Two of our biggest strengths at B2B International are our commitment to both customise and add value to the quantitative market research data we collect as part of our research studies.

What we offer

We dig deeper and go further with the data we collect in order to provide real valuable insights for our clients. Our research experience and know-how help us to understand and select the most appropriate analytical techniques for every research project we carry out.

To find out more about the different types of analytical techniques available, and how they bring value to each research study, take a look at the descriptions below.

Brand Mapping

Brand mapping is most often used in brand research, and involves placing product and service attributes on one map and interpreting them simultaneously. For example, a brand is most strongly associated with those attributes placed nearby on the map. If products are placed close to each other, it suggests they hold a similar position within the market.

CHAID Analysis

CHAID analysis is most often used to understand the different characteristics of the most and least satisfied, or interested, customers or employees. For example, CHAID analysis could be used by clients to target potential customers more efficiently, such as in direct marketing to identify customers who have reacted to a specific campaign.

Cluster Analysis

Cluster analysis is most often used in segmentation research and is designed to reveal natural groupings within large observations, such as segmenting a survey sample of respondents or companies into smaller groups. For example, looking at the answers respondents provide can help to describe and quantify customer segments, enabling the client to target specific customers according to their needs, rather than having one general marketing strategy.

Conjoint Analysis

Conjoint analysis is most often used in new or improved product development research and is used to measure respondent preferences on a number of different attributes for a product or service. For example, conjoint analysis can help to determine the relative importance of each product/service attribute (e.g. price, specific features, brand) as well as the levels at which each attribute is preferred to another (e.g. how much is a price of £200 more preferred than a price of £400).

Correlation Analysis

Correlation analysis is more often used in customer satisfaction and employee satisfaction research and is used to identify the factors that satisfy your customers or employees and what keeps them loyal. For example, correlation analysis can help to determine which factors contribute most to overall customer/employee satisfaction or loyalty levels.

Discriminant Analysis

Discriminant analysis is used to identify the differences between two or more groups based on different characteristics. For example, it can explain why respondents belong to certain groups and also classify new respondents based on the scores they give. Discriminant analysis can therefore be used to answer questions such as: which customers are likely to buy certain products; whether or not a bank should offer a loan to a new company; and to identify which patients may be at high risk for medical problems.

Factor Analysis

Factor analysis is used to describe a large number of different variables or questions by using a reduced set of underlying variables, called factors. For example, in customer satisfaction studies it is used to determine underlying service dimensions, and in profiling studies it can be used to identify core attitudes.


MAXDIFF analysis is used to quantify the importance of different product and service benefits on the basis that not all of them can be offered to everyone, all of the time. For example, as choices are often driven by several factors such as brand, product/service features and price, MAXDIFF aims to understand the importance of each specific feature and determine the differences between the key drivers of decision-making.

Multidimensional Scaling

Multidimensional Scaling (MDS) is an alternative to factor analysis and is used to identify meaningful underlying dimensions that can explain any similarities or differences between objects. For example, respondents in b2b markets may say there is little difference between products and suppliers, but multidimensional scaling can be used to detect whether this is true or not. Particularly useful in brand research, this type of analysis can help to assess the positioning of a brand and determine whether any work needs doing to differentiate the brand from its competitors.