MKTG 203 Chap.10 Digital Marketing and Customer Journeys
Digital Marketing and Customer Journeys
Digital marketing connects discoverability, interaction, data and service across a customer journey. This chapter maps customer tasks and contact points rather than assuming a neat funnel, distinguishes owned, paid and earned media, and links measures to decisions. It also treats personalisation, automation and experimentation as governed choices that require relevant data, valid comparisons and respect for customer trust.
What this chapter covers
- 01
Journeys map customer tasks, states and contact points over time
- 02
Owned, paid and earned media provide different control and credibility
- 03
Metrics must connect exposure to behaviour and organisational value
- 04
Experiments require a clear outcome and a valid comparison
- 05
Personalisation trades relevance against data, fairness and trust risks
Worked example · free
Repair a subscription journey
- +2Map the customer task and friction around trial and renewal.
- +2Separate channel exposure from meaningful behaviour.
- +2Propose one controlled change and outcome measure.
- +2State a trust or data boundary.
Key terms
- Customer journey
- A representation of customer tasks, experiences and contact points across an extended decision process.
- Owned media
- Communication channels and assets controlled by the organisation.
- Conversion
- Completion of a defined action that advances a stated marketing objective.
- Attribution
- The rule or analysis used to assign contribution to contact points associated with an outcome.
Digital Marketing and Customer Journeys FAQ
Is a customer journey the same as a sales funnel?
Not exactly. A funnel aggregates movement toward an outcome, while a journey represents customer tasks, states and contact points that may loop, pause or occur after purchase. Both can be useful, but the journey is especially valuable for finding friction and coordination problems across channels and service moments.
Which digital metric matters most?
There is no universal metric. The useful measure follows the objective and decision: reach for potential exposure, qualified engagement for considered interaction, conversion for a defined action, retention for continuing value and contribution for economics. Metrics should have clear definitions, time windows and known limitations.
Does personalisation always improve relevance?
No. Personalisation can reduce search effort or improve timing, but weak data and opaque inference can produce poor recommendations, exclusion or discomfort. The expected relevance gain should be weighed against consent, sensitivity, fairness and security. A less personalised experience may preserve greater trust and still meet the customer's task.
What makes a digital metric useful?
Define the customer behaviour, denominator, period and decision before interpreting movement. Clicks or views describe activity but may not establish fit, conversion or continuing value. Compare relevant cohorts, validate tracking and identify what the organisation will adopt, revise, investigate or stop if the measure changes.
Exam move
Map one unfamiliar journey from the customer's task rather than from the organisation chart. Mark contact points, questions, emotions, friction and the data created, then identify which team can change each point. Compare owned, paid and earned media by control, reach and credibility. For metrics, write the decision beside every number and distinguish activity from customer behaviour and organisational value.
Practise experimentation by specifying the change, comparison, outcome and time window before interpreting a result. End digital cases with a governance check covering necessity, consent, fairness, security and trust. This sequence prevents technology vocabulary from substituting for a marketing decision. Add handoffs to the journey map.
Draw lines between advertisement, page, form, service delivery, support and return, and note what information or expectation crosses each boundary. Friction often sits between owned systems rather than inside a single screen. For each touchpoint, write its primary job and the next meaningful customer action. Reject extra content or automation that does not reduce a barrier or create useful learning.
Build a metric ladder with activity at the bottom, customer behaviour in the middle and organisational value at the top. Give every metric a definition, denominator, time window and decision. Compare cohorts rather than mixing customers acquired under different promises or experiences. For attribution, apply two different rules to the same journey and explain why the result changes; treat each rule as a lens rather than proof.
Design a small test by naming the hypothesis, single consequential change, comparison, outcome, guardrail and adoption rule before reading data. Check whether tracking quality, capacity or seasonality could create the observed movement. For personalisation, state the customer benefit and minimum data required, then test consent, sensitivity, fairness, security and the consequence of error.
A less tailored route may be preferable when the relevance gain is weak or the trust cost is high. Finish each case with one learning decision: adopt, revise, investigate or stop. The answer should say what the evidence permits next without claiming that a dashboard removes judgement. As a final check, explain the result without platform vocabulary.
If the customer task, barrier, evidence and decision remain clear, the digital recommendation is serving the marketing strategy rather than replacing it.
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