This page provides you with instructions on how to extract data from Bing Ads and analyze it in Tableau. (If the mechanics of extracting data from Bing Ads seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
What is Bing Ads?
Bing Ads is a pay-per-click (PPC) advertising platform used to display ads based on the keywords that appear in Bing users' search queries.
What is Tableau?
Tableau is one of the world's most popular analysis platforms. The software helps companies model, explore, and visualize their data. It also offers cloud capabilities that allow analyses to be shared via the web or company intranets, and its offerings are available as both installed software and as a SaaS platform. Tableau is widely known for its robust and flexible visualization capabilities, which include dozens of specialized chart types.
In addition to its business software, Tableau also offers a free product called Tableau Public for analyzing open data sets. If you're new to Tableau, this offering is a great way to experience Tableau's capabilities at no cost and share your work publicly.
Getting data out of Bing Ads
Microsoft makes Bing Ads data available through a Bing Ads API, which offers data on things like ad insights, estimated bids, estimated positions, and many other kinds of data. Because it’s a SOAP API, scripts must call data objects by making SOAP request messages.
For example, to get data about bid opportunities, you could use the Bing Ads API GetBidOpportunities service. The service’s syntax includes four header elements and three body elements, two of which are optional. Once you decided exactly what information you wanted, you could code a SOAP request that might look like this:
<s:Envelope xmlns:i="http://www.w3.org/2001/XMLSchema-instance" xmlns:s="http://schemas.xmlsoap.org/soap/envelope/"> <s:Header xmlns="Microsoft.Advertiser.AdInsight.Api.Service.V11"> <Action mustUnderstand="1">GetBidOpportunities</Action> <ApplicationToken i:nil="false">ValueHere</ApplicationToken> <AuthenticationToken i:nil="false">ValueHere</AuthenticationToken> <CustomerAccountId i:nil="false">ValueHere</CustomerAccountId> <CustomerId i:nil="false">ValueHere</CustomerId> <DeveloperToken i:nil="false">ValueHere</DeveloperToken> <Password i:nil="false">ValueHere</Password> <UserName i:nil="false">ValueHere</UserName> </s:Header> <s:Body> <GetBidOpportunitiesRequest xmlns="Microsoft.Advertiser.AdInsight.Api.Service.V11"> <AdGroupId i:nil="false">ValueHere</AdGroupId> <CampaignId i:nil="false">ValueHere</CampaignId> <OpportunityType>ValueHere</OpportunityType> </GetBidOpportunitiesRequest> </s:Body> </s:Envelope>
Sample Bing Ads data
The Bing Ads API returns XML objects. In response to a bid opportunities request, for example, the service would provide a SOAP response that might look like this:
<s:Envelope xmlns:s="http://schemas.xmlsoap.org/soap/envelope/"> <s:Header xmlns="Microsoft.Advertiser.AdInsight.Api.Service.V11"> <TrackingId d3p1:nil="false" xmlns:d3p1="http://www.w3.org/2001/XMLSchema-instance">ValueHere</TrackingId> </s:Header> <s:Body> <GetBidOpportunitiesResponse xmlns="Microsoft.Advertiser.AdInsight.Api.Service.V11"> <Opportunities xmlns:e63="http://schemas.datacontract.org/2004/07/Microsoft.BingAds.Advertiser.AdInsight.Api.DataContract.V11.Entity" d4p1:nil="false" xmlns:d4p1="http://www.w3.org/2001/XMLSchema-instance"> <e63:BidOpportunity> <e63:AdGroupId>ValueHere</e63:AdGroupId> <e63:CampaignId>ValueHere</e63:CampaignId> <e63:CurrentBid>ValueHere</e63:CurrentBid> <e63:EstimatedIncreaseInClicks>ValueHere</e63:EstimatedIncreaseInClicks> <e63:EstimatedIncreaseInCost>ValueHere</e63:EstimatedIncreaseInCost> <e63:EstimatedIncreaseInImpressions>ValueHere</e63:EstimatedIncreaseInImpressions> <e63:KeywordId>ValueHere</e63:KeywordId> <e63:MatchType d4p1:nil="false">ValueHere</e63:MatchType> <e63:SuggestedBid>ValueHere</e63:SuggestedBid> </e63:BidOpportunity> </Opportunities> </GetBidOpportunitiesResponse> </s:Body> </s:Envelope>
Preparing Bing Ads data
If you don’t already have a data structure in which to store the data you retrieve, you’ll have to create a schema for your data tables. Then, for each value in the response, you’ll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. The source API documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.
Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. This means you’ll likely have to create additional tables to capture the unpredictable cardinality in each record.
Loading Data into Tableau
Analyzing data in Tableau requires putting it into a format that Tableau can read. Depending on the data source, you may have options for achieving this goal, but the best practice among most businesses is to build a data warehouse that contains the data, and then connect that data warehouse to Tableau.
Tableau provides an easy-to-use Connect menu that allows you to connect data from flat files, direct data sources, and data warehouses. In most cases, connecting these sources is simply a matter of creating and providing credentials to the relevant services.
Once the data is connected, Tableau offers an option for locally caching your data to speed up queries. This can make a big difference when working with slower database platforms or flat files, but is typically not necessary when using a scalable data warehouse platform. Tableau's flexibility and speed in these areas are among its major differentiators in the industry.
Analyzing Data in Tableau
Tableau's report-building interface may seem intimidating at first, but it's one of the most powerful and intuitive analytics UIs on the market. Once you understand its workflow, it offers fast and nearly limitless options for building reports and dashboards.
If you're familiar with Pivot Tables in Excel, the Tableau report building experience may feel somewhat familiar. The process involves selecting the rows and columns desired in the resulting data set, along with the aggregate functions used to populate the data cells. Users can also specify filters to be applied to the data and choose a visualization type to use for the report.
You can learn how to build a report from scratch for free (although a sign-in is required) from the Tableau documentation.
Keeping Bing Ads data up to date
At this point you’ve coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.
Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Bing Ads.
And remember, as with any code, once you write it, you have to maintain it. If Microsoft modifies the Bing Ads API, or if the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.
From Bing Ads to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Bing Ads data in Tableau is to store that data inside a data warehousing platform alongside data from your other databases and 3rd party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Bing Ads to Redshift, Bing Ads to BigQuery, and Bing Ads to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Bing Ads data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Tableau.