Internal Secondary DATA AND Analytics PDF

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Summary

Notes from the course Introductory Marketing in English during course 2020-2021. They include both the slides and complementary comments....

Description

INTERNAL SECONDARY DATA AND ANALYTICS INTERNAL SECONDARY DATA What products customers buy? Which customers buy the most products? Are there any seasonal patterns of purchasing behaviour by product types and customer types? These are examples of data generated from invoices that could help to understand consumer behaviour. On the other hand, the researcher’s benefits of loyalty card and product scanning systems are the following: 

Profile of customers can be built up.

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One big laboratory.

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Refining the marketing process.

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Developing a clear understanding of gaps in the knowledge of consumers.

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Linking behavioural and attitudinal data.

GEODEMOGRAPHIC DATA ANALYSES At a base level, a geodemographic information system matches geographic information with demographic information. Thus, it allows subsequent data analyses to be presented on maps. It groups consumers together based on the types of neighbourhood in which they live. If a set of neighbourhoods are similar across a wide range of demographic measures, they will also offer similar potential across most products, brands, services and media.

CUSTOMER RELATIONSHIP MANAGEMENT It includes the processes involved in identification of profitable customers and/or customer groups and the cultivation of relationships with these customers. The aim of such processes is to build customer loyalty, raise retention rates and therefore, increase profitability. 

ESOMAR’s Basic Principles: Market Research shall be clearly distinguished and separated from non-research activities including any commercial activity directed at individual participants, such as advertising, sales promotion, direct marketing, direct selling…

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ESOMAR’s Global Market Research report 2010: addressing one of the biggest threats to the industry: as databases improve and become increasingly broad (big data), a growing amount of data collection needs to be non-anonymous and linked to customer databases- at the moment marketing research is not well placed to contribute to that process.

BIG DATA It describes a range of technological and commercial trends enabling the storage analysis of huge amounts of customer data. The commercial promise of big data is in the ability to generate valuable insights from collecting new types and volumes of data in ways that were not previously economically viable. The four dimensions of Big Data are the following: 1. Volume. 2. Velocity. 3. Variety. 4. Veracity. WEB ANALYTICS Web analytics is the process of collection, measurement and analysis of user activity on a website. When linked to other databases within and organization or integrated with a GIS or CRM system, this form of electronic observation can measure and model customers in ways that marketing research techniques may find impossible to replicate. Building consumer profiles from website events

Visitor profile database and methods of segmenting markets

CHALLENGES

IN

INTEGRATING

MARKETING RESEARCH, CUSTOMER ANALYTICS, SOCIAL MEDIA RESEARCH AND BUSINESS INTELLIGENCE

In this picture, we can seeapproaches to gaining consumer insight. However, there are some challenges regarding the following aspects: 

Data challenges: to source, gather, validate, store, integrate, feed, model, analyse and interpret internally and externally generated quantitative and qualitative data.

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Information systems challenges: to manage this array of data, develop and support emergent knowledge in an organization.

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Marketing challenges: to keep abreast of the nature and dynamism of what should be an organization’s target markets and performance in those markets.

Distinctive role of markeing research 1. It focus upon existing and potential customers. 2. Use quantitative and qualitative approaches to tap into a rich array of consumer attitudes, emotions and aspirations. 3. Tap into a rich array of sensory and experiential characteristics of consumers. 4. Draw on a well established and robust theorical base in measuring and understanding consumers. 5. Draw on well established and robust theoretical base in being representative and generalizing populations. 6. Demonstrate social responsibility through a long established, proactive and open code of conduct. Data mining The process of discovering meaningful correlations, patterns and trends by sifting through large amounts of data stored repositories, using pattern recognition as well as statistical and mathematical techniques. It aims to do the following: 

Classify customers into specific categories that are meaningful to decision-makers.

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Identify potential target markets that possess the characteristics that decision-makers seek.

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Forecast scales or the use of services.

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Discover which types of products or services are purchased together.

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Discover patterns and trends over time, such as “after graduation, students take a holiday”, and be able to show the probabilities associated with different holiday types....