Marketing Strategy and Competitive Positioning pdf ebook

Figure 4.8 
Marketing decision 
support systems
Marketing decision-maker
Managerial interface
Statistical
techniques
Market
models
Raw data
Marketing research
interface
Marketing environment
Responses
Decisions

---

109
ORGANISING CUSTOMER INFORMATION
This is essentially their internal model of the price/sales relationship. In examining data 
through the use of statistical techniques, the analyst may wish to test out the model of the 
market he or she already has. Alternatively, the objective may be to build a new model of 
the market to help managerial understanding of the forces that affect demand and overall 
company performance. There are models covering all parts of marketing activity. Lilien
et al. (1992) undertook a comprehensive review of these attempts ‘to bring order to the 
chaos of collected facts’.
Marketing analytics enables marketers to evaluate their marketing initiatives by the sys-
tematic use of processes and technologies. It is seen as a way to measure ROI and is used for 
a variety of purposes, from customer acquisition to omnichannel marketing and marketing 
mix strategies (see Sorger, 2013). Artificial intelligence is used more and more to enhance 
marketing analytics. For example, predictive analysis can be used to predict purchasing 
patterns and deep learning (through learning to recognise sophisticated objects) in order to 
understand customer brand perception and usage through images.
Currently, customer analytics (48 per cent), operational analytics (21 per cent), fraud and 
compliance (12 per cent), new product and service innovation (10 per cent) and enterprise 
data warehouse optimisation (10 per cent) are among the most popular Big Data uses in 
sales and marketing (Columbus, 2016).
Another example of the use of Big Data is customer value analytics (CVA), which enables 
marketers to deliver consistent customer experiences across all channels.
Big Data
Source: mamanamsai/Shutterstock.