如果您指定一個關鍵字，此部分的流行度值將讓您了解該關鍵字在某些位置之間的流行度。為了計算結果，我們確定所有位置中相關關鍵字的最高流行度值，并將所有其他值表示為最高關鍵字流行度值的百分比。
如果您指定多個關鍵字，關鍵字流行度值將幫助您了解每個關鍵字的流行度——與所有給定位置的其他關鍵字相比。在這種情況下，我們確定所有指定位置的所有關鍵字中的最高流行度值。然后，我們將每個關鍵字的流行度表示為最高關鍵字流行度值的百分比。
您可以試驗關鍵詞并嘗試自己獲取一些搜索興趣信息，只需在此處試用我們的 DataForSEO 趨勢工具即可，它是免費的！
您可能想知道我們的新 DataForSEO Trends API 和 Google Trends API 之間的區別。這些 API 可能看起來相似，但兩者之間存在關鍵性的差異，使得 DataForSEO Trends API 成為了更為通用且高效的解決方案。
首先，與 Google Trends API 相比，DataForSEO Trends API 的可靠性更勝一籌。盡管 Google Trends 提供了高度準確的數據，但其平臺偶爾會遇到服務中斷或提供不完整數據的情況，這使得頻繁調用 API 來獲取大量的歷史趨勢數據變得困難。
另一方面，DataForSEO Trends API 被設計成一個可靠且可擴展的解決方案，用于檢索關鍵字趨勢和流行度數據。此外，我們還確保了與 Google Trends API 的參數保持最大兼容性，以便于用戶的平穩過渡。
下一個不同之處在于我們的 API 采用的方法。Google Trends API 基于 Google Trends 的“探索”功能，提供關鍵字隨時間變化的流行度、特定地區的流行度以及相關主題和查詢的信息。此外，您可以指定搜索類別代碼以獲取更精確的結果，并檢索特定時間點的數據。
另一方面，正如我們之前提到的，DataForSEO Trends API 分析來自多種來源的數據，包括頁面內容的流行度和網絡性能指標，并處理點擊流數據以更好地理解用戶行為。
我們的算法的最大優勢之一是，它允許您按性別和年齡細分搜索興趣，并立即比較估計結果。Google Trends API 本身沒有這樣的機會。為此，我們在 DataForSEO Trends API 中引入了四個獨立的端點：
每個端點都提供了比較關鍵字流行度并給出相關結果的功能。另外，與 Google Trends API 不同，DataForSEO Trends API 僅支持“實時”數據檢索，這意味著每次請求都會獲得最新數據。
如果您想了解更多關于 DataForSEO Trends API 的細節，請參閱我們的文檔。
總而言之，以下是 DataForSEO Google Trends API 和 DataForSEO Trends API 之間的主要區別。
?Google Trends API：
?DataForSEO Trends API：
請注意，DataForSEO Trends API 提供的是基于算法分析的估計數據，并不提供確切的搜索量數據或其他關鍵字指標。若需要精確的搜索量數據，建議使用我們的關鍵字數據 API 的“搜索量”端點。
現在您已經了解到 DataForSEO Trends API 是一個多功能且可擴展的搜索趨勢數據檢索解決方案。接下來，我們來看看如何通過幾個關鍵的應用案例，將它的功能轉化為您項目和工具的實際價值。
如何利用 DataForSEO Trends API 的強大功能？需要考慮的主要用例
出于這些目的，DataForSEO Trends API 以其經濟實惠和多功能性脫穎而出。為了說明這一點，讓我們比較一下在我們的 Trends API、Google Trends API 和 Google & Bing Ads API 中檢索 10,000 個關鍵字數據的成本。在實時模式下通過 Google Ads API 獲取 10,000 個關鍵字的數據將花費 0.75 美元（10 個任務，每個任務 1,000 個關鍵字）。在 Bing Ads API 中，50 個任務，每個任務 200 個關鍵字的成本為 3.75 美元。雖然這些價格似乎很實惠，但估算關鍵字流行度值需要手動計算和比較結果。（在實時模式下通過 Google Ads API 獲取 10,000 個關鍵字的數據將花費 0.75 美元（10 個任務，每個任務 1,000 個關鍵字）。而在 Bing Ads API 中，50 個任務，每個任務 200 個關鍵字的成本為 3.75 美元。盡管價格看似合理，但估算關鍵字流行度值仍需手動計算和比較。）
使用 Google Trends API 在“實時”模式下提供 10,000 個關鍵字的關鍵字流行度數據將花費 18 美元（2,000 個任務，每個任務 5 個關鍵字）。但是，如前所述，Google Trends 并不總是可靠地運行，這在檢索大量關鍵字數據時可能會導致問題。另一方面，對于相同數量的關鍵字，使用 DataForSEO Trends API“探索”端點只需支付 2 美元。此外，它以實時模式檢索數據并立即比較結果。（使用 Google Trends API 在“實時”模式下獲取 10,000 個關鍵字的流行度數據將花費 18 美元（2,000 個任務，每個任務 5 個關鍵字）。然而，如前所述，Google Trends 在可靠性方面存在問題，尤其是在處理大量關鍵字數據時。相比之下，使用 DataForSEO Trends API 的“探索”端點只需支付 2 美元，并且它能在實時模式下檢索數據并即時比較結果。）
| API | 價格 |
|---|---|
| Google 廣告 API | 10,000 個關鍵詞的數據價格為 0.75 美元。 |
| Bing 廣告 API | 10,000 個關鍵詞的數據價格為 3.75 美元。 |
| Google 趨勢 API | 10,000 個關鍵詞數據售價 18 美元。 即時關鍵詞流行度值估算和比較。 |
| DataForSEO 趨勢 API | 10,000 個關鍵詞數據售價 2 美元。 即時關鍵詞流行度值估算和比較。 |
?
如您所見，DataForSEO Trends API 可讓您以最高的成本效益訪問關鍵字趨勢數據。要了解有關 DataForSEO Trends API 定價的更多信息，請訪問我們的定價頁面。
以下是使用 DataForSEO Trends API 檢索歷史關鍵字熱度數據的示例。假設您想比較過去 90 天內美國對“iPhone”和“Samsung”的搜索興趣變化情況。圖表本身可能如下所示：
在圖表上，您可以觀察到在 90 天的時間段內搜索查詢“iphone”和“samsung”的流行度值如何逐日波動。
為了生成下圖，我們需要向DataForSEO Trends API 的“探索”端點發出請求。
POST https://api.dataforseo.com/v3/keywords_data/dataforseo_trends/explore/live
示例請求
[
{
"keywords": [
"iphone",
"samsung"
],
"location_name": "United States",
"date_from": "2024-01-11",
"date_to": "2024-04-06",
"type": "web"
}
]結果將返回如下
{
"version": "0.1.20240313",
"status_code": 20000,
"status_message": "Ok.",
"time": "0.6720 sec.",
"cost": 0.001,
"tasks_count": 1,
"tasks_error": 0,
"tasks": [
{
"id": "04101608-1535-0570-0000-7037e2c7e5ec",
"status_code": 20000,
"status_message": "Ok.",
"time": "0.6149 sec.",
"cost": 0.001,
"result_count": 1,
"path": [
"v3",
"keywords_data",
"dataforseo_trends",
"explore",
"live"
],
"data": {
"api": "keywords_data",
"function": "explore",
"se": "dataforseo_trends",
"keywords": [
"iphone",
"samsung"
],
"location_name": "United States",
"date_from": "2024-01-11",
"date_to": "2024-04-06",
"type": "web"
},
"result": [
{
"keywords": [
"iphone",
"samsung"
],
"type": "trends",
"location_code": 2840,
"language_code": null,
"datetime": "2024-04-10 13:08:27 +00:00",
"items_count": 1,
"items": [
{
"position": 1,
"type": "dataforseo_trends_graph",
"keywords": [
"iphone",
"samsung"
],
"data": [
{
"date_from": "2024-01-11",
"date_to": "2024-01-11",
"timestamp": 1704931200,
"values": [
88,
63
]
},
{
"date_from": "2024-01-12",
"date_to": "2024-01-12",
"timestamp": 1705017600,
"values": [
88,
54
]
},
{
"date_from": "2024-01-13",
"date_to": "2024-01-13",
"timestamp": 1705104000,
"values": [
90,
53
]
},
{
"date_from": "2024-01-14",
"date_to": "2024-01-14",
"timestamp": 1705190400,
"values": [
86,
60
]
},
{
"date_from": "2024-01-15",
"date_to": "2024-01-15",
"timestamp": 1705276800,
"values": [
82,
58
]
},
{
"date_from": "2024-01-16",
"date_to": "2024-01-16",
"timestamp": 1705363200,
"values": [
92,
52
]
},
{
"date_from": "2024-01-17",
"date_to": "2024-01-17",
"timestamp": 1705449600,
"values": [
87,
93
]
},
{
"date_from": "2024-01-18",
"date_to": "2024-01-18",
"timestamp": 1705536000,
"values": [
80,
100
]
},
{
"date_from": "2024-01-19",
"date_to": "2024-01-19",
"timestamp": 1705622400,
"values": [
86,
84
]
},
{
"date_from": "2024-01-20",
"date_to": "2024-01-20",
"timestamp": 1705708800,
"values": [
91,
72
]
},
{
"date_from": "2024-01-21",
"date_to": "2024-01-21",
"timestamp": 1705795200,
"values": [
75,
79
]
},
{
"date_from": "2024-01-22",
"date_to": "2024-01-22",
"timestamp": 1705881600,
"values": [
89,
73
]
},
{
"date_from": "2024-01-23",
"date_to": "2024-01-23",
"timestamp": 1705968000,
"values": [
75,
56
]
},
{
"date_from": "2024-01-24",
"date_to": "2024-01-24",
"timestamp": 1706054400,
"values": [
74,
54
]
},
{
"date_from": "2024-01-25",
"date_to": "2024-01-25",
"timestamp": 1706140800,
"values": [
79,
58
]
},
{
"date_from": "2024-01-26",
"date_to": "2024-01-26",
"timestamp": 1706227200,
"values": [
76,
53
]
},
{
"date_from": "2024-01-27",
"date_to": "2024-01-27",
"timestamp": 1706313600,
"values": [
79,
74
]
},
{
"date_from": "2024-01-28",
"date_to": "2024-01-28",
"timestamp": 1706400000,
"values": [
83,
58
]
},
{
"date_from": "2024-01-29",
"date_to": "2024-01-29",
"timestamp": 1706486400,
"values": [
79,
62
]
},
{
"date_from": "2024-01-30",
"date_to": "2024-01-30",
"timestamp": 1706572800,
"values": [
70,
56
]
},
{
"date_from": "2024-01-31",
"date_to": "2024-01-31",
"timestamp": 1706659200,
"values": [
60,
54
]
},
{
"date_from": "2024-02-01",
"date_to": "2024-02-01",
"timestamp": 1706745600,
"values": [
60,
48
]
},
{
"date_from": "2024-02-02",
"date_to": "2024-02-02",
"timestamp": 1706832000,
"values": [
62,
49
]
},
{
"date_from": "2024-02-03",
"date_to": "2024-02-03",
"timestamp": 1706918400,
"values": [
68,
58
]
},
{
"date_from": "2024-02-04",
"date_to": "2024-02-04",
"timestamp": 1707004800,
"values": [
62,
42
]
},
{
"date_from": "2024-02-05",
"date_to": "2024-02-05",
"timestamp": 1707091200,
"values": [
66,
63
]
},
{
"date_from": "2024-02-06",
"date_to": "2024-02-06",
"timestamp": 1707177600,
"values": [
61,
60
]
},
{
"date_from": "2024-02-07",
"date_to": "2024-02-07",
"timestamp": 1707264000,
"values": [
73,
47
]
},
{
"date_from": "2024-02-08",
"date_to": "2024-02-08",
"timestamp": 1707350400,
"values": [
68,
50
]
},
{
"date_from": "2024-02-09",
"date_to": "2024-02-09",
"timestamp": 1707436800,
"values": [
68,
44
]
},
{
"date_from": "2024-02-10",
"date_to": "2024-02-10",
"timestamp": 1707523200,
"values": [
67,
55
]
},
{
"date_from": "2024-02-11",
"date_to": "2024-02-11",
"timestamp": 1707609600,
"values": [
67,
43
]
},
{
"date_from": "2024-02-12",
"date_to": "2024-02-12",
"timestamp": 1707696000,
"values": [
69,
59
]
},
{
"date_from": "2024-02-13",
"date_to": "2024-02-13",
"timestamp": 1707782400,
"values": [
57,
43
]
},
{
"date_from": "2024-02-14",
"date_to": "2024-02-14",
"timestamp": 1707868800,
"values": [
65,
43
]
},
{
"date_from": "2024-02-15",
"date_to": "2024-02-15",
"timestamp": 1707955200,
"values": [
67,
49
]
},
{
"date_from": "2024-02-16",
"date_to": "2024-02-16",
"timestamp": 1708041600,
"values": [
70,
49
]
},
{
"date_from": "2024-02-17",
"date_to": "2024-02-17",
"timestamp": 1708128000,
"values": [
85,
55
]
},
{
"date_from": "2024-02-18",
"date_to": "2024-02-18",
"timestamp": 1708214400,
"values": [
68,
50
]
},
{
"date_from": "2024-02-19",
"date_to": "2024-02-19",
"timestamp": 1708300800,
"values": [
79,
56
]
},
{
"date_from": "2024-02-20",
"date_to": "2024-02-20",
"timestamp": 1708387200,
"values": [
71,
47
]
},
{
"date_from": "2024-02-21",
"date_to": "2024-02-21",
"timestamp": 1708473600,
"values": [
75,
54
]
},
{
"date_from": "2024-02-22",
"date_to": "2024-02-22",
"timestamp": 1708560000,
"values": [
92,
45
]
},
{
"date_from": "2024-02-23",
"date_to": "2024-02-23",
"timestamp": 1708646400,
"values": [
81,
44
]
},
{
"date_from": "2024-02-24",
"date_to": "2024-02-24",
"timestamp": 1708732800,
"values": [
79,
47
]
},
{
"date_from": "2024-02-25",
"date_to": "2024-02-25",
"timestamp": 1708819200,
"values": [
83,
40
]
},
{
"date_from": "2024-02-26",
"date_to": "2024-02-26",
"timestamp": 1708905600,
"values": [
78,
51
]
},
{
"date_from": "2024-02-27",
"date_to": "2024-02-27",
"timestamp": 1708992000,
"values": [
68,
45
]
},
{
"date_from": "2024-02-28",
"date_to": "2024-02-28",
"timestamp": 1709078400,
"values": [
79,
53
]
},
{
"date_from": "2024-02-29",
"date_to": "2024-02-29",
"timestamp": 1709164800,
"values": [
60,
35
]
},
{
"date_from": "2024-03-01",
"date_to": "2024-03-01",
"timestamp": 1709251200,
"values": [
68,
45
]
},
{
"date_from": "2024-03-02",
"date_to": "2024-03-02",
"timestamp": 1709337600,
"values": [
62,
52
]
},
{
"date_from": "2024-03-03",
"date_to": "2024-03-03",
"timestamp": 1709424000,
"values": [
74,
58
]
},
{
"date_from": "2024-03-04",
"date_to": "2024-03-04",
"timestamp": 1709510400,
"values": [
78,
47
]
},
{
"date_from": "2024-03-05",
"date_to": "2024-03-05",
"timestamp": 1709596800,
"values": [
66,
48
]
},
{
"date_from": "2024-03-06",
"date_to": "2024-03-06",
"timestamp": 1709683200,
"values": [
76,
53
]
},
{
"date_from": "2024-03-07",
"date_to": "2024-03-07",
"timestamp": 1709769600,
"values": [
79,
45
]
},
{
"date_from": "2024-03-08",
"date_to": "2024-03-08",
"timestamp": 1709856000,
"values": [
63,
52
]
},
{
"date_from": "2024-03-09",
"date_to": "2024-03-09",
"timestamp": 1709942400,
"values": [
69,
46
]
},
{
"date_from": "2024-03-10",
"date_to": "2024-03-10",
"timestamp": 1710028800,
"values": [
71,
43
]
},
{
"date_from": "2024-03-11",
"date_to": "2024-03-11",
"timestamp": 1710115200,
"values": [
61,
35
]
},
{
"date_from": "2024-03-12",
"date_to": "2024-03-12",
"timestamp": 1710201600,
"values": [
58,
40
]
},
{
"date_from": "2024-03-13",
"date_to": "2024-03-13",
"timestamp": 1710288000,
"values": [
76,
48
]
},
{
"date_from": "2024-03-14",
"date_to": "2024-03-14",
"timestamp": 1710374400,
"values": [
72,
39
]
},
{
"date_from": "2024-03-15",
"date_to": "2024-03-15",
"timestamp": 1710460800,
"values": [
71,
47
]
},
{
"date_from": "2024-03-16",
"date_to": "2024-03-16",
"timestamp": 1710547200,
"values": [
73,
51
]
},
{
"date_from": "2024-03-17",
"date_to": "2024-03-17",
"timestamp": 1710633600,
"values": [
63,
49
]
},
{
"date_from": "2024-03-18",
"date_to": "2024-03-18",
"timestamp": 1710720000,
"values": [
63,
43
]
},
{
"date_from": "2024-03-19",
"date_to": "2024-03-19",
"timestamp": 1710806400,
"values": [
75,
49
]
},
{
"date_from": "2024-03-20",
"date_to": "2024-03-20",
"timestamp": 1710892800,
"values": [
58,
38
]
},
{
"date_from": "2024-03-21",
"date_to": "2024-03-21",
"timestamp": 1710979200,
"values": [
65,
46
]
},
{
"date_from": "2024-03-22",
"date_to": "2024-03-22",
"timestamp": 1711065600,
"values": [
79,
56
]
},
{
"date_from": "2024-03-23",
"date_to": "2024-03-23",
"timestamp": 1711152000,
"values": [
73,
51
]
},
{
"date_from": "2024-03-24",
"date_to": "2024-03-24",
"timestamp": 1711238400,
"values": [
74,
57
]
},
{
"date_from": "2024-03-25",
"date_to": "2024-03-25",
"timestamp": 1711324800,
"values": [
83,
52
]
},
{
"date_from": "2024-03-26",
"date_to": "2024-03-26",
"timestamp": 1711411200,
"values": [
73,
49
]
},
{
"date_from": "2024-03-27",
"date_to": "2024-03-27",
"timestamp": 1711497600,
"values": [
22,
21
]
},
{
"date_from": "2024-03-28",
"date_to": "2024-03-28",
"timestamp": 1711584000,
"values": [
1,
7
]
},
{
"date_from": "2024-03-29",
"date_to": "2024-03-29",
"timestamp": 1711670400,
"values": [
5,
12
]
},
{
"date_from": "2024-03-30",
"date_to": "2024-03-30",
"timestamp": 1711756800,
"values": [
null,
null
]
},
{
"date_from": "2024-03-31",
"date_to": "2024-03-31",
"timestamp": 1711843200,
"values": [
9,
9
]
},
{
"date_from": "2024-04-01",
"date_to": "2024-04-01",
"timestamp": 1711929600,
"values": [
2,
7
]
},
{
"date_from": "2024-04-02",
"date_to": "2024-04-02",
"timestamp": 1712016000,
"values": [
2,
2
]
},
{
"date_from": "2024-04-03",
"date_to": "2024-04-03",
"timestamp": 1712102400,
"values": [
0,
9
]
},
{
"date_from": "2024-04-04",
"date_to": "2024-04-04",
"timestamp": 1712188800,
"values": [
4,
10
]
},
{
"date_from": "2024-04-05",
"date_to": "2024-04-06",
"timestamp": 1712275200,
"values": [
0,
15
]
}
],
"averages": [
65,
48
]
}
]
}
]
}
]
}在items數組中，您將找到dataforseo_trends_graph element包含您指定的關鍵字的 。此元素本身包含數組data，您可以在其中找到給定時間范圍內特定時間戳的相對關鍵字流行度。此外，在請求的末尾，還有一個averages數組，提供整個時間范圍內平均的估計關鍵字流行度值。
估算的搜索興趣數據可以成為您現有營銷工具和產品的寶貴補充。API 趨勢數據可以無縫集成到現有的 SEO 工具、插件和關鍵字排名跟蹤解決方案中。除了提供搜索量和其他關鍵字指標的數據外，您還可以讓客戶實時監控搜索趨勢，并深入了解區域搜索興趣和人口統計細分。
例如，您可以根據關鍵字趨勢數據創建預覽關鍵字研究工具。此工具可讓您的客戶在購買完整產品之前分析某些搜索查詢在不同地區、年齡組和性別中的受歡迎程度。
為了說明其如何工作，讓我們檢查一下從 API 請求生成的圖表示例。
第一張圖顯示了過去 90 天內，美國哪些州對“壽司外賣”這一搜索查詢的搜索熱度最高。第二張圖顯示了不同年齡段的男性和女性用戶對此查詢的搜索興趣分布。
通過DataForSEO API 的“合并數據”端點可以獲取兩個圖表的值。
POST https://api.dataforseo.com/v3/keywords_data/dataforseo_trends/merged_data/live
示例請求
[
{
"keywords": [
"sushi delivery"
],
"location_name": "United States",
"date_from": "2024-01-11",
"date_to": "2024-04-06",
"type": "web"
}
]以下是您將獲得的響應示例
{
"version": "0.1.20240313",
"status_code": 20000,
"status_message": "Ok.",
"time": "4.8994 sec.",
"cost": 0.005,
"tasks_count": 1,
"tasks_error": 0,
"tasks": [
{
"id": "04101640-1535-0575-0000-a8964098467b",
"status_code": 20000,
"status_message": "Ok.",
"time": "4.8385 sec.",
"cost": 0.005,
"result_count": 1,
"path": [
"v3",
"keywords_data",
"dataforseo_trends",
"merged_data",
"live"
],
"data": {
"api": "keywords_data",
"function": "merged_data",
"se": "dataforseo_trends",
"keywords": [
"sushi delivery"
],
"location_name": "United States",
"date_from": "2024-01-11",
"date_to": "2024-04-06",
"type": "web"
},
"result": [
{
"keywords": [
"sushi delivery"
],
"type": "trends",
"location_code": 2840,
"language_code": null,
"datetime": "2024-04-10 13:40:08 +00:00",
"items_count": 3,
"items": [
{
"position": 1,
"type": "dataforseo_trends_graph",
"keywords": [
"sushi delivery"
],
"data": [
{
"date_from": "2024-01-11",
"date_to": "2024-01-11",
"timestamp": 1704931200,
"values": [
2
]
},
{
"date_from": "2024-01-12",
"date_to": "2024-01-12",
"timestamp": 1705017600,
"values": [
2
]
},
{
"date_from": "2024-01-13",
"date_to": "2024-01-13",
"timestamp": 1705104000,
"values": [
10
]
},
{
"date_from": "2024-01-14",
"date_to": "2024-01-14",
"timestamp": 1705190400,
"values": [
7
]
},
{
"date_from": "2024-01-15",
"date_to": "2024-01-15",
"timestamp": 1705276800,
"values": [
9
]
},
{
"date_from": "2024-01-16",
"date_to": "2024-01-16",
"timestamp": 1705363200,
"values": [
4
]
},
{
"date_from": "2024-01-17",
"date_to": "2024-01-17",
"timestamp": 1705449600,
"values": [
3
]
},
{
"date_from": "2024-01-18",
"date_to": "2024-01-18",
"timestamp": 1705536000,
"values": [
18
]
},
{
"date_from": "2024-01-19",
"date_to": "2024-01-19",
"timestamp": 1705622400,
"values": [
9
]
},
{
"date_from": "2024-01-20",
"date_to": "2024-01-20",
"timestamp": 1705708800,
"values": [
3
]
},
{
"date_from": "2024-01-21",
"date_to": "2024-01-21",
"timestamp": 1705795200,
"values": [
2
]
},
{
"date_from": "2024-01-22",
"date_to": "2024-01-22",
"timestamp": 1705881600,
"values": [
4
]
},
{
"date_from": "2024-01-23",
"date_to": "2024-01-23",
"timestamp": 1705968000,
"values": [
2
]
},
{
"date_from": "2024-01-24",
"date_to": "2024-01-24",
"timestamp": 1706054400,
"values": [
3
]
},
{
"date_from": "2024-01-25",
"date_to": "2024-01-25",
"timestamp": 1706140800,
"values": [
14
]
},
{
"date_from": "2024-01-26",
"date_to": "2024-01-26",
"timestamp": 1706227200,
"values": [
4
]
},
{
"date_from": "2024-01-27",
"date_to": "2024-01-27",
"timestamp": 1706313600,
"values": [
2
]
},
{
"date_from": "2024-01-28",
"date_to": "2024-01-28",
"timestamp": 1706400000,
"values": [
0
]
},
{
"date_from": "2024-01-29",
"date_to": "2024-01-29",
"timestamp": 1706486400,
"values": [
21
]
},
{
"date_from": "2024-01-30",
"date_to": "2024-01-30",
"timestamp": 1706572800,
"values": [
4
]
},
{
"date_from": "2024-01-31",
"date_to": "2024-01-31",
"timestamp": 1706659200,
"values": [
2
]
},
{
"date_from": "2024-02-01",
"date_to": "2024-02-01",
"timestamp": 1706745600,
"values": [
2
]
},
{
"date_from": "2024-02-02",
"date_to": "2024-02-02",
"timestamp": 1706832000,
"values": [
2
]
},
{
"date_from": "2024-02-03",
"date_to": "2024-02-03",
"timestamp": 1706918400,
"values": [
2
]
},
{
"date_from": "2024-02-04",
"date_to": "2024-02-04",
"timestamp": 1707004800,
"values": [
5
]
},
{
"date_from": "2024-02-05",
"date_to": "2024-02-05",
"timestamp": 1707091200,
"values": [
12
]
},
{
"date_from": "2024-02-06",
"date_to": "2024-02-06",
"timestamp": 1707177600,
"values": [
3
]
},
{
"date_from": "2024-02-07",
"date_to": "2024-02-07",
"timestamp": 1707264000,
"values": [
13
]
},
{
"date_from": "2024-02-08",
"date_to": "2024-02-08",
"timestamp": 1707350400,
"values": [
5
]
},
{
"date_from": "2024-02-09",
"date_to": "2024-02-09",
"timestamp": 1707436800,
"values": [
3
]
},
{
"date_from": "2024-02-10",
"date_to": "2024-02-10",
"timestamp": 1707523200,
"values": [
6
]
},
{
"date_from": "2024-02-11",
"date_to": "2024-02-11",
"timestamp": 1707609600,
"values": [
23
]
},
{
"date_from": "2024-02-12",
"date_to": "2024-02-12",
"timestamp": 1707696000,
"values": [
19
]
},
{
"date_from": "2024-02-13",
"date_to": "2024-02-13",
"timestamp": 1707782400,
"values": [
5
]
},
{
"date_from": "2024-02-14",
"date_to": "2024-02-14",
"timestamp": 1707868800,
"values": [
3
]
},
{
"date_from": "2024-02-15",
"date_to": "2024-02-15",
"timestamp": 1707955200,
"values": [
2
]
},
{
"date_from": "2024-02-16",
"date_to": "2024-02-16",
"timestamp": 1708041600,
"values": [
3
]
},
{
"date_from": "2024-02-17",
"date_to": "2024-02-17",
"timestamp": 1708128000,
"values": [
1
]
},
{
"date_from": "2024-02-18",
"date_to": "2024-02-18",
"timestamp": 1708214400,
"values": [
3
]
},
{
"date_from": "2024-02-19",
"date_to": "2024-02-19",
"timestamp": 1708300800,
"values": [
9
]
},
{
"date_from": "2024-02-20",
"date_to": "2024-02-20",
"timestamp": 1708387200,
"values": [
3
]
},
{
"date_from": "2024-02-21",
"date_to": "2024-02-21",
"timestamp": 1708473600,
"values": [
2
]
},
{
"date_from": "2024-02-22",
"date_to": "2024-02-22",
"timestamp": 1708560000,
"values": [
1
]
},
{
"date_from": "2024-02-23",
"date_to": "2024-02-23",
"timestamp": 1708646400,
"values": [
3
]
},
{
"date_from": "2024-02-24",
"date_to": "2024-02-24",
"timestamp": 1708732800,
"values": [
2
]
},
{
"date_from": "2024-02-25",
"date_to": "2024-02-25",
"timestamp": 1708819200,
"values": [
4
]
},
{
"date_from": "2024-02-26",
"date_to": "2024-02-26",
"timestamp": 1708905600,
"values": [
15
]
},
{
"date_from": "2024-02-27",
"date_to": "2024-02-27",
"timestamp": 1708992000,
"values": [
10
]
},
{
"date_from": "2024-02-28",
"date_to": "2024-02-28",
"timestamp": 1709078400,
"values": [
5
]
},
{
"date_from": "2024-02-29",
"date_to": "2024-02-29",
"timestamp": 1709164800,
"values": [
2
]
},
{
"date_from": "2024-03-01",
"date_to": "2024-03-01",
"timestamp": 1709251200,
"values": [
2
]
},
{
"date_from": "2024-03-02",
"date_to": "2024-03-02",
"timestamp": 1709337600,
"values": [
2
]
},
{
"date_from": "2024-03-03",
"date_to": "2024-03-03",
"timestamp": 1709424000,
"values": [
2
]
},
{
"date_from": "2024-03-04",
"date_to": "2024-03-04",
"timestamp": 1709510400,
"values": [
3
]
},
{
"date_from": "2024-03-05",
"date_to": "2024-03-05",
"timestamp": 1709596800,
"values": [
2
]
},
{
"date_from": "2024-03-06",
"date_to": "2024-03-06",
"timestamp": 1709683200,
"values": [
2
]
},
{
"date_from": "2024-03-07",
"date_to": "2024-03-07",
"timestamp": 1709769600,
"values": [
4
]
},
{
"date_from": "2024-03-08",
"date_to": "2024-03-08",
"timestamp": 1709856000,
"values": [
47
]
},
{
"date_from": "2024-03-09",
"date_to": "2024-03-09",
"timestamp": 1709942400,
"values": [
7
]
},
{
"date_from": "2024-03-10",
"date_to": "2024-03-10",
"timestamp": 1710028800,
"values": [
9
]
},
{
"date_from": "2024-03-11",
"date_to": "2024-03-11",
"timestamp": 1710115200,
"values": [
6
]
},
{
"date_from": "2024-03-12",
"date_to": "2024-03-12",
"timestamp": 1710201600,
"values": [
1
]
},
{
"date_from": "2024-03-13",
"date_to": "2024-03-13",
"timestamp": 1710288000,
"values": [
1
]
},
{
"date_from": "2024-03-14",
"date_to": "2024-03-14",
"timestamp": 1710374400,
"values": [
2
]
},
{
"date_from": "2024-03-15",
"date_to": "2024-03-15",
"timestamp": 1710460800,
"values": [
20
]
},
{
"date_from": "2024-03-16",
"date_to": "2024-03-16",
"timestamp": 1710547200,
"values": [
3
]
},
{
"date_from": "2024-03-17",
"date_to": "2024-03-17",
"timestamp": 1710633600,
"values": [
1
]
},
{
"date_from": "2024-03-18",
"date_to": "2024-03-18",
"timestamp": 1710720000,
"values": [
1
]
},
{
"date_from": "2024-03-19",
"date_to": "2024-03-19",
"timestamp": 1710806400,
"values": [
2
]
},
{
"date_from": "2024-03-20",
"date_to": "2024-03-20",
"timestamp": 1710892800,
"values": [
2
]
},
{
"date_from": "2024-03-21",
"date_to": "2024-03-21",
"timestamp": 1710979200,
"values": [
3
]
},
{
"date_from": "2024-03-22",
"date_to": "2024-03-22",
"timestamp": 1711065600,
"values": [
3
]
},
{
"date_from": "2024-03-23",
"date_to": "2024-03-23",
"timestamp": 1711152000,
"values": [
2
]
},
{
"date_from": "2024-03-24",
"date_to": "2024-03-24",
"timestamp": 1711238400,
"values": [
0
]
},
{
"date_from": "2024-03-25",
"date_to": "2024-03-25",
"timestamp": 1711324800,
"values": [
1
]
},
{
"date_from": "2024-03-26",
"date_to": "2024-03-26",
"timestamp": 1711411200,
"values": [
1
]
},
{
"date_from": "2024-03-27",
"date_to": "2024-03-27",
"timestamp": 1711497600,
"values": [
4
]
},
{
"date_from": "2024-03-28",
"date_to": "2024-03-28",
"timestamp": 1711584000,
"values": [
4
]
},
{
"date_from": "2024-03-29",
"date_to": "2024-03-29",
"timestamp": 1711670400,
"values": [
1
]
},
{
"date_from": "2024-03-30",
"date_to": "2024-03-30",
"timestamp": 1711756800,
"values": [
0
]
},
{
"date_from": "2024-03-31",
"date_to": "2024-03-31",
"timestamp": 1711843200,
"values": [
2
]
},
{
"date_from": "2024-04-01",
"date_to": "2024-04-01",
"timestamp": 1711929600,
"values": [
28
]
},
{
"date_from": "2024-04-02",
"date_to": "2024-04-02",
"timestamp": 1712016000,
"values": [
17
]
},
{
"date_from": "2024-04-03",
"date_to": "2024-04-03",
"timestamp": 1712102400,
"values": [
91
]
},
{
"date_from": "2024-04-04",
"date_to": "2024-04-04",
"timestamp": 1712188800,
"values": [
100
]
},
{
"date_from": "2024-04-05",
"date_to": "2024-04-06",
"timestamp": 1712275200,
"values": [
17
]
}
],
"averages": [
8
]
},
{
"position": 2,
"type": "subregion_interests",
"keywords": [
"sushi delivery"
],
"interests": [
{
"keyword": "sushi delivery",
"values": [
{
"geo_id": null,
"geo_name": "California",
"value": 9
},
{
"geo_id": null,
"geo_name": "Florida",
"value": 24
},
{
"geo_id": null,
"geo_name": "Georgia",
"value": 21
},
{
"geo_id": null,
"geo_name": "Illinois",
"value": 22
},
{
"geo_id": null,
"geo_name": "Iowa",
"value": 62
},
{
"geo_id": null,
"geo_name": "Kansas",
"value": 100
},
{
"geo_id": null,
"geo_name": "Louisiana",
"value": 81
},
{
"geo_id": null,
"geo_name": "Maryland",
"value": 36
},
{
"geo_id": null,
"geo_name": "New York",
"value": 26
},
{
"geo_id": null,
"geo_name": "Pennsylvania",
"value": 15
},
{
"geo_id": null,
"geo_name": "Texas",
"value": 11
}
]
}
],
"interests_comparison": null
},
{
"position": 3,
"type": "demography",
"keywords": [
"sushi delivery"
],
"demography": {
"age": [
{
"keyword": "sushi delivery",
"values": [
{
"type": "18-24",
"value": 100
},
{
"type": "25-34",
"value": 80
},
{
"type": "35-44",
"value": 51
},
{
"type": "45-54",
"value": 48
}
]
}
],
"gender": [
{
"keyword": "sushi delivery",
"values": [
{
"type": "female",
"value": 83
},
{
"type": "male",
"value": 100
}
]
}
]
},
"demography_comparison": null
}
]
}
]
}
]
}在items響應的數組中，您將找到subregion_interests元素，其中包含每個指定術語的子區域關鍵字流行度數據，以及相應子區域的名稱。此外，在該demography元素中，您可以找到提供age不同年齡組中關鍵字流行度分布的數組。此外，該gender數組還按性別細分了關鍵字流行度值。
正如您所看到的，您只需一個簡單的請求即可獲得所需的所有基本搜索趨勢數據，并以方便且易于訪問的格式呈現。
如果您正在尋找超出 Google Trends 能力范圍的詳細而獨特的搜索趨勢數據洞察，DataForSEO Trends API 是一個值得考慮的絕佳替代方案。如果您需要以可承受的價格為大型項目獲取廣泛的關鍵字流行度數據，那么它尤其有價值。此外，整合搜索趨勢洞察可以顯著提升您現有關鍵字研究工具和相關產品的價值主張。
此外，使用 DataForSEO 趨勢 API，您可以快速獲取大量趨勢數據，而不必擔心請求限制和系統可用性。??您每分鐘最多可以進行2000 次 API 調用，如果您想進行更多調用，我們將根據您的需求提高限制。
最后，您可以輕松地將 DataForSEO Trends API 集成到您的網站或應用程序中——它與幾乎所有編程語言兼容，并且所有內容都在我們的文檔中進行了詳細說明。如果您想測試我們 API 的功能，請免費試用并熟悉DataForSEO Trends 工具。
原文鏈接：A Versatile Alternative to Google Trends: Exploring the Power of DataForSEO Trends API