Categories
Customer Prediction Persona Analytics

Performance analysis of keyword advertising campaign using gender-brand effect of search queries

Performance analysis of keyword advertising campaign using gender-brand effect of search queries
Performance Analysis of Keyword Advertising Campaign Using Gender-Brand Effect of Search Queries

In this research, we analyze the relationship among (1) the performance metrics of a sponsored search campaign, (2) the gender orientation of queries, and (3) the occurrence of branded terms in queries.

The aim of this research is to investigate the effectiveness of increased personalization of search engine advertising in order to improve the consumer’s online experience.

We segregate keyphrases from a dataset covering thirty-three consecutive months from a major US retailer consisting of 7 million daily records of a real time keyword advertising campaign into three gender categories (male, female and neutral) each with two groups (branded and unbranded) term usage.

Using ANOVA, we analyze the effect of gender and brand keyphrases on critical sponsored search performance metrics of impressions, clicks, cost-per-clicks, sales revenue, orders, items purchased and return on advertising.

Research findings show that the combination of brand focus with the gender-orientation of keyphrases is a significant factor in predicting sponsored search performance and behavior. There are statistically significant variations in consumer behavior as measured by sponsored search metrics among the gender categories.

Specifically, females are more attracted to the use of branded terms than males, perhaps due to the trust and customer loyalty generated by brand image.

Our results establish that positive brand reputation creates dramatic influence on consumer’s loyalty over the brand and hence strongly affects their interests, activities and purchasing behavior in e-commerce environment.

Mukherjee, P. and  Jansen, B. J. (2014) Performance Analysis of Keyword Advertising Campaign Using Gender-Brand Effect of Search Queries. Electronic Commerce Research and Applications. 13(2), 139–149.

Categories
Customer Prediction Persona Analytics

Evaluating The Performance of Demographic Targeting Using Gender in Keyword Advertising

Evaluating The Performance of Demographic Targeting Using Gender in Keyword Advertising
Evaluating The Performance of Demographic Targeting Using Gender in Keyword Advertising

This research evaluates the effect of gender-targeted advertising on the performance of sponsored search advertising.

We analyze nearly 7,000,000 records spanning 33 consecutive months of a keyword advertising campaign from a major US retailer. In order to determine the effect of demographic targeting, we classify the campaign’s key phrases by a probability of being targeted for a specific gender.

We then compare the key performance indicators among these groupings using the critical sponsored search metrics of impressions, clicks, cost-per-click, sales revenue, orders, and items, and return on advertising.

Findings from our research show that the gender orientation of the key phrase is a significant determinant in predicting behaviors and performance, with statistically different consumer behaviors for all attributes as the probability of a male or female keyword phrase changes.

However, gender-neutral phrases perform the best overall, generating 20 times the advertising return than any gender-targeted category.

Insights from this research could result in sponsored advertising efforts being more effectively targeted to searchers and potential consumers.

Jansen, B. J., Moore, K., and Carman, S. (2013) Evaluating The Performance of Demographic Targeting Using Gender in Keyword Advertising. Information Processing & Management. 49(1), 286-302.

Categories
Persona Analytics

Classifying ecommerce information sharing behaviour by youths on social networking sites

Classifying ecommerce information sharing behaviour by youths on social networking sites
Classifying Ecommerce Information Sharing Behaviour by Youths on Social Networking Sites

Teenagers and young adults form an economically critical demographic group and are confronted with an array of internet social networking services just as they are forming online information seeking and sharing habits.

Using a survey of 34,514 respondents from myYearbook.com, the research reported in this paper is an inferential analysis of information seeking and sharing behaviors in the ecommerce domain on four social networking sites (Facebook, MySpace, myYearbook and Twitter).

Using k-means clustering analysis, we find clusters within this demographic based on levels of being connected on and being engaged with social networking services.

Research results show that the majority of this demographic have accounts on multiple social networking sites, with more than 40% having profiles on three social networking sites and an additional 20% having four social networking accounts.

We also investigate the motivations for using different social media sites, showing that the reasons for engaging differ among sites. Companies and organizations interested in marketing to this demographic cannot cluster social networking users for more personalized targeting of advertisements and other information.

Jansen, B. J., Sobel, K. and Cook, G. (2011) Classifying Ecommerce Information Sharing Behaviour by Youths on Social Networking Sites. Journal of Information Science. 37(2), 120-136.

Categories
Persona Analytics

Bidding on the Buying Funnel for Sponsored Search Campaigns

BIDDING ON THE BUYING FUNNEL FOR SPONSORED SEARCH AND KEYWORD ADVERTISING
Bidding on the Buying Funnel for Sponsored Search Campaigns

The buying funnel is a common method of segmenting customers.

In this research, we evaluate the effectiveness of the buying funnel as a model for understanding consumer interaction with keyword advertising campaigns on web search engines.

We analyze data of nearly 7 million records from a 33 month, $56 million (US) search engine marketing campaign of a major US retailer.

We classify key phrases used in this campaign into stages of the buying funnel (i.e., Awareness, Research, Decision, and Purchase) and then compare the consumer behaviors associated with each stage of the buying funnel using the critical keyword advertising metrics of impressions, clicks, cost-per-click, sales revenue, orders, and items sold.

Findings from our analysis show that the stages from the buying funnel are effective for classifying types of queries, with statistically different consumer behaviors for all attributes among all stages. However, results also indicate that the buying funnel model does not represent the actual process that consumer engage in when contemplating a potential purchase, as the stages do not seem to be associated with expected consumer actions as predicted by the model.

Results show that Awareness key phrases cost less and generate more sales revenue than Purchase queries, indicating that these broader phases can be a lucrative advertising segment for sponsored search campaigns.

The results reported in this paper are important to researchers interested in understanding online consumer interaction with search engines and beneficial to search engine marketers striving to design successful advertising campaigns.

Insights from this research could produce keyword advertising efforts being more effectively targeted to consumers in order to achieve campaign goals.

Jansen, B. J. and Schuster, S. (2011) Bidding on the Buying Funnel for Sponsored Search Campaigns. Journal of Electronic Commerce Research. 12(1), 1-18.

Categories
Persona Analytics

Interested in Short Form Advertising?

Which Message? Which Channel? Which Customer?: Exploring Response Rates in Multi-Channel Marketing Using Short Form Advertising
 Which Message? Which Channel? Which Customer?: Exploring Response Rates in Multi-Channel Marketing Using Short Form Advertising

Formulating short-form advertising messages with little ad content that work and choosing high-performing channels to disseminate them are persistent challenges in multichannel marketing. 

Drawing on the persuasive systems design (PSD) model, we experimented with 33,848 actual customers of an international telecom company. 

In a real-life setting, we compared the effectiveness of three persuasion strategies (rational, emotional, and social) tested in three marketing channels (short message service (SMS)social media advertising, and mobile application), evaluating their effect on influencing customers to purchase international mobile phone credits. 

Results suggest that companies should send rational messages when using short-form advertising messages regardless of the channel to achieve higher response rates. Findings further show that certain customer characteristics are predictive of positive responses and differ by channel but not by message type. 

Findings from crowdsourced evaluations also indicate that people noticeably disagree on what persuasive strategy was applied to these short messages, indicating that consumers are not well-equipped to identify persuasive strategies or that what advertisers see as a “pure” strategy actually involves elements from multiple strategies as interpreted by consumers. 

The results have implications for the theoretical understanding of persuasive short-form commercial messaging in multichannel marketing and practical insights for advertising within a limited amount of space and attention afforded by many digital channels.

Marzouk, O., Salminen, J., Zhang, P., and Jansen, B. J. (2021) Which Message? Which Channel? Which Customer?: Exploring Response Rates in Multi-Channel Marketing Using Short Form AdvertisingData and Information Management.

Categories
Persona Analytics

Why is segmentation analytics important?

Why is segmentation analytics important?
Why is segmentation analytics important?

Customer segmenting is the process of dividing a group of people into homogeneous subgroups that differ from other subgroups, typically based on behaviors and demographics, grounded on some product, brand, advertisement, or content, with many factors affecting product engagement by customer.

The identification of customer segments has been important in marketing and advertising for some time, and it is increasingly important in the technology and online content publishing domains, such as online news media and online content.

The identification of customer segments is typically aimed at the understanding of a subset of people’s reactions, interactions, uses, etc., based on one or more key performance indicators to achieve some goal or objective, such as increasing revenue, increasing market share, or designing future content.

Categories
Persona Analytics

Comparing Persona Analytics and Social Media Analytics for a User-Centric Task Using Eye-Tracking and Think-Aloud

Comparing Persona Analytics and Social Media Analytics for a User-Centric Task Using Eye-Tracking and Think-Aloud
Comparing Persona Analytics and Social Media Analytics for a User-Centric Task Using Eye-Tracking and Think-Aloud

We compare a data-driven persona system and an analytics system for efficiency and effectiveness for a user identification task.

Findings from the 34-participant experiment show that the data-driven persona system affords faster task completion, is easier for users to engage with, and provides better user identification accuracy.

Eye-tracking data indicates that the participants focus most of their attention on the persona content while focusing more on navigation features when using the analytics system.

The combined results provide empirical support for the use of data-driven personas for a user identification task, which we surmise to be a result of the persona system following a user-centered design paradigm instead of an information-centered paradigm.

That analytics system afforded capabilities and insights that the persona system did not suggest that the triangulation of features may lead to a better overall user understanding.

Salminen, J., Sercan, S., Jung, S.G., and Jansen, B. J. (2021) Comparing Persona Analytics and Social Media Analytics for a User-Centric Task Using Eye-Tracking and Think-Aloud. CHItaly2021, 11-13 July 2021, Remote via Internet & ​Bozen-Bolzano, Italy.

Categories
Persona Analytics

Persona Analytics: Implementing Mouse-tracking for an Interactive Persona System

 

Persona Analytics: Implementing Mouse-tracking for an Interactive Persona System
Persona Analytics: Implementing Mouse-tracking for an Interactive Persona System

Using an interactive persona system, user behavior and interaction with personas can be tracked with high precision, addressing the scarcity of behavioral persona user studies.

In this research, lead by Soon-gyo Jung and Joni Salminen, we introduce and evaluate an implementation of persona analytics based on mouse tracking, which offers researchers new possibilities for conducting persona user studies, especially during times when in-person user studies are challenging to carry out.

Jung, S.G., Salminen, J., and Jansen, B. J. (2021) Persona Analytics: Implementing Mouse-tracking for an Interactive Persona System. ACM CHI Conference on Human Factors in Computing Systems (CHI2021), Yokohama, Japan. 8-13 May. Article No.: 342.

Categories
Persona Analytics

Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data

Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data
Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data

In this research, my co-researchers and I propose a novel approach for isolating customer segments using online customer data for products that are distributed via online social media platforms. We use non-negative matrix factorization to first identify behavioral customer segments and then to identify demographic customer segments.

We employ a methodology for linking the two segments to present integrated and holistic customer segments, also known as personas. Behavioral segments are generated from customer interactions with online content. Demographic segments are generated using the gender, age, and location of these customers.

In addition to evaluating our approach, we demonstrate its practicality via a system leveraging these customer segments to automatically generate personas, which are fictional but accurate representations of each integrated behavioral and demographic segment.

Results show that this approach can accurately identify both behavioral and demographical customer segments using actual online customer data from which we can generate personas representing real groups of people.

An, J., Kwak, H., Salminen, J., Jung, S.G., and Jansen, B. J. (2018) Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data. Social Network Analysis and Mining. 8(1), 54.

Categories
Persona Analytics

Making Meaningful User Segments from Datasets Using Product Dissemination and Product Impact

Making Meaningful User Segments from Datasets Using Product Dissemination and Product Impact
Making Meaningful User Segments from Datasets Using Product Dissemination and Product Impact

Online companies face large user populations, making segmentation a daunting exercise. 

In this research, we demonstrate an approach that facilitates user segmentation. The approach leverages product dissemination and product impact metrics with normalized Shannon entropy. 

Using 4,653 products from an international news and media organization with 134,364,449 user-product engagements, we isolate the key products with the widest product dissemination and the least product impact using entropy-based measures, effectively capturing the engagement levels. 

We demonstrate that a small percentage (0.33% in our dataset) of products are so widely disseminated that they are non-discriminatory, and a large percentage of products (17.02%) are discriminatory but have so little dissemination that their impact is negligible. 

Our approach reduces the product dataset by 17.35% and the number of user segments by 8.18%. Implications are that organizations can isolate impactful products useful for user segmentation to enhance the user focus.

Jansen, B. J., Salminen, J. O., & Jung, S. (2020). Making Meaningful User Segments from Datasets Using Product Dissemination and Product ImpactData and Information Management. doi: https://doi.org/10.2478/dim-2020-0048