smart ideas for big data in social media

The Bigger The Better: Smart Ideas for Big Data in Social Media

Aleksander Zawalich Analyst Sotrender Research Teamaleksander@sotrender.comGo Mobile #10, Warsaw, 10.02.2016

About Sotrender

5 20 800 16 3Team

MembersYears Marketers Countries Research

Team

• Research company specializing in measuring social media, as well as developing it’s own analytics software with usage of big data technologies.

• Founders: Jan Zając (CEO), Paweł Kucharski (CTO), Dominik Batorski (Chief Scientist).

Our Software

Our Software

How are we doing it?

• Apache Big Data: Hadoop, Hive, Spark, HBase

• R-software Analyses: R-Studio, own algorithms, machine learning, decision trees and random forests, open-source packages (e.g. dplyr, ggplot2)

Data statistics

35 4 30k

PlatformsTB of data Profiles

We have BIG DATA.What else do we need?

Big & SmartIDEAS

Idea #1 Trends Reports

Trends Reports

• Monthly reports for marketers

• Facebook, Twitter, YouTube• 3 countries (PL, UK,

Indonesia)• Have been published,

developed and improved since 4 years.

sotrender.pl/reports

PL Fanpage Trends Report – 03.2011

Rank Name Fans Growth Growth %

PL Fanpage Trends Report – 01.2016

PL Fanpage Trends Report – 01.2016

% of Engaged users Engaged users

PL YouTube Trends Report – 01.2016

Ratings number % of positive ratings

PL Twitter Trends Report – 01.2016

Most often mentioned profiles

Idea #2 Facebook Fans Interests

Facebook Fans Interests

• On-demand analysis about your fans: where else are they active and what are their interests.

• Useful for planning communication strategy and choosing brand ambassadors.

Facebook Fans Interests

Hey, which cars do you like apart from Honda?

Facebook Fans Interests

Example data

Facebook Fans Interests

Example data

Coactivity with categories

Facebook Fans Interests

Idea #3 Paid Posts Prediction

Facebook Paid Posts Prediction

• Estimation of paid posts on fanpages made with machine-learning and random forests algorighms.

• Based only on public data derived from 14 000 profiles.

• >90% accuracy of the model.

$$$

Facebook Paid Posts Prediction

Example data

The share of paid communication vs reach per post N

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r of a

ll po

sts

Number of paid posts

Facebook Paid Posts Prediction

Example data

Non-paidPaid

Paid vs non-paid communication usage

Facebook Paid Posts Prediction

Example data

Non-paidPaid

Efficacy of paid posts – mean number of activities

Idea #4 Personal e-marketing

trainer

Sombrero App – personal E-marketing trainer

• iOS and Android app aimed at helping beginners at the beginning of their social media adventure.

• Big-data and experience-based everyday simple tasks.

sombreroapp.com

Sombrero App – personal E-marketing trainer

Add profiles Complete tasks See results

Thanks for your attention Let’s stay in touch!

Aleksander Zawalich Analyst Sotrender Research Teamaleksander@sotrender.com

sotrender.comfacebook.com/Sotrendertwitter.com/Sotrenderslideshare.net/FanpageTrender

Go Mobile #10, Warsaw, 10.02.2016