Just as Amazon knows which products you may be interested in buying, based on your past purchase-history, it should be possible for independent shop-owners to offer a more intelligent shopping-experience to their users.
As a E-Commerce technology service-provider, our mission is to bring sustainable technology-trends to merchants and E-Commerce agencies in Germany and Switzerland.
– In 2015, we added Shopware to our portfolio.
– In 2016, we worked hard on building a Multichannel E-Commerce Framework, including ERP capabilities based on odoo.
– In 2017, we are looking forward to adding Machine Learning capabilities for E-Commerce to our technology portfolio. This will immediately enhance our HyperSearch® capabilities, and give us new a business-spaces to play in.
Our efforts will primarily be structured around these problems:
Ranking Top Sellers
Based on the sum of order-data available in a database, we would like to derive the top-sellers on specific web-shops. Such a result can of course be derived using SQL database queries. But we intend to solve the same problem using Machine Learning techniques.
User Profiling
Based on user-behavior while on a specific web-shop, we’d like to profile the user, without the user having to log in. For example, we’d like to determine if a user is male or female without he/she telling us. Similarly, we’d like to guess the approximate spending capacity of the user, and promote products in that price-segment.
- Gender
- Spending-power
- Product-category
Trend List
Based on click-pattern of a user, we’d like to guess which type of products the user is interested in, and load those with a higher priority into the shop-interfaces. In other words, utilise ML to provide a highly relevant user-experience.
Demand Forecasting
Based on daily sales data, predict demand, to help the online-seller with purchase decisions. While analysing sales data, consider:
- Seasonality: Correlation between date of the year the sales happened and quantity
- Volumes of product sold (without correlations)
Search Results
Search features, especially “enriched” results like those from HyperSearch® can be enriched even more by learning from past click-patterns (matched with search keywords).
Dynamic Pricing
As we perfect our odoo based Omnichannel E-Commerce solution at Simplify-ERP®, we look for more and more ways AI can help our retailer (B2C) and wholesale (B2B) customers derive ever more economic value from their data.
Liquidating inventory is a well known trader’s strategy to maintain high liquidity, to invest cash in new products, and hence offer a better selection to buyers. ML can help sellers determine price-propositions for products, correlated to costs and gross-margins, and help him “push” the product on specific online-sales-channels. Very useful for this learning would of course be competitive price-data on specific channels. So a sub-problem to the Dynamic Pricing problem would be to learn the market-price (on specific channel/market, ex. Amazon) for a specific product.
HyperSearch® becomes Smarter
Our E-Commerce Search solution should get smarter by training the algorithm to learn keywords that humans type-in and the search-results they have selected to follow. Based on these, suggestion can be pre-loaded (below search-field).
The above learning can be correlated to purchase-data of articles, such that those that have been known to have been bought in the past can be promoted to the top of the SERP.
OXID E-Shop
Of course, we are most keen to apply our solutions to our favorite E-Commerce product, the OXID E-Shop!
We invite friendly agencies to participate as a stakeholder in this research and reap the benefits from an early stage.
Resources
- Google Cloud ML Platform (Documentation)
- Google Prediction API
- Learning Works, As Explained By Google
- Machine Learning 101 – Supervised Learning
- Learning Machines 101: A Gentle Introduction to Artificial Intelligence and Machine Learning
- Machine Learning is Fun!
- BIG ML- Machine Learning 101
- Essentials of Machine Learning Algorithms (with Python and R Codes)
- Simple Sample Program. After understanding basics of theory, try this code.
- 50 Useful Machine Learning & Prediction APIs
- Amazon Machine Learning – Developer Guide
- Quora answers on Applications of Machine Learning in E-Commerce.
- Stanford Univ. MOOC by Prof. Andrew Ng – Machine Learning
- Building Machine Learning Predictive Model – Data-Sailors.com
- Classifying E-Commerce Products based on Images and Text – Christopher Bonnet
Important Concepts
- Model, Parameters & Learner
- Gradient Descent, Learning-rate
- Neural Networks (NN)
- Constitutional Neural Networks (CNN)
- Binary, multiclass, regression classifications
- Algorithms
- Validation dataset
- Supervised vs. Unsupervised learning
- Reinforced learning
- Softmax Classification Layer
- Deep Learning
- Visual Geometry Group (VGG), VGGNet
- Image-Net
- Fine-tuning
- Convolution Filters
- Convolutional Computer Vision
- Annotation
- Logistic Regularisation
- L1, L2 Regularisation
- Random Search
- Random Forest
- Hyperparameter Optimisation
- Logistic Regression
- Transfer Learning
- Bag of Words Model
Deep Learning Libraries
- Keras
- TensorFlow
