Synerise - Behavioral Data Infrastructure Driven by AI (original) (raw)

"Synerise is able to track every event, across every channel, for the customer - whether it's mobile, it's web, it's retail, physical presence."

"All of that is signal that's being continuously collected, processed, and then in turn AI is being applied, workflows are being applied to drive the experience"

Satya Nadella about Synerise

CEO, Microsoft

Unified Synerise Experience Platform

A fully integrated suite of behavioral intelligence products

We created an end-to-end experience & continuous intelligence framework - connecting modern data collection, processing methods & analytics with AI-driven business scenarios execution.

Integrate data

Heterogeneous data sources integration

Calls, surveys & chats logs

Unify Information

Behavioral profiles & actions in one place

Deep Analytics (LTV, CLV,RFM)

Expressions & Dynamic Aggregates

Manage Data & Controll Access

Security by design

Data self-service importer

Analyze lifetime data streams

Actionable & advanced analytics & BI

Predict, decide & personalize

ML & deep learning supported decisions

Automate, Optimize & Execute

Experience orchestration

Deliver content & activate profiles

Omnichannel communication

Create, connect and extend features

Custom extensions, APPs & dedicated portals

Hot & Cold storage access

Recommendations on an empty basket

Use the potential of emptied basket by displaying AI recommendations

Segment creation based on quantiles

Reach a specific audience by defining a segment based on quantiles

Recommendations compliant with the Omnibus

Create personalized recommendations that show the lowest price over the last 30 days for discounted products

Low-stock abandoned cart campaign

Create low-stock campaign for customers with abandoned carts

Dynamic report for products bought together with top 10 products

Create a report with top 10 complementary products to top 10 bestsellers

Recommendations of similar products with item context

Create a carousel of product recommendations similar to items recently added to favorites

Personalized promotion in the mobile app with display time limitation

Create a personalized mobile promotion available to the customer within a specified time frame

Send a list of profiles from Synerise to Google Ads

Send propenisty-based customer segmentation to Google Ads

Dailymotion.com has applied EMDE/BaseModel.ai to personalize video recommendations in native applications, leading to improved relevance and catalog coverage

EMDE (BaseModel.ai) gives us a generalised framework for recommendations. The embedding generation was superfast (i.e <5 minutes). For context, do remember that GraphSAGE took ~20hours for the same data in the NCR region.

BaseModel.ai is core for all AI services offered in Synerise platform

BaseModel’s powerful behavioral analysis enables us to make sure that our clients receive communication tailored specifically to their preference.

BaseModel.ai

Apply science to behavioral data. Automatically.

Get answers for all crucial questions.

Reduce your modeling life-cycle to days instead of months

General

How do daily customer interactions influence their future behaviors?

Retail

How much will the customer spend in a specific category next week?

Travel

What is the customer’s expected number of trips this year?

Customer Service

What is the customer’s likelihood of using a special offer?

Telco

How much data traffic will the customer use this month?

Health

How many diagnostic tests will the patient need this year?

Insurance

How many insurance policies will the customer subscribe to this year?

Gaming

How many power-ups/bundles will the gamer buy this month?

Banking

What is the customer’s projected profitability in the next quarter?

Ecommerce

Which products/promotions/ offers/categories the customer is interested in?

Home & Furniture

How to split the customer population into behaviorally distinctivegroups?

Automotive

What kind of product/category is the customer interested in and why?

Software

Will the customer churn in the near future and what events had an impact on that?

Payments

What is the utility of customer for your business and what arethe behavioral and sociodemographic factors affecting it?

Fashion

Will the customer make a purchase next week? Whatsteps need to be taken to increase the chance of purchase?

Security

Is recent behavior of the customer inconsistent with past habits?

Compliance

Are there outlier customers in the population, who might be worth looking into?

News & Publishing

Will the reader subscribe to a premium plan?

Science

Lab

Sair is a lab focused on behavioral modeling, recommendations, large-scale data and graphs processing. We share our ideas, models, and experimental results, also presenting our take on important breakthroughs and interesting technologies. We hope to build a better and more thorough understanding of the field. We believe in the importance of this research not only from a business perspective but most importantly as a study of human decision-making processes.

BaseModel vs TIGER for sequential recommendations

The comparison between BaseModel and TIGER reveals substantial differences in their architectural choices and performance.

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BaseModel vs HSTU for sequential recommendations

To evaluate BaseModel against HSTU, we replicated the exact data preparation, training, validation, and testing protocols described in the HSTU paper.

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Fourier Feature Encoding of numerical features

Pre-processing raw input data is a very important part of any machine learning pipeline, often crucial for end model performance

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Why We Need Inhuman Artificial Intelligence

We continuously wonder how much longer it will take until AI reaches human skill level in these tasks - or, when does AI become "truly" intelligent.

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EMDE vs Multiresolution Hash Encoding

When we created our EMDE algorithm we primarily had in mind the domain of behavioral profiling.

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Efficient integer pair hashing

Mental models are simple expressions of complex processes or relationships.

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Cleora: how we handle billion-scale graph data

We have recently open sourced Cleora — an ultra fast vertex embedding tool for graphs & hypergraphs.

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Towards a multi-purpose behavioral model

In various subfields of AI research, there is a tendency to create models which can serve many different tasks with minimal fine-tuning effort.

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EMDE Illustrated

In this article we provide some intuitive explanations of our objectives and theoretical background of the Efficient Manifold Density Estimator (EMDE)

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How we challenge the Transformer

Having achieved remarkable successes in natural language and image processing, Transformers have finally found their way into the area of recommendation.

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We are sharing our ideas with others!

Our research papers based on Synerise BaseModel.ai framework

Multidimensional Hopfield Network

Redefining Graph Clustering: A Convergence of Algorithms and Networks

A Foundation Model for Behavioral Event Data

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

Real-Time Multimodal Behavioral Modeling

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

An Efficient Manifold Density Estimator for All Recommendation Systems

International Conference on Neural Information Processing (ICONIP 2021)

Cleora: a Simple, Strong and Scalable Graph Embedding Scheme

International Conference on Neural Information Processing (ICONIP 2021)

Twitter User Engagement Prediction with a Fast Neural Model

15th ACM Conference on Recommender Systems RecSys Challenge Workshop, 2021

Node Classification in Massive Heterogeneous Graphs

ACM's Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) KDD Cup Open Graph Benchmark (OGB) Challenge Workshop, 2021

Efficient Manifold Density Estimator for Cross-Modal Retrieval

The 43th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) eCom Workshop, 2020

Modeling Multi-Destination Trips with Sketch-Based Model

14th ACM International Web Search and Data Mining Conference (WSDM) WebTour Workshop on Web Tourism, 2021

On the Unreasonable Effectiveness of Centroids in Image Retrieval

International Conference on Neural Information Processing (ICONIP 2021)

Interpretable Efficient Multimodal Recommender

Thirty-seventh International Conference on Machine Learning (ICML) Machine Learning for Media Discovery (ML4MD) Workshop, 2020

Temporal graph models fail to capture global temporal dynamics

We propose a trivial optimization-free baseline of "recently popular nodes" outperforming other methods on all medium and large-size datasets in the Temporal Graph Benchmark.