Use cases

Engagement snapshot

Fetch the most recent month’s engagement metrics for a company such as total visits, bounce rate, pages per visit, and time on site.

curl -G "https://api.akta.pro/api/v1/company/website-traffic/" \
  --data-urlencode "company=https://canva.com" \
  -H "x-api-key: your api key"

import requests

url = "https://api.akta.pro/api/v1/company/website-traffic"

payload = {}
headers = {
  'x-api-key': 'your api key'
}
params = {"company": "https://canva.com"}

response = requests.request("GET", url, headers=headers, params=params, data=payload)

print(response.text)

Sample response:

{
  "data": {
    "uuid": "00000jw-canva",
    "engagements": {
      "bounce_rate": "0.2602897105526746",
      "month": "5",
      "year": "2026",
      "page_per_visit": "6.55412715753744",
      "visits": "974544088",
      "time_on_site": "349.0280264736948"
    },
    "estimated_monthly_visits": {
      "2026-03-01": 981605012,
      "2026-04-01": 973355461,
      "2026-05-01": 974544088
    },
    "traffic_sources": {
      "social_organic": 0.042621396495100196,
      "social_paid": 0.017813082048215217,
      "mail": 0.027515646067013535,
      "referrals": 0.04181835401212351,
      "search_organic": 0.2075475145136343,
      "search_paid": 0.007554620736976237,
      "direct": 0.634763534328985,
      "gen_ai": 0.0154293188059966,
      "affiliate": 0.0020269187393783146,
      "display_ads": 0.0029096142525770747
    }
  },
  "credits_consumed": 1.5
}

Trend analysis over time

Extract the estimated_monthly_visits series to chart traffic growth or decline, and compute month-over-month change.

import requests

HEADERS = {"x-api-key": "<YOUR_API_KEY>"}

response = requests.get(
    "https://api.akta.pro/api/v1/company/website-traffic",
    headers=HEADERS,
    params={"company": "canva.com"}
)

monthly = response.json()["data"]["estimated_monthly_visits"]

# Sort by date — dict key order is not guaranteed
series = sorted(monthly.items(), key=lambda kv: kv[0])

print(f"{'Month':<12} {'Visits':<15} {'MoM Change'}")
print("-" * 40)
prev_visits = None
for date, visits in series:
    if prev_visits is not None:
        change = (visits - prev_visits) / prev_visits * 100
        change_str = f"{change:+.1f}%"
    else:
        change_str = "—"
    print(f"{date:<12} {visits:<15,} {change_str}")
    prev_visits = visits

Traffic channel mix

Break down where a company’s traffic actually comes from which could be useful for assessing brand strength versus paid dependency, or spotting emerging channels like AI-assisted discovery.

import requests

HEADERS = {"x-api-key": "<YOUR_API_KEY>"}

response = requests.get(
    "https://api.akta.pro/api/v1/company/website-traffic",
    headers=HEADERS,
    params={"company": "canva.com"}
)

sources = response.json()["data"]["traffic_sources"]

CHANNEL_GROUPS = {
    "Owned/Brand":   ["direct"],
    "Organic":       ["search_organic", "social_organic"],
    "Paid":          ["search_paid", "social_paid", "display_ads"],
    "Referral":      ["referrals", "affiliate", "mail"],
    "Emerging (AI)": ["gen_ai"]
}

for group, channels in CHANNEL_GROUPS.items():
    share = sum(sources.get(c, 0) for c in channels)
    print(f"{group:<15} {share * 100:5.1f}%")

Competitive benchmark across multiple companies

Fetch traffic engagement and channel mix for a set of companies and compare side by side which could be useful for market share and share-of-voice style analysis.

import requests

HEADERS   = {"x-api-key": "<YOUR_API_KEY>"}
COMPANIES = ["canva.com", "figma.com", "notion.so", "miro.com"]

def get_traffic(company):
    r = requests.get(
        "https://api.akta.pro/api/v1/company/website-traffic",
        headers=HEADERS,
        params={"company": company}
    )
    data = r.json()["data"]
    eng = data["engagements"]
    src = data["traffic_sources"]
    return {
        "company":      company,
        "visits":       int(eng["visits"]),
        "bounce_rate":  float(eng["bounce_rate"]),
        "direct_share": src.get("direct", 0),
        "organic_share": src.get("search_organic", 0) + src.get("social_organic", 0)
    }

results = [get_traffic(c) for c in COMPANIES]
results.sort(key=lambda x: x["visits"], reverse=True)

print(f"{'Company':<15} {'Visits':<15} {'Bounce':<9} {'Direct%':<10} {'Organic%'}")
print("-" * 65)
for r in results:
    print(
        f"{r['company']:<15} "
        f"{r['visits']:<15,} "
        f"{r['bounce_rate'] * 100:<9.1f} "
        f"{r['direct_share'] * 100:<10.1f} "
        f"{r['organic_share'] * 100:.1f}"
    )

Getting Started

API Products

Resources

Developer Tools

Engagement snapshot

Trend analysis over time

Traffic channel mix

Competitive benchmark across multiple companies

​Engagement snapshot

​Trend analysis over time

​Traffic channel mix

​Competitive benchmark across multiple companies

Engagement snapshot

Trend analysis over time

Traffic channel mix

Competitive benchmark across multiple companies