Surveys can be a rich source of information, including not only factual questions, but asking about attitudes, behaviours, and activities.
Factor analysis is a statistical technique that combines questions that are related (correlated) into a smaller number of factors, to create more robust measures.
In this blog we show factor analysis in action.
Cumulative gains and lift curves are a simple and useful approach to understand what returns you are likely to get from running a marketing campaign and how many customers you should contact, based on targeting the most promising customers using a predictive model.
read moreMost statistical analyses assume that the data collected are from a simple random sample of the population of interest. However, it’s not always possible or practical to take a SRS, and complex samples can be used to create more efficient or cheaper sampling designs.
read moreIn this latest blog post in our Market Research terminology series, we discuss CHAID - an algorithm that is useful when looking for patterns in datasets with lots of categorical variables and that offers a convenient way of visualising relationships in the data.
read moreCustomer segmentation breaks down large groups of current and/or potential customers in a given market into smaller groups that are “similar” in terms of their preferences or characteristics. This allows you to adopt a different marketing mix for each segment of the market.
read moreMaxDiff or Best-Worst Scaling is a survey method that is used to try to gain an understanding of consumers’ likes and dislikes. The goal is to rank product attributes in terms of their importance to customers, so that comparisons and trade-offs between them can be made.
read moreIt’s important to identify and understand the drivers of key business outcomes. You might want to understand which aspects of your service influence how likely a customer will be to recommend you, for example. A so called key driver analysis can be used to address this sort of question.
read moreIn a previous blog post, we discussed how supermarkets use data to better understand consumer needs and, ultimately, increase their overall spend. One of the key techniques used by the large retailers is called Market Basket Analysis (MBA), which uncovers associations between products by looking for combinations of products that frequently co-occur in transactions.
read moreClassification means assigning an outcome to an individual or case, usually for the purpose of making a decision. Examples include predicting which individuals will default on personal debt to decide who could be offered a credit card, or predicting which visitors to a retail website will make a purchase. In this blog we will discuss some ways of measuring the performance of a classification model.
read moreWhen conducting research about your customers, patients or products, it's crucial to consider what effect your sample size will have on how valid and reliable your conclusions will be. Larger sample sizes give more reliable results with greater precision and power, but they also cost more time and money.
read moreRetailers have been finding more and more sophisticated ways to use customer data to their advantage. By collecting and analysing consumer data, together with other socio-economic data, supermarkets are able to make evidence-based decisions when devising their marketing and operational strategies.
read moreInteracting with social media is a key element of any strategy for businesses wishing both to better understand and interact with their customers. But how can you measure the success of a marketing campaign on Twitter?
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