GET /candidate/projects/credit_transparency

{
  "title": "Making AI credit costs visible lifted activations ~12%",
  "summary": "An in-context credit estimate in Data Studio showed customers what an AI action would cost before they ran it, and Data Studio activations rose about 12%.",
  "details": {
    "company": "HubSpot, Data Studio (Data Hub)",
    "role": "Senior Product Designer, UX DRI for credits and AI UX in Data Studio",
    "team": "[PM], [N engineers] across Data Studio teams, Content Design",
    "stakeholders": "The usage-based billing (UBB) platform team, Legal, Data Hub leadership, adjacent product teams (Events, Email/SMS)",
    "methods": "Usage data analysis, customer research, hypotheses and guardrails, in-product experiment, content testing",
    "tools": "Figma, FigJam, Lovable, NotebookLM, Granola"
  },
  "overview": {
    "problem": "Customers avoided paid AI features in Data Studio because they couldn’t predict how many credits an action would cost. CSAT was falling, with billing friction as a top cause, and some customers were paying for features they didn’t need.",
    "solution": "Narrowed a broad adoption study to one fast question (does showing cost upfront increase activation?), then shipped an in-context credit estimate next to each AI action plus clearer credit copy, with Legal and cross-org approval despite pushback.",
    "impact": [
      "~12% more Data Studio activations after the full rollout on 1/12/26",
      "2 adjacent teams (Events in Data Hub, Email/SMS in Marketing Hub) asked to adopt the pattern",
      "Shaped new UBB platform patterns and content standards"
    ]
  },
  "context": {
    "business": "Data Studio launched at INBOUND ’25 as HubSpot’s workspace for building and cleaning datasets. Its AI features, such as Smart Columns, run on credits under a new usage-based billing (UBB) model; their adoption drives customer value and revenue.",
    "customer_problem": "Customers couldn’t tell what an AI action would cost until after running it. Many didn’t activate; others spent credits inefficiently, e.g. paying for Smart Properties where free enrichment would do.",
    "evidence": "CSAT declining with UBB friction as a top driver; executive visibility. [CSAT figures, interviews or support tickets, activation drop-off.]",
    "quote": "[Customer quote about not knowing what an action would cost] — [Role, company type]",
    "problem_statement": "How might we help Data Studio users understand what an AI action will cost before they run it, so that they activate features with confidence instead of avoiding them?"
  },
  "role": {
    "owned": "Discovery and synthesis; problem framing; hypotheses, success metrics and guardrails; end-to-end UX of the credit estimate; Legal and cross-org approval to ship.",
    "partnered_on": "UBB pricing model and credit-usage UX with the UBB platform team; in-product copy with Content Design; implementation with Data Studio engineering teams.",
    "constraints": [
      "Organizational: pushback against showing end users real-time costs, for fear visible prices would scare people away",
      "Legal: any cost shown had to be accurate enough for Legal approval",
      "Platform: had to fit the shared UBB billing system and the HubSpot design system",
      "Time: [any deadline]"
    ]
  },
  "process": {
    "discover": "As UX DRI for Smart Columns (credit usage, web search, UBB, error states), gathered evidence of credit anxiety from usage data and research, synthesized with NotebookLM and Granola. Key insight: fear of an unpredictable bill was a problem on its own, risking trust and activation.",
    "define": "Narrowed a plan that mixed “why don’t people adopt Smart Columns?” with “are people averse to credits?” to one question: does showing cost upfront increase activation? Set hypotheses, success metrics and guardrails.",
    "explore": "[Concepts tried: placement and timing of the estimate, ranges vs. exact numbers, copy variations; what was rejected and why.]",
    "validate": "After Legal approval, launched an in-context credit estimate experiment for Data Studio activations in December 2025; fixed credit copy comprehension issues with Content Design."
  },
  "decisions": [
    { "decision": "Research scope", "options": "Broad adoption study / narrow credit transparency test", "chose": "Narrow and fast; one clear answer would unblock pricing UX across several products", "tradeoff": "Broader adoption questions set aside for later" },
    { "decision": "Whether to show cost at all", "options": "Hide costs / show them before the action", "chose": "Show them before the action; evidence pointed to uncertainty, not price, as the barrier", "tradeoff": "Leadership worried visible prices would reduce usage, so it had to be proven otherwise" },
    { "decision": "[How to show the estimate]", "options": "[Exact number / range; inline / confirmation step]", "chose": "[Choice + why]", "tradeoff": "[Tradeoff]" }
  ],
  "buy_in": "Turned UBB constraints and usage data into concrete customer problems, ran a problems and opportunities exercise across Data Agent and UBB, and set guardrails so stakeholders could approve a contained experiment instead of a permanent change.",
  "solution": [
    "The estimated credit cost appears next to each AI action, such as a Smart Column, before the user runs it",
    "Clearer credit copy, rewritten with Content Design, so users understand what they pay for and why",
    "A reusable UBB platform pattern and content standards; Events and Email/SMS asked to adopt it"
  ],
  "impact": {
    "metrics": [
      "Data Studio activations: ~+12% (early data, after full rollout on 1/12/26)",
      "Pattern adoption: 2 teams requested it (Events, Email/SMS)",
      "Platform influence: shaped UBB platform patterns and content standards",
      "CSAT / support: [to add]"
    ],
    "qualitative": "Manager: the work “identified and addressed systemic risk to customer activation and trust.”",
    "wider_context": "Since INBOUND ’25, 400+ portals use credits in Data Studio (+292% since September 2025); Smart Columns account for 320K+ credits.",
    "quote": "[Customer or PM quote] — [Role]"
  },
  "reflection": {
    "do_differently": "Narrow the scope sooner; a smaller, faster test would have reached the same answer earlier.",
    "learned": "Transparency is easier to argue for with guardrails and a contained experiment; it turns a values disagreement into a question the data can answer.",
    "next": "[Whether other teams shipped the pattern; whether it extended to the Data Agent landing page.]"
  }
}