Recommending the right products or services to your customers or stakeholders using machine learning
What are personalized recommendations?
Personalized recommendations has several denominations : predictive marketing, hyperpersonalized marketing, individualized marketing...It consists in improving customer journey and increasing conversions by showing to customers products recommendations that are relevant to them. In Machine Learning terms,this is called a recommender engine.
Why use personalized recommendations?
Whether in B2B or in B2C, predictive marketing is becoming more and more common for companies that have large pools of products or customers. Hyperpersonalization is about promoting the right product or service to the right customer at the right time.
Cross-selling is the action or practice of selling an additional product or service to an existing customer. AI can help you increase the value of a customer or customer basket by recommending new products to an existing customer.
Upselling is the practice of encouraging customers to purchase a comparable higher-end product than the one in question. AI can help you increase crosssell to certain customer segments or markets.
Improve customer journey and retention
Studies show that customers are looking for personalization in their journey. Helping a customer find something that she/he may like, through relevant product recommendation, increases customer satisfaction and retention.
Develop new business models
What if you could use product recommendation to provide additional services to your clients, suppliers or partners? This is the case for some companies who develop new business models or extend their business model so they can find an additional lever for their data.
Kantify's approach to personalized recommendations
Kantify has developed a performing AI solution that can be tuned to your needs and business objectives. Before initiating the development work, we usually start a new predictive marketing project by helping you frame and refine your objectives. In that way, we ensure that your recommender engine (the predictive marketing solution) will be a long term growth vehicle for your company. The development of the solution can be performed autonomously or in close collaboration with your teams. The final solution, once tested, is conceived to be easily deployed and embedded in your testing systems, websites or apps.
Case study on Predictive Marketing
One of our latest case studies is a recommender engine for a lunch benefit company, Monizze. Monizze is a growing Belgium scaleup, part of the Up Group, that uses technology as a vector of its growth and competitive advantage.
The challenge was to create a solution to provide the users of the Monizze mobile application with relevant restaurant recommendations at all points of the day and in the whole of Belgium.
Kantify has developed a personalised recommender engine that can define in real time what will be the relevance of a restaurant for a specific user. The customer gets a feeling of ‘serendipity’ (i.e find something that you like, by chance). His satisfaction and etention are increased through the relevance of the restaurant recommendations.