Zero-Coding UMAP in Marketing
A Scalable Platform for Profiling and Predicting Customer Behavior by Just Clicking on the Screen
Takuya Kitazawa
Arm Treasure Datakitazawa@treasure-data.com

ABSTRACT

2 DEMONSTRATION

Customer Data Platform (CDP) is an integrated customer database
operated by marketers. In the context of UMAP, this paper demonstrates a real-world CDP with a special focus on (1) simple and deterministic text-based behavioral profiling technique, and (2) GUIbased versatile tool for predictive analytics. Those functionalities
are designed for those who have no expertise in machine learningand natural language processing, so the only thing marketers haveto do is clicking some buttons on UI. Meanwhile, their back-end
system ensures scalability and utility of the entire workflow fromdata collection and management to prediction and visualization.

Fig. 1 shows that the CDP stands on a data management layer using
open-sourced big data processing tools such as Digdag2 , Presto3 ,Apache Hive and Hivemall [4]. These components guarantee scalability and maintainability of the whole system in accordance withthe widely recognized best practices [6]. Plus, seamless, accurate,
and reliable integration with abundant third-party tools ensures
the reproducibility of data modeling as discussed in [5].

• Information systems → Enterprise applications; Information integration; Personalization; Decision support systems; Computing platforms; Data warehouses;

ACM Reference Format:
Takuya Kitazawa. 2019. Zero-Coding UMAP in Marketing: A Scalable Platform for Profiling and Predicting Customer Behavior by Just Clicking on
the Screen. In 27th Conference on User Modeling, Adaptation and Personalization Adjunct (UMAP’19 Adjunct), June 9–12, 2019, Larnaca, Cyprus. ACM,New York, NY, USA, 2 pages. https://doi.org/10.1145/3314183.3324970

1Behaviors
Click log

Purchase history

APIQuery engines

KEYWORDS
Customer data platform; user profiling; predictive analytics

Customer

NameAge
SexEmailAddress…

…

CCS CONCEPTS

A5ributes

Hive
DigdagDistributed workﬂow engine

Batch

Hivemall+ ML toolkit

Presto Ad-hoc

Data transfer from/to external services(e.g., web logs, CRM, social networks, BI tool, email)Figure 1: Architecture of the demonstrated CDP. Underlying solid data layer stores massive data on the cloud.

BACKGROUNDA rapidly growing need for optimizing day-to-day marketing activities has recently introduced a new concept named Customer
Data Platform (CDP). Earley [3] explained that CDP is a centralized place for creating customer profiles, implementing marketing
campaigns, and predicting customer behavior in connection witha variety of data and signal sources. In fact, UMAP techniques potentially play an important role in making deeper insights about alarge number of customers on CDP. However, the implementation
is not straightforward due to the end user’s limited technical expertise and complexity of real data. Therefore, this paper provides
a practical solution to the unique challenge by demonstrating acommercially available CDP, Arm Treasure Data enterprise CDP1 .
1 https://www.treasuredata.com/

Permission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must behonored. For all other uses, contact the owner/author(s).
UMAP’19 Adjunct, June 9–12, 2019, Larnaca, Cyprus© 2019 Copyright held by the owner/author(s).
ACM ISBN 978-1-4503-6711-0/19/06.
https://doi.org/10.1145/3314183.3324970

On the other hand, our web-based GUI application covers thosetechnical details with dedicated views for end users. Fig. 2 andthe rest of this section describe how marketers interact with theircustomer data on the application. Most importantly, no coding isneeded at every stage as follows.
First, once CDP users specified desired data and signal sources,unified customer profiles are systematically populated by aggregating static attributes and time-stamped behavioral data. At thesame time, the system conducts simple, deterministic text-based
customer profiling on titles and descriptions of customer’s visited
web pages as needed. Consequently, customer records are enrichedwith their interest words and affinity categories as seen in an individual’s profile view like Fig. 2 (a). Unfortunately, we cannot explain the detail of the profiling technique due to space limitations,but it basically relies on (i) TF-IDF weighting for interest word extraction in a similar way to [2], and (ii) a large word-to-category
mapping table generated from the Wikipedia corpus.
Subsequently, marketer defines a subset of customer profiles(i.e., customer segment) as illustrated in Fig. 2 (b), with a flexible
2 https://www.digdag.io/
3 https://prestodb.io/1st party data
(e.g., web logs)

StorageQuery

2nd & 3rd party data
(e.g., demographics)

Workﬂow

Data layer(Fig. 1, boMom)

Customer proﬁling with ML/NLP-based enrichment

Text-based behavior proﬁling
Interest words
Aﬃnity categories

GUI applica*on(Fig. 1, top)

Segmenta*on

Campaign implementa*on…

Data inges*on & management

Predic*ng customer behavior
Probability of belonging totarget segment

Target customers

PopulaKon(b) Extract subset of customers based on business interests(a) Customer 360-degree proﬁle view(c) Suggest promising aMributes for feature engineering(d) Dashboard for predicKve customer scoring

Figure 2: End-to-end UMAP workflow implemented with the demonstrated CDP. Demo scenario follows this entire flow4 .
choice of filtering conditions. The CDP then enables to implement
marketing campaigns on arbitrary customer segment. For example, if you create a segment consisting of dormant customers, you
may want to send a special offer to them for customer reactivation.
Moreover, an advanced use case of the platform is to implement
look-ahead-based marketing campaigns by predicting unseen customer’s behavior. In the previous example, it means that marketers
can preliminarily find out customers who are likely to become dormant before they actually stop using your service. The predictive
analytics capability, namely predictive customer scoring, technically solves binary classification problem on customer profiles,
and the classifiers eventually give the probability of belonging toa specific “target segment” (e.g., “set of customers who have notlogged in for a year” for dormant customer prediction). The probabilities are finally visualized on a dashboard view as Fig. 2 (d) withsome auxiliary metrics such as evaluation accuracy and feature importance. In the end, marketers can easily and effectively performpredictive marketing campaigns.
As the scenario above suggests, the CDP simply requires users
to click buttons on UI at each step of customer profiling, segmentation, predictive analytics, and campaign execution. It should be
noted that, while practitioners normally spend a huge amount oftime on feature engineering, the predictive analytics functionality makes the task semi-automated by suggesting features as captured in Fig. 2 (c). The process internally takes a heuristics-based
approach with single-column profiling [1] on customer attributes.
4 A supplemental screencast is available at: https://youtu.be/iwbqb5D2uPw

3 DISCUSSIONAs an example of productized UMAP solution, we have demonstrated the enterprise-grade implementation of CDP that provides
a solid data layer and marketer-friendly GUI-based application.
From an algorithmic point of view, the CDP employs limited conventional UMAP techniques like TF-IDF weighting and binary classification to make the application as explainable, scalable, and accurate as possible for non-expert users. On that point, future challenges might be related to a field of interactive and explainable usermodeling e.g., based on topic modeling and clustering.

4 ACKNOWLEDGMENTS
The author would like to thank Kazuki Ohta, Sadayuki Furuhashi,
Takahiro Tanaka, Tsubasa Tomoda, and all contributors to this project
for their professional work. We would also like to thank the usersof our CDP solution for providing valuable feedback.

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