Everyone wants to know where they rank. Ranking data from Bing Webmaster Tools helps in getting to know this important information allows you to run experiments and watch for the best performers across multiple tactics.
"You've got to start somewhere, the saying goes, and that's often "beyond 100" in the rankings. Whether you find value after page three, or after page ten matters little," Duane Forrester stated. "What matters is that first moment you see hard data indicating a change is happening. That your page is ranking, and getting clicks." And even "those two elements - ranking & getting clicks - are two entirely different animals as your data will show. You could easily rank and get no clicks. What's the point then?"
Bing Webmaster Tools provides deep insights into this information, with two paths to discovery. Forrester explains, you can come at this problem from the URL end or the keyword end. The two paths are as follows:
- Log into your Bing Webmaster Tools account
- Select a site to work on (if you only have one site verified, you'll automatically be taken to this Dashboard)
- In the navigation, click on the small arrow next to Reports & Data (circled in orange below)
- Click on either Page Traffic or Search Keywords (orange arrows)
- On the far right of the report, click on (View)
- Inside the report popup, click on the + symbol to the left of the keyword or URL to see detailed information
"If you click on Search Keywords, you'll see a randomized list in the report. You can click on any heading to resort the data around that column. Clicking the same heading again reverses the order of the data shown," Forrester explains.
"If you click on the (View) link on the right of the report, you'll see a popup appear with information available related to just the item you clicked on. Clicking on the + symbol, expands the data you can see related to the keyword, and the URL that ranked for that keyword."
Now you're seeing not average ranking positions, but actual rank positions. You're also seeing clicks in each position and click through rates for each position. "All of this data is reflected against the date range you selected at the top of the reports page, so you can tailor this information for a specific period of time if needed," he said.
In a latest Quality Insights series, Paul Yiu, Principal Group Program Manager for Bing Social, provides an overview of how they've incorporated people into Bing's latest release. "No matter what query you submit to Bing, you may be amazed that some of your friends, influentials or experts often know something that you're searching for, in addition to high quality web documents that you always count on," posted Dr. Harry Shum, Corporate Vice President, Bing R&D.
Friends Who Might Know
Bing will recommend people in the sidebar that might be able to help, based on "what your friends have been paying attention to, and their profile information," Paul informs.
In terms of how we order these friends in the sidebar, Paul explains, "it's a combination of how many activities and attributes match your query, the type of activities and attributes that made the friend relevant, and how likely our ranking system thinks you'll find that information useful." Since, its launch, "the system is learning quickly which types of information inspire the most engagement from users. The more you use the product, the more accurate that Bing gets at recommending friends that might be able to help," Paul stated.
People Who Know
Bing can also help you find people who're influential about the topic you're searching, based on what they've publically blogged or tweeted about. You can follow them, ask them a question or see what they've shared in the past.
"While results may vary when it comes to Friends Who Might Know, in the People Who Know section of the Sidebar, for now, Bing displays the same results for each user. The idea is to recommend people that are influential or popular in the context of your query or topic," Paul said.
"There is some similarity here to how we think about ranking documents. There are signals that are relatively static, and there are signals that are more dependent on the query," Paul wrote. In terms of static signals, we look at:
- "Followers in Twitter, and how many there are
- How influential the person is in general, i.e., how much does he or she get re-tweeted
- Who he or she follows on Twitter
- The likelihood that the Twitter user is a spammer based on peculiarities in his or her connectivity graph," explained.
When it comes to query-dependent signals, "we look at a user's influence, i.e., how well does his or her content get retweeted around this particular topic," Paul said. In a way, a retweet is like "social anchor text."
"While general authority matters, our machine learning techniques try to surface the people who're Influential or Popular for the particular query topic at hand. As a result, people who are generally influential on Twitter (i.e. have a large general following) may not be guaranteed to appear for a query or topic where he or she has little influence," explains Paul.
For example, Kim Kardashian is influential on Twitter but probably won't appear for the query "machine learning."