##About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
#Who should attend Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI, DL and ML engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to firstname.lastname@example.org
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Large scale Machine Learning and data storage for CDP: transforming Digital Marketing
We will talk about why do we need a single end-to-end customer data platform to enable truly personalised digital marketing. We also explain what pain-points, such as cold-start problem, do we solve for marketers if we collate and utilise data from first, second and third party sources rather than relying on just first party data. Then we will focus on the motive to use Machine Learning to create behavioral audience segments, instead of relying on plain historical numbers.
The major end goals are:
- cost effective customer acquistion
- higher purchases, subscriptions, engagement etc
- better return on marketing spends
- higher user retention
Then we shall cover the underlying tasks. First is that of acquisition and ingestion of data from multiple data sources into the platform in such a way that it adheres to the data laws, is secure, and can be used combinedly. These data sources include telecom operators, location service providers, ad-exchanges, push notification service providers, social media platforms etc.
Secondly, we shall discuss how is these data enriched and engineered into a single, unified, secure and anonymised user profile database. Example of data enrichment are location based such as polygon mapping, mobile device based, app usage based etc.
Then we discuss a series of user behavioral scores built using Machine Learning such as LTV, Churn Rate, propensity to transact. lookalike etc. Here we cover feature engineering, model accuracy, choice of algorithm, scalability etc. Scalability is a big challenge as certain scores are predicted for hundreds of millions of users. The business proposition of how these scores can be used to create audience segments in order to target users as per their life-cycle will come next.
- Introduction to CDP
- Pain points of marketers
- How Machine Learning and CDP work in tandem
- Data acquisition, collection, ingestion, enrichment and management
- Machine Learning based user behavioral scores
- Audience and persona creation
- Nothing specific. People should have a basic understanding of Machine Learning.
Kunal Kishore completed his Bachelor of technology degree from IIT Kharagpur in Electronics and Communication Engineering. Currently he works as Research Scientist at Inmobi where he leads the data science efforts on Inmobi’s CDP offering. He has previously worked on data science areas such as large scale content recommendation systems, ad response prediction for display advertising bidder and e-commerce product recommendation.