The Web is the largest source of publicly available information in the world. Today’s businesses see the immense potential of harnessing external and competitive information from the Web to aid in enhanced business strategies and decision-making.
The catch? Data from the Web is hard to aggregate and analyze in a meaningful way, as it is typically noisy, unstructured, and transient. This is where we come in.
Our large-scale data aggregation platform can acquire millions of data points from the Web across geographies, ZIP codes, and languages, every day. And it’s not just from web pages, but mobile apps as well, providing a comprehensive view of the online competitive environment.
Our data aggregating engine is built to operate across complex Web environments, capable of acquiring data from diverse industry verticals and online platforms. We also maintain a historical store of all data acquired, enabling us to deliver unique, time series insights.
Once we acquire all data points of interest from the Web, we then go about processing it for information.
Due to the inherent noise and lack of structure in Web data, we use advanced normalization techniques to clean the data, including AI-powered image and text analytics. On organizing the data, we begin unearthing meaningful information from it.
Complex machine-learning and information retrieval algorithms are used to build semantic models, which in combination with proprietary knowledge bases built since our inception, help us make sense of the data. For example, by using deep-learning techniques, we classify products into retail taxonomy, identify product attributes based on unstructured text features, and tag complex product features like the length of a skirt, a collar's type, and more by analyzing images.
This information is used in categorizing and matching eCommerce products across websites, grouping together similar products, and providing similar product recommendations.
Businesses make critical decisions that impact their top line and bottom line using the insights delivered by us. Therefore, in scenarios when the confidence score of the machine-driven math is low, we leverage human intelligence to ensure 95%+ accuracy.
Our quality assurance team takes three actions – confirms the veracity of the conclusion, investigates why the confidence score is low, and figures out a way to encode this knowledge into an algorithmic rule.
This way, we’ve built a self-improving feedback loop which, by its very nature, performs better over time. This system has accumulated knowledge over the last 7 years of our operations, resulting in a very high accuracy output.
All our insights are of little use if businesses are unable to consume them easily and put them into action.
Our SaaS-based web portal provides businesses access to our insights through dashboards, reports, and visualizations. We present customized insights for each persona, enabling swift actions on relevant competitive intelligence. These include day-to-day tactical recommendations or inputs for long-term strategies.
What’s more, our data can be accessed using plug and play APIs as well, enabling businesses to combine their external and internal data to generate predictive intelligence.
Retailers are looking for ways to grow their revenues and margins with smarter data-driven strategies. And they come to us.
Digitally-enabled consumer brands are looking for ways to better manage and optimize their eCommerce channel. And they come to us.
Natt Fry, a renowned thought leader in the world of retail and analytics, published recently an article expoun...
Insightful Forecast on Expansion of Global Retail Intelligence Market During 2018 – 2028...
The retail industry has undergone a rapid transformation ove...
25th Sep, 2018