conjoint analysis, different question types can be used: 1. rating task: participants rate each presented product profile on a scale to indicate how preferred it is compared to other profiles. 2. ranking task: participants rank the presented product profiles in order of preference. 3. choice-based task: participants are presented with different product profiles to choose from and must select the one they prefer. By evaluating the ratings or rankings given, conjoint analysis can answer several questions, including: 1. importance of product features: Which features have the greatest influence on consumers’ decisions? 2. Preference for specific feature attributes: Which specific feature attributes are preferred? 3. price acceptance: how much are consumers willing to pay for specific feature combinations? 4. market share estimates: How likely are different product variants to be chosen by consumers?
A correlation analysis is a statistical method used to examine the relationship between two or more variables. It measures the strength and direction of the linear relationship between variables. Correlation analysis provides information about how much changes in one variable affect another variable. The correlation coefficient varies between -1 and 1. A value close to -1 or 1 indicates a strong linear relationship, while a value close to 0 indicates a weak relationship.
CX Insights Platforms
CX Insights platforms are software solutions designed specifically for customer experience management. They collect, analyze, and visualize data to help companies gain insights into their customers’ experiences and identify opportunities for improvement. These platforms combine multiple data sources, such as surveys, social media feedback, customer reviews and behavioral data, to provide a comprehensive picture of customer perceptions. CX Insights platforms measure a variety of metrics to assess customer satisfaction and the quality of the customer experience. Some of the most common metrics are: 1. Net Promoter Score (NPS): NPS measures how likely customers are to recommend a company to others. It is based on a single question, “On a scale of 0 to 10, how likely are you to recommend our company/product/service to a friend or colleague?” 2. customer satisfaction (CSAT): CSAT measures customer satisfaction with a specific aspect of the customer experience. It is often determined by asking: “How satisfied are you with [product/service/support]?”. 3. customer loyalty: This metric assesses how loyal customers are to a company and how likely they are to purchase or do business with the company again. CX Insights platforms can answer several questions to improve the customer experience. Here are some examples: 1. What factors most influence customer satisfaction? 2. What is the NPS for different customer segments and how can it be improved? 3. What product features are most important to customers and how well do we meet their expectations? 4. How do customers react to specific marketing campaigns or product changes? 5. What complaints or problems do customers often have and how can they be resolved? 6. How good is customer service and how can processes be optimized to ensure a better customer experience? These platforms provide companies with the ability to collect data, analyze it, and derive actions to improve the customer experience, build customer loyalty, and ultimately drive business success.