=Paper= {{Paper |id=Vol-1383/paper11 |storemode=property |title=Using Semantic Technologies to Mine Customer Insights in Telecom Industry |pdfUrl=https://ceur-ws.org/Vol-1383/paper11.pdf |volume=Vol-1383 |dblpUrl=https://dblp.org/rec/conf/semweb/KanagasabaiVNYD14 }} ==Using Semantic Technologies to Mine Customer Insights in Telecom Industry== https://ceur-ws.org/Vol-1383/paper11.pdf 
Using Semantic Technologies to Mine Customer Insights in Telecom Industry
Rajaraman Kanagasabai, Anitha Veeramani, Le Duy Ngan, Ghim-Eng Yap
Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore
Email: {kanagasa,vanitha,dnle,geyap}@i2r.a-star.edu.sg

James Decraene, Amy Shi-Nash
R&D Labs, Living Analytics, Singapore Telecommunication Ltd, Singapore
Email: {jdecraene,amyshinash}@singtel.com

Background : Many telecommunication companies (telcos) today have actively started to transform
the way they do business, going beyond communication infrastructure providers and repositioning
themselves as data-driven service providers to create new revenue streams. New business
opportunities, notably in terms of market research, can be realised using the telco data especially
when it is complemented with external open data sources. Indeed, significant efforts have gone into
mining customer insights from the massive mobile data while preserving the end customers’ privacy.
In this paper, we present a novel industrial application where semantic technologies are successfully
used to mine commercial interactions of anonymous mobile customers, to get new aggregated
insights from their call records.

Our Method: The case study is inspired by an observed semantic gap between the contextual
business categories that can be derived from the customers’ call records and the industry-standard
classification scheme that is often a requirement for consistent ad targeting. In particular, the
Internet Advertising Bureau (IAB) Contextual Taxonomy, with 23 Tier-1 classes and 371 Tier-2 classes,
is an international standard that is adopted e.g. by the Google Display Network. Mapping from
thousands of contextual business categories to such a far more concise taxonomy is clearly a
daunting task for the market researchers.

Traditional approaches using machine learning techniques like the Support Vector Machines (SVM)
can help automate this task but it is not straightforward to apply them over thousands of categories
spread across a wide variety of domain areas. Our experience mapping a total of 2532 contextual
business categories to the IAB Taxonomy shows that applying text feature extraction and matching
resulted in merely 263 categories being matched (approximately 10% in recall). The challenge is that
this is in fact a large-scale multi-class multi-label classification problem that needs sufficient training
examples to generalize well. Obtaining such training labels specific to each application is expensive
and it is not feasible to repeat this for all applications. In this paper, we leverage domain knowledge
in semantic machine learning methodologies and avoid the need to invest in expensive training data.

Using public knowledge bases WordNet, DMOZ, and Yahoo! Answers as our domain ontology model
references, we investigated the three different semantic IAB classification methods described below:

   I.   We employ WordNet features to build an extended text vector for classification, and use it
        as a baseline for comparison.
  II.   DMOZ Open Directory is among the largest human-curated directories online. We ingested
        RDF dumps of DMOZ into AllegroGraph triplestore as our DMOZ ontology model. Low-level
         text features were extracted from each contextual category to find its semantically-matching
         categories in the DMOZ ontology and the DMOZ categories are used to find best IAB classes.
 III.    Yahoo! Answers is a community-driven Q&A site hosted by Yahoo! Inc. The site categorizes
         questions and answers in a shallow categorical hierarchy that is similar to IAB, though the
         category names are very different. We capitalize on this by first searching Yahoo! Answers
         with the contextual category and using the returned Yahoo! Categories to match IAB classes.

We built a corpus with just 525 contextual business categories and created IAB class assignments by
using two human experts to classify independently and a third expert to cross-check the assignment.
The corpus was used in our research to fine-tune parameters in all the three classification methods.
Following that, we validated the methods on a full set of 2532 contextual categories and manually
evaluated their classifications. Method III performed best, followed by Method II and then Method I.

Deployment: A customer insights dashboard product was developed to provide user behavioural
insights based on users’ geo-location traces and call details records (“who called who”). All records
were anonymised via a one-way AES encryption-hashing process and neither personal data nor calls
content was used.

A key offering of the product is its market segmentation service where in-depth user profiling was
conducted to infer various people traits of interest such as demographics, occupation, housing type,
and travel pattern. To illustrate, the example screenshot in Figure 1 shows the distribution of work
locations for people who are living within a specific location inside Singapore (marked as a blue cell).




   Figure 1 Example screenshot of the customer insights product showing work locations distribution and commercial
  interactions profile of people living in Buona Vista (blue cell) – the commercial interactions profile were successfully
      generated using the novel Yahoo! Answers-based semantic IAB classification method presented in this paper

To enrich the customer profiling, we used call detail records to identify user calls to local businesses
by identifying the business numbers being called and extracting the associated contextual business
category from open data that was available online. We applied Method III (using Yahoo! Answers) to
map the arbitrarily-defined 2532 contextual business categories into the IAB Contextual Taxonomy.
As shown in Figure 1, the proposed method enables the end product to effectively provide customer
insights in terms of market segments based on such commercial interactions of mobile device users.