AI is becoming a one of the main drivers of shopping in retail, e-commerce and consumer goods. Machine learning gives businesses a strong competitive advantage and is available to businesses of any size or budget.
Next, we list our solutions for retail, e-commerce & consumer goods enterprises.
For staying competitive in today’s world, organizations need to constantly reconfigure
supply chains and continually manage the changing demand and supply. Intelligent decisions
in inventory management can drive large cost saves and avoid missing selling opportunities.
A recommendation system uses machine learning to understand the customers preferences with the
objective of recommending them products. In e-commerce, retail and consumer goods, personalized
product recommendations are key to run a profitable business.
In the fashion and luxury retail industries, recommending products based on just traditional
features like brand or price might not be enough. The visual appearance plays an important role
on what the customer decides to buy. Machine learning can be used to analyze a garment or a
luxury piece to extract visual information and provide a more meaningful recommendation.
Optimising a web using AI can dramatically boost Click-Through Rate (CTR) and conversion. The
system monitors the performance analytics of a website to discover insights and automatically
understands the audience preferences. Using A/B or multivariate testing, different contents
are generated to segmented users for improving traffic and revenue.
Traditional sales forecasting is difficult, time-consuming and
incorporates human bias in the process. AI-enabled forecasting tools
uses data mining, statistics and machine learning to analyze
previous wins, performance patterns and other historical data.
This technique can calculate the probability of winning deals, predict expected
revenue and help you create more accurate sales forecasts.
Traditional static pricing keeps prices absolute, dynamic pricing, on the contrary, adjusts prices in real-time based on external factors and customers individual buying habits. Dynamic pricing solutions use machine learning and big data to find customer’s data patterns, aggregate prices from competitors, analyze consumption in different regions and predict customer behavior over promotions and loyalty programs.
Multi-channel e-commerces and specially sales aggregators are particularly susceptible to fraud. AI reduces fraud by applying real-time continuous learning and by intelligently flagging transactions that appear suspicious, identifying falsified products and detecting fraudulent merchants.