Insight: Wix.com

Wix.com

Wix operates as a leading global SaaS platform providing end-to-end digital presence solutions, combining AI-driven tools, web design, hosting, domains, and business applications to serve a broad customer base ranging from individuals to enterprises. The company has demonstrated strong execution against its strategic objectives, notably exceeding its 2023 Three-Year Plan by achieving positive GAAP net income and Rule of 40 metrics ahead of schedule, supported by disciplined cost management and robust free cash flow generation. Financial performance in 2025 reflects continued growth, with total revenue reaching $1.99 billion (+13% YoY), driven by both Creative Subscriptions (71% of revenue, +11%) and higher-growth Business Solutions (+18%), indicating successful diversification and monetization beyond core subscriptions. Wix’s freemium model, high share of annual and multi-year subscriptions (83%), and focus on user engagement, pricing optimization, and partner ecosystem development support revenue visibility and retention. Additional upside is driven by expanding business solutions such as payments, applications, and marketing tools, which enhance customer lifetime value. The company’s data-driven user acquisition strategy and cohort-based return optimization further reinforce efficient growth. Overall, Wix exhibits a scalable and resilient business model, though future performance remains dependent on continued subscription growth, increased adoption of higher-margin business solutions, and the company’s ability to adapt to evolving technologies, particularly in AI, while maintaining infrastructure efficiency.

Example DCF valuation

The discounted cash flow (DCF) analysis is based on a 5-year explicit forecast period with a weighted average cost of capital (WACC) of 9% and a terminal growth assumption of 0–3% depending on the scenario. The company’s free cash flow (FCF) shows a strong recovery from -33.5M in 2022 to 182.2M in 2023 and 478.1M in 2024, followed by 573.0M in 2025, reflecting a rapid normalization and stabilization of cash generation. However, for valuation purposes, the base case assumes a flat or conservative long-term FCF profile after the initial growth period.

Under these assumptions, the projected cash flows are discounted over the forecast horizon, resulting in an estimated enterprise value of approximately 14.38 billion USD in the higher-growth scenario and 6.37 billion USD in the more conservative flat-growth scenario. After adjusting for net debt, the implied equity value ranges from about 13.97 billion USD down to 5.96 billion USD. This translates into an intrinsic share price estimate between 88.77 USD and 208.06 USD, depending on the growth and terminal value assumptions applied. The wide valuation range highlights the sensitivity of the model to long-term growth expectations and underscores the importance of conservative terminal assumptions in DCF analysis.

import numpy as np
import pandas as pd

# =========================================================
# 1. INPUTS (ALL ASSUMPTIONS IN ONE PLACE)
# =========================================================

# -----------------------------
# Historical FCF (for reference only)
# -----------------------------
historical_fcf = {
    "year": [2022, 2023, 2024, 2025],
    #"fcf": [-33_512, 182_197, 478_079,572_957]
    "fcf" : [ -33_512_000, 182_197_000, 478_079_000, 572_957_000]
}

# -----------------------------
# Base Year FCF (MOST IMPORTANT INPUT)
# -----------------------------
last_year_fcf = 572_957_000   # Latest reported Free Cash Flow (USD)

# -----------------------------
# Growth Assumptions
# -----------------------------
fcf_growth_rate = 0.0      # Annual FCF growth rate (e.g. 12%)
forecast_years = 5            # Projection period (years)

# -----------------------------
# Discounting Assumptions
# -----------------------------
wacc = 0.09                   # Weighted Average Cost of Capital (9%)
terminal_growth = 0.00        # Long-term perpetual growth (3%)

# -----------------------------
# Capital Structure Inputs
# -----------------------------
#cash = 660_000_000            # Cash & equivalents correction wring number
cash = 1_180_495_000           # Cash & equivalents
#debt = 969_946 * 1000         # Total debt (converted to USD) 2024
debt = 1_586_609 * 1000         # Total debt (converted to USD) 2024
#shares_outstanding = 60247.51 * 1000  # Shares outstanding 2024
shares_outstanding = 67143.96 * 1000  # Shares outstanding


# =========================================================
# 2. HISTORICAL ANALYSIS (OPTIONAL)
# =========================================================

df_hist = pd.DataFrame(historical_fcf)

df_hist["YoY_growth_%"] = df_hist["fcf"].pct_change() * 100
print(df_hist)

fcf_start = df_hist.loc[df_hist["year"] == 2023, "fcf"].values[0]
fcf_end = df_hist.loc[df_hist["year"] == 2024, "fcf"].values[0]

cagr = (fcf_end / fcf_start) - 1
print(f"\nFCF CAGR (2023–2024): {cagr:.2%}")


# =========================================================
# 3. FORECAST FCF
# =========================================================

years = np.arange(1, forecast_years + 1)

fcf_forecast = np.array([
    last_year_fcf * (1 + fcf_growth_rate) ** t
    for t in years
])


# =========================================================
# 4. DISCOUNTING
# =========================================================

discount_factors = 1 / (1 + wacc) ** years
discounted_fcf = fcf_forecast * discount_factors


# =========================================================
# 5. TERMINAL VALUE (GORDON GROWTH MODEL)
# =========================================================

terminal_value = (
    fcf_forecast[-1] * (1 + terminal_growth)
    / (wacc - terminal_growth)
)

discounted_terminal_value = terminal_value / (1 + wacc) ** forecast_years


# =========================================================
# 6. VALUATION
# =========================================================

enterprise_value = discounted_fcf.sum() + discounted_terminal_value
equity_value = enterprise_value + cash - debt
intrinsic_share_price = equity_value / shares_outstanding


# =========================================================
# 7. OUTPUT
# =========================================================

results = pd.DataFrame({
    "Year": years,
    "Projected FCF": fcf_forecast,
    "Discount Factor": discount_factors,
    "Discounted FCF": discounted_fcf
})

print(results)

print("\n--- DCF VALUATION SUMMARY ---")
print(f"Enterprise Value: {enterprise_value/1e9:.2f} B USD")
print(f"Equity Value: {equity_value/1e9:.2f} B USD")
print(f"Intrinsic Share Price: {intrinsic_share_price:.2f} USD")

Conclusion and quality of earnings.

The company is valuable, which is great news. However, true skill is in assessing the expected growth and this is the billion dollar question. One approach can be used to assess the likelihood of growth is to evaluate the quality of earnings (see checklist bellow).