Widget Analytics – Measuring the widgets in the wild

Helping web analysts navigate the measurement and tracking of widgets.

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How Do You Value Your Performance Metrics?

Posted by widgetgirl on October 30, 2009

Return on investment of Social Media campaigns was a big topic a few weeks ago at the Social Ad Summit in New York. As brand advertisers are venturing further into the space, the instinct to put an ROI on their marketing efforts to support their six figure spends is only natural.

As an analyst the method for measuring traditional online advertising is pretty baked.  We measure impressions, clicks, clickthrough rate and conversion. What are the metrics for measuring efficacy in social media though?   As the shift of ad dollars within online to Social Media happens, the most relevant models emerging for advertisers focus on performance metrics. You “direct to response” advertisers know exactly what I am talking about.  For brands the value is in the impression, but how does this mindset shift to valuing friends and fans or other social media actions?

Just a few of the examples of pay for performance metrics are as follows:

  • Cost per click (CPC) – I am sure you are all familiar with this one
  • Cost per view (CPV) – I am specifically referring to video and sometimes to specific levels of watchthrough such as 10 or 20 seconds into the video clip
  • Cost per install (CPI) – this may apply to widgets or social applications
  • Cost per action (CPA, but not in the traditional sense) – this metric is configurable based on unique actions such as friending or fanning on Facebook, sharing, commenting or ranking.

So what you are willing to pay for each one of these performance based actions?  And what measurable value will they bring back to your organization? Tying the value of the paid action back to the value that you get from having a user interact with your brand is still evolving for most marketers.  It is a way for brands to have a dialogue with their customers and be part of the conversation. In many organizations these campaigns are still coming out of the “testing” budget – albeit a larger allocation each quarter.

Getting to that ROI is not an easy problem to solve. For example, have you ever asked yourself if you can you put a value on a friend? Some friends you value much more than others because they “get you”.  Other friends fall into buckets of “a good person to have dinner with”, “a friend that makes you laugh” or “a good business contact”. You can probably rank order the importance of each of those categories, but can you put an intrinsic value on them?  In many cases you cannot unless you are specifically tying the lifetime value of that “friend” specifically to how much they spend with your company.  The value comes from what your organization sees in having a user engage with your brand in a positive or negative manner.  It comes from knowing that positive interaction that resonates inside of a user’s social network to influence others cannot always be measured – but we know it happens as research has proven it.

The market has determined the value of an impression – check!  The value of a friend, a fan, a post or install is still a little up in the air, but the market will help determine that as well.  Transactions are taking place and the ability to measure the actions of users is there, but the “true” value of how much a brand is willing to pay to acquire their friends and fans doesn’t start with the action, it extends with how they build value with that relationship that they have created. How are you measuring the value of a friend or fan?

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Bon Jovi Widget

Posted by widgetgirl on October 14, 2009

Check out this Bon Jovi widget that is on the Clearspring platform.

[clearspring_widget title=”Bon Jovi Widget” wid=”4aca4a1074c730b7″ pid=”4ad619a777905d9d” width=”400″ height=”430″ domain=”widgets.clearspring.com”]

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Visualizing Data

Posted by widgetgirl on September 4, 2009

When an engineering colleague of mine (95% of my immediate surroundings) emailed me a link this past week, I thought for sure it was for a YouTube video.  This link was different though in that it introduced me to Anscombe’s quartet, a set of four datasets that share the exact same statistical properties.  Each one of these datasets has the same mean, variance and mean of each y variable, variance and mean of each x variable and the exact same correlation between the x and y variables.   A little geeky, eh?

What’s my point you might ask?  When each one of these datasets is plotted out visually, they have completely different appearances (just click on the link above and you’ll see what I mean).  There are outliers where one would not expect to see them – identifying both opportunities and risks in your data depending on what you are analyzing.  However, one would never see the variance in data patterns if it was not plotted in a chart or graph (or analyzed data point by data point).

Looking at your data is just as important as reading your data.  Not all of us can see the obvious by just looking at numbers, even if we don’t consider ourselves “visual” people.  I’ve learned this quite extensively in my current job when presenting data findings to a combination of both finance and product teams.  Some people are “table” folks and others are “chart” folks.  Regardless, the combination of the two data presentation methods jogs the brain and forces you to see the data in new ways and patterns. Here are three reasons why you need to visualize your data:

  1. Visualizing data allows you to segment elements within your results set.  For example, when analyzing campaign data, the average clickthrough rate or cost per acquisition might meet your campaign goals.  However, deeper segmentation via visualization can help you determine where further optimization opportunities lie via eliminating waste and exploiting upside outliers.
  2. Ability to see relationships and correlations within the data.  Scatterplots and grids can help sift out opportunities where you might have huge upside if you can optimize your key trigger points (whether it is behavior metrics like CTR or margin management).
  3. Trend analysis over time is more obvious when it is visualized. Trends in data can shift dramatically or slowly over time. Visualizing trends can allow you to see the slope of a metric or group of metrics to identify seasonal and market trends that may not pop out at you when you are just staring at a table of numbers.

Segmentation, correlation and trending are just three of the reasons for data visualizations – but there are so many more.  As analysts we sometimes do not see the forest through the trees.  Visualizing data forces you to stop and look at the results and ponder the bigger picture.  Perhaps a picture is worth a thousand data points in this case.

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