An approach to use social media data for the characterization of a company’s follower base and the identification of influential players within the network

1. by Moritz Osterberger
An approach to use social media data
for the characterization of a company’s
follower base and the identification of
influential players within the network
Instagram network analytics

2. Objective
Analysis of potential customer characteristics
detection of influencers by publicly available
Instagram data
and

3. Which data?
Instagram data about followers of the online
home & living shopping club Westwing and
those followers’ followings:
• General Information: Username, full name,
bio, user id, usernames of followings,
number of followers, -followings & -posts
• Information about a users’ post:
Engagement (number of comments & likes)
and upload frequency (posts per day)
31.000 users

4. How collected?
Four different scrapers written in Python language with the
libraries Selenium, Requests & Beautiful Soup:
1. Usernames of Westwings‘ followers
2. Usernames of those followers‘ followings
3. General user information
4. Information about posts
Total (successful) runtime: 138,5 hours

5. Data exploration
Part 1: Characteristics of followers based on their user
page information
Part 2: Creation of a graph with different communities
based on the users’ followings

6. Where are the followers from?
• Information gained from
users’ bio
• Top cities:
City Frequency Population
Munich 8,26% 1,46m
Hamburg 8,08% 1,81m
Berlin 7,57% 3,57m
Cologne 3,44% 1,07m
Vienna 3,37% 1,87m

7. How is the gender distributed?
Information gained from
user’s full name by
identifying the first name and
then categorizing it to a
gender
Female 94,59%
Male
5,41
%
Word cloud of first names in user profiles

8. What are the followers’ interests?
• Information gained from
users’ bio
• Top words:
Word Frequency
travel 5,37%
mother 3,50%
design 3,08%
fashion 3,07%
food 2,96%
interior 2,95%

9. What are Graphs?
• The fundament & the mathematical
representation of every network
• A graph G is a pair of sets of all nodes V and all
edges E: G = (V, E).
• Directed or undirected
• Centrality: Measurement of a node‘s importance
• Indegree centrality captures number of
direct incoming relationships of an actor
• Communities: Subsets of nodes that are „near“
to each other in a specific network
Edge
Node A
Node B
Graph

10. Big Players of Instagram
Influencer & Models
Celebrities
Big Brands
Home and Living
Health, Beauty & small shops
How was the graph created?
• Top 1.000 followings of
Westwing’s followers were
selected by their indegree
centrality
• Followers that don’t follow
those were excluded
• Six communities found

11. What are the communities?
Optimal target group
➔further analysis

12. Analysis on Home & Living
• Follower within this community
fit best to the company
• To find the influential
accounts, analysis just on the
followers’ followings within
network
• Manual categorization into 5
groups

13. Who are the optimal influencers?
• Optimality is defined as the
combination of Centrality &
Engagement
• By taking the product of both
the most fitting influencers
could be identified
• Ideal choice for influencer
marketing
➔„karlas_view“, „solebich“ &
„svenja_traumzuhause“
300.000
100.000
50.000

14. Who are fitting brands?
• Optimality is defined as the
combination of Centrality &
Engagement
• By taking the product of both the
most fitting influencers could be
identified
• Addition to the product palette
and/or extra promotion
➔„sostrenegrene“, „thejungalow“
& „eulenschnitt“
1.000.000
500.000
250.000

15. 7,5
5,0
2,5
1,0
Who are the competitors?

16. Thanks!