eComtics combines in-built predictive analytics and multi-source customer data, unlocking meaningful relationships between customers and products to drive conversions and increase revenue.
Functions of eComtics
eComtics combines Machine Learning algorithms with Big Data technologies to create state-of-the-art eCommerce analytic platform with:
- Personalized marketing
- Recommendation Engine
- Pricing Analytics Engine
- Sentiment Analysis
How eComtics works?
eComtics combines big data technologies such as Hadoop, NoSQL data stores, in-memory analytic engines and massively parallel processing analytic databases with multiple machine learning algorithms to enable your eCommerce website to deliver a captivating user experience.
Unified AI Powered eCommerce Analytics
eComtics has everything under one roof: data integration, data preparation and profiling, advanced analytics, machine learning, visualization and data export.
eComtics supports cloud, on-premise or hybrid deployments to align with your goals.
eComtics provides API Endpoints to manage data and deliver analytics, bridging the gap between data and delivery.
eComtics delivers state-of-the-art relevant analytics to the ecommerce platform without the need to write a single line of code through a simple, configurable interface: analytical power at your fingertips, instantly.
On Demand Scalability
eComtics provides the ability to scale the system dynamically based on application demands without any downtime.
Integration with major eCommerce Platforms
eComtics enables integration with major eCommerce platforms such as Shopify, BigCommerce, Magento Commerce and WooCommerce.
eComtics helps reach customers with granular one-to-one personalised messaging and promotions.
- Devise communication strategies to deliver relevant marketing messages to targeted customers
- Predict the customer’s response based on product appeal, offer receptivity and frequency of use
eComtics AI models help identify effective cross-sell / up-sell strategies based on historic purchase and customer preferences.
- Identify products that sell in conjunction with each other on a dynamic basis, linking them with historic purchases to recommend offers and promotions
- Identify products that drive the purchase of primary items
Pricing Analytics Engine
eComtics’ AI-based pricing engine is designed to recommend optimum pricing of a product based on dynamically forecasted demand as well as effect of offers, promotions, competitor analysis, etc.
- Maximize overall e-tailer profit through dynamic pricing: daily, seasonal, promotional and markdown
- Manage competitive and complementary product pricing (cross-elasticity)
eComtics uses advanced NLP algorithms to analyze overall sentiment using customer comments, feedback and social media interactions for effective reputation management.
- Helps to understand the overall customer sentiment for the products to increase brand/product affinity
- Assess the market needs from the voice of the customer
Other unique features of eComtics
Some other unique features of eComtics are ontologies, knowledge graphs, image search and SEO.
- eComtics search engine boosts the relevance of the queries dynamically based on user profile and browsing activity. Not only in words, but search by images too
- eComtics knowledge graph enables insights into customer preferences by correlating customer profiles with products. This enhances customer experience and drives revenue
Clustering Algorithms, Regression Analysis, Association rules, Markov Chain.
Drawing from the existing customer data eComtics machine learning models analyses billions of consumer interest variables and touch points, identifying specific customer’s interests and group customers with similar interests for effective targeting
Content based filtering, Collaborative filtering, Multi-criteria recommender systems
eComtics uses appropriate filtering algorithms to extract the relevant information required to make the final recommendations.
Simulations, Heuristic Solvers, Multi Armed Bandit Algorithm
eComtics machine learning models dynamically forecasts demand, and uses a heuristic solver to search for relevant promotional campaigns to maximize total profit across all products in the category. At the end, a what-if analysis is done to quantitatively assess the pricing decisions.
Natural Language Processing (NLP), Rules-based Sentiment Analysis
eComtics’s sentiment analysis combines machine learning techniques and rule based systems to assign weighted scores to topics, entities, themes within customer comments to find out customer sentiments.