Customer experience personalization, for better conversions and customer relations

By the end of this year, a quarter of the total investment in digitization in all markets will be aimed at improving the user experience. This significant portion will be even greater in the retail market: in this sector, more so than others, the collection and processing of data is of decisive importance for the improvement and greater personalization of the digital experience, in order to build a truly one-to-one relationship and customer journey.

Indeed, Forrester’s research “The State of Retail Innovation 2019” puts Shopper Personalization at the forefront of retailers’ investments. Although this consideration may seem obvious, it is now becoming increasingly feasible thanks to the unprecedented availability of tools and technologies. Thanks to these types of application solutions designed specifically to transform collected data sets into business-enabling information, a data-driven company can now shape and modify its offering and services in real time to the requirements of individual customers.

Twenty-five years ago, thanks to an algorithm from MIT, Amazon was able to identify books related to a user’s choice of a single title. This was probably the first automation in history designed for up-selling and cross-selling. Nowadays, presenting related products on an e-commerce website is a commodity, and over time other functionalities have been added to this simple one, but always with the same goal: anticipate the consumer’s wishes to generate up-selling and brand loyalty.

The MIT algorithm could not, due to obvious technical limitations, provide a unique list of products for each consumer, but only a list based on the specific choice, by cluster, which could be the same for many users. Today, however, the fullest possible personalization of the user experience aims to create a unique list of actions and suggestions for each user; not only this, but the goal is to customize the experience – the relationship with the brand – rather than the individual choice of purchase.  The starting point for these algorithms is always the data that the consumer generates in their contact with any of the company’s touchpoints, both offline and online, and others. Today, these application solutions allow additional data to be obtained from information sources other than the company’s touchpoints. 

This means creating a “shareable alter ego” for every single user, which tells you much more than can be found out from a consumer – a complete stranger – who enters a store, makes a single purchase and leaves. These new strategies, indeed, aim to collect information on behavior before, during, and after contact. And each group of this information is linked to a series of potential actions to be carried out, even in real time, in full compliance with the achievement of business objectives and production optimization. 

Modern Data Intelligence goes beyond the generic collection of Big Data, but rather focuses on more specific types, such as Alternative Data. This is information which can be recovered from scanning open touchpoints (blogs, marketplaces such as Amazon, social networks, forums, etc.) that allow you to better profile the clusters of consumers. With Alternative Data, for example, we can see whether a specific product line actually meets the desires of the target or pay more attention to offers that are neglected and are proving to be trending at some point. The algorithms scanning these sources of information are also able to pay more attention to the Weak Signals, i.e. new growth trends that will soon manifest themselves. This is the level of sophistication which is possible with the AI algorithms available today, made necessary by the need to immediately implement an upstream filtering process that helps to select and process only genuinely useful data.

If, moreover, privacy can, rightly, represent a constraint, it must be made clear that the analysis of consumer data at this level, first of all, relies on information which is freely provided by consumers and which is freely circulating on the Web. Secondly, reliance on sensitive data should be reduced in favor of “anonymized” profiling data, which are intended to contribute to the creation of increasingly small clusters of consumers who are not necessarily identifiable. The retailer thus becomes fully GDPR compliant.

What are the personalized experiences that customers want to have? 
In general, customers want to:

• Save money - Buyers are still motivated by the best price and want more and more offers tailored to their needs and items that really interest them.
• Immediacy and simplification - Personalization makes the shopping experience more “agile” and intuitive, facilitating the path of the customer and retailer.
• Better service - Personalization allows you to provide a better service, through targeted content and offers and customized communications based on customer actions, even in the aftersales phase.

A strategy aimed at achieving maximum personalization of the user experience must include some specific interventions, such as:

• Unification of real-time flows of customer data 
• Dynamic presentation of the contents in the different touchpoints
• Activation of up-selling and cross-selling (product recommendation) tools
• Automatic generation of contact messages (e-mail, SMS, push notifications, etc.) related to the individual user action
• Identification of critical issues (e.g. session abandonment) and real-time monitoring of digital touchpoints, starting from e-commerce platforms
• Incorporation of information from third-party platforms (social networks, marketplaces, etc.)

Today, the solutions aimed at personalizing the user experience rely on Artificial Intelligence algorithms, in particular machine learning, and solutions for the creation and analysis of content.

These tools primarily leverage the cloud and do not impact the existing IT infrastructure and the performance of the services that offline and online touchpoints provide. 
Personalization algorithms are applied to the specific context in which individual users interact, building messages, content, offers and other types of personalized interaction through digital channels, supporting marketing, customer experience and e-commerce. These personalized interactions can increase conversion rates, marketing effectiveness and customer satisfaction, thus improving business results.

Therefore, the Customer Engagement Platforms and Marketing Automation solutions allow targeted content to be sent to individual users, even in real time, following a trigger event: content targeted at and personalized for the context, at the right time, and on the right channel.

Moreover, Digital Experience Platforms (and Content Management Systems) allow the management of storytelling content and the distribution of personalized content on an omni-channel basis, which is a real differentiating factor in the relationship between brand and consumer. 

Finally, Unified Commerce platforms allow targeted offers to be made to each customer, based on their browsing, brand interaction, and purchase experience, always in relation to their behavior with respect to the brand audience.  

Ultimately, it should be noted that in the in-store experience, the personalization logic changes the role of the shopper assistant. As a matter of fact, the physical point of sale has the chance to become the core of the digital experience: the employee who works at the store, as well as the call-center or back-office operator, can turn the inputs resulting from customer behaviors into business actions. In particular, clienteling systems allow the salesperson to have a single view of the customer, both in terms of their on-line actions and purchases and in the retail network, allowing the sales assistant to become an active part in the dynamic process of personalizing the offer for the individual consumer. 

Lutech, in addition to leading projects which implement the best customer experience personalization solutions, provides its clients with consulting and service which follows two main directions: integration of the tools with the customer’s application architecture, and the fine-tuning necessary to meet the individual client requirements according to an agreed priority scale. A partner in end-to-end paths who understands right from the get-go what data to collect, what to discard, and how to analyze them in order to achieve business goals.