Yonder Data Solutions is a trading name of Yonder Consulting Limited which is registered in England No. 4153928. Registered Office: Northburgh House, 10 Northburgh Street, London EC1V 0AT
Our analytics team, led by Karsten Shaw, provides expert advice and consultation on advanced analytical methodologies and allows us to offer a seamless and integrated solution with our data collection services.
We ensure that the statistical outputs we provide are in plain English and are commercially focussed, making them easy to communicate with stakeholders and integrate into meaningful insights.
Please click on the below to view background information on these techniques and to understand the business questions they answer.
Driver modelling uses varied range of techniques, from correlation to regression models or prediction trees (CHAID) in order to investigate the relationship between brand attributes and performance.
This analysis is a powerful tool which helps brands describe, diagnose or predict their most important KPIs and is packaged in a simple easy-to-understand deliverable.
Segmentation analysis uses a varied range of techniques, from factor analysis to various forms of cluster analysis in order to divide the market of potential customers into groups, or segments. The segments created are composed of customers that respond similarly to marketing strategies and share similar traits such as attitudes, interests or location.
This analysis is a great tool for understanding your customers and knowing how to best target them.
Conjoint analysis is a statistical technique used to determine how customers value different attributes that make up an individual product or service. The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on customer’s likelihood to purchase the product.
Conjoint is a great tool for testing current products against competitors, or against future own products.
MaxDiff, short for Maximum Difference Scaling, is an analysis based on choice, where customers engage in a trade-off exercise regarding different pairs of product attributes or features. This analysis allows us to determine how customers evaluate all possible pairs of attributes within the displayed set and choose the one that reflects the maximum preference or importance to them.
The outcome of this analysis allows for an absolute ranking of importance, preference, relevance, etc. of attributes.
TURF analysis, short for Total Unduplicated Reach and Frequency, is a type of analysis used in order to find the best mixture of features or messages that will reach the widest possible audience or customers.
TURF is a simple tool but can be very powerful and should be used to compliment other measures of gauging appeal in order to understand how attributes work together.
Media Effectiveness modelling is not a single technique, rather it refers to a range of approaches developed by Yonder which aim to measure the impact of advertising and marketing activity.
These approaches use aggregate information about media spend along with aggregate reads of KPIs to detect trends and be able to determine the impact of multiple channels on those KPIs. Other information can also be incorporated such as economic data and product level data such as pricing.
Correspondence mapping is a data reduction technique, allowing simple interpretation of the market trends by using visual two-dimensional quadrant mapping.
This analysis aims to identify and describe links or ‘correspondence’ between different statements and brands, which can then be mapped.
Pricing models comprise different techniques suitable for new or existing products or services. For new products, pricing scenarios can be pre-defined by the client or acquired using a Conjoint design. However, simpler techniques such as Gabor Granger and Van Westendorp are also used to understand fundamental responses to different prices of products and services.
We align this to likelihood of purchase of customers and compare with product demand and revenue estimates in order to determine the optimum price of product delivery, whilst ensuring maximum revenue.
Data fusion is not a statistical technique, rather it refers to a range of different ways in which we can align internal company data with survey metrics, in order to provide a more comprehensive picture of the results.
Internal data sources that we can work with include financial data, transactional data, HR data, training data. It could also include things such as dates for store refurbishment or service changes. These are then fused with information from survey data.