Advanced analytics as a tool to boost interactions with customers

What is advanced analytics and what methods does it offer for your business? Why is it important and how does Like Reply uses it to boost engagement with customers? What types of advanced analytics are there and what are the differences between them? 

The approach of Like Reply offers a paradigm shift: the transition from a descriptive use of analytics to the vast range of possibilities offered by advanced analytics. 


With full appreciation for the value of data, Like Reply tackles the challenges that businesses and institutions present to them, precisely selecting which data to collect and, most importantly, determining how they are to be used. 

The team collects and processes data series for the development of models that provide appropriate responses to the needs of consumers. They include behavioral data as one of the major sources, the primer for contact, and thus demonstrate an effective focus on the value of data, whose use the consumer must always consent to. 

In the project evaluations Like Reply shares with companies, they demonstrate the most appropriate approach to analytics collection as an opportunity for engagement with a company's own customers, as well as the link with the return on investment. To this end, the company carefully selects usage cases and activate phases of experimentation and relevant comprehension of the phenomena with the greatest impact. 

In many cases, the team makes use of advanced analytics tools that enable them to analyze the phenomena that have led to the current result (diagnostic analytics). The same goes for predictive analytics, which enables them to predict phenomena by means of forecasting models based on techniques involving machine learning and regressions. Like Reply also uses prescriptive analytics, which enables them to make more active suggestions as to what decisions the brand should take on the relevant matter. 

Understanding the occasions, the methods and the primary reasons that induce the consumer to interact with the brand becomes an inexhaustible source of moments of engagement, which are given greater value through mutual exchange of information. 

Behavioral data represent the short-term memory of the interaction with the consumer, undoubtedly crucial for structuring opportunities for contact, but evidently incomplete. In complementary fashion, it is CRM data that enable the company to extend the profile of the consumer, permitting the brand to interact on the basis of a relationship history and to avoid the chance approach that is all too often the cause of distance from customers. 

When they extend the scope of the data domain provided to them by advanced analytics to include transactional and operational data, then the capacities for application increase considerably. Diagnostic analytics enables the company, for example, to understand the determining factors in the customer acquisition process, while predictive analytics helps them to advance their understanding of the costs relating to such a process. Prescriptive analytics can be employed, for example, in the identification of key factors in the churn rate, with the aim being to automate action with respect to consumers through proactive customer care interventions. 


In this scenario, where data are the source feeding opportunities for interaction with the consumer, equal importance must be assigned to communication channels, which must be introduced in a single, harmonious process. Information symmetry between channels, the personalization of communication, and the application of push-and-pull techniques become crucial factors in the exploitation of the potential offered by advanced analytics. 

Media channels, which are useful for guiding traffic towards the company's own touchpoint, must convey a message that is consistent with the consumer's experience of using the site and app, and it is likewise important that contact via direct channels (e.g. email, SMS and push notifications) takes into account the consumer's online and offline behavior

From the initial consultation to activation, Like Reply makes use of models in which the integration of data with the communication channels and their orchestration for the purposes of conversation and relationship building exploits the value offered by advanced analytics, with the aim being to evaluate, select and test the use cases that best contribute to the return on investment.