Zetech University Library - Online Catalog

Mobile: +254-705278678

Whatsapp: +254-706622557

Feedback/Complaints/Suggestions

library@zetech.ac.ke

Amazon cover image
Image from Amazon.com
Image from Google Jackets
Image from OpenLibrary

Marketing analytics: a practical guide to improving consumer insights using data techniques/ Mike grigsby.

By: Material type: TextTextPublication details: London: Kogan Page, c2018.Edition: 2nd edDescription: x, 217p.: ill.; 23 cmISBN:
  • 9780749482169
LOC classification:
  • HF5415. 2 .G75 2018
Contents:
A brief statistics review -- Brief principles of consumer behaviour marketing strategy -- What is an insight? -- What drives demand? Modelling dependent variable techniques -- Who is most likely to buy and how do I target them? -- When are my customers likely to buy? -- Panel regression - How to use a cross-sectional time-series -- Systems of equations for modelling dependent variable techniques -- What does my ( Customer) market look like? Modelling inter-relationship techniques -- Segmentation - tools and techniques -- Statistical testing - how do I know what works? -- Impementing big data and big data analytics -- The finale - what should you take away from this?
Tags from this library: No tags from this library for this title. Log in to add tags.
Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode
Books Books Zetech Library - TRC General Stacks Non-fiction HF 5415.2 .G75 2018 (Browse shelf(Opens below)) C1. Available Z009542

Includes indexes.

A brief statistics review -- Brief principles of consumer behaviour marketing strategy -- What is an insight? -- What drives demand? Modelling dependent variable techniques -- Who is most likely to buy and how do I target them? -- When are my customers likely to buy? -- Panel regression - How to use a cross-sectional time-series -- Systems of equations for modelling dependent variable techniques -- What does my ( Customer) market look like? Modelling inter-relationship techniques -- Segmentation - tools and techniques -- Statistical testing - how do I know what works? -- Impementing big data and big data analytics -- The finale - what should you take away from this?

There are no comments on this title.

to post a comment.