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The Live Pitch to a New Client

Work through budget and media mix live on the model in the first meeting - instead of asserting them on static slides. The prospect names their numbers and objections; you show the effect at once. A presentation becomes a shared line of reasoning.

Why static pitch slides are weaker in the first meeting

A slide is a one-way street

A slide asserts a budget recommendation, but the prospect can't test it. Every objection - "What if we halve Paid?", "Why so much offline?" - lands as an open question and gets pushed to a follow-up call. Trust leaks away in exactly that gap.

A live model turns the objection into input

A live model reverses that. The objection becomes an input: you change the assumption in the conversation and show what happens to the mix. Not because the result impresses, but because the reasoning is open - that convinces differently than a polished claim.

How the live pitch on the model works

  1. Prepare a baseline. Before the meeting, build a realistic starting scenario from industry benchmarks - the rough starting point, still without the client's real numbers.
  2. Open it live. Share the tool in the meeting instead of clicking through slides. The baseline is the starting point, not the result.
  3. Gather the real numbers. Have the prospect name their real figures and constraints - budget, audience, no-go channels. These replace the benchmark assumptions.
  4. Work through objections. Answer every "what if" question straight on the sliders and show the effect on mix, saturation and contribution.
  5. Hand over the result. Export the worked-through scenario as a local JSON file and hand it over as a concrete takeaway.

Build a baseline scenario for your next pitch: try the tool

Talking points for the conversation

  • "Give me your number - we'll run it right now." Turns skepticism into participation.
  • Show saturation rather than assert it: why pouring the whole budget into one channel doesn't pay off.
  • Make the halo effect visible to defend classic offline media against pure performance channels.
  • Instead of a single recommendation, put an optimistic and a cautious scenario side by side - that signals prudence, not sales pressure.

Common thinking traps

  • Selling benchmark results as a guarantee. The model simulates forward on empirical values - it proves nothing on the client's own data. Promising precision the method doesn't have loses back the very trust the live pitch builds.
  • Presenting the baseline as a finished recommendation. It's the conversation opener, not the result. The value only emerges once the client's real numbers flow in.
  • Obscuring where the numbers come from. The strength lies in openness: show the assumption, not a black box. The moment you dodge, the model looks like a trick instead of a tool.

Frequently asked questions about the live pitch

Why a live model instead of a finished budget recommendation on slides?

A slide asserts a number; a live model shows how it comes about. Objections can be worked through on the spot instead of being deferred to a follow-up call. That builds more trust than a polished presentation.

Does the prospect already need their own data?

No. The live pitch runs on benchmark and empirical values; the client's real numbers flow in as soon as they name them. Precisely because no clean data history is needed yet, the model suits the first meeting.

How do you handle the question "Where do these numbers come from?"

Openly. The calculation is deterministic and traceable - you show the assumption, not a black-box result. A forecast is a simulation, not a guaranteed outcome; that very honesty is an advantage in the pitch.

What does the prospect take away from the meeting?

The worked-through scenario can be exported as a local JSON file and handed over as a takeaway - a concrete artifact instead of a vague promise.

Does the live pitch replace a data-driven MMM?

No. It is the upstream planning and validation layer for the first meeting. Where a robust causal proof on your own data is required later, a data-driven MMM is added on top.

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