Complex Analysis
This section provides more detail on each of the data visuals presented in the Complex Analysis section of the Analytics tab for Attitude-X studies. Complex analysis is not currently available for custom studies.
Attitude-X now offers two forms of Know Your Customer studies - the complete version and a short form. Differences between the two forms are described in this section.
Customer Preferences
Analysis of the customer preference questions is broken into two sections:
- Business attributes affecting Customer choices; and
- Relative business attribute detail.
Customer preference analysis is based on a customer selecting their preference based on specific combinations of attributes and attribute options (levels). It is designed to mimic real-world choices where a combination of business attributes are presented to customers. The analysis returns the preference of each attribute plus the relative importance of each option included within a specific attribute.
This type of analysis can offer powerful insights into customer preferences, pricing decisions and more, which can help business owners to better understand how to target product and service offerings to customers to align with customer expectations.
This analysis is considered a partial sample design - not every combination of business attributes and options is tested. This is done to manage the size of the Attitude-X study while still allowing indicative response data. Analysis presented in Attitude-X is designed to be considered together with other analysis included in a study. It shouldn’t be considered in isolation from other inputs as it may not accurately represent the opinions of study respondents.
Business attributes affecting Customer choices
The business attributes referred to in this analysis are core business pillars for businesses for which the Attitude-X study is prepared. The business attribute analysis is based on responses from customers completing the Attitude-X study.
The Business attributes affecting Customer choices displays a horizontal bar chart with each bar representing a business attribute (core business pillar). Each bar shows the relative importance of business attribute compared to the other attributes included in the data visual. The longer the bar, the greater the relative importance based on customer responses. The number shown in the bar represents the relative importance. This means a business attribute with an importance of twenty is twice as important as a business attribute with an importance of ten. The number shown in the bar represents the measure of relative importance and all numbers should add up to 100% (forgiving any rounding errors).
In the example below, the light blue and purple business attributes are the most important attributes of a business through the eyes of the customer:
Interpreting the Analysis
There are several ways of reviewing the data included in the analysis:
- Measure of Importance: Business attributes that show higher relative importance scores will likely have a stronger influence on customers’ decision making.
- Comparison of Options: Business attributes that show higher relative importance scores may be considered as “must-have” or “deal-breaker” attributes required for customers to continue to visit a business.
Relative business attribute detail
The detail shown in the second part of the Customer Preferences visuals represents the relative value customers place on each level of an attribute. The higher the score, the higher the customer preference for a particular level within an attribute.
In the example below, the blue level shows a strong positive preference from customers. The blue level represents a strong preference toward the $50.00 - $80.00 level in the Price per Person attribute. It can also be seen that Coffee and Cake style food options is the least preferred Food Style Options attribute levels and Bar-style Seating is also not a preference for customers at this venue:
Interpreting the Analysis
There are several ways of reviewing this visual:
- Measure of Preferences: Levels of a business attribute are shown as positive and negative scores. A positive score indicates a specific level of a business attribute is preferred by customers, while a negative score indicates a lower preference for a particular business attribute level.
- Magnitude of Preferences: Level values within business attributes show the most and least preferred levels, and also how positive and negative the preferences are within each business attribute. Level preferences can be compared directly to each other within a business attribute. Level preferences cannot be directly compared between business attributes as each attribute is scaled differently.
Customer likes and dislikes
Customer likes and dislikes is another way of asking customers make a decision relating to the most and least important items in a list. By providing a number of lists, each with a different combination of elements, customers will provide data to create a preference ranking of those elements.
Customer likes and dislikes is similar to the Customer Preferences analysis and both should be reviewed together to get a more complete picture of customer preferences. Customer likes and dislikes asks customers to make a judgement about what is the most and least important element in a list. Unlike Customer preferences (where options are selected as a group), customers respond based on choosing singular options. This approach helps to establish clearer preferences and priorities while reducing bias and response style differences.
Customer likes and dislikes and Customer preference analysis may not always align. There are several reasons for this including the number of options presented, the way questions are interpreted, and the choice of options within each question. Nevertheless, together, they provide a more complete picture of customer preferences in terms of core business pillars which supports important data-driven decision making by business owners.
In the example below, the Number of items on the menu, Friendly and helpful staff, and Reasonable prices are more important to this business’s customers than a Diverse food or drink selection and a Variety of seating options:
The text-based table included in the above visual represents the data in another way but also shows the comparable differences between Like and Dislike in the customer responses.
Interpreting Customer likes and dislikes
Customer likes and dislikes represent relative preferences - scores represent how each element compares to other elements in the list rather than absolute measures of importance. Elements with increasing prefereneces over time may reflect shifting priorities or emerging trends. Decreasing relative preference scores may suggest a trend of losing favour or interest.
Attitude-X uses this method of analysis to reduce the chance of data being skewed by responders. For this reason Customer likes and dislikes can be relied on to show areas of focus for new strategic thinking. By combining the analyis of Customer likes and dislikes with other data included in Know Your Customer Analysis, it is easier to make informed decisions for change.
Customer Segmentation
Customer Segmentation groups a business’s customers based on things they have in common. By sorting customers into categories, it is possible to better understand needs and preferences, and tailor products and services to reflect the population of customers in each group. In Attitude-X, customers are grouped using the following categories:
- Age - understanding whether customers are younger or older and the relative size of the age groups.
- Work Commitments - how many hours customers are at work per week to contribute to analysis on different peak busy times throughout the week.
- Social Media Usage - contributing to analysis of social media as a method of interacting with customers.
- Employment Status - more data to help understand possible peak and low busy times throughout the week. Also useful to identify opportunities to offer unique products and services specifically targeted at a particular customer group.
- Number of children - understanding how best to deliver a range of products and services that accommodate both children and adults.
- Location - how close customers live to the business to determine if “surprise” specials may be a way to excite customers to visit a business at short notice. This data also contributes to determining how to market offerings to nearby customers.
- Transport Options - understanding how to accommodate a variety of transport options for customers.
- Relationship status - understanding how to create interest or activity for special events for customers.
Interpreting Customer Segmentation
The number of members in a group is described as a percentage of all members of all groups (based on customer responses). The simplest analysis is to understand the proportional size of a group in relation to other groups of the same type. If this information is not already known, it is a great way to start to assess the current relevance of a business’s products and services to its customer segmentation analysis. Over time, the percentage of group members in any particular group may change. Tracking and understanding how and when segmentation group percentages change over time is one way to ensure a business remains current and relevant to its customers. Understanding and reacting to changes in segmentation group data can also contribute to keeping customer retention high.
Customer segmentation can help to identify new target market opportunities. Analysis of customer responses may identify a new or different group of customers that could be targeted with specific offerings of products and services which may assist to grow new customers. By understanding customer segmentation together with customer preferences, products and services can be fine-tuned to perfectly match what each group values. It’s all about attracting more customers and giving them exactly what they want.
Monthly Spend Analysis
Monthly Spend Analysis is the process of understanding how customers believe they spend money with a small business every month. This is important to the small business to assist with identifying customer trends and spending patterns. By understanding spend analysis from the customer perspective, small businesses can build stronger customer relationships, react to downward trends to maximize revenue, and make smarter decisions to grow their business.
In the example below, the “line of best fit” shows a very positive trend in customer monthly spend response data. By tracking this data regularly, a business owner will notice a change in the trend of customer spend estimates enabling a timely response and ensuring it doesn’t continue for an extended period of time.
Interpreting Monthly Spend Analysis
Estimated monthly spend data shows how much customers are likely to spend with a small business every month. It highlights an important trend and gives a clearer picture of customer purchasing habits - helping to predict future sales and plan ahead. It is a tool that helps a business owner understand the impact of pricing and marketing on customers, and to see the effect of any new ideas are having on encouraging customers to spend more with any business.
When estimated monthly spend begins to trend downwards, check external factors such as seasonal changes, external events or economic conditions. If changes are outside a small business owner’s control, adjust expenditure and expectations accordingly. If changes in external factors are within a small business owner’s control, make changes as necessary to address. Changes might include focusing on customer service and making products and services more appealing, considering a change in marketing campaigns, introducing new products or services and/or reviewing product and service pricing.
Factors important to business (Long form only)
Factors important to business is a way to uncover hidden patterns in customer responses. It looks at how different questions are related and groups them into broader themes or categories. For example, if customers respond to several questions about price, quality, and value in similar ways, this may relate to responders relating to an overall theme such as “product satisfaction.” It helps the business owner understand what’s really driving people’s responses without needing to analyze every question individually.
In the example below, the items with the highest and lowest score in the column labeled Factor 1 can be reviewed to establish a “theme” or similarity between the questions with the highest and lowest scores. Lower numbers represent less importance in the eyes of the customer and can be considered less “front-of-mind” when customers responded. A similar approach is taken for the scores in the column labeled Factor 2.
Interpreting Factors important to business (Long form only)
Factors are things that can’t be identified and measured directly. Factors are themes that are based on understanding whether there is a correlation between customer responses to each type of question presented in the study. When customers respond in a similar way to specific questions, there may be a theme or correlation between question responses to be explored. As the number of responses grows, themes will become more readily identifiable.
Factors important to business uses two types of questions to help to understand how customers think when they wish to spend money in a business. The questions try to capture whether a customer spends money based on how they feel or how they think. Customers may spend money because they feel comfortable in an environment, or because they have a positive opinion about aspects of a business.
Factors important to business are useful when used together with Customer Attributes and Response Analysis by providing further or complementary detail to identified themes.
Customer Attributes (Long form only)
Customer attributes is a way to find out whether which attributes are related or connected between customers, and if so, how strong that connection is. For example, customers of the same age may have the same business visiting habits, or customers with children will be more forgiving of a bad experience with a business.
A positive correlation means that as one thing increases, the other does too (like warmer weather and ice cream sales). A negative correlation means that as one thing increases, the other decreases (like fuel prices going up and fewer road trips being taken). If there’s no correlation, it means the two things aren’t related. Understanding more about customer attributes helps to spot patterns and make smarter business decisions!
Interpreting Customer Attributes
Understanding customer attribute analysis is like finding patterns in relationships between two things. It helps to see if changes in one thing are linked to changes in another. For example, customers who visit a business more often tend to spend more. The strength of the correlation between customer attributes is shown by the size and colour of the circle. The larger the circle (and the more blue in the colour), the stronger the relationship or link between the responses to questions.
In the example above, there is a strong link between the age of the customer and the time customers visit (the blue circle with a value of 0.808). When looking back at the study Response Analysis we can see the customers to this business are of older age and prefer to visit later in the morning rather than early in the day. Understanding these types of behavioural patterns helps to target products and services to specific audiences at specific times.