免費(fèi)API攔截工具入門指南：初學(xué)者必備教程與實(shí)用技巧
如果您指定一個(gè)關(guān)鍵字，此部分的流行度值將讓您了解該關(guān)鍵字在某些位置之間的流行度。為了計(jì)算結(jié)果，我們確定所有位置中相關(guān)關(guān)鍵字的最高流行度值，并將所有其他值表示為最高關(guān)鍵字流行度值的百分比。
如果您指定多個(gè)關(guān)鍵字，關(guān)鍵字流行度值將幫助您了解每個(gè)關(guān)鍵字的流行度——與所有給定位置的其他關(guān)鍵字相比。在這種情況下，我們確定所有指定位置的所有關(guān)鍵字中的最高流行度值。然后，我們將每個(gè)關(guān)鍵字的流行度表示為最高關(guān)鍵字流行度值的百分比。
您可以試驗(yàn)關(guān)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(shì)工具即可，它是免費(fèi)的！
您可能想知道我們的新 DataForSEO Trends API 和 Google Trends API 之間的區(qū)別。這些 API 可能看起來(lái)相似，但兩者之間存在關(guān)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(zhǔn)確的數(shù)據(jù)，但其平臺(tái)偶爾會(huì)遇到服務(wù)中斷或提供不完整數(shù)據(jù)的情況，這使得頻繁調(diào)用 API 來(lái)獲取大量的歷史趨勢(shì)數(shù)據(jù)變得困難。
另一方面，DataForSEO Trends API 被設(shè)計(jì)成一個(gè)可靠且可擴(kuò)展的解決方案，用于檢索關(guān)鍵字趨勢(shì)和流行度數(shù)據(jù)。此外，我們還確保了與 Google Trends API 的參數(shù)保持最大兼容性，以便于用戶的平穩(wěn)過(guò)渡。
下一個(gè)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(guān)鍵字隨時(shí)間變化的流行度、特定地區(qū)的流行度以及相關(guān)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(jié)果，并檢索特定時(shí)間點(diǎn)的數(shù)據(jù)。
另一方面，正如我們之前提到的，DataForSEO Trends API 分析來(lái)自多種來(lái)源的數(shù)據(jù)，包括頁(yè)面內(nèi)容的流行度和網(wǎng)絡(luò)性能指標(biāo)，并處理點(diǎn)擊流數(shù)據(jù)以更好地理解用戶行為。
我們的算法的最大優(yōu)勢(shì)之一是，它允許您按性別和年齡細(xì)分搜索興趣，并立即比較估計(jì)結(jié)果。Google Trends API 本身沒(méi)有這樣的機(jī)會(huì)。為此，我們?cè)?DataForSEO Trends API 中引入了四個(gè)獨(dú)立的端點(diǎn)：
每個(gè)端點(diǎn)都提供了比較關(guān)鍵字流行度并給出相關(guān)結(jié)果的功能。另外，與 Google Trends API 不同，DataForSEO Trends API 僅支持“實(shí)時(shí)”數(shù)據(jù)檢索，這意味著每次請(qǐng)求都會(huì)獲得最新數(shù)據(jù)。
如果您想了解更多關(guān)于 DataForSEO Trends API 的細(xì)節(jié)，請(qǐng)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(qū)別。
?Google Trends API：
?DataForSEO Trends API：
請(qǐng)注意，DataForSEO Trends API 提供的是基于算法分析的估計(jì)數(shù)據(jù)，并不提供確切的搜索量數(shù)據(jù)或其他關(guān)鍵字指標(biāo)。若需要精確的搜索量數(shù)據(jù)，建議使用我們的關(guān)鍵字?jǐn)?shù)據(jù) API 的“搜索量”端點(diǎn)。
現(xiàn)在您已經(jīng)了解到 DataForSEO Trends API 是一個(gè)多功能且可擴(kuò)展的搜索趨勢(shì)數(shù)據(jù)檢索解決方案。接下來(lái)，我們來(lái)看看如何通過(guò)幾個(gè)關(guān)鍵的應(yīng)用案例，將它的功能轉(zhuǎn)化為您項(xiàng)目和工具的實(shí)際價(jià)值。
如何利用 DataForSEO Trends API 的強(qiáng)大功能？需要考慮的主要用例
出于這些目的，DataForSEO Trends API 以其經(jīng)濟(jì)實(shí)惠和多功能性脫穎而出。為了說(shuō)明這一點(diǎn)，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(gè)關(guān)鍵字?jǐn)?shù)據(jù)的成本。在實(shí)時(shí)模式下通過(guò) Google Ads API 獲取 10,000 個(gè)關(guān)鍵字的數(shù)據(jù)將花費(fèi) 0.75 美元（10 個(gè)任務(wù)，每個(gè)任務(wù) 1,000 個(gè)關(guān)鍵字）。在 Bing Ads API 中，50 個(gè)任務(wù)，每個(gè)任務(wù) 200 個(gè)關(guān)鍵字的成本為 3.75 美元。雖然這些價(jià)格似乎很實(shí)惠，但估算關(guān)鍵字流行度值需要手動(dòng)計(jì)算和比較結(jié)果。（在實(shí)時(shí)模式下通過(guò) Google Ads API 獲取 10,000 個(gè)關(guān)鍵字的數(shù)據(jù)將花費(fèi) 0.75 美元（10 個(gè)任務(wù)，每個(gè)任務(wù) 1,000 個(gè)關(guān)鍵字）。而在 Bing Ads API 中，50 個(gè)任務(wù)，每個(gè)任務(wù) 200 個(gè)關(guān)鍵字的成本為 3.75 美元。盡管價(jià)格看似合理，但估算關(guān)鍵字流行度值仍需手動(dòng)計(jì)算和比較。）
使用 Google Trends API 在“實(shí)時(shí)”模式下提供 10,000 個(gè)關(guān)鍵字的關(guān)鍵字流行度數(shù)據(jù)將花費(fèi) 18 美元（2,000 個(gè)任務(wù)，每個(gè)任務(wù) 5 個(gè)關(guān)鍵字）。但是，如前所述，Google Trends 并不總是可靠地運(yùn)行，這在檢索大量關(guān)鍵字?jǐn)?shù)據(jù)時(shí)可能會(huì)導(dǎo)致問(wèn)題。另一方面，對(duì)于相同數(shù)量的關(guān)鍵字，使用 DataForSEO Trends API“探索”端點(diǎn)只需支付 2 美元。此外，它以實(shí)時(shí)模式檢索數(shù)據(jù)并立即比較結(jié)果。（使用 Google Trends API 在“實(shí)時(shí)”模式下獲取 10,000 個(gè)關(guān)鍵字的流行度數(shù)據(jù)將花費(fèi) 18 美元（2,000 個(gè)任務(wù)，每個(gè)任務(wù) 5 個(gè)關(guān)鍵字）。然而，如前所述，Google Trends 在可靠性方面存在問(wèn)題，尤其是在處理大量關(guān)鍵字?jǐn)?shù)據(jù)時(shí)。相比之下，使用 DataForSEO Trends API 的“探索”端點(diǎn)只需支付 2 美元，并且它能在實(shí)時(shí)模式下檢索數(shù)據(jù)并即時(shí)比較結(jié)果。）
| API | 價(jià)格 |
|---|---|
| Google 廣告 API | 10,000 個(gè)關(guān)鍵詞的數(shù)據(jù)價(jià)格為 0.75 美元。 |
| Bing 廣告 API | 10,000 個(gè)關(guān)鍵詞的數(shù)據(jù)價(jià)格為 3.75 美元。 |
| Google 趨勢(shì) API | 10,000 個(gè)關(guān)鍵詞數(shù)據(jù)售價(jià) 18 美元。 即時(shí)關(guān)鍵詞流行度值估算和比較。 |
| DataForSEO 趨勢(shì) API | 10,000 個(gè)關(guān)鍵詞數(shù)據(jù)售價(jià) 2 美元。 即時(shí)關(guān)鍵詞流行度值估算和比較。 |
?
如您所見(jiàn)，DataForSEO Trends API 可讓您以最高的成本效益訪問(wèn)關(guān)鍵字趨勢(shì)數(shù)據(jù)。要了解有關(guān) DataForSEO Trends API 定價(jià)的更多信息，請(qǐng)?jiān)L問(wèn)我們的定價(jià)頁(yè)面。
以下是使用 DataForSEO Trends API 檢索歷史關(guān)鍵字熱度數(shù)據(jù)的示例。假設(shè)您想比較過(guò)去 90 天內(nèi)美國(guó)對(duì)“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(shí)間段內(nèi)搜索查詢“iphone”和“samsung”的流行度值如何逐日波動(dòng)。
為了生成下圖，我們需要向DataForSEO Trends API 的“探索”端點(diǎn)發(fā)出請(qǐng)求。
POST https://api.dataforseo.com/v3/keywords_data/dataforseo_trends/explore/live
示例請(qǐng)求
[
{
"keywords": [
"iphone",
"samsung"
],
"location_name": "United States",
"date_from": "2024-01-11",
"date_to": "2024-04-06",
"type": "web"
}
]結(jié)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(shù)組中，您將找到dataforseo_trends_graph element包含您指定的關(guān)鍵字的 。此元素本身包含數(shù)組data，您可以在其中找到給定時(shí)間范圍內(nèi)特定時(shí)間戳的相對(duì)關(guān)鍵字流行度。此外，在請(qǐng)求的末尾，還有一個(gè)averages數(shù)組，提供整個(gè)時(shí)間范圍內(nèi)平均的估計(jì)關(guān)鍵字流行度值。
估算的搜索興趣數(shù)據(jù)可以成為您現(xiàn)有營(yíng)銷工具和產(chǎn)品的寶貴補(bǔ)充。API 趨勢(shì)數(shù)據(jù)可以無(wú)縫集成到現(xiàn)有的 SEO 工具、插件和關(guān)鍵字排名跟蹤解決方案中。除了提供搜索量和其他關(guān)鍵字指標(biāo)的數(shù)據(jù)外，您還可以讓客戶實(shí)時(shí)監(jiān)控搜索趨勢(shì)，并深入了解區(qū)域搜索興趣和人口統(tǒng)計(jì)細(xì)分。
例如，您可以根據(jù)關(guān)鍵字趨勢(shì)數(shù)據(jù)創(chuàng)建預(yù)覽關(guān)鍵字研究工具。此工具可讓您的客戶在購(gòu)買完整產(chǎn)品之前分析某些搜索查詢?cè)诓煌貐^(qū)、年齡組和性別中的受歡迎程度。
為了說(shuō)明其如何工作，讓我們檢查一下從 API 請(qǐng)求生成的圖表示例。
第一張圖顯示了過(guò)去 90 天內(nèi)，美國(guó)哪些州對(duì)“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(duì)此查詢的搜索興趣分布。
通過(guò)DataForSEO API 的“合并數(shù)據(jù)”端點(diǎn)可以獲取兩個(gè)圖表的值。
POST https://api.dataforseo.com/v3/keywords_data/dataforseo_trends/merged_data/live
示例請(qǐng)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(yīng)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(yīng)的數(shù)組中，您將找到subregion_interests元素，其中包含每個(gè)指定術(shù)語(yǔ)的子區(qū)域關(guān)鍵字流行度數(shù)據(jù)，以及相應(yīng)子區(qū)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(guān)鍵字流行度分布的數(shù)組。此外，該gender數(shù)組還按性別細(xì)分了關(guān)鍵字流行度值。
正如您所看到的，您只需一個(gè)簡(jiǎn)單的請(qǐng)求即可獲得所需的所有基本搜索趨勢(shì)數(shù)據(jù)，并以方便且易于訪問(wèn)的格式呈現(xiàn)。
如果您正在尋找超出 Google Trends 能力范圍的詳細(xì)而獨(dú)特的搜索趨勢(shì)數(shù)據(jù)洞察，DataForSEO Trends API 是一個(gè)值得考慮的絕佳替代方案。如果您需要以可承受的價(jià)格為大型項(xiàng)目獲取廣泛的關(guān)鍵字流行度數(shù)據(jù)，那么它尤其有價(jià)值。此外，整合搜索趨勢(shì)洞察可以顯著提升您現(xiàn)有關(guān)鍵字研究工具和相關(guān)產(chǎn)品的價(jià)值主張。
此外，使用 DataForSEO 趨勢(shì) API，您可以快速獲取大量趨勢(shì)數(shù)據(jù)，而不必?fù)?dān)心請(qǐng)求限制和系統(tǒng)可用性。??您每分鐘最多可以進(jìn)行2000 次 API 調(diào)用，如果您想進(jìn)行更多調(diào)用，我們將根據(jù)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(wǎng)站或應(yīng)用程序中——它與幾乎所有編程語(yǔ)言兼容，并且所有內(nèi)容都在我們的文檔中進(jìn)行了詳細(xì)說(shuō)明。如果您想測(cè)試我們 API 的功能，請(qǐng)免費(fèi)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
免費(fèi)API攔截工具入門指南：初學(xué)者必備教程與實(shí)用技巧
LLM內(nèi)容創(chuàng)作能力評(píng)估
使用Python語(yǔ)言調(diào)用零一萬(wàn)物API實(shí)戰(zhàn)指南
探索中國(guó)特色內(nèi)容知識(shí)的API合輯：構(gòu)建智慧應(yīng)用的橋梁
API 市場(chǎng)在 5 個(gè)領(lǐng)域中的作用
有道翻譯API：開(kāi)啟多語(yǔ)言交流新篇章
如何免費(fèi)調(diào)用手機(jī)歸屬地查詢API實(shí)現(xiàn)用戶定位
WordPress REST API 初學(xué)者指南
Python 輕松實(shí)現(xiàn)天氣提醒：通過(guò)天氣實(shí)況 API 獲取城市氣溫
對(duì)比大模型API的內(nèi)容創(chuàng)意新穎性、情感共鳴力、商業(yè)轉(zhuǎn)化潛力
一鍵對(duì)比試用API 限時(shí)免費(fèi)