How to Build Innovative Products with Facebook Topic Data

How to Build Innovative Products with Facebook Topic Data

Tim BuddenVP, Data Science

DATASIFT

Jay KrallDirector of Product

ManagementDATASIFT

Dary HsuProduct Marketing Manager

DATASIFT

Intro to Facebook topic data

Agenda1

2

3

4

5

Evolution of social data

Philosophy of Facebook topic data

Product Differentiation

Q&A

Intro to Facebook Topic Data

1

For years, companies struggled to get a complete view of their audience on Facebook and turn that information into

useful insights until….

DATASIFT + FACEBOOK PartnershipENGAGEMENT ACROSS FACEBOOK

FACEBOOK TOPIC DATA

Topic Data Unlocks Unique Insights for Marketers

What is Facebook Topic Data?

What’s on your mind?

CONTENT DEMOGRAPHICS

LIKES and SHARES

Anonymized and aggregate topic data• Posts• Pages Posts

Plus engagement data• Likes on Posts• Shares on Posts• Comments (no text) on Posts

Data enriched with• Demographics• Topics• Sentiment

• Real-time access to the entire newsfeed with over 4.75 billion pieces of content shared a day.

• Gain anonymous & aggregated insights about specific activities, events, brand names, and other subjects that people are sharing on Facebook.

Insights From a Network of 1.59 Billion People

WITHOUT FACEBOOK TOPIC DATA + FACEBOOK TOPIC DATA

Analysis across public social data sources

Example: Analysis of automotive brand

6xAnalysis includes Twitter, Tumblr, blogs, forums.

Evolution of social data2

The evolution of social dataFrom public to non-public spaces:

Public Walled 1 to 1 Image-based

Public

Where brands and consumers most commonly engage directly. This is where customer support and brand perception can be addressed directly by a brand.

Walled garden

Users engage each other in a non-public but large network. This is where users are more candid about their aspirations and attitudes toward brands.

1 to 1

Users engage each other directly on a one-to-one or small group basis. Thus far this space has been considered largely off limits to brands, but that is starting to change.

Image-based

Public spaces where people showcase their best visual content.

Philosophy of PYLON3

How can information useful for businesses be extracted from these non-public spaces, while wholeheartedly respecting people’s privacy?

Think in terms of audiences and demographics not individuals

17

Djokovic

Federer

female male

Henman Hill at Wimbledon

Come on

Djokovic!

Come on

Roger!

Great shot

Federer!Go for it Novak!

Think in terms of topics and attitudes not verbatim

Sumptuous interior!

Lots of storage

Beautiful lines!

How does PYLON support this?

User identity is removed from posts and engagement data processing.

Text and meta data from anonymized posts are indexed within Facebook’s infrastructure for analysis.

Developers query data collected in real-time to perform analysis. Data is aggregated at query time to provide aggregate results.

Privacy controls ensure results only provided if audience size thresholds are met.

CONTENTGender: MaleAge Range: 35-44Region: California, USA

CONTENTNegativeNeutralPositive

DEMOGRAPHICS

SENTIMENT

Automatic classification of related topics

e.g. Star Wars VII (Film)

TOPIC ANALYSIS

CONTENT

LINKSAnalyze

URLs shared across Facebook

Engagement and Demographics around Likes, Comments and Shares

ENGAGEMENT

Can’t wait to take the kids to watch Star Wars VII

CONTENTPrivacy-safe

aggregate analysis of text

TEXT ANALYSIS

Topic Data is Multi-Dimensional. Build Insights into Content, Engagement, Audiences

Product differentiation4

VEDO custom tags

Create custom tagging and scoring rules using VEDO to apply your unique understanding of the industry and product to add value to the data and surface deeper insights.

Example:• Expressions of intent• Expressions of emotions• Product features (style, cost, reliability

…)• Media types (blogs, news, video …)• Domain expertise

Baselining comparisons

Example:• Comparing engagements with a

car maker vs engagement around automotive in general.

Baselining is a technique for understanding data in context that allows you to compare one set of results to another and find the outliers.

Complex queries

Nested analysis queries allow each result of a frequency distribution analysis to be broken down by the values of another target with only a single request to the API.

Industry-specific indexes

Build industry specific insights by leveraging your domain expertise to create repeatable indexes specific to the needs of the market segment you serve.

Example:• Film• TV• Fashion• Sports

Historical archive of insights

Export your analysis results and build an archive of insights to measure the evolution of topics or simply understand the impact of a topic at any given time in the past.

Q&A

THANK YOU