In January of this year, B2B delinquency reached a record of 7,1 million companies with overdue debts that, added, totaled R$ 154,9 billion – an increase of R$ 4,3 billion compared to the previous month. Here we are talking about 31,4% of the companies operating in the country. These are data collected by Serasa Experian, that reached in that month the highest volume recorded by the historical series of the survey, held monthly since 2016. To get an idea, in January 2024 this number was 6,7 million and consolidated a growth trend throughout the year.
This scenario is just an example of how delinquency among companies in general has shown an evolution that deserves attention and, of course, effective actions. The industrial sector, although it represents a smaller share in this reality of overdue payments (8% compared to 52,4% of Services and 35,3% of Commerce, also faces great challenges in credit recovery.
It is a fact that, when delays are not properly managed, can seriously compromise cash flow, reduce investment capacity and even increase financial costs, if it is necessary to resort to credit under unfavorable interest conditions.
This leads us to look at the different lines of defense against default, something that ranges from credit analysis to the collection model adopted. After all, at a time when the consolidation of Industry 4.0 already points to a future 5.0, it is necessary to discuss from the same perspective the traditional billing models in comparison with the new possibilities brought by technology.
There is a lack of automation in traditional models
Naturally, when we talk about traditional models it is not about practices that are almost completely out of use, how to send a letter or have a collector in person. At least not when we talk about massive and high-performance collection processes used by medium and large companies. We can say that traditional models are those that, although they are already digital to some extent, they still do not efficiently explore all the capabilities that technological resources allow nowadays.
A phone call schedule based on an aging list – a list of delinquent clients organized by the length of delay – maybe it is the most basic example. From this, we can move on to digital channels email, WhatsApp and SMS. It turns out that without a strategy based on automation and full integration of these channels, it will be just a simple transposition of the telephone model. More agile and scalable for sure, but, even so, below its maximum potential.
We need to start from the understanding that, in B2B credit recovery, the approach dynamics need to be intelligent and discerning. It is a collection with a more sophisticated profile, targeted at well-informed professionals, with greater willingness for a renegotiation in more complex terms and conditions. Thus, personalization and data intelligence become key words to improve results in the collection of this sector. And this requires new resources.
Advances brought by the new billing models
The new billing models are strategies and tactics based on tools that use artificial intelligence, predictive algorithms and automations. They are forms of action capable of responding accurately to different patterns of default.
An example of this is the concept "digital first", an approach that prioritizes digital channels as a means of contact and service. This not only brings more efficiency and cost optimization but also meets a demand from the public, who increasingly prefers the convenience and flexibility of digital service. The basis of this concept is channels like email, SMS, WhatsApp and social networks, combined with chatbot technologies and virtual assistants.
The structuring of a digital first approach requires steps such as mapping the customer journey, process automation, definition of channels and data analysis. This requires a robust infrastructure, with advanced resources, especially regarding the processing capacity of a large volume of information, how data lakes and machine learning solutions. In our experience at Global, we have proven that this set of resources goes far beyond optimizing collection results, as it also brings predictive analysis capability, from which it is possible to outline strategies and plan proactive actions that mitigate the risks of default.
Service must remain humanized
With such a wide range of technologies and the constant crossing of information, the efficient integration of all this repertoire becomes essential for its maximum utilization and for its most important goal which is the reduction of default rates. But total integration is also the best way to solve a common paradox of digital billing channels: people prefer this automated method, but they do not want to give up on humanized care, next and personalized.
The simple adoption of digital channels and automations disconnected from data intelligence is not enough. See an example of what a well-integrated structure can do. Let's say that a digital solution makes an approach through an automatic message. A negotiation begins through a chatbot that offers some optimized condition options for that customer. So, in the face of a counterproposal, the tool understands the complexity of the response and scales this service up to a human, in a fluid manner, perhaps imperceptible to the person on the other side.
An operation like this example represents in practice a higher success rate, why not seize the opportunity that arose with the opening for dialogue, does not bureaucratize the service, do not make the customer wait, don't even ask him to access another channel. Everything is resolved in the same contact.
Why new models are better for the industry?
Many particularities of the industrial sector and the type of default it faces justify the urgency to modernize its collection models. The high values negotiated in this sector require more complex contracts and payment terms and, therefore, a charge that understands the different agreements.
Long payment terms are another factor, since delays affect production planning, essential part of the strategy of any industry, and mitigating this risk requires agility in credit recovery. The seasonality, that strongly affects many segments, it is another very specific issue that impacts financial planning and should be considered in collection strategies and, above all, in predictive models.
Give agility, precision, customization and consistent results for this set of characteristics depend on technologies such as artificial intelligence and highly refined data analysis. Resources that only the new and modern billing models can deliver.
Finally, it is necessary to remember the basics, something that neither the old nor the new models can overlook in building a strategy: collection is relationship. And it is always in the pursuit of the best relationship that digital and automation technologies should be focused. Without this guidance and extreme care in each approach, the results will never be satisfactory.
