E-commerce Case Studies
At Addepto, we specialize in creating custom machine learning solutions that meet our clients’ current needs as well as to adapt to future changes.
We have had the opportunity to work on various projects for many companies in the E-commerce & Retail sector.
Customer Lifetime Value Prediction
In order to optimize your approach to tailored marketing, customer acquisition, long-term retention, and continuous customer engagement you need to implement data analytics in your company.
You need machine learning models to predict how much money each client will make in the future (CLTV).
Such a solution will guarantee you an accurate estimation of the customer’s future value, effective allocation of marketing expenses, identification and increased care for valuable customers, and reduced exposure to losses.
155% increased customer satisfaction
Product managers must anticipate the amount of stock and supplies needed to meet demand. It’s not enough to just use historical trends to get accurate results.
You need a machine learning system that uses all available data from a variety of sources to predict demand with high accuracy using advanced self-learning algorithms.
With AI, product managers can more intelligently and efficiently predict customer behavior and demand for individual product SKUs and offer relevant and helpful recommendations.
25% cost decrease
Acquiring new customers may be several times more expensive than keeping the existing ones. Identifying the customers that are likely to churn and preventing it is a challenging task.
You need a machine learning system that estimates chances of churn and forecasts losses based on massive amounts of IoT data, customer behavior, and sentiment.
Benefits of this solution include quick reaction to retain customers, increased retention, and reduced overall customer acquisition costs.
30% cost decrease
Purchase Probability Prediction
To survive in a competitive environment, online malls need to learn from all available data. You need a machine learning system to calculate the purchase probability.
The model should be built on the basis of user demographic data, activity in the online store, activity in the loyalty program, preferences, and interests of each user.
It will provide your company with a product recommendation system, improved internet marketing, increase in sales and customer satisfaction.
20% increase in sales
Customer-oriented Visual Search
Consumers are often disappointed with the e-commerce experience as the displayed product results are often irrelevant.
You need an AI-based system that uses natural language processing (NLP) to narrow down, contextualize and improve search results for online shoppers. It enables users to visually search, find and match products, as well as discover complementary products and improve customer experience.
170% increase in customer engagement
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