ML-Powered Data Warehouse for SERP Analytics

Monitoring website position inside global and local search engines.

DRL Team

AI R&D Center

15 Jun 2020

6 min read

ML-Powered Data Warehouse for SERP Analytics

Client Services

AI Solutions Development

Industries

Performance Marketing

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Summary

Modern approaches for monitoring the site position inside global and local search engines require huge amounts of different textual queries.
A leading digital marketing agency wanted a scalable and automated service, dedicated to performing search engine optimization analysis.
The agency used such engine for day-to-day and long term analytics and monitoring of performance of SEO optimized sites.
Natural Language Processing and other Machine Learning methods were the foundations of the solution implemented by our team.

Tech Stack

Akka

Apache Spark

Cassandra

GlusterFS

Kafka

PostgreSQL

Python

Scala

TensorFlow

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Timeline

2 Weeks

Data Gathering Parser

Data Engineer

1 Week

Solution Architecture Design

Solution Architect

1 Week

Feature Extraction Pipeline Development

Deep Learning Engineer

Deep Learning Researcher

1 Week

Development of Customised LSH

Deep Learning Researcher

Deep Learning Engineer

1 Week

Clustering Performance Optimization

Deep Learning Engineer

Data Engineer

2 Weeks

Data Warehouse Configuration

Data Engineer

6 Weeks

Web Platform Development

Backend Developer

Frontend Developer

2 Weeks

Integration & Deployment

Backend Developer

Dev Ops

Tech Challenge

Implementation required removal of the laborious manual tasks from the SEO team, allowing the client to considerably improve quality and revenues of the services.
It was important that the entire range of analytics, from days to years, is in full disposal of the SEO specialist to adjust the parameters and predict the outcome.
Parsing of Google Search Console of the websites and then parse google for search queries results taken from GSC.
Clustering query-results matrix by links where the size of the matrix could be tens of millions squared.

Solution

Peek queries are formed automatically by our own Natural Language Processing algorithm, applying modern approach to monitoring the site position inside global and local search engines.
This algorithm considers structure and content of the target site pages and builds huge amounts of different textual queries to get the whole picture of the site’s performance.
Those queries are performed and stored inside the database on a daily basis for whole range of sites. Each site is then analyzed against the competition, using the tool we have built.
Different ranges of analytics on day-to-years scale are accessible to the SEO specialist for further iterations.
Customized and optimized Apache Spark based LSH (Locality Sensitive Hashing) for approaching near linear clustering complexity - O(bn). Avgerage clustering time for 10M x 10M matrix takes near 15min.

Impact

Our team has built a scalable service, which performs search engine optimization analysis.
It is used for daily as well as long-term analytics and monitoring the performance of the SEO optimized sites.
By eliminating the need for manual creation of SEO queries, our solutions has saved the agency at least thousands of working hours allowing to allocate people resources elsewhere.

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