This unit will develop learners’ understanding of the sales funnel and the customer experience, and the types of data generated during the customer journey. They will learn how and why organisations measure digital marketing activities and the key metrics and performance indicators they use to determine the success of digital marketing campaigns.
Understand the sales funnel in digital marketing
Stages of the sales funnel/customer experience: awareness, education, repertoire, consideration, purchase.
Factors: customer life cycle, quality of marketing activity, quantity of marketing activity.
Explain the stages of the sales funnel.
Explain the different factors that impact on sales funnel conversion rates.
Understand how digital marketing metrics are generated.
Reasons for measuring data: refinement and adaptation of marketing activities, budget allocation.
Types of data: page impression, unique visits, click through rates (CTR), average number of page views per visit, average duration, sales, bounce rates, validity, reliability, sample size, usefulness.
Key Performance Indicators (KPIs): Return on Investment (ROI), customer satisfaction and engagement, customer trends, Cost per acquisition (CPA), sales, retention rates, win back, engagement, re-engagement, perception, customer satisfaction, brand awareness, average revenue per user.
Key metrics: hit rate/visits (total), unique visitors, bounce rate, exit rate, dwell time (stickiness), click-through rate, download rates, visitor origin (country/region), time of day, top page views, Pages Per Visit (PPV), Daily Active Users (DAU), Monthly Active Users (MAU).
Tools for tracking data: tracking codes, pixel tracking, first party and third party cookies.
Tools for viewing data: analytic reports, dashboards, aggregators.
Key features of analytics software: big data processing (involve the collection and organisation of raw data to produce meaning), intuitive user interface, flexible, user friendly, predictive applications, identity management, analytics, filtering/sorting, reporting features, security, support, version control.
Explain the customer experience during different stages of the sales journey and the types of data generated.
Explain the types of data required to evaluate success in relation to Key Performance Indicators (KPIs)
Compare software tools for tracking and viewing data.
Explain the key features of analytics software.
Understand how to use analytics software to optimise a digital marketing campaign.
Reasons for measuring data: refinement and adaptation of marketing activities, budget allocation.
Types of data: page impression, unique visits, click through rates (CTR), average number of page views per visit, average duration, sales, bounce rates, validity, reliability, sample size, usefulness.
Key Performance Indicators (KPIs): Return on Investment (ROI), customer satisfaction and engagement, customer trends, Cost per acquisition (CPA), sales, retention rates, win back, engagement, re-engagement, perception, customer satisfaction, brand awareness, average revenue per user.
Key metrics: hit rate/visits (total), unique visitors, bounce rate, exit rate, dwell time (stickiness), click-through rate, download rates, visitor origin (country/region), time of day, top page views, Pages Per Visit (PPV), Daily Active Users (DAU), Monthly Active Users (MAU).
Tools for tracking data: tracking codes, pixel tracking, first party and third party cookies.
Tools for viewing data: analytic reports, dashboards, aggregators.
Key features of analytics software: big data processing (involve the collection and organisation of raw data to produce meaning), intuitive user interface, flexible, user friendly, predictive applications, identity management, analytics, filtering/sorting, reporting features, security, support, version control.
Analyse digital marketing campaign data for insights and trends.
Suggest and justify methods to optimise a digital marketing campaign.