Dim lecture 5 2 dim analytics website blog and mobile apps 2018

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DIM Analytics: Website (microsite) / blog and mobile apps Digital (Interactive) Marketing (Lecture 5.2) Dr Elvira Bolat C113, Christchurch House, Talbot campus ebolat@bournemouth.ac.uk @dimbsbu

https://dimbsbu.wordpress.com


Learning Outcomes • To explore key digital metrics related to measuring a website (microsite) effectiveness • To explore key digital metrics related to measuring a blog effectiveness • To overview digital metrics related to measuring a mobile web effectiveness


“You can’t manage what you can’t measure”

(Peter Drucker 1960s)


Objectives determine Tactics Tactics determine Metrics (sMart) Metrics determine the analytics (tools)


What can you measure? and where?


Key questions in evaluating process, metrics and tools for improving the contribution of digital marketing within an organisation

Chaffey and Ellis-Chadwick (2016), p. 550


The performance measurement process: summary

Chaffey and Ellis-Chadwick (2016), p. 355


The five diagnostic categories for digital marketing measurement

Chaffey and Ellis-Chadwick (2016), p. 357


(Digital Analytics Association 2014)


Different types of data within a performance management system for Internet marketing

Chaffey and Ellis-Chadwick (2016), p. 369


Main categories of Metrics COUNT = single number

RATIO = a count / a count (‘per’) KPI

DIMENSION


Main categories of Metrics AGGREGATED SEGMENTED INDIVIDUAL


Key definitions


(a) Channel promotion metrics • Traffic drivers and referrals (where do people visiting your website/blog come from) • Percentage of all referrals • CPA (cost per acquisition) • Contribution to sales (or alternatives)


Google Analytics will create Tracking ID (code) to be embedded within every single web page





(b) Channel buyer behaviour

Pages and Visits Understand audience!

Unique visitors = number of distinctive people visiting your website/blog in a day. (using IP or tracking cookies)

Pageviews = number of total pages within the website that the total of visitors checked out during a visit Understand engagement!

A view = the loading of a page





Pages and Visits (cont.) Pages/visits = average number of pages that a person checks during one visit Understand engagement!


average number of pages that a person checks during one visit = 29/15 = 1.9



Pages and Visits (cont.)

Bounce rate* = percentage of Understand engagement! people that leave immediately after arriving at your website

*for e-commerce websites the bounce rate is low whereas for blogs - high



Pages and Visits (cont.) Engagement rate (%) = (100 – bounce rate (%))


Pages and Visits (cont.)

Repeats Understand engagement!

= visitor sessions / visitors


What else? (b) Channel buyer behaviour


Other metrics to consider: Understand engagement!

Understand audience!

• Time spent on website/blog • New visitors / returning visitors • Landing pages (entry) / exit pages Understand engagement!




Examples of different measures of visitor volume to a website

PPV – pages per visit VPV – visits per unique visitor Chaffey and Ellis-Chadwick (2016), p. 361


Source: Chaffey and Eliis-Chadwick, 2012. Digital marketing, p.469


Summary: Analytics – key metrics and dimensions

Read more: http://mashable.com/2012/07/27/marketing-metrics/


• Audience and Behaviour dimensions – clickstream analysis and visitor segmentation • Clickstream – (1) pathway analysis (2) on-site search effectiveness • Visitor segmentation


(C) Channel outcomes - conversion • Registration on website / subscription to newsletters • A sale on website • A download • Request further information



Conversion rate = (1) Visitors (5000) to purchase (100) = 2% (100/5000*100%) (2) Visitors (100) to registration (20) = 20% (20/100*100%)


Conversion rate = Reach (1) Ad impressions (2) Click-through rate


Conversions and conversion attribution



What about Mobile Apps? • Marketing • Analytics


Mobile Apps: metrics that matter • • • • • • • • •

Number of downloads and installations Number of users Lifetime value Retention rate Active users Session length ARPU Cohort analysis Virality (e.g. Candy Crush)


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