E-Marketing Training for Business Consultants: From Diagnosis to Recommendation

E-marketing training for HKU Business Consulting Practicum

Effective e-marketing consulting begins with diagnosis, not a list of channels. Consultants must connect customer evidence, business economics and execution capability before recommending activity.

What informed this framework

In a six-hour HKU Business Consulting Practicum programme in 2020, Social Stand founder Dr Bernie Wong guided students through digital and social media marketing, storytelling and applied business recommendations.

A consulting sequence that produces better advice

1. Clarify the commercial question

Define the desired outcome, baseline, constraints and decision that the client needs to make.

2. Diagnose the customer journey

Use interviews, search behaviour, analytics, sales data and service feedback to identify friction and unmet needs.

3. Prioritise interventions

Score opportunities by expected value, evidence, feasibility, cost and time to learn. Avoid recommending every channel.

4. Design the test and scorecard

Specify audience, message, execution, owner, budget, leading indicators and commercial outcome.

How to present a credible recommendation

Separate facts, assumptions and hypotheses. Explain alternatives and risks. Provide a phased roadmap that the client can fund, operate and review.

Common mistakes

  • Confusing competitor activity with customer evidence.
  • Using engagement as a substitute for business impact.
  • Ignoring the client’s people, data and approval constraints.
  • Presenting tactics without ownership or review dates.

See Dr Bernie Wong’s original engagement note and the Social Stand resource hub.

For applied consulting, digital strategy or corporate capability programmes, speak with Social Stand.

Frequently asked questions

Should a consultant recommend quick wins?

Yes, when they are connected to a longer-term model and produce useful evidence rather than temporary activity.

How much research is enough?

Enough to distinguish a customer problem from an internal assumption and to design a responsible first test.

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