Data Analytics

anelvr
Mind Map by anelvr, updated 11 months ago
anelvr
Created by anelvr about 4 years ago
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Description

Post Grad ELM Mind Map on Data Analytics, created by anelvr on 11/04/2015.
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Resource summary

Data Analytics
1 Concepts
1.1 Perf. monitoring & Trends
1.1.1 Monitor user beh. + mkt campaign perf.
1.1.2 Only clear pic if presented in context
1.2 Big Data
1.2.1 Massive data sets, require specialised software & massive PCs to process
1.2.2 Measure trends not absolute figures
1.3 Data Mining
1.3.1 Find patterns hidden in lrg numbers & databases
1.3.2 Auto comp program
1.4 A world of data
1.4.1 Variety of sources
1.4.2 eg online data: social media, emails, forums
1.5 Databases
1.5.1 CRM info
1.5.2 Loyalty progr.
1.6 Software data
1.6.1 Some browsers gather info
1.6.2 Habits • Crashes • Problems
1.7 App store data
1.7.1 Way people download / pay for / use
1.7.2 EG Google, Google app store
1.8 Offline data
1.8.1 POS records
1.8.2 Cust. service logs
1.8.3 In person surveys
1.8.4 In store foot traffic
2 Objectives
2.1 Align with strategic outcome of B
2.2 "What want to achieve with mkt campaign"
2.3 Eg: Increase sales, web traffic, brand awareness
2.4 To optimize website & e-mkt
2.5 NB: to ID unique visitors
3 Goal
3.1 Action user takes
3.2 Type of user beh.
3.3 Eg: Purchase, view # pages
3.4 Completed goal = conversion
3.5 Derives from obj.
3.6 "What need user to do to achieve obj."
4 KPI
4.1 Metric indicate whether obj been met
4.2 "What data need to see if goals completed"
4.3 Eg. Obj to incr. web traffic, have to incr. # visitors, % new, how long stay on
5 Targets
5.1 Actual target KPI needs to meet to decline campaign success
5.2 Eg. newsletter sub, target 100 p/m
6 Funnel analysis
6.1 Path analysis: annalize each step
7 Events / micro conversions: Process of achieving ultimate goal broken down into steps
8 How info captured
8.1 Cookie-based tracking
8.1.1 Page tagging, info to 3rd party server eg Google Anal.
8.1.2 Can be used by comp.
8.1.3 Less accurate
8.1.4 Good level support
8.2 Server-based tracking
8.2.1 Log files - web servers - raw data readily available.
8.2.2 Comp must have access to server
8.2.3 Very accurate, record every lick & visits from SE spiders => SEO
8.2.4 Switch vendors still able to analyze
8.2.5 Often done in house
8.3 Universal analytics
8.3.1 New Google feature
8.3.2 Track people not just sessions
8.3.3 How beh. depend on device
8.3.4 How beh. changes the longer fan of brand
8.3.5 How often interacting with brand
8.3.6 Lifetime value & engagement
8.3.7 Allows import data from other sources into Google Anal.
9 TAO
9.1 Track Analyse Optimise
9.1.1 Key elements to analyse
9.1.1.1 Behaviours (Intent of visitors)
9.1.1.2 Outcomes (how many perf. intended goals)
9.1.1.3 UX (Patterns of user beh., how influence to achieve obj?)
9.1.1.4 Bounce Rate: do users engage?
9.1.1.5 Key words / phrases, Best?
9.1.1.6 Top content - what attract & keep
9.1.1.7 Geo. distr.
9.1.1.8 Traffic source?
10 Tracking Collecting Measuring Reporting Analyzing
10.1 Online data
10.1.1 Easy, Quick, Affordable
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