Web Analytics: Lecture Presentation. Covers All Content on Mobile Retention Dashboards for Mobile Marketing (123 Pages)

Document Content and Description Below

Topics Mobile Marketing How to measure it Retention Metrics Creating Dashboards Putting it all together Measuring traffic in the mobile ecosystem Geo targeting is what’s new – It know... s where you are Google and Facebook have majored on location for some years How the idea of a phone has evolved Mobile payment is now the default for many transactions Tap Summary Contradictions between what we want and how we behave How does mobile change behaviours 9 things to consider It’s grown quickly by enabling the sharing economy via mobile apps creating & sharing content and tracking user trajectory. And changes classic e - commerce businesses like ours. Payments at shows. It allows contextualisation of offers into ACTUAL relationship management Why is the customer there? What value are they looking for? The primacy of messaging - 98 percent of text messages are read within 90 seconds of delivery, Why Geography Matters Geo fencing and geo targeting – beacons and GPRS Discount and Distance – Ghose’s experiments His work on offers in store The secret of timeliness If we test in hour long chunks what do we find? Triggering the urge to buy The effect of intrusion. It depends on where we are in the AIDA sequence Salience - How do we grab their attention Crowdedness Trajectory - knowing where you’ve been, where you’re going and why The art is to get permission. If you have it and don’t abuse it then you have the customer Why are trajectory based offers so powerful? The future of mobile advertising depends on a bargain that consumers and firms need to strike with each other. The data mining approach Tracking issues for mobile devices Probabilistic matching with nonpersonally identifiable information: Deterministic matching with personally identifiable information: App registration becomes the gold standard More information here So what should we be measuring? Customer Retention is a key metric The basis of CRM and data mining analysis From Marketing Metrics: The Manager's Guide to Measuring Marketing Performance, 3/e by Paul Farris, Neil Bendle, Phillip E. Pfeifer and David J. Reibstein (0134085965) Copyright © 2016 Pearson Education, Inc. All rights reserved Not all customers earn you money CLV depends on customer perceived value The customer lifetime value is the value of everything the customer will ever buy less the costs of acquiring the customer and the annual maintenance costs discounted back to the present day. Another way of looking at it factoring in the churn Jeffery’s Template shows us how it works – I’ve put this onto blackboard What other retention metrics might there be https://www.userlike.com/en/blog/customer-retention-metrics Return on Marketing Investment Is what you’re doing worth it? How do we know the extra sales were due to our programme? When you start a business you have no customers so you have to buy them all usiness Disovery. Market Analysis to understand the business and impact of campaign or new product launch Base Case – define existing market sales, costs and net cash flows from current activity Sensitivity Analysis – vary the assumptions for best, worst and expected cases ROMI Analysis for a Web Portal New Product Launch Dashboards and Metrics Creating High impact dashboards Benchmark / segment / Trend Trinity Metrics Some Critical things to Measure Segmentation Goal conversion by Device So now it’s up to you Good luck with the assignment Quick recap of the main ideas Different Marketing Mixes – Mobile Version B How it works Traditional structure of the web Traditional Measures as per Farris and Bendle Kaushik’s wisdom Key Business Questions The Trinity Approach 4 attributes of great Metrics Site Design with Google in Mind Using Keywords – the phrase to own in Google’s Mind for SEO and PPC These are the words we ended up with How it works you bet on phrases and on ads and then test the returns you get – it’s pure A vs B testing We are competing for the answer box How traffic gets to a site What we have to measure Facebook advertising So what are the goals we need to set and how do we track them? Making analytics actionable Mr Kaushik’s words of wisdom Conversion Improvement Basics Continually work to improve the site Site design for results Advanced concepts Get to 95% significance easily Seven steps to a data driven culture 1. Go for the bottom line first 2. Reporting is not analysis – which is what you want 3. Depersonalise decision making 4. Be proactive 5. Empower the analysts 6. Solve for the trinity 7. Think in terms of process What Data Science has to do Data science uses automated methods to analyse vast amounts of data and extract knowledge You need a subject expert – a business man, an IT person for data and a data analyst to build models The analytic modelling toolkit Types of techniques Predictive Models Descriptive Models Decision Tree Analysis Factor Analysis and principal components analysis Cluster Analysis Regression Analysis Latent Class Analysis Supervised Neural Network Self Organising Map Association Analysis Regression for Prediction Straightforward but consuming Marketing with Smart Machines Alexander Borek and Joerg Reinold Geo targeting is what’s new – It knows where you are Geo fencing and geo targeting – beacons and GPRS [Show More]

Last updated: 1 year ago

Preview 1 out of 123 pages