Electronic Commerce

Semester 2 Term 2 Lecture 24

Technology & Marketing Strategy - Historically

• Historically, marketing managers have developed marketing strategy using the 4Ps: product, price, promotion and place

• Strategy was developed by focusing on individual products and their associated metrics (market share, product performance and market penetration)

Technology & Marketing Strategy - Today

• Today, increasingly customised products and services, new distribution and communication channels, and multiple pricing options are making the marketing process more complex and difficult to manage

• Also, the vast amounts of detailed real-time data that must be collected and analysed to make effective decisions in this environment will require new ways of building information systems

Emerging Questions

• How can technology help marketing to respond to challenges such as global competition, rising customer expectations, and emerging markets?

• What architectures, tools and applications will be ideal for technology-enabled marketing?

• What impact will information availability have on marketing strategy and decision-making processes?

More Emerging Questions

• Entering new markets is time consuming, risky and expensive. How can technology help firms expand tinto new markts at a low cost?

• What cost-effective strategices can organisations use to market and advertise effectively?

• To answer these questions, marketing organisations are increasingly looking at new applications of technoogy in developing marketing strategy

Marketing Decision Support Systems

• Sharper communication means that there is a business imperative to make decisions quickly

• Product innovations and marketing strategies are quickly copied, so the ability to respond quickly to a changing market becomes the crucial determinant of both profitability and survival

• To do this, marketing managers need information that is timely, accurate, and relevant, and that contains the right level of detail

Data Mining & Decision Support

• The architecture that has emerged as an industry solution for marketing decision support is data mining

• The most successful data mining solutions are tuned to deliver optimum performance for knowledge workers – easy data access and manipulation – enabling them to use data creatively

Data Mining Compared to Operational Systems

• Operational systems such as relational databases and online transaction processing (OLTP) systems, are typically tuned for repetitive, continual updating with each task typically using very small amounts of data

Characteristics of the Data in a Data Warehouse

• The data is subject oriented – organised according to the different ways the managers view it, not for a specific function, process or application

• When data resides in many separate sources, encoding is usually insonsistent, so when it is transferred to the data warehouds, it must be integrated through a consistent naming convention

• The data must be time-variant – collected and organised consistently over time for use in comparisons, identifying trends and forecasting

• The data is nonvolatile, it is not updated or changed only reloaded and accessed