Personalisation of the browsing and purchasing experience in omnichannel contexts, chatbots and virtual assistants, virtual and augmented reality: artificial intelligence is revolutionising the relationship between brands and consumers, offering the former increasingly indispensable tools and applications for successful customer-centric strategies.
Although data analysis has always been a relevant aspect in marketing, it has taken time and significant paradigm shifts for companies to realise its value, so much so that they have come to consider it a strategic asset.
It is precisely in this context that artificial intelligence and machine learning have revealed their full potential, especially in the analysis phase, allowing them to reach levels of precision and efficiency even in the predictive aspect.
But what is meant by predictive analysis and what is its relevance in the realisation of strategies aimed at personalising the customer experience?
Let’s delve into it together!
Predictive analytics and artificial intelligence: the winning combination!
Market trends, pricing policies, customer base analysis: marketing and sales departments have always been looking for models and tools that can offer something more than a purely descriptive analysis of the context.
Artificial intelligence and machine learning nowadays allow precisely this: analysis activities that are no longer limited to drawing a well-defined picture of the present or the past, also with regard to the characteristics, habits and surfing and purchasing behaviour of users and customers, but which enable them to anticipate their needs, expectations and problems.
It is precisely in this ability to intercept and anticipate desires and needs, even latent ones, that brands strengthen their relationship with their consumers.
The objective remains that of loyalty: the more one knows the customer and the user one is dealing with, the more one is able to implement strategies and activities aimed at enhancing the customer experience.
Specifically, predictive analysis is based on the use of artificial intelligence and machine learning algorithms to process large amounts of data. The aim is to identify patterns capable of predicting future behaviour.
As can be easily understood, this type of approach goes through a series of well-defined phases such as:
- identification of the objectives to which the analysis must respond;
- data collection and normalisation;
- data analysis and identification of recurring patterns and trends;
- prediction of results and development of models capable of predicting user and customer needs.
The use of predictive analysis thus makes it possible to develop ideal user/customer profiles and to identify, at the strategic stage, the most rewarding activities for each specific cluster and profile.
“Intelligent” customer service: evolution under the banner of customer experience
Anticipating users’ needs, meeting them before they manifest themselves: the use of a marketing approach based on data analysis and the use of sophisticated artificial intelligence algorithms make it possible to personalise users’ browsing and purchasing experience, down to the smallest detail. From the proposal of customised content and products to ‘tailor-made’ communications and offers, but not only: one of the salient aspects in which the use of predictive analytics can play an important role is that of customer care, still one of the most significant touchpoints in the user’s customer journey.
Speaking of customer care and artificial intelligence, the association with chatbots is immediate, but among the uses of artificial intelligence it is certainly not the only one.
The use of predictive analytics can in fact be particularly useful, not only to anticipate customer questions and thus provide quick and precise answers, but also to predict a customer’s churn rate and thus intervene before the customer decides to stop buying the brand’s products and services.
Finally, let us not forget how the use of artificial intelligence and machine learning in predictive contexts is also important for up-selling and cross-selling strategies.
Customer service with Blendee: more information, more effective services
Proactive customer care services? The secret lies in an in-depth knowledge of the user with whom the operator interfaces. Blendee equips the company’s customer service department with tools to identify and predict consumer behaviour, significantly increasing not only conversions but also the level of service provided.
Detailed master data, but not only that: artificial intelligence and machine learning algorithms make it possible to propose products, discount codes and promotions, based on the profile of the user who is interacting with the customer care service.
DATA COLLECTION AND NORMALISATION
Data collected at the different touchpoints of the user’s customer journey are collected and normalised at the single customer view level.
UNIFIED CUSTOMER VIEW
Blendee equips customer care operators with up-to-date user-insights in real time that provide an extremely detailed overview of individual user and customer data from a multitude of channels. Identity resolution processes make it possible to uniquely recognise the user in real time thanks to the convergence and resolution of the different IDs assigned at the various touchpoints.
A.I. POWERED PERSONALISATION
Thanks to the information and data collected on customers, Blendee’s artificial intelligence and machine learning algorithms can propose to the customer care service products and recommendations more in line with the individual customer’s needs.
OMNICHANNEL EXPERIENCE MANAGER
The personalisation of the customer experience also seeps through customer care service, the problem/service contact encountered by the customer can turn into an opportunity for personalised up-selling and cross-selling strategies.
Data analysis and the use of artificial intelligence on this front are transforming the customer experience: there are no good relationships without good information. Companies are called upon to offer their customers increasingly personalised and valuable customer experiences, but to do so they need to be able to rely on tools that allow them to have a holistic and integrated view of their users and customers.
Customer satisfaction and loyalty is increasingly linked to these factors.
