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It isnít easy to find the posts in the social media ocean that reveal whoís talking about your product or service, what theyíre saying, and their intentions. You can use search to ask a question, count the responses, and make inferences from whatís said, which may be useful. Often though, this is a self-fulfilling exercise. You only hear what you expect to hear because of how youíve framed the questions. At Networked Insights we take a more illuminating approach by discovering information, attitudes and trends in conversations that are unfolding naturally. Using three analytical techniques ñ full-text search, clustering and classification ñ we can help you uncover who is interested in buying your product or service, and who is in a state of mind that could be converted into purchase. Read how our approach can discover hot, relevant topics in social posts and help you align your media strategy to maximize paid, earned and owned spends.
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A look behind the curtain at Networked Insights
The Anatomy of Social Analytics
In “The Wizard of Oz,” when Toto tugged at the curtain, Dorothy and friends were disappointed to learn that the Wizard was merely a man — the rest was an illusion.
When we open the curtain at Networked Insights, clients are pleasantly surprised. Not only are there real people, but they’re using very sophisticated analytical techniques to reveal insights into social media data that were unimaginable even two or three years ago.
Searching the ocean of social media data to find relevant posts isn’t easy. It’s a real challenge to unearth the ones that reveal who’s talking about your product or service, what they’re saying, and where their intentions are.
With this in mind, it’s useful to think of search as a giant funnel. You ask a question and start counting responses. You then make inferences, which may be useful, based on the information you obtain. Often, though, the exercise is self-fulfilling. You only hear what you expect to hear from a search because of how you’ve framed the questions. Also, the process is focused on the top results — with each page of results (and there are often hundreds if not thousands of pages), the quality deteriorates.
Networked Insights takes a very different approach to this process — and dramatically extends the process — by discovering information, attitudes and trends in conver-sations that are unfolding naturally. In fact, we do three things right now (and soon a fourth) that have significantly advanced the practice of social media analysis:
Full-text search, which many others do, as well — we just add a twist that others don’t.
Clustering, which a few others do, but which we take to an entirely new level.
Classification, which few others do right now, and which gives our clients unheard-of insight into social media data.
Full-text search Many others do as well — we just add a twist that others don’t.
Clustering Few others do, but which we take to an entirely new level
ClassificationFew others do right now, and which gives our clients unheard- of insight into social media data
Three things we do
© 2011, Networked Insights, Inc. All rights reserved. 2
A look behind the curtain at Networked Insights
The Anatomy of Social Analytics
Using clustering and classification techniques along with search, our approach can find patterns in the data that position our clients where those conversations are hap-pening. They can then set a baseline for measuring their advertising campaigns and maximizing their paid, earned and owned media spends.
Read on to learn more about what goes on behind the curtain at Networked Insights and how it can benefit you.
Tailoring full-text searchNetworked Insights is among many companies that use “full-text search” to find and count posts across social media and other unstructured data from the Internet. The process, known as Boolean search, basically involves entering a Google-like query and getting results. Boolean search allows you to look for combinations of words that may not necessarily be connected.
Networked Insights’ Boolean search approach uses a dataset focused on social media. It contains fields that nor-malize data into different dimensions, such as a domain, an author or a subject. An audience filter then focuses the searches into the right areas. For example, a search for Target, the retail store chain, is pre-filtered to focus on consumer purchasing. This eliminates results for such topics as target practice or Human Target, the TV series.
ClusteringSearch is one of two ways you can retrieve information. As discussed, search can provide information you seek, but the questions used to frame the search can bias the results. An alternative to search is to use clustering, which is a true discovery approach.
Simply stated: with clustering, you get back what the data tells you rather than what you’re asking for. We perform clustering using Networked Solutions’ proprietary Topic Discovery Engine (TDE), a semantic analysis system finely tuned to discover and label topics in social media posts.
© 2011, Networked Insights, Inc. All rights reserved. 3
Networked Insights’ Boolean search approach uses a dataset focused on social media. Target, the retail store chain, is pre-filtered to focus on con-sumer purchasing. This eliminates re-sults for such topics as target practice or Human Target, the TV series.
Tailoring full-text search
Target, we use their brand. If I’m too lazy to run to a different store though.
Where do you buy them
Mine loves the Children’s museum and Target (lol), pet stores and aquariums...
Fun things to do...
I love my grocery shopping list and Target app...oh and my hautelook app :)
Favorite iPhone apps???
Target
Source: baby-gaga.com/Parents
Source: cafemom.com/Newcomers
Source: cafemom.com/Newcomers
The TDE has no bias or preconceived notion of what it will find. The engine runs and identifies a primary concept, and then breaks that into sub-concepts. This type of discovery lessens the influence of anyone’s bias in the process.
The TDE uses an advanced form of semantic analysis to organize topics it discovers into a hierarchy of concepts, which we call “topic trees.” From a main topic, the TDE drills down into subtopics (Figure 1). The size of each orange node in Figure 1 represents the volume of conver-sation around each subtopic. The TDE enables you to look at topics that bubble up, and then cluster them naturally. For example, data that simply mentions Starbucks can be analyzed for discussions around different drinks and their popularity. Extra-hot coffee or caramel lattes might emerge as more important than gingerbread lattes.
This is a powerful part of the process, because the labels assigned to the cluster are highly descriptive and together create a “topic cloud.” Along with being able to click on one of the topics and see the verbatim discussions around it, the labels themselves provide a good understanding of what someone is looking at. The result is less verbatim reading and more discovery. Someone can look at a topic tree, understand what’s being said and drill down into topics of interest.
Once the topics have been discovered through clustering, each one can be modeled across the data, and more posts can be found that align with it. Networked Insights’ ability to uncover topics of interest is enhanced by historical data on social media activity that we have amassed. (See our Semantic vs Sentiment Report)
ClassificationAt a high level, classification is the process of separating data into predefined categories. Classification uses the same technology and artifacts as clustering.
The TDE enables you to look at topics that bubble up, and then cluster them naturally. For example, data that simply mentions Starbucks can be analyzed for discussions around different drinks and their popularity.
vent
i
Starbucks favorite drink
Extra hot coffee
doub
le m
ocha Vi
aPi
ke P
lace
orga
nic,
resp
onsi
bilit
y
Rewards
Gold
car
d
free
drin
kbi
rthda
y
bagg
ed b
eans
Frappuccino
Tum
bler
s
love
, lov
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vors
iced
, moc
ha
© 2011, Networked Insights, Inc. All rights reserved. 4
Figure 1
However, with classification we direct the clustering process by pre-assigning a label and instructing the TDE to find that pattern among social media conversations.
This approach enables us to:
• Use labels from “discovered clusters” — i.e., clusters of conversations that naturally bubble up during the clustering process — to refine our analysis and search out more detailed or specific patterns in the data.
• Identify more “nuanced” topics within the data — i.e., topics that don’t occur in conversations at a high enough volume to naturally bubble to the surface through clustering — by creating a category “classifier”upfront that directs the clustering process.
For example, the different stages of purchasing — awareness, consideration and purchase — could be established as categories (Figure 2). To create classifiers capable of identifying posts that belong to these catego-ries, we start by finding examples of conversations where people are in those states. They may not be the absolute right conversations, but that’s all right.
The conversations are then passed through a panel of Networked Insights reviewers who confirm the state of the data. These people essentially provide the “artificial intelligence.” Humans physically look at the posts or conversations and say this person is in the awareness phase, that person is ready to buy and so on.
Ultimately, though, technology, rather than people, does the bulk of the work. Once enough examples of each of the three stages are identified, machine learning can be used to model those conversations. That model then runs across millions of posts, classifying each into one of the three target categories or a fourth “unrelated” category. In this way, we discover many more relevant conversations.
The different stages of purchasing — awareness, consideration and purchase —could be established as categories. To create classifiers capable of identifying posts that belong to these categories, we start by finding examples of conversations where people are in those states.
Establishing categories through classification
Awareness
Consideration
Purchase
© 2011, Networked Insights, Inc. All rights reserved. 5
Figure 2
Search, clustering and classification can be used together. Search and clustering can identify all the topics of interest, then classification can refine the analysis within the resulting clusters, such as positive, negative and neutral sentiment.
More powerful tools in the futureNetworked Insights’ revolutionary approach to topic discovery can uncover who is interested in purchasing your product or service, as well as who is in a state of mind that could be converted into purchase. In the near future, we’ll be able to conduct regression analysis on the 15 months of data we have gathered and use it to predict likely future events and activities – adding a fourth capability to the three things we do very well today behind the curtain at Networked Insights.
Questions about this report? Want a free consultation on how social data can improve your media planning and other marketing? Contact us.
Phone: 608.237.1867
Web: www.networkedinsights.com
Email: [email protected]
Networked Insights was founded in 2006 by industry leaders and seasoned entrepreneurs in the fields of social media and customer intelligence. Headquarters are in Madison, WI, with offices in New York and Chicago.
Search, clustering and classification can be used together. Search and clustering can identify all the topics of interest, then classification can refine the analysis within the resulting clusters, such as positive, negative and neutral sentiment.
© 2011, Networked Insights, Inc. All rights reserved. 6
