1. TITLE
Using Public APIs to Create a Tool
for Social Network Analysis:
Lessons Learned
Alan Raphael

2. About Us
• API through Mashery
• Tools for social data analysis, matching, and segmentation
• Currently working on a tool to give brands an “instant focus group” using
social analytics and passive segmentation
• Data from Facebook and Twitter with the option of using a custom data
source (CRM, forums, etc.)

3. Psychographics are the New Demographics
• Demographics like gender, age, and marital status have long dominated
marking studies and brand outreach programs – easy to pull from social
media like Facebook
• Brands are moving away from them towards segmentation models based
on attitudes, beliefs, personality, interests, and values (combined with
demographics)
• Harder to derive than demographics but it’s the sheer amount of data
available in social media that makes derivation possible

4. Psychographics in Action
• Best Buy stores based on target:
• Jill – busy suburban mom – personal shopping assistants
• Barry – affluent tech enthusiast – separate theatre
department, specialists in mobile electronics
• Buzz – young gadget fiend – lots of video games
• Ray – price-conscious family guy
• Mr. Storefront – small business owner
• Forrester “Technographics”
• Fast Forwards
• New Age Nurturers
• Mouse Potatoes
• Companies typically use surveys and focus groups for this data, often at a
high cost and turnaround time

5. Lesson: Vary Your Data Sources
• A lasting and robust product needs redundancy
• Companies change their data use policies and what is available in their
APIs all the time – especially when they are free to use
• Our case:
• Facebook limits on public information via the API
• Use of an app created by customer to capture information
• Twitter call limits and fire hose

6. Lesson: Get Creative in the Hunt for More Data
• Public APIs out there can be leveraged to compensate for otherwise sparse
data
• Data enrichment
• Geographic information
• Photo analysis (my favorite)
• Photo analysis in more detail:
• Technology has come a long way
• Even a basic profile picture can lead to things like age, gender, marital
status, and the presence of kids
• In the future, even location may be possible (without embedded
geocoding)

7. DELL SEGMENTATION
<attributes >
<age_est>
Photo Analysis Example <value>12</value>
<confidence>88</confidence>
</age_est>
<age_max>
<value>16</value>
<confidence>88</confidence>
</age_max>
<age_min>
<value>10</value>
<confidence>88</confidence>
</age_min>
<face>
<value>true</value>
<confidence>95</confidence>
</face>
<gender>
<value>female</value>
<confidence>39</confidence>
</gender>
<glasses>
<value>false</value>
<confidence>97</confidence>
</glasses>
<lips>
<value>sealed</value>
<confidence>68</confidence>
</lips>
<mood>
<value>surprised</value>
<confidence>62</confidence>
</mood>
<smiling>
<value>false</value>
<confidence>90</confidence>
</smiling>
</attributes>

8. Lesson: Know Your Audience
• Our case: the marketing or brand manager at a company
• Need to come up with metrics and a display that was simple, engaging, not
overtly technical, and actionable
• Take your analysis one step further – what do raw statistics and numbers
mean?
• Limited time to grab and keep someone’s attention – need to give the end
user information as quickly as possible

9. Lesson: Prioritize Your Calls
• We want the greatest amount of data in the least amount of time
• Connection limitations – response time dependencies on API
• Batch your calls when possible
• Public APIs often rate limit facilitating the need for filtering so that calls
and time are not wasted
• Possible ways:
• Influence / Klout
• Raw metrics like number of friends and number of tweets
• Be careful not to bias your sample

10. Lesson: Give Your Users Something Actionable
• Interpret data, don’t just display it (audience in mind)
• What does it mean that most of my page’s fans are soccer moms that like
to bowl?
• What does a 1 pt drop in sentiment mean for the bottom line?
• I ran an ad campaign on these dates…how successful was it?
• How does my brand’s image stack up against my competitors? How can it
be improved?

11. Lesson: Know Your Data and Play to Its Strengths
• Some correlations are hard to logically explain
• What is the value of a “like”
• Our case: looking at FB likes to help come up with meaningful analysis
• “like” Ferrari doesn’t necessarily mean that the person has money let
alone a Ferrari
• “like” Honda does make it more likely that they own a Honda
• Need to bring in probabilities
• Given that someone “likes” product X, what is the probably that they
own/use it
• False Positive Paradox – false positives are more probable than true
positives

12. Social Network Analysis and Marketing in General
• Identify core influencers in network as well as gauge the best ways to
spread information
• Size of network vs. the quality of network – more “likes” (nodes) is not
always better when those nodes lead to a decrease in connectivity in the
network
• Predict behavior of whole network based on an identified subset
• Similar to epidemiology studies
• Predict large drops in sentiment to have a response and plan in place
before it hit the whole network

13. Interesting Fact