Zero-Coding UMAP in Marketing
A Scalable Platform for Profiling and Predicting Customer Behavior by Just Clicking on the Screen
Takuya Kitazawa (Arm Treasure Data)

Demonstrated Solution: Arm Treasure Data Enterprise CDP

Customer Data Platform (CDP)▶ A rapidly growing need for optimizing day-to-day marketing activities.

A5ributes▶ CDP is a centralized place for:- creating customer profiles,- implementing marketing campaigns, and- predicting customer behavior
in connection with a variety of data and signal sources.

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

WorkﬂowData layer

GUI applica*on(Fig. 1, boMom)(Fig. 1, top)

…
Batch

DigdagDistributed workﬂow engine

Ad-hoc+Scalable ML library

Cloud-based scalable data layer- ML in query language- Query execuKon- Workﬂow management

Data transfer from/to external services(e.g., web logs, CRM, social networks, BI tool, email)Segmenta*on

Campaign implementa*on…

Text-based behavior proﬁling
Interest words
Aﬃnity categories

Query

Purchase history

Query engines

Customer proﬁling with ML/NLP-based enrichment

Storage

Click log

CDP: GUI-based UMAP applicaEon- Uniﬁed, enriched customer proﬁles- StaKc aPributes- Time-stamped behavioral dataAPI▶ UMAP techniques play an important role in making deeper insights.
↔ Implementation is NOT straightforward due to:
1. the end user’s limited technical expertise, and
2. complexity of real data.

1st party data
(e.g., web logs)

Behaviors

NameAge
Sex
EmailAddress…

— S. Earley. The Role of a Customer Data Platform. IT Professional, 20 (1), pp. 69–76, 2018.

Data inges*on & management

CustomerPredic*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

Zero-Coding UMAP #1: Text-Based Behavior Profiling
TF-IDF-based keyword extraction from web articles (title + description)
Olympic

game

medal

president

ci2zenrule

datacloud

CDP

law

equa2on

math

curry

rice

US

na2on

SocietyScience
Food, Culture

historytd_interest_words
cdp_customer_id word
word

Zero-Coding UMAP #2: Predicting Customer Behavior▶ Implement look-ahead-based marketing campaigns by predicting unseen
customer’s behavior.
▶ Solve binary classification problem on customer profiles by just clicking on GUI.
→ Classifier gives the probability of belonging to a specific “target segment.”law

Aggregating customer’s web visits — sum(), l1_normalize(), each_top_k()
poli2cs(d) Dashboard for predicKve customer scoring

td_affinity_categories▶ Semi-automated ML workflow, including:- Logistic regression training and prediction- Over- or down-sampling, and probability calibration- Min-max normalization- 8:2 validation▶ Dashboard visualizes probability, evaluation accuracy, and feature importance.

score
category

probability

Pre-deﬁned mapping table generated with Wikipedia corpus

Discussions▶ Be as explainable, scalable, and accurate as possible for non-expert users.
→ Limited UMAP techniques like TF-IDF weighting and logistic regression.
↔ More interactive and explainable user modeling e.g., based on topic modeling.