Product Personalization

What Is Product Personalization?

In the current era, consumers expect the firms they engage with to provide personalized service and offers. They believe that companies have the technological tools to analyze their specific needs and can perform this task with minimal effort, so businesses should develop such ability.

One may imagine that this kind of mass operation will need some resources and financial investment but by implementing artificial intelligence and automated processes, this capability can be readily available for any business.

Traditionally, personalization was focused on a set of rules based on existing data. Firms used to collect data in advance without any consideration for real time data but now better and much faster results can be obtained by using AI that allows businesses to conduct profiling and real-time analysis to optimize each conversion. This process is defined as predictive personalization and is driven by machine learning.

Why Is It Important to Have a Good Product Personalization Model?

The great progress of the recent years in the field of personalization technology, data, and analytics will soon allow marketers to create much more personal experiences across each moment of engagement with clients or potential clients. A good product personalization model could drive 5 to 15 percent increases in revenue and 10 to 30 percent increases in marketing spend efficiency, mostly by using product recommendations and triggered communications.

The model is designed in such way that its predictions are based on probability. Basically, the model converts visitor characteristics into a number that represents the probability of a user to take certain actions. By doing so, the learning algorithm can create more parameters to explain the relation between the variables and, by finding the explanation, businesses can provide a personalized service to consumers.

What Internal Data Should I Have for a Good Product Personalization Model?

Most applications of the product personalization model would need examples of inputs and outputs in order to learn the mapping between them. In order to use the models effectively, we will need to use enough valuable data, of which there are three main types:

1. Intent: information collected based on actions
2. Transaction: an exchange, agreement or transfer in a given time period
3. Psycho-demographics: psychographic factors include values, interests, and opinions.
   Demographic factors include variables like age, location, gender and more

What External Data is Essential for a Good Product Personalization Model?

The general idea is to collect complete and comprehensive data that reflects the entire customer interaction from all touch-points. This means companies need to track every interaction a customer has with them, such as how the customer uses the product, which emails a customer opens, when a customer contacts support, and how many times clients choose to engage in a live chat.

What External Data May Prove Useful for a Good Product Personalization Model?

Personalization aims at recognizing special customer traits such as inclinations and preferences in order to individualize the inter-linkage process. The consulting team should adapt the website layout to customer’s requirements and personalize the preparation of customer dialogs. The customer requirements captured during this interaction process have to be correctly interpreted into product specifications through mapping methods that translate customer preferences and requirements into product-specific characteristics and ensure that the product specifications are adequately portrayed to the customer.

What Are the Main Challenges of the Product Personalization Use Case?

Personalization can be a powerful tool for any business, but the large databases that have been created must be securely stored as they contain private and sensitive information. Any leak of information can cause a lot of legal and financial trouble.

Interesting Case Studies and Blogs to Look Into

Science Direct: User-experience Based Product Development for Mass Personalization: A Case Study
Smart Insights: A case study showing the impact of Personalised Product recommendations on retail merchandising

Tangible Examples of Impact

As restaurants navigate through an uneasy climate in 2020, developing a strategy to improve the customer experience and maximize long-term profitability is paramount. Restaurants need new customers, more visits, and larger checks, and they need it all to come with a positive ROI. In the COVID and post-COVID environments, it may sound like an impossible feat.

It’s becoming increasingly clear that personalization is the key ingredient restaurants need in order to survive—and then thrive in a post-COVID world. Research shows that personalization influences consumer behavior, improves the consumer experience, and optimizes marketing ROI and profit.

MRM EXCLUSIVE: The Power of Personalization