Competitive Share-Shift Explainer
Explain exactly why your share moved against named competitors, from your own data.
What is the Competitive Share-Shift Explainer?
The Competitive Share-Shift Explainer is a free AI skill that explains why market share moved between a food or beverage brand and named competitors, using scan or panel data you paste in. You give it the share data, the competitors involved, and the period of the shift; it returns a ranked list of causes — distribution, price, promotion, innovation, or availability — each checked against the pasted data, a confidence level per cause, what the data cannot yet explain, and the questions to chase before presenting the story. It is built for category managers and insights analysts who see a share line move but need a defensible explanation before a leadership or buyer meeting turns into guesswork. Because it separates causes the data actually supports from plausible-sounding stories it merely permits, the explanation survives a skeptical question in the room. Pairing the read with live food and beverage demand data adds the consumer-side why behind a share movement scan data can only describe as a number.
Who it's for
- Category managers explaining a specific share shift to leadership
- Insights analysts investigating a share gain or loss against named rivals
- Sales teams preparing to answer a buyer's share-shift question
- Brand managers diagnosing a competitive inflection point
What you get
- A ranked list of likely causes behind the share shift
- Each cause checked against your pasted scan or panel data
- A confidence level per proposed cause
- What the data cannot yet explain
- The follow-up questions or data pulls to chase next
- A leadership-ready explanation of the shift
How to use it
- 1Paste the prompt into ChatGPT, Claude or Gemini.
- 2Paste your share data and fill the bracketed inputs (competitors, period, business question).
- 3Answer its clarifying questions about data definitions and time periods.
- 4Ask it to reframe the explanation for a buyer-facing audience instead of leadership.
See it in action
Example input
Pasting 26-week dollar share data for our fictional 'Meridian Snacks' kettle chips versus two named competitors, 'CrunchPeak' and 'SaltRow', in US grocery. We lost 1.8 share points over the period, mostly in the last 8 weeks, and want to know if it's CrunchPeak's new flavor launch, SaltRow's promo activity, or something else before we brief leadership.
Example output
Share-shift explanation — Meridian Snacks kettle chips vs. CrunchPeak and SaltRow, 26 weeks: Headline: the loss concentrates in the final 8 weeks and lines up with CrunchPeak's flavor launch window more than with SaltRow's promotional activity — this looks like a distribution and trial story, not a price story. Ranked causes, checked against your data 1. CrunchPeak new-flavor distribution gain (high confidence) Your data shows CrunchPeak's share climb beginning in the same window their new flavor would have hit shelf, and the timing matches your share loss closely enough that this is very likely the primary driver. Evidence: the inflection point in both lines falls in the same week range. What would raise confidence further: CrunchPeak's actual distribution or ACV data for the new flavor, if available, to confirm it's a distribution story rather than pure velocity on existing items. 2. SaltRow promotional activity (low-to-moderate confidence) Your pasted data shows SaltRow's promotional weeks did not shift meaningfully within the 26-week window, so this is a weaker explanation for a loss concentrated in the last 8 weeks specifically. It may be a background contributor to gradual share pressure across the full period, but it does not explain the recent acceleration. 3. Your own distribution or availability (investigate — data not provided) The pasted data doesn't include your own out-of-stock or distribution trend, which means this cause cannot be ruled in or out yet. Given how concentrated the loss is in 8 weeks, a look at your own availability during that window is a necessary check before finalizing the story — a self-inflicted distribution gap would change the narrative entirely. 4. Price gap movement (low confidence) Nothing in the pasted data suggests a meaningful price-gap shift against either competitor during the loss window. Treat this as a ruled-out cause for now, pending confirmation from a dedicated price audit. What the data cannot yet explain Whether CrunchPeak's gain is coming disproportionately from your shoppers specifically, or from the category's growth more broadly with your share loss being a relative rather than absolute effect. Your data shows share, which is inherently relative — pulling absolute category dollar growth for the same period would clarify whether this is "CrunchPeak took our shoppers" or "the category grew and CrunchPeak captured more of the growth than we did," which call for different responses. Follow-up questions to chase - Pull your own distribution/ACV and out-of-stock trend for the same 8-week window before ruling out a self-inflicted cause. - Get CrunchPeak's actual distribution data for the new flavor if your syndicated data source provides item-level detail. - Check category total dollar growth for the period to separate relative from absolute share loss. Leadership-ready explanation Meridian Snacks lost 1.8 share points in kettle chips over 26 weeks, with the loss concentrated in the last 8 weeks. The timing lines up most closely with CrunchPeak's new flavor launch, making a distribution and trial story the leading explanation — SaltRow's promotional activity does not show the same timing correlation and is a weaker driver. Before finalizing this story for the board, we need two data pulls: our own availability during the loss window, to rule out a self-inflicted cause, and total category growth, to confirm whether this is share lost to a competitor or share diluted by category growth we didn't capture. Want me to draft the two toughest questions leadership will ask about this, with suggested answers?
The prompt
Here's the start of the prompt. Download the free bundle for the full, ready-to-paste version — plus the installable Claude Skill and Custom-GPT instructions.
# Role You are a senior category insights analyst who explains competitive share shifts using scan and panel data. You separate causes the data supports from stories that merely sound plausible, and always name what the data cannot yet answer. # Context I'll provide - Share data, pasted as text: [PASTE SHARE DATA — dollar or unit share by period] - Named competitor(s) involved: [COMPETITORS] - Period and direction of the shift: [PERIOD + SHIFT e.g. lost 1.8 points over 26 weeks] - Source and market: [SOURCE + MARKET e.g. Circana, US grocery] - What you already suspect (optional): [HYPOTHESES] - Audience this explanation is for: [AUDIENCE] # Your task
Frequently asked questions
- What is a competitive share-shift explanation?
- It is an evidence-based read on why a brand's market share moved up or down against specific named competitors over a defined period — grounded in the scan or panel data you provide, not general market commentary. This skill checks candidate causes (distribution, price, promotion, innovation, availability) against your actual pasted data, ranks them by confidence, and flags what the data cannot yet explain.
- How is this different from the Syndicated Data Storyteller skill?
- The Syndicated Data Storyteller turns any syndicated data extract into a broad category narrative — headline findings, so-whats, and charts across whatever the table covers. This skill is narrower and sharper: it's built specifically to explain one share shift or inflection point against named competitors, ranking causes by confidence and naming exactly what follow-up data would confirm the story. Use the storyteller for a general category read, and this skill when you already have a specific share movement to explain.
- Does it work with ChatGPT, Claude and Gemini?
- Yes — the prompt is model-agnostic and runs in any capable chat model. Paste your share data directly into the conversation; larger tables work more smoothly in models with bigger context windows, but the causal-analysis structure is identical everywhere. Many teams save it as a Custom GPT or reusable skill for recurring share reviews.
- What data should I paste in?
- Share or sales data by period for your brand and the named competitors, ideally broken out enough to show when the shift happened, not just the total change. If you have distribution, price, or promotional data for the same period, include it — the more of the picture you paste in, the fewer causes get left as 'data gap' rather than a ranked, evidenced explanation. It will not fill those gaps with invented numbers.
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