# Visualising Business Data: A Survey
## Secondary Data as a Business By-Product (BE):
### Customer Centric Literature
#### 3.3.1 Customer Behaviour (CB)
A dashboard approach is taken that visualises the geographical location of the business alongside the tweet feedback. The user can select what company to view, and then all of the stores from that company are marked on the map. The sentiment of the tweets about these stores are computer and displayed to the user through the colour of a marker on the map. This system enables the user to quickly find \"problem stores\" and then read the twitter feedback of the customers who have been there. User options provide filtering of customer demographics and competitor comparisons. Twitter data have often been used for sentiment and business analytics purposes [180; 181]; the novelty of this application is that it collects customer opinions of physical stores and visualises the sentiment behind them, removing the need for a review to be explicitly written. **Secondary Data as A Priori Database (CB):** This subsection contains research that derives and depicts customer behaviour from pre-existing secondary databases. [PERSON] et al. presented a method of conveying customer targeting data using a heat map [64]. This method depicts value distribution across customer needs and characteristics which helps the planning of a customer-oriented business strategy. A pre-existing database appears to have been used for this research. A customer need is interpreted as any voiced opinion about a product. This can be represented linearly by taking the volume of expressed opinions without a positive or negative correlation. The customer characteristic refers to a linear scale that can quantify a customer. Using a number of metrics, customers are placed on a single sliding scale. The customer map is drawn on a 2D plane with one axis representing a key customer characteristic and the other a key customer need. Each customer will occupy their own space on the \(x\)-\(y\) grid and therefore a heat map can be created to see where the customers fall. Clusters of customers can be identified as well as trends in the data. Customer segmentation visualisation methods previously use neural networks to draw a self-organising map [182]. [PERSON] suggested a framework that emphasises the importance of segment-based target marketing as calculated by visually inspecting profit curves [183]. [PERSON] and [PERSON] presented methods for displaying market segmentation data to inform marketing strategies [65]. Utilising a pre-existing database, clustering techniques alongside self-organising maps are used to segment customers into behavioural demographics. Each cell of the SoM grid is coloured using a combination of RGB values which are derived from the attributes associated with the customer such as education level or income. These customer nodes are arranged into clusters, isolating groups of similar purchasers. This segmentation enables the user to see distinct groups of customers and the motivation behind their purchases. The self-organised map was introduced by [PERSON] [184] in 1981 and is used to present data of high dimensionality. The maps have previously been used to present business-related data [185]. The self-organising map was more recently explored by [PERSON] et al. [66] who continued this area of research by visualising customer segmentation data, as opposed to market segmentation data. The dataset that was used contained over 100k members of a Japanese supermarket loyalty scheme. The SoM clusters loyalty club members into unique categories which can be used to more effectively market selected products to the different groups. [PERSON] et al. presented \"TelCoVis\", a visual analytics system that highlights behavioural patterns in potential customers through data obtained from China's largest telecommunications company [67]. This system focuses on co-occurrence (people from two regions visiting the same urban space during the same time span). See Figure 23 on page 23. They used two visual designs to convey this data.