Consumer spending for teams

Granular visibility into consumer spending across Europe's biggest brands. Integrate anonymized merchant-level transaction data directly into your research workflow.

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Transaction History: 0+ Years
Public Companies: 0+
European Markets: 0 Countries

Alternative Data

Spending insights, built for developers

Access aggregated consumer spending data from Fable Data's European transaction panel. Normalize by panelist count for accurate trend analysis, filter by country, channel, and consumer type, and track week-over-week momentum across any merchant.

✓Daily transaction aggregates (released after 7-day embargo)
✓Spend normalized by 28-day rolling panelist count
✓Filter by online, offline, or buy now pay later channels
✓Cross-border analysis across UK, DE, FR, ES, IT, AT

# Merchant Aggregates
import requests

API_KEY = "YOUR_API_KEY"
URL = "https://api.massive.com/consumer-spending/eu/v1/merchant-aggregates"
PARAMS = {
    "name": "nike",
    "transaction_date.gte": "2024-01-01",
    "limit": 100,
    "apiKey": API_KEY
}

response = requests.get(URL, params=PARAMS)

if response.status_code == 200:
    data = response.json()
    for record in data["results"]:
        spend_per_panelist = record["total_spend"] / record["twenty_eight_day_rolling_category_accounts"]
        print(f"{record['transaction_date']}: €{spend_per_panelist:.2f}/panelist")
else:
    print(f"Request failed with status {response.status_code}")

Competitive intelligence

Track relative performance

Compare week-over-week spending momentum between competitors. Identify when one company is gaining wallet share relative to another, spot seasonal outperformance, and track the impact of promotions and market events.

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Nike vs Adidas— Who's winning?

Nike (NKE)

Adidas (ADDYY)

Seasonal patterns

Discover spending seasonality

Analyze spending patterns across years to identify consistent seasonal behavior. See how travel bookings peak in summer, how retail spends during Black Friday, and how COVID-19 disrupted normal patterns before recovery.

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Airbnb Spending Seasonality

Year	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
2016	+0.21	-0.06	+0.02	+0.09	-0.04	-0.04	+0.04	-0.10	-0.16	-0.09	-0.13	-0.23
2017	-0.08	+0.00	-0.03	-0.04	-0.01	-0.06	-0.01	-0.17	-0.21	-0.17	-0.17	-0.23
2018	-0.04	-0.01	-0.03	+0.01	-0.02	-0.00	+0.08	-0.08	-0.17	-0.12	-0.16	-0.11
2019	+0.06	-0.02	-0.03	-0.01	-0.00	+0.04	+0.16	-0.07	-0.17	-0.12	-0.16	-0.13
2020	+0.03	+0.01	-0.29	-0.11	+0.30	+0.56	+0.42	-0.02	-0.20	-0.22	-0.27	-0.09
2021	-0.05	+0.17	+0.22	+0.40	+0.65	+0.37	+0.36	+0.06	-0.10	-0.06	-0.12	-0.07
2022	+0.07	+0.15	+0.12	+0.14	+0.16	+0.14	+0.38	+0.09	-0.07	-0.03	-0.07	-0.06
2023	+0.12	+0.14	+0.16	+0.12	+0.14	+0.15	+0.31	+0.09	-0.08	-0.05	-0.10	-0.04
2024	+0.07	+0.07	+0.06	+0.06	-0.01	+0.02	+0.18	-0.04	-0.16	-0.13	-0.16	-0.15
2025	-0.03	+0.01	-0.02	+0.00	-0.04	-0.02	+0.12	-0.07	-0.17	-0.16	-0.14	-0.18
2026	-0.11	-0.11	-0.13

Low spend

High spend

Peak Month

Jul

+0.21 vs avg

Low Month

Sep

-0.15 vs avg

Range

93%

peak vs low

Years

2016-2026

Corporate structure

Navigate the merchant hierarchy

Access reference data mapping 3,500+ merchants to their parent companies and stock tickers. Understand corporate hierarchies, track spending across all subsidiaries, and link transaction data directly to public equity analysis.

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MC FPLVMH Moët Hennessy Louis Vuitton SE

Fashion & Leather

Louis Vuitton

Fendi

Celine

Givenchy

Kenzo

Loro Piana

Marc Jacobs

Berluti

Moynat

Stella McCartney

Thomas Pink

Watches & Jewelry

Tiffany & Co.

Bulgari

Tag Heuer

Hublot

Chaumet

Perfumes & Cosmetics

Sephora

Benefit Cosmetics

Guerlain

Acqua Di Parma

Make Up For Ever

Fenty

Wine, Spirits & Travel

Moët Hennessy

Rimowa

MAR USMarriott International Inc

Luxury

The Ritz Carlton

St. Regis Hotel

Premium

Marriott

Westin Hotels

Renaissance Hotels

Sheraton

Delta Hotels

Select

Aloft

Four Points

Protea Hotels

Longer Stays

Residence Inn

TownePlace Suites

SpringHill Suites

Fairfield Inn

Applications

Power your investment research

Revenue Estimation

Estimate merchant revenue trends before earnings announcements using normalized spend per panelist across markets.

Sector Benchmarking

Compare spending trends across retailers, restaurants, and travel companies to identify sector-wide momentum shifts.

Cross-Border Analysis

Analyze regional performance differences across UK, Germany, France, Spain, Italy, and Austria markets.

Event Impact

Measure the spending impact of product launches, promotions, and macroeconomic events on consumer behavior.

Channel Mix

Track the shift between online, offline, and buy-now-pay-later channels to understand changing consumer behavior.

Fraud & Returns

Analyze refund patterns and identify merchants with high return activity to assess product quality and customer satisfaction.

Market Share Tracking

Monitor competitive positioning and track market share shifts over time to identify emerging winners and losers.

Promotional Effectiveness

Measure the impact of marketing campaigns, seasonal promotions, and discount strategies on consumer spending patterns.

Core data fields

Everything you need to analyze consumer trends

Each record includes daily spending totals, panelist counts for normalization, and dimensional filters. All data is aggregated and anonymized.

Where and how are people spending?

name — the merchant (Nike, Zara, Lidl, etc.)
user_country — where the consumer is (UK, DE, FR, ES, IT, AT)
channel — how they paid (online, in-store, or buy now pay later)
transaction_date — when the spending happened

How much are people spending?

spend_out_spend — money spent by consumers on purchases (negative, like a withdrawal from your bank account)
spend_in_spend — money returned to consumers from refunds (positive, like a deposit to your bank account)
total_spend — net spending after refunds

Where does this spending go?

parent_name — the company that owns the merchant
ticker — the company's stock ticker
sector — the broad economic category for this company
industry — the specific business category for this company

// GET https://api.massive.com/consumer-spending/eu/v1/merchant-aggregates?name=nike

{
  "results": [{
    "type": "merchant",
    "name": "nike",
    "transaction_date": "2026-03-15",
    "published_date": "2026-03-20",
    "mcc_group": "sports clothing & apparel",
    "merchant_ticker": "NKE US",
    "parent_name": "NIKE Inc",
    "merchant_industry": "Apparel/ Footwear & Acc Design",
    "user_country": "DE",
    "channel": "offline",
    "consumer_type": "consumer_credit",
    "transaction_currency": "EUR",
    "total_spend": -1791.75,
    "total_transactions": 25,
    "total_accounts": 25,
    "spend_out_spend": -1962.74,
    "spend_in_spend": 170.99,
    "eight_day_rolling_category_accounts": 3054,
    "twenty_eight_day_rolling_category_accounts": 9284
  },
  {
    "type": "merchant",
    "name": "nike",
    "transaction_date": "2026-03-15",
    "published_date": "2026-03-20",
    "mcc_group": "sports clothing & apparel",
    "merchant_ticker": "NKE US",
    "parent_name": "NIKE Inc",
    "merchant_industry": "Apparel/ Footwear & Acc Design",
    "user_country": "DE",
    "channel": "online",
    "consumer_type": "consumer_credit",
    "transaction_currency": "EUR",
    "total_spend": -2335.28,
    "total_transactions": 21,
    "total_accounts": 21,
    "spend_out_spend": -2515.26,
    "spend_in_spend": 179.98,
    "eight_day_rolling_category_accounts": 3054,
    "twenty_eight_day_rolling_category_accounts": 9284
  }]
}