=Paper=
{{Paper
|id=Vol-2608/paper75
|storemode=property
|title=Decision tree based targeting model of customer interaction with business page
|pdfUrl=https://ceur-ws.org/Vol-2608/paper75.pdf
|volume=Vol-2608
|authors=Hrystyna Lipyanina,Anatoliy Sachenko,Taras Lendyuk,Serhiy Nadvynychny,Sergii Grodskyi
|dblpUrl=https://dblp.org/rec/conf/cmis/LipyaninaSLNG20
}}
==Decision tree based targeting model of customer interaction with business page==
https://ceur-ws.org/Vol-2608/paper75.pdf
Decision Tree Based Targeting Model of Customer
Interaction with Business Page
Hrystyna Lipyanina [0000-0002-2441-6292], Anatoliy Sachenko [0000-0002-0907-3682], Taras
Lendyuk [0000-0001-9484-8333], Serhiy Nadvynychny [0000-0002-5567-1114], Sergii Grodskyi [0000-
0002-9366-2243]
Ternopil National Economic University, Lvivska Str., 11, Ternopil, 46000, Ukraine
xrustya.com@gmail.com, as@tneu.edu.ua, tl@tneu.edu.ua, nad-
vynychnyy@tneu.edu.ua, sergij80@yahoo.com, www.tneu.edu.ua
Abstract: Company branding through social networks is most effective if it
reaches the right customers. This study explores how to improve business page
targeting on Facebook by customer behavior targeting, age and gender to form a
more comprehensive contextual advertising strategy. Paper focuses on empiri-
cal modeling of targeting based on decision trees. The general practice of such
models developing does not take into account business goals sufficiently. To
correct this, we propose to create an algorithm for customer interaction simulat-
ing with a business page on Facebook based on decision trees within the control
and optimization of a company's marketing strategy. The resulting algorithm
combines statistical training principles and business goals in the form of cam-
paign income maximizing. The basic approach to the marketing strategy forma-
tion is considered, the parameters of the algorithm and the algorithm of forming
the client interaction targeting with the business page on the basis of decision
tree are established. Based on the above algorithm, we build a model of cus-
tomer interaction targeting with a business page based on the decision tree us-
ing the data of contextual advertising campaign on Facebook. Based on the
simulation results, a re-formation of the advertising campaign and analysis with
the input data were performed. The results of the study confirm the value of the
proposed method, since the targeting model of customer interaction with a
business page based on decision trees recommends significantly more profitable
target groups than a few benchmarks.
Keywords: model, targeting, algorithm, method of decision tree, R language.
1 Introduction
One of the main tasks of modern business is to attract new customers and maintain
relationships with existing customers. The importance of this problem is determined
by the fact that social networks have become an integral part of modern life of most
people whom the business views as potential customers. Social networks have be-
come the landscape for business and attracting new customers. This space has its own
features that set it apart from the traditional market.
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
All social networks are distinguished type. There are networks for people finding:
classmates, colleagues and other people. There are business networks for searching of
job, partners, professional communication and other business matters. Some networks
are video-based, some are oriented on audio and specifically music and some are
photo-based. There are also specialized networks that target a specific, well-defined
group of people, such as the Habrahabr network that integrates everything related to
IT. Advantages of social networks are the ability to use them to establish relationships
with customers and monitor them [29].
All digital marketers must have the skills to build distributed computing systems,
data warehouses, and electronic document management systems based on the client's
network, as well as be able to register domain names, hosting and colocation tradi-
tionally targeted at large non-governmental enterprises and companies, government
agencies, small businesses.
The global cost of online advertising has caught up newspaper advertising in 2013.
The figures are huge: $ 82.09 billion spent on digital advertising in the US only in
2017, and it is expected to cost more than $ 113 billion in 2020 [1]. In March 2019,
the most popular social network in Ukraine was Facebook, which was used by 50% of
respondents. This is confirmed by the survey data from Research & Branding Group
[2].
Facebook allows companies placing audience-targeted ads in the news feed, the
right column of their account, show them on the mobile app, promote videos, etc.
Properly setting and selecting the target group will save your campaign budget and
time for setting your campaign up, so this is an issue for nowadays business commu-
nity.
This work proposes Model of Customer Interaction Targeting with Business Page
based on Decision Tree. The rest of the paper has the following structure: Section 2
examines existing methods and approaches to online advertising, including the use of
targeting techniques to improve it. Section 3 describes the proposed algorithm for
customer targeting interaction with a business page based on a decision tree. Section 4
discusses the findings of the study and Section 5 summarizes the findings.
2 Related Work
The study [3] investigates how to enhance geographical targeting by a suite of
other targeting strategies, including behavioral targeting, temporal targeting, and use
of discount in an online-to-offline commerce context, to form a more comprehensive
contextual targeting strategy. However, there is no specification of the obtained re-
sults in this study, in particular the targeting group is not clearly defined.
Authors [4] stay that the level of focus on consumer advertising significantly af-
fects the idea of ordering informativity, whereas specialized online remarketing ad-
vertising have a direct adverse effect on the customer, in addition, it increases the
irritation caused by targeting behavior.
In general, work [5] can be considered as a first step in studying of large-scale,
fine-grained digital traces of online human behavior and how they can be used to
predict people’s future marketing behavior.
Attention in articles [6, 7] focus on empirical targeting models. The papers argue
that the general practice of such models developing does not sufficiently take into
account business goals. The results of a comprehensive empirical study confirm that it
recommends significantly more profitable target groups.
The authors of [8] use content analysis to study topics and formats of 5932 Face-
book posts from leading US colleges and universities. The results show that there are
content topics, such as athletics, that significantly increase engagement, and others
tend to be less active. In addition, format, as well as user-generated content, is another
contributing factor to engagement.
The study [9] outlines the risks of using Facebook for both users and marketers.
The suggested scenarios will help marketers understand how Facebook marketing
uses knowledge management tools like plan-do-check-act (PDCA) and root cause
analysis (RCA).
A study [10] examines the effectiveness of advertising on Instagram and Facebook
in terms of advertising attitude, advertising persistence and loyalty intentions. The
results show that Instagram Stories not only improves consumer attitudes towards
advertising, but also increases its responsiveness compared to Facebook Wall.
The results of a study [17] revealed that attitudes towards advertising on the social
network, that is, any efforts to transmit product messages between network members,
who are also consumers of different products, are formed and persuaded by hedonic
motivation (HM), a source of derogation (SD), self-concept (SC), messages informal-
ity (MI), and experience messages (EM).
A study [18] examines Facebook advertising, namely social influence theory and
regulatory focus theory. Structural equation modeling results show that in both devel-
oped countries (Australia) and developing countries (South Africa), there are signifi-
cant relationships between the considered parameters in the model (privacy, trust,
advertising, advertising, attitude to advertising, promotional values, Facebook adver-
tising attitudes, and promotional behavior).
A study [19] proved that different advertising content in Facebook leads to differ-
ent levels of recruitment and involvement of users in the advertisement. Different
advertisements also change the choice of advertisement in terms of demographic and
mental health characteristics.
Research findings [20] have shown that Facebook advertising has had a significant
impact on brand image and price, both of which contribute to increased brand sales.
The authors of a study [21] examined whether customer involvement in social media
brands has a bearing on brand trust, brand loyalty, and brand creation, and the results
indicated that motivation to participate in SNSs significantly influences customer
engagement, which in turn, has a positive effect on brand credibility, brand loyalty.
The authors of [22] confirmed that Facebook, among users of social networks, is
perceived as an effective means of advertising, and it is strongly associated with the
benefits of “customer relationship management” and “promotion of new products”.
Article [23] investigates the impact of two adverticement placements on Facebook,
a sidebar ad, and a message bar to avoid advertising. The results indicate the crucial
role of product engagement in Facebook ads targeting to the right audience and
choosing the right ad placement.
Article [24] defines the influence of online advertising on students' decision-
making and the choice of their universities. The results revealed that social media and
websites have a positive effect on student decision making, which then has a signifi-
cant impact on students’ choices at a particular university.
The of the study result [25] revealed the two-sided nature of community size: uni-
versities with a strong reputation tend to have more Facebook fans, but the presence
of numerous Facebook fans has a detrimental effect on attracting individual fans.
Article [26] analyzes the trends of video marketing. The algorithm of video content
creation is defined and the ways of video marketing using in the activity of universi-
ties are suggested, namely: creation of high quality video, video format should dem-
onstrate social proof of quality of educational services; universities’ education video
services to increase conversions.
We consider the work [11] as the most relevant one, where authors developed a
two-step method based on the Gaussian filter and decision tree (M-GFDT). The Gaus-
sian filter corrects the distribution of business data in the first phase, and classifies the
decision tree to remove inefficient online advertising while achieving high accuracy
in predicting effective advertising. The second step provides testing of the method
experimentally with data from a cross-border e-commerce company. In our opinion
such approach is too complicated.
Thus, the above mentioned works, on the one hand, mostly analyze user actions in
response to online advertising, such as clicks and visits to brand sites, etc. On the
other hand, a number of analogs require relatively sophisticated tools for their imple-
mentation, that is, the question remains the simplification of data flows intellectual
processing, their interpretation, classification in the process of formation and, accord-
ingly, the adoption of targeted management decisions in the formation of advertising
strategy of customer interaction with the business side.
3 Algorithm for Customer Engagement Targeting with a
Facebook Business Page
Facebook is suitable for finding customers among the most solvent customers in
age 25–50. This segment of the audience responds well to ads that they find useful to
both themselves and their friends. This is manifested in the “natural” user’s activity:
in likes, reposts, comments. Due to this activity, the ad can gain additional reach and,
conditionally, “free” customers who have seen their friend’s repost or comment and
also are interested.
Currently, targeted Facebook ads have great business opportunities. Artificial Intel-
ligence analyzes user reactions to promotional content and concentrates impressions
only on those users who are more likely to become your customers. Advertising
works great even with small budgets. The algorithm is fast-paced and capable of de-
livering clients with limited budgets.
With a quality strategy, it is possible to achieve the desired result in social net-
works, because, quite often, marketers work with an audience that does not know the
advertised brand. Based on the recommendations [12, 13, 27, 28] it is possible to form
a strategy for advertising on social networks (Fig. 1).
Figure 1. Step-by-step formation of advertising strategy on social networks
The last step (see Fig. 1) is worth considering in more details, as it takes a long
time to process and is important for budget redistribution.
The objectives of controlling advertising include the following: analysis of the
goals compliance and objectives of the advertising campaign with the goals and ob-
jectives of the company marketing strategy; Determining the difference between the
planned and actual costs of the advertising campaign; determining specific results of
advertising by certain time; development of measures for advertising activity im-
provement in future.
The control of advertising campaign results has seven stages [12, 14, 15]:
Stage 1. Conducting an audit, i.e. situational analysis.
Stage 2. Establishment of planned values and standards (goals and norms).
Stage 3. Measuring of actually achieved results for a certain period (day, week,
month, quarter, year).
Stage 4. Comparison of actual values with planned and standard values.
Step 5. Analysis of the comparison results, which allows making changes in the
planned values and standards or in the course of the advertising campaign.
Stage 6. Reflect on the effectiveness.
Stage 7. Ongoing project management.
The last two steps require the most time, so they will target the customer interac-
tion with the business page based on the decision tree, and accordingly, it allows mak-
ing changes in the advertising campaign.
The decision tree is a fairly common approach now to identify and visualize logical
patterns in data. Dichotomous trees are used in this paper, here only two branches
emerge from the top. Each node is mapped to a certain attribute, and branches to ei-
ther specific values for qualitative features or a range of values for quantitative fea-
tures. The decision tree allows constructing a model of the dependence of many cases
on many characteristic features.
The decision tree algorithm, first proposed by Quinlan, operates on the principle of
recursively partitioning a dataset and incremental tree construction [16].
Table 1 lists the notations and descriptions used in the model tree targeting model.
The table shows all the performance parameters that are displayed when targeting a
Facebook ad campaign.
Table 1. Targeting options forming for customer interaction with a Facebook business page
Parameter Value Description
Age Age Age groups: 13-18, 18-25, 25-30, 30-35,
35-40, 40-45, 45-50, 50-55
Sex Sex female, male
r Results The number of times that an ad has
reached a goal-specific result and setting.
W Interaction The total number of actions taken by peo-
with page ple on a Facebook business page and posts
on it as a result of viewing ads.
Let’s build an algorithm for customer interaction targeting with a business page
based on the decision tree (Fig. 2). The algorithm of customer interaction targeting
with the business page on the basis of the decision tree will allow to reduce time-
consuming and on the basis of it is possible to make changes in the advertising cam-
paign.
For algorithm implementing the R programming language is used, which is free
and has significant capabilities for statistical analysis, time series analysis, cluster
analysis etc.
Initially, the data should be prepared for analysis (Block 1), with the R language
requiring a database file (file with the extension “*.xlsx”) (Block 2), where the model
parameter values are located in the columns. Parameters are converted into data with
factor values. In our case, there were parameters Age, Sex and r.
Next it is needed installation and running of the libraries (Unit 3) to build the
model based on the decision tree.
The next step is to create control samples (Block 4). Splitting data from vector r
into two sets in a predetermined ration, keeping the relative ratios of different labels
in r. Also returned (Block 5) are subsets of vectors, matrices, or data frames that meet
certain conditions.
In block 6, the model is constructed based on the recursive separation and regres-
sion tree. Due to the fact that the dependent parameter r is a factor, the construction of
the model is determined by the class method. Next (Block 7) the targeting model of
customer interaction with the business page is based on the decision tree, built on the
breakdown of independent variables (minbucket, minsplit, cp (Complexity parameter).
Begin
Database 2
face
1
Data preparation:
as.factor(Age, Sex, r) 7
F<-Sex+Age+W
3
Installation and run of libraries
library(rpart) 6
library(rpart.plot) Model building.
library(rattle) FaceTree <- rpart(r ~ F, data = Train,
library(RColorBrewer) method = "class",
library(caTools) control=rpart.control(minsplit =1, cp = 0.0005))
4 8
Creation of control sample. Graphic representation of decision trees
split <- sample.split(face$r, SplitRatio prp(FaceTree, box.palette=c("Greys"))
= 0.6) fancyRpartPlot(FaceTree, palettes=c("Greys"),
type=2)
5
Return of vectors subset 9
Train <- subset(face, split == TRUE) Building of cp table.
Test <- subset(face, split == FALSE) printcp(FaceTree)
10
View graph of error.
plotcp(FaceTree)
with(FaceTree, {lines(cptable[, 2] + 1, cptable[, 3], type = "b", col = "red")
legend("topright", c(Error in learning", "Cross-Verification Error (CV)", "min(CV Error)+SE"),
lty = c(1, 1, 2), col = c("red", "black", "black"), bty = "n") })
End
Figure 2. Algorithm for customer interaction targeting with business page based on decision
tree
The graphs of the decision tree are then constructed, with the choice of the best
representation (Block 8), the table of cp optimal segments based on the parameter of
complexity (Block 9) and the graph of model error are presented.
4 Results and Discussion
Based on the above algorithm, we will build a model of targeting customer interac-
tion with the business page based on the decision tree using data (more than 1 thou-
sand indicators) of contextual advertising campaign on Facebook during the admis-
sion campaign, this is considered on the example of specialty “Computer Science” of
Ternopil National Economic University (see Fig. 3).
Built model will reduce time spent, redistribute budget and make changes in the
course of advertising campaign.
Figure 3. Advertising “Computer Science” of Ternopil National Economic University on
Facebook
The targeting model of customer interaction with a business page based on a deci-
sion tree (see. Fig. 4) is usually the highest result (r) of a customer interaction with a
Facebook business page “Computer Science TNEU” are for male and female in age
group 40-55.
Fig. 5 shows the typical error behavior during ensemble training, the result is stabi-
lized on the tree branch no. 5, the minimum relative error during cross-checking is cp
= 0.0021.
Figure 4. Targeting model of customer interaction with business page based on decision tree
Figure 5. Errors in the targeting model for customer interaction with the business page based
on the decision tree
Re-targeting results made it possible to improve advertising campaign results by
30% over first ad campaign option. The average r for all ad groups increased from
2.78 to 3.7. These results indicate the model adequacy.
The novelty of the work is following. In contrast to analogues [3, 11], a model of
customer interaction targeting with a Facebook business page based on decision trees
enables for the edges ("branches") of the tree to make changes to the advertising cam-
paign strategy by the attributes on which the target function depends. Moreover, the
use of the rpart library in the R programming language makes it possible to clean and
filter data quickly, which makes target groups forming easily comparing with [11].
The developed model of customer interaction targeting with a business page based
on the decision tree has a practical importance for forming an advertising strategy of
admission campaign in higher educational institutions.
5 Conclusions
The algorithm for forming the client interaction targeting with business page, based
on the decision tree, is proposed. It enables to simplify the data flows intelligent proc-
essing, their interpretation, classification according to making the targeted manage-
ment decisions.
Based on the algorithm, a model of customer interaction targeting with a business
page based on the decision tree has built. It made it possible to evaluate the targeting
of advertising campaign and form an advertising strategy of admission campaign.
According to the built model, the strategy of the advertising campaign for admis-
sion was formed as well as the basic rule was highlighted: the male and female clients
in the age category 40-55 had the greatest interaction with the business page of Com-
puter Science at Ternopil National Economic University. It enables to consider this
rule to be taken into account during the advertising campaign for admission in higher
educational institutions.
References
1. Zenith – https://www.zenithmedia.com/
2. Research & Branding Group – http://rb.com.ua/blog/praktika-polzovanija-socsetjami-
v-
ukraine/?fbclid=IwAR3LAJVpOwYPgDWdQ7uqvkWXrUnX8S_rkXry9nhmtuxsNU
sinpNnn96zIvs
3. Lian, S., Cha, T., & Xu, Y.: Enhancing geotargeting with temporal targeting, behav-
ioral targeting and promotion for comprehensive contextual targeting. Decision Sup-
port Systems. 117:28-37 (2019).
4. Ozcelik, A. B., & Varnali, K.: Effectiveness of online behavioral targeting: A psycho-
logical perspective. Electronic Commerce Research and Applications. 33:100819
(2019).
5. Ghose, A., Li, B., & Liu, S.: Mobile targeting using customer trajectory patterns.
Management Science. 65(11): 5027-5049 (2019).
6. Lessmann, S., Haupt, J., Coussement, K., & De Bock, K. W.: Targeting customers for
profit: An ensemble learning framework to support marketing decision-making. In-
formation Sciences. in press. (2019).
7. Lo, Y. C., & Fang, C. Y. Facebook marketing campaign benchmarking for a fran-
chised hotel. International Journal of Contemporary Hospitality Management. 30(3):
1705-1723 (2018).
8. Peruta, A., & Shields, A. B.: Marketing your university on social media: a content
analysis of Facebook post types and formats. Journal of Marketing for Higher Educa-
tion. 28(2) 175-191 (2018).
9. Jaman, S. F. I., & Anshari, M.: Facebook as marketing tools for organizations:
Knowledge management analysis. In Dynamic perspectives on globalization and sus-
tainable business in Asia. IGI Global: 92-105 (2019).
10. Belanche, D., Cenjor, I., & Pérez-Rueda, A: Instagram Stories versus Facebook Wall:
an advertising effectiveness analysis. Spanish Journal of Marketing-Esic. 23(1): 69-
94 (2019).
11. Wang, H., & Hong, M.: Online ad effectiveness evaluation with a two-stage method
using a Gaussian filter and decision tree approach. Electronic Commerce Research
and Applications. 35: 100852 (2019). DOI: 10.1016/j.elerap.2019.100852.
12. Heinze, A., Fletcher, G., Rashid, T., & Cruz, A.: Digital and social media marketing:
a results-driven approach. Routledge. (2016).
13. Paleha O.I., Horban Yu.I.: Informational Business. Kyyiv National University of Cul-
ture and Arts. Kyyiv: Lira-K. (2015). (in Ukrainian)
14. Mygal O. F., Gupalo O.V.: Features of Goods Promotion on Social Networks. Pro-
ceedings of S.I. Yuriy third Scientific Readings, Ternopil, TNEU, 28 November
2017, pp. 82-86 (2017).
15. Perna, S. N., Clifton, M. A., Jongjin, K. I. M., Varma, B. S., Piro, S. J., Mapen, B. E.,
& Davis, T. J.: U.S. Patent No. 10,025,982. Washington, DC: U.S. Patent and
Trademark Office. (2018).
16. Shareef, M. A., Mukerji, B., Alryalat, M. A. A., Wright, A., & Dwivedi, Y. K.: Ad-
vertisements on Facebook: Identifying the persuasive elements in the development of
positive attitudes in consumers. Journal of Retailing and Consumer Services. 43: 258-
268 (2018).
17. Wiese, M., Martínez-Climent, C., & Botella-Carrubi, D.: A framework for Facebook
advertising effectiveness: A behavioral perspective. Journal of Business Research.
109: 76-87 (2020).
18. Choi, I., Milne, D. N., Glozier, N., Peters, D., Harvey, S. B., & Calvo, R. A.: Using
different Facebook advertisements to recruit men for an online mental health study:
engagement and selection bias. Internet Interventions. 8: 27-34 (2017).
19. Dehghani, M., & Tumer, M.: A research on effectiveness of Facebook advertising on
enhancing purchase intention of consumers. Computers in Human Behavior. 49: 597-
600 (2015).
20. Kamboj, S., Sarmah, B., Gupta, S., & Dwivedi, Y.: Examining branding co-creation
in brand communities on social media: Applying the paradigm of Stimulus-
Organism-Response. International Journal of Information Management. 39: 169-185
(2018).
21. Ertugan, A.: Using statistical reasoning techniques to describe the relationship be-
tween Facebook advertising effectiveness and benefits gained. Procedia computer
science. 120: 132-139 (2017).
22. Evert, V., Poels, K., &Walrave M.: An experimental study on the effect of ad place-
ment, product involvement and motives on Facebook ad avoidance. Telematics and
Informatics. 35: 470-479 (2018).
23. Jan M. T. & Ammari D.: Advertising online by educational institutions and students'
reaction: a study of Malaysian Universities. Journal of Marketing for Higher Educa-
tion. 26(2): 168-180 (2016). DOI: 10.1080/08841241.2016.1245232
24. Brech F. M., Messer U., Vander Schee B. A., Rauschnabel P. A. & Ivens B. S.: En-
gaging fans and the community in social media: interaction with institutions of higher
education on Facebook. Journal of Marketing for Higher Education. 27(1), 112-130
(2017). DOI: 10.1080/08841241.2016.1219803
25. Kovpak, V., & Trotsenko, N.: The Pragmatic Potential of Native Advertising: Forms,
Trends (on the Example of Native Content on the Social Network Facebook by the
Brand of the Journalism Department of ZNU). State and Regions. Series: Social
Communications. 1(41): 113-121 (2020).
26. Semeniuk, S. B.: Video Marketing in the Activities of Universities. Marketing and
Digital Tehnologies. 3(1): 68-77 (2019). (in Ukrainian)
27. Drewniany, B. L., & Jewler, A. J.: Creative strategy in advertising. Cengage Learning.
(2013).
28. Altstiel, T., Grow, J., & Jennings, M.: Advertising creative: Strategy, copy, and de-
sign. Sage Publications. (2018).
29. Bomba, A., Kunanets, N., Pasichnyk, V., & Turbal, Y.:Process modeling of message
distribution in social networks based on socio-communicative solitons. International
Journal of Computing. 17(4): 250-259 (2018).