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Simplicity is not Key
GIJS OVERGOOR, WILLIAM RAND, WILLEMIJN VAN DOLEN & MASOUD MAZLOOM
UNIVERSITY OF AMSTERDAM, NC STATE UNIVERSITY
Motivation
• Opportunity: Social Media are becoming more important for achieving key marketing objectives.
• Challenge: More content gets crowded out by the vast amount that is out there.
• Solution: Create visual content that is appealing and engaging to the consumers.
• Posts with visual content have 2.3 times more engagement than posts without. (Buzzsumo 2016)
• Goal: Create a tool that brand managers can use to identify visual content that is likely to increase
consumer engagement.
Theory
• Research on consumer engagement in social media such as arousal (Berger and Milkman 2012),
interactivity (de Vries, Gensler and Leeflang 2012), positivity (Hewett et al. 2016), and media
persuasiveness (Stephen, Sciandra and Inman 2015), is mainly focused on textual content.
• People’s perceptions, preferences and behavior with regards to visual objects, scenes and display are
influenced by visual complexity (Machado et al. 2015).
• Divide in advertisement literature:
• emphasis on simplicity to increase engagement (Aitchinson 1999; Book and Schik 1997)
• emphasis on complexity to increase engagement (Nelson 1985, Putrevu, Tan and Lord 2004).
Objective
How does visual complexity impact consumer engagement on social media?
Inspired by (Pieters, Wedel, and Batra 2010), the divide in advertising literature and recent advances in
computer science we aim to empirically explain the effect of visual complexity on the engagement
towards firm generated content on Instagram.
Methodological Framework
Firm-Generated Image Feature Complexity
Color
Luminance
Edges
Design Complexity
Concepts
Clutter
Likes
Instagram Post
Feature
Extraction Visual Complexity Influence Engagement
Visual Complexity
“Visual complexity is broadly defined as the level of detail or intricacy contained within an image
(Snodgrass & Vanderwar t, 1980). It has been suggested that perceived complexity correlates positively
with the amount of variety in a picture (Heylighen, 1997) and that it corresponds to the degree of difficulty
people show when describing a visual stimulus (Heaps & Handel, 1999)” - Palumbo et al. (2014)
• Feature Complexity – Unstructured pixel-level variation.
• Design Complexity – Structured, design variation.
Feature Complexity
Detail and variation in the three basic visual features (color, luminance and edges) across an image
constitute the feature complexity of an image.
• Color - variation in color, measured by a color variation entropy.
• Luminance – variation in brightness, measured by a luminance variation entropy.
• Edge Density – variation in edges, measured by the percentage of pixels on an ”edge”.
Feature Complexity - Color
◦ Color has been shown to have an effect on people’s perception (Bevan & Dukes, 1953; Warden & Flynn
1926; Tom et al. 1987), people’s beliefs (Gordon et al., 1994; Belizzi et al., 1983) and people’s
psychological reactions (Nakshian, 1964; Wilson, 1966, Bellizzi & Hite 1992).
◦ Particularly it has shown that color causes emotions that consequently affect a consumer’s attitude
toward an advertisement (Holbrook & Bartra 1987; Burke & Edell 1989).
Feature Complexity - Color
Low HighColor Variation Entropy
Feature Complexity - Luminance
◦ Luminance is apparent brightness, how bright an object appears to the human eye.
◦ Nuthman (2017) and Nuthman and Eindhauser (2015) investigate the factors that influence fixation. The
authors show that luminance and luminance contrast, among others, significantly impact the fixation
and fixation duration
◦ Schindler (1986) hypothesized that a majority of firms would employ advertisements with high contrast
or luminance variation to gain audience attention to the ad.
Feature Complexity - Luminance
Low HighLumiance Variation Entropy
Feature Complexity – Edge Density
◦ An edge in an image is a boundary or contour at which a significant change occurs in some physical
aspect of an image, such as the surface reflectance, illumination or the distances of the visible surfaces
from the viewer.
◦ Rosenholtz (2007) shows that edge density is significantly correlated to perceived complexity and that it
takes more cognitive effort for taking in an image.
Feature Complexity – Edge Density
Low HighEdge Density Percentage
Design Complexity – Concepts
◦ The design complexity of an image captures the complexity in terms of the semantic information of a
scene in the image. Images with a higher variation in terms of concepts present are more complex.
◦ Concepts impact pleasure and arousal when viewing an image which in turn influences the formation of
a first impression (Tuch et al. 2009). Consequently it impacts the attitude towards advertisements
(Pieters, Wedel and Bartra 2010).
Concepts Detected:
resort hotel
resort
sun deck
oceanfront
sunbather
outrigger
water jump
hideaway
outrigger canoe
beach
flip
Number of concepts: 11
Dissimilarity score: 0.11
Design Complexity
Low HighQuantity and Dissimilarity of Concepts
Concepts
Detected:
Ornament
Snow
Ice
Tree
Concepts
Detected:
oceanfront
resort
littoral
shore
point
seashore
shoreline
resort hotel
seaside
promontory
bight
waterside
Coastland
villa
beach
waterfront
Design Complexity – Clutter
◦ Clutter is the state in which excess items, or their representation or organization, lead to a degradation
of performance at some task. (Rosenholtz 2007)
◦ Visual perception researchers because excess and/or disorganized display items can cause crowding,
masking, decreased recognition performance due to occlusion, greater difficulty at both segmenting a
scene and performing visual search, etc. (Rosenholtz 2007)
Design Complexity – Clutter
Low HighFeature Congestion Score
Instagram
• Visual Social Media Platform
• 500 Million active monthly users
• 95 Million shared photos or videos per day
• 4.2 Billion likes per day
• 70% of US brands now use Instagram
Data
• 633 active brands selected based on their L2 Digital IQ index
• Public Instagram API for accessing the content
• Per brand, we collected:
• All firm-generated content between 01-05-2015 and 30-04-2016.
• Brand profile, industry and number of followers.
• Per posts, we collected:
• Number of likes
• Number of comments
• Filters
• Date
• Caption (Hashtags)
• Image
Model
Negative Binomial Regression for count data
𝐿𝑜𝑔 𝜆𝑖 = 𝛼 + 𝛽1 𝐶𝑖 + 𝛽2 𝐶𝑖
2
+ 𝛽3 𝐿𝑖 + 𝛽4 𝐿𝑖
2
+ 𝛽5 𝐸𝑖 + 𝛽6 𝐸𝑖
2
+ 𝛽7 𝑄𝑖 + 𝛽8 𝐷𝑖 + 𝛽9 𝑄𝑖 ∗ 𝐷𝑖 + 𝛽9 𝑉𝐶𝑖
+𝛽6 𝐹𝑜𝑙𝑙𝑜𝑤𝑒𝑟𝑠 𝑏 + 𝛽7 𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑏
+𝛽8 𝑇𝑖𝑚𝑒𝑖 + 𝛽9 𝑊𝑒𝑒𝑘𝑑𝑎𝑦𝑖 + 𝛽10 𝑆𝑒𝑎𝑠𝑜𝑛𝑖
+𝛽11 𝐹𝑖𝑙𝑡𝑒𝑟𝑖 + 𝛽12 𝑇𝑎𝑔𝑠𝑖 + 𝛽13 𝐶𝑎𝑝𝑡𝑖𝑜𝑛_𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑖 + 𝛽14 𝐶𝑎𝑝𝑡𝑖𝑜𝑛_𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑖 + 𝜖𝑖
Engagement Feature Complexity
Control Variables: brand fixed-effects
Control Variables: time dependent
Control Variables: post specific
Design Complexity
Variables
𝐿𝑜𝑔 𝜆𝑖 = 𝛼 + 𝛽1 𝐶𝑖 + 𝛽2 𝐶𝑖
2
+ 𝛽3 𝐿𝑖 + 𝛽4 𝐿𝑖
2
+ 𝛽5 𝐸𝑖 + 𝛽6 𝐸𝑖
2
+ 𝛽7 𝑄𝑖 + 𝛽8 𝐷𝑖 + 𝛽9 𝑄𝑖 ∗ 𝐷𝑖 + 𝛽9 𝑉𝐶𝑖
+ 𝛽6 𝐹𝑜𝑙𝑙𝑜𝑤𝑒𝑟𝑠 𝑏 + 𝛽7 𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑏 +𝛽8 𝑇𝑖𝑚𝑒𝑖 + 𝛽9 𝑊𝑒𝑒𝑘𝑑𝑎𝑦𝑖 + 𝛽10 𝑆𝑒𝑎𝑠𝑜𝑛𝑖 + 𝛽11 𝐹𝑖𝑙𝑡𝑒𝑟𝑖 + 𝛽12 𝑇𝑎𝑔𝑠𝑖 + 𝛽13 𝐶𝑎𝑝𝑡𝑖𝑜𝑛_𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑖 + 𝛽14 𝐶𝑎𝑝𝑡𝑖𝑜𝑛_𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑖 + 𝜖𝑖
• Dependent Variables (Engagement):
• Number of likes
• Independent Variables:
• Ci: Feature Complexity - Color
• Li: Feature Complexity - Luminance
• Ei : Feature Complexity – Edge Density
• Qi : Design Complexity - Quantity of concepts
• Di : Design Complexity - Dissimilarity of concepts
• VCi : Design Complexity - Visual Clutter
• Control Variables
• Brand specific: Number of followers, activity
• Time dependent: Time of day, day of the week, season
• Other: Filter, number of tags, textual sentiment of caption
Operationalization – Feature Complexity
• Color: Color complexity entropy (Zhou et al. 2015) : 𝐶𝑖 = − 𝑗=1
𝑀
𝑛𝑗log(
𝑛 𝑗
𝑁
)
• M: number of unique colors in image
• j: unique color j
• nj : number of pixels with color j
• N : total number of pixels in the image = 480 x 480. (all images are resized if size differs)
• Luminance: Luminance complexity entropy : Li = − 𝑘=1
𝑇
𝑝 𝑘log(
𝑝 𝑘
𝑁
)
• T: number of unique levels of luminance in image. m is derived from Y in YUV color space.
• k: unique luminance level k
• pj : number of pixels with luminance k
• N : total number of pixels in the image = 480 x 480
• Edge Density: 𝐸𝑖 =
𝑒 𝑖
𝑁
• Canny (1986) edge detector for binary classification of all pixels in an image.
• ei: all pixels classified as 1.
• N: total number of pixels in image 480x480
Operationalization - Design Complexity
• Quantity of concepts: Concept extraction by using a pretrained Deep Neural Network to identify
1000 ImageNet (Deng et al. 2009) concepts. A count of concepts with a confidence score above
the threshold for correct classification determines the quantity of concepts.
• Dissimilarity of concepts: WordNet Concept Similarity (Pedersen, Patwardhan, and Michelizzi
2004) to calculate similarities between detected concepts. The dissimilarity of concepts is one
minus the average similarity between all the concepts detected in the image.
• Visual Clutter: A feature congestion map of visual clutter was computed for each image, using
the algorithm described by Rosenholtz et al. (2007). Normalization of the feature congestion
map results in a single measure for visual clutter.
Results
• We observe an inverted u-shape for the influence of both luminance and edge density of the
feature complexity on engagement.
• Due to multicollinearity, color complexity was excluded.
Variable Beta – Coefficients Std.
Luminance 0.2424*** (0.0699)
Luminance Squared - 0.2030*** (0.0463)
Edge Density 2.3010*** (0.0842)
Edge Density Squared - 2.1947*** (0.1252)
*** p< 0.01
Results
• Only the interaction effect between quantity and dissimilarity of concepts is significant.
• The design complexity measures were tested for a quadratic relationship, but the hypothesis for
linearity could not be rejected.
• We observe a positive relationship between the interaction of quantity and dissimilarity of
concepts and the engagement.
• We observe a negative relationship between visual clutter and engagement.
Variable Beta – Coefficients Std.
Quantity -0.2424 (0.0371)
Dissimilarity -0.0364 (0.0364)
Interaction Q * D 0.2183*** (0.0515)
Visual Clutter - 0.4933*** (0.0860)
*** p< 0.01
Engagement
Followers 0.924***
Posts -0.0303***
Caption Positive 0.001*
Caption Negative 0.004
Tags -0.024***
Filter 0.064***
Afternoon 0.508
Evening 0.034***
Night 0.086***
Weekend 0.058***
Spring -0.220***
Summer -0.239***
Fal -0.198***
Constant -1.910***
===============================================================
Observations 151,987
Adjusted R2 0.397
θ 1.906***
• Influence of including variables, where:
• Extended model includes post-specific variables: Caption sentiment, number of tags and filter.
• Full model includes post- and time-specific variables: time of day, day of week and season.
• Chi-squared test for significance of the regression model (significant).
• LR-test to justify use of Negative Binomial Model over Poisson model (significant).
• Exclusion of color complexity variable due to multicollinearity.
Robustness
Basic Model Extended Model Full Model
Adjusted R2 0.348 0.364 0.397
θ 1.899*** 1.903*** 1.906***
AIC 2,232,536 1,998,824 1,951,816
Conclusion
• Feature complexity: needed to stop consumer and hold attention within resources of the user
(inverted u-shape). In contrary to Pieters, Wedel and Bartra (2010).
• A higher number of disimilar concepts detected leads to an increase in engagement.
• Visual complexity has engaging aesthetic qualities (Berlyne 1958)
• Visual clutter harms the engagement because excess and/or disorganized display items can cause
crowding, masking, decreased recognition and therefore cause confusion.(Rosenholtz 2007)
As a brand manager you need to make sure that your content is in the mid section of complexity.
Next Steps
• Constructing a filter guide such that content managers can manipulate the feature complexity
for the optimal point of engagement..
• Deriving clear managerial guide lines regarding design complexity.
Thanks!
Questions?
g.overgoor@uva.nl

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Data Science meets Digital Marketing

  • 1. Simplicity is not Key GIJS OVERGOOR, WILLIAM RAND, WILLEMIJN VAN DOLEN & MASOUD MAZLOOM UNIVERSITY OF AMSTERDAM, NC STATE UNIVERSITY
  • 2. Motivation • Opportunity: Social Media are becoming more important for achieving key marketing objectives. • Challenge: More content gets crowded out by the vast amount that is out there. • Solution: Create visual content that is appealing and engaging to the consumers. • Posts with visual content have 2.3 times more engagement than posts without. (Buzzsumo 2016) • Goal: Create a tool that brand managers can use to identify visual content that is likely to increase consumer engagement.
  • 3. Theory • Research on consumer engagement in social media such as arousal (Berger and Milkman 2012), interactivity (de Vries, Gensler and Leeflang 2012), positivity (Hewett et al. 2016), and media persuasiveness (Stephen, Sciandra and Inman 2015), is mainly focused on textual content. • People’s perceptions, preferences and behavior with regards to visual objects, scenes and display are influenced by visual complexity (Machado et al. 2015). • Divide in advertisement literature: • emphasis on simplicity to increase engagement (Aitchinson 1999; Book and Schik 1997) • emphasis on complexity to increase engagement (Nelson 1985, Putrevu, Tan and Lord 2004).
  • 4. Objective How does visual complexity impact consumer engagement on social media? Inspired by (Pieters, Wedel, and Batra 2010), the divide in advertising literature and recent advances in computer science we aim to empirically explain the effect of visual complexity on the engagement towards firm generated content on Instagram.
  • 5. Methodological Framework Firm-Generated Image Feature Complexity Color Luminance Edges Design Complexity Concepts Clutter Likes Instagram Post Feature Extraction Visual Complexity Influence Engagement
  • 6. Visual Complexity “Visual complexity is broadly defined as the level of detail or intricacy contained within an image (Snodgrass & Vanderwar t, 1980). It has been suggested that perceived complexity correlates positively with the amount of variety in a picture (Heylighen, 1997) and that it corresponds to the degree of difficulty people show when describing a visual stimulus (Heaps & Handel, 1999)” - Palumbo et al. (2014) • Feature Complexity – Unstructured pixel-level variation. • Design Complexity – Structured, design variation.
  • 7. Feature Complexity Detail and variation in the three basic visual features (color, luminance and edges) across an image constitute the feature complexity of an image. • Color - variation in color, measured by a color variation entropy. • Luminance – variation in brightness, measured by a luminance variation entropy. • Edge Density – variation in edges, measured by the percentage of pixels on an ”edge”.
  • 8. Feature Complexity - Color ◦ Color has been shown to have an effect on people’s perception (Bevan & Dukes, 1953; Warden & Flynn 1926; Tom et al. 1987), people’s beliefs (Gordon et al., 1994; Belizzi et al., 1983) and people’s psychological reactions (Nakshian, 1964; Wilson, 1966, Bellizzi & Hite 1992). ◦ Particularly it has shown that color causes emotions that consequently affect a consumer’s attitude toward an advertisement (Holbrook & Bartra 1987; Burke & Edell 1989).
  • 9. Feature Complexity - Color Low HighColor Variation Entropy
  • 10. Feature Complexity - Luminance ◦ Luminance is apparent brightness, how bright an object appears to the human eye. ◦ Nuthman (2017) and Nuthman and Eindhauser (2015) investigate the factors that influence fixation. The authors show that luminance and luminance contrast, among others, significantly impact the fixation and fixation duration ◦ Schindler (1986) hypothesized that a majority of firms would employ advertisements with high contrast or luminance variation to gain audience attention to the ad.
  • 11. Feature Complexity - Luminance Low HighLumiance Variation Entropy
  • 12. Feature Complexity – Edge Density ◦ An edge in an image is a boundary or contour at which a significant change occurs in some physical aspect of an image, such as the surface reflectance, illumination or the distances of the visible surfaces from the viewer. ◦ Rosenholtz (2007) shows that edge density is significantly correlated to perceived complexity and that it takes more cognitive effort for taking in an image.
  • 13. Feature Complexity – Edge Density Low HighEdge Density Percentage
  • 14. Design Complexity – Concepts ◦ The design complexity of an image captures the complexity in terms of the semantic information of a scene in the image. Images with a higher variation in terms of concepts present are more complex. ◦ Concepts impact pleasure and arousal when viewing an image which in turn influences the formation of a first impression (Tuch et al. 2009). Consequently it impacts the attitude towards advertisements (Pieters, Wedel and Bartra 2010). Concepts Detected: resort hotel resort sun deck oceanfront sunbather outrigger water jump hideaway outrigger canoe beach flip Number of concepts: 11 Dissimilarity score: 0.11
  • 15. Design Complexity Low HighQuantity and Dissimilarity of Concepts Concepts Detected: Ornament Snow Ice Tree Concepts Detected: oceanfront resort littoral shore point seashore shoreline resort hotel seaside promontory bight waterside Coastland villa beach waterfront
  • 16. Design Complexity – Clutter ◦ Clutter is the state in which excess items, or their representation or organization, lead to a degradation of performance at some task. (Rosenholtz 2007) ◦ Visual perception researchers because excess and/or disorganized display items can cause crowding, masking, decreased recognition performance due to occlusion, greater difficulty at both segmenting a scene and performing visual search, etc. (Rosenholtz 2007)
  • 17. Design Complexity – Clutter Low HighFeature Congestion Score
  • 18. Instagram • Visual Social Media Platform • 500 Million active monthly users • 95 Million shared photos or videos per day • 4.2 Billion likes per day • 70% of US brands now use Instagram
  • 19. Data • 633 active brands selected based on their L2 Digital IQ index • Public Instagram API for accessing the content • Per brand, we collected: • All firm-generated content between 01-05-2015 and 30-04-2016. • Brand profile, industry and number of followers. • Per posts, we collected: • Number of likes • Number of comments • Filters • Date • Caption (Hashtags) • Image
  • 20. Model Negative Binomial Regression for count data 𝐿𝑜𝑔 𝜆𝑖 = 𝛼 + 𝛽1 𝐶𝑖 + 𝛽2 𝐶𝑖 2 + 𝛽3 𝐿𝑖 + 𝛽4 𝐿𝑖 2 + 𝛽5 𝐸𝑖 + 𝛽6 𝐸𝑖 2 + 𝛽7 𝑄𝑖 + 𝛽8 𝐷𝑖 + 𝛽9 𝑄𝑖 ∗ 𝐷𝑖 + 𝛽9 𝑉𝐶𝑖 +𝛽6 𝐹𝑜𝑙𝑙𝑜𝑤𝑒𝑟𝑠 𝑏 + 𝛽7 𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑏 +𝛽8 𝑇𝑖𝑚𝑒𝑖 + 𝛽9 𝑊𝑒𝑒𝑘𝑑𝑎𝑦𝑖 + 𝛽10 𝑆𝑒𝑎𝑠𝑜𝑛𝑖 +𝛽11 𝐹𝑖𝑙𝑡𝑒𝑟𝑖 + 𝛽12 𝑇𝑎𝑔𝑠𝑖 + 𝛽13 𝐶𝑎𝑝𝑡𝑖𝑜𝑛_𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑖 + 𝛽14 𝐶𝑎𝑝𝑡𝑖𝑜𝑛_𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑖 + 𝜖𝑖 Engagement Feature Complexity Control Variables: brand fixed-effects Control Variables: time dependent Control Variables: post specific Design Complexity
  • 21. Variables 𝐿𝑜𝑔 𝜆𝑖 = 𝛼 + 𝛽1 𝐶𝑖 + 𝛽2 𝐶𝑖 2 + 𝛽3 𝐿𝑖 + 𝛽4 𝐿𝑖 2 + 𝛽5 𝐸𝑖 + 𝛽6 𝐸𝑖 2 + 𝛽7 𝑄𝑖 + 𝛽8 𝐷𝑖 + 𝛽9 𝑄𝑖 ∗ 𝐷𝑖 + 𝛽9 𝑉𝐶𝑖 + 𝛽6 𝐹𝑜𝑙𝑙𝑜𝑤𝑒𝑟𝑠 𝑏 + 𝛽7 𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑏 +𝛽8 𝑇𝑖𝑚𝑒𝑖 + 𝛽9 𝑊𝑒𝑒𝑘𝑑𝑎𝑦𝑖 + 𝛽10 𝑆𝑒𝑎𝑠𝑜𝑛𝑖 + 𝛽11 𝐹𝑖𝑙𝑡𝑒𝑟𝑖 + 𝛽12 𝑇𝑎𝑔𝑠𝑖 + 𝛽13 𝐶𝑎𝑝𝑡𝑖𝑜𝑛_𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑖 + 𝛽14 𝐶𝑎𝑝𝑡𝑖𝑜𝑛_𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑖 + 𝜖𝑖 • Dependent Variables (Engagement): • Number of likes • Independent Variables: • Ci: Feature Complexity - Color • Li: Feature Complexity - Luminance • Ei : Feature Complexity – Edge Density • Qi : Design Complexity - Quantity of concepts • Di : Design Complexity - Dissimilarity of concepts • VCi : Design Complexity - Visual Clutter • Control Variables • Brand specific: Number of followers, activity • Time dependent: Time of day, day of the week, season • Other: Filter, number of tags, textual sentiment of caption
  • 22. Operationalization – Feature Complexity • Color: Color complexity entropy (Zhou et al. 2015) : 𝐶𝑖 = − 𝑗=1 𝑀 𝑛𝑗log( 𝑛 𝑗 𝑁 ) • M: number of unique colors in image • j: unique color j • nj : number of pixels with color j • N : total number of pixels in the image = 480 x 480. (all images are resized if size differs) • Luminance: Luminance complexity entropy : Li = − 𝑘=1 𝑇 𝑝 𝑘log( 𝑝 𝑘 𝑁 ) • T: number of unique levels of luminance in image. m is derived from Y in YUV color space. • k: unique luminance level k • pj : number of pixels with luminance k • N : total number of pixels in the image = 480 x 480 • Edge Density: 𝐸𝑖 = 𝑒 𝑖 𝑁 • Canny (1986) edge detector for binary classification of all pixels in an image. • ei: all pixels classified as 1. • N: total number of pixels in image 480x480
  • 23. Operationalization - Design Complexity • Quantity of concepts: Concept extraction by using a pretrained Deep Neural Network to identify 1000 ImageNet (Deng et al. 2009) concepts. A count of concepts with a confidence score above the threshold for correct classification determines the quantity of concepts. • Dissimilarity of concepts: WordNet Concept Similarity (Pedersen, Patwardhan, and Michelizzi 2004) to calculate similarities between detected concepts. The dissimilarity of concepts is one minus the average similarity between all the concepts detected in the image. • Visual Clutter: A feature congestion map of visual clutter was computed for each image, using the algorithm described by Rosenholtz et al. (2007). Normalization of the feature congestion map results in a single measure for visual clutter.
  • 24. Results • We observe an inverted u-shape for the influence of both luminance and edge density of the feature complexity on engagement. • Due to multicollinearity, color complexity was excluded. Variable Beta – Coefficients Std. Luminance 0.2424*** (0.0699) Luminance Squared - 0.2030*** (0.0463) Edge Density 2.3010*** (0.0842) Edge Density Squared - 2.1947*** (0.1252) *** p< 0.01
  • 25. Results • Only the interaction effect between quantity and dissimilarity of concepts is significant. • The design complexity measures were tested for a quadratic relationship, but the hypothesis for linearity could not be rejected. • We observe a positive relationship between the interaction of quantity and dissimilarity of concepts and the engagement. • We observe a negative relationship between visual clutter and engagement. Variable Beta – Coefficients Std. Quantity -0.2424 (0.0371) Dissimilarity -0.0364 (0.0364) Interaction Q * D 0.2183*** (0.0515) Visual Clutter - 0.4933*** (0.0860) *** p< 0.01
  • 26. Engagement Followers 0.924*** Posts -0.0303*** Caption Positive 0.001* Caption Negative 0.004 Tags -0.024*** Filter 0.064*** Afternoon 0.508 Evening 0.034*** Night 0.086*** Weekend 0.058*** Spring -0.220*** Summer -0.239*** Fal -0.198*** Constant -1.910*** =============================================================== Observations 151,987 Adjusted R2 0.397 θ 1.906***
  • 27. • Influence of including variables, where: • Extended model includes post-specific variables: Caption sentiment, number of tags and filter. • Full model includes post- and time-specific variables: time of day, day of week and season. • Chi-squared test for significance of the regression model (significant). • LR-test to justify use of Negative Binomial Model over Poisson model (significant). • Exclusion of color complexity variable due to multicollinearity. Robustness Basic Model Extended Model Full Model Adjusted R2 0.348 0.364 0.397 θ 1.899*** 1.903*** 1.906*** AIC 2,232,536 1,998,824 1,951,816
  • 28. Conclusion • Feature complexity: needed to stop consumer and hold attention within resources of the user (inverted u-shape). In contrary to Pieters, Wedel and Bartra (2010). • A higher number of disimilar concepts detected leads to an increase in engagement. • Visual complexity has engaging aesthetic qualities (Berlyne 1958) • Visual clutter harms the engagement because excess and/or disorganized display items can cause crowding, masking, decreased recognition and therefore cause confusion.(Rosenholtz 2007) As a brand manager you need to make sure that your content is in the mid section of complexity.
  • 29. Next Steps • Constructing a filter guide such that content managers can manipulate the feature complexity for the optimal point of engagement.. • Deriving clear managerial guide lines regarding design complexity.