The term ‘Big Data’ appeared in the early 2000s, as part of several major facts including automation and the expansion of data exchange, the evolution of their possibilities for storage and the evolution of advanced analytics. Big Data is therefore interfering with various sectors: life sciences, web media or marketing. It opens up new horizons that were, in the past, unimaginable, such as the example to work with any personalization.
In B2B, the segmentation criteria generally used is based on data that can be described as geography, the size of the company, the market or even the role in the decision-making process. A company considering some marketing could find this data in their CRM. By segmenting its market in this way, there is a risk of using the same segments than those of its competitors, and thus can fail to stand out.
Another approach is to work on more specific segments from more important but also relevant data, such as:
Transactional data
It can be data extracted from an e-commerce site. This type of data can result, for example, in assessing the importance of delivery.
Behavioral data
You think among other things, data from the analysis of the navigation of a website. You can identify that visits from such sources will have tracking in the site and learn concrete facts to include in the segmentation criteria.
Semantic data
This refers to the semantics used for search engine queries or in social media. This type of data is likely to bring concrete elements, which provide clarification and thus allows for a better segment.
Attitudinal data
This data is obtained from various commitments and can be seen, for example, in social media with the words “I love”, 1 + Google + and mentions on twitter.
This data therefore constitutes a chance for businesses to enhance their segmentation by the addition of new criteria until now, unused, and therefore the opportunity to identify micro-segments. From this, the company will be better able to target them by marketing more specific campaigns, which as a result, will have chances to prove more effective.
In conclusion, I would add that this access to data and their operations opens other opportunities for businesses. Among them, to accentuate their client knowledge and especially their customer experience knowledge.
Data segmentation is very important to increase marketing effectiveness and boost potential ROI. To do so, companies must develop a predictive analytics program to target messages with more precision. Customer segmentation provides a significant opportunity to increase revenue through a better understanding of customers.
This article had examples of different types of data that can be used in segmentation. Microsegmentation will get more automated as the systems evolve.