AI customer approach for product launch

At a glance

As part of the market launch of a new furniture collection, a large furniture manufacturer developed an AI-based recommendation system for targeting customers. This system used a combination of online behavioural data and CRM information to precisely identify potential buyers and implement personalized marketing strategies.

Client: Furniture manufacturer
Industry: Furniture
Business segment: B2C
Technologies: Artificial intelligence, machine learning, data analysis

Short facts

Project: AI-based recommendation system for customer approach
Target group: Customers of the furniture manufacturer, including new and existing customers
Data sources: Online behavior (e.g. website visits, click behavior) and CRM data (e.g. purchase history, customer profiles)

Our fields of work

KI Consulting IT architecture Development

The initial situation

A large furniture manufacturer was planning the launch of a new collection and aimed to optimize targeting to maximize market entry. Previous marketing strategies were often generic and based on demographic information, resulting in inefficient campaigns and insufficient targeting. To increase the effectiveness of the product launch campaign, it was necessary to develop a data-driven recommendation system that targeted the right customers at the right time.

The solution

We developed an AI-based recommendation system that involved several steps:

Data integration
Merging online behavioral data (e.g. interactions with the website, product views) and CRM data (e.g. previous purchases, preferences).
Customer analysis
Use of artificial intelligence and machine learning algorithms to analyze the data. These algorithms identified patterns in customer behavior and segmented the customer base based on their preferences and purchasing behavior.
Recommendation engine
Development of an intelligent recommendation engine that generates personalized product recommendations. These recommendations were based on the interests and behavior of customers as well as seasonal trends.
Campaign optimization
Implementation of personalized recommendations in the marketing campaign, including targeted advertising via email, social media and the company website.

Result

The introduction of the AI-based recommendation system led to significant improvements in the marketing strategy and the results of the product launch:

Increased conversion rates
The personalized recommendations led to a 35% increase in conversion rates compared to previous campaigns.
Optimized marketing spend
By targeting relevant customers, marketing costs were reduced by 25% while increasing the reach and ROI of the campaign.Increased customer satisfaction
Customers appreciated the personalized offers, which led to a significant increase in customer satisfaction and loyalty.

Overall, this project shows how AI-supported recommendation systems can optimize the way target groups are addressed during product launches and thus significantly increase the success of marketing campaigns.