Segmentation is, as we know, the best solution to structure big data in such a way that it can be read and used. Dividing the total amount of data into useful sub-areas according to categories is what it takes to do so, not to mention that filtering according to the company's individual needs is a prerequisite for adding value to the data and thus the mantra of every web analyst.
The usual segmentation categories so far have been the division of data according to marketing channels (SEA, SEO, social ...), regional differences (geo-location) or the end devices from which purchases were made, and whether there is a focus on a device or cross-devices. Product groups, buyer groups, if known, and return behavior, are also considered.
Segmentation plays a key role in the preparation of data for artificial intelligence (AI). Through them, the AI can learn and act in the interests of the company. Segmentation usually follows the company's key performance indicators (KPIs). However, if the company serves several sales and customer contact channels, it must be handled consistently across the organization, as only then AI can reliably access the data of the entire company. Because, if each department would follow its own segmentation rules and create data silos, contradictions would arise in the evaluation and the associated terminology. In other words, the AI gets confused and draws wrong conclusions, which can have serious consequences, such as e.g., advertising unprofitable products, ignoring customer needs or low-reach advertising strategies. Web analysts who live in constant scrutiny with the contradicting definitions in companies know a thing or two about it.
Therefore, segmentation needs several perspectives: one on the company's future goals and that on the core of the company when it comes to defining the segments, which can even take time factors into account, since the (holiday) seasons play a relevant role in business. In general, however, every company needs to establish the procedure on a broad basis, across all channels, when it comes to cooperation between departments and functional automation for AI and personalization.
Corona brings a new aspect into play and it affects almost every company, regardless of the business area. During the lockdown, and with communication and shopping conditions changing almost every week, the needs and behavior of customers change as well, regionally, and worldwide. No artificial intelligence should automatically assume that the data read during COVID-19 can be transferred to times after the end of the pandemic. A simple pre- or post-corona is not enough to view the data either, even if it promises a segmentation solution that can be implemented relatively quickly, since the corona situation has long-term and short-term consequences.
Many phenomena, such as the growing online customer base, online communication by companies that previously only had personal contact with customers, or the online use of technical solutions (for example, video tutorials) will continue to be used by many after the pandemic—even if there are certainly fluctuations in the intensity and range of use depending on the lockdown situation. However, segments of interest will certainly vary, like party outfits being bought again when major events are allowed; while the demand for garden, balcony, and home improvement supplies will probably decrease again; or people will want to choose their vegetables in the supermarket themselves again if the infection rate is low. While if the number of cases increases dramatically, the delivery service may be called upon.
Anyway, what becomes clear is that it is worth considering the respective corona situation as a factor in the segmentation so that the AI can identify the causes of changed numbers without considering the sold-out toilet paper to be a long-term trend and recommends stocking an absurdly high number of it.
That is why it makes sense to segment new customers according to the period before and after the occurrence of the corona epidemic and, of course, the product groups should also be segmented accordingly to avoid making wrong decisions later.
For instance, those who segment according to the corona situation can control advertising measures in a more needs-based manner too. Especially since the use of devices depends on the customer's activity and office behavior, which raises the question if customers also buy from their mobile device when the desk is available nearby? Will the computer be used by different family members with different interests during lockdown and home schooling, etc.?
The fact is that the experience of the pandemic has changed our behavior in a very short time. One of the consequences for all online companies is an increase in data that often come from customers who have previously remained anonymous in the store and are now, for the first time, revealing their interests in a comprehensible manner.
This represents an advantage for businesses since many of the new online customers will certainly continue to use the convenient sides of online communication even after Corona and will probably also stay true to some aspects of online shopping. But this added value for customer communication and corporate strategy can only take effect if the AI can differentiate between exceptional situations and rules.
With segmentation according to Corona criteria, the company can use the newly gained knowledge to expand personalization, automation, and AI by feeding the flexibility and changeability of the situation into the knowledge of the AI and thus, make it suitable for the time during and after the Corona pandemic.
By Daniela La Marca