The success of an automated marketing project is closely linked to user segmentation.Is segmentation in marketing dead? I wouldn’t say so since we still have the goal of finding ways to analyze users to best serve them. Has segmentation become more complicated? Perhaps, because the Internet has confronted us with more complexities. Can automated marketing help us segment better? Undoubtedly yes, let’s see how.
Segmenting how?
This 2009 article in Advertising Age already listed some of the considerations that we still face today, almost ten years later, in understanding how to segment users in this new level of complexity. The interesting points are three:
- Fast change and segment reliability: people change homes, jobs, get divorced, suddenly become poor or rich. How long is a segment reliable? Clearly, segmentation cannot remain static and must update quickly.
- The presence of users in multiple segments: in the morning we are workers, in the evening we are sportsmen, on the weekend parents who are TV series fans…difficult to describe people using only one variable, better to consider more than one by creating archetypes that help us describe this greater degree of complexity. Using Personas can be very useful for us to get a clearer picture of our consumers.
- Self-profiling by interests: social media and all interest-based aggregation ecosystems on the Internet have forced upon us a revolution in the analysis of people’s needs. The value proposition of a product comes first from the pain point and the expression of a need. The ability to monitor online behavior helps us intercept and infer it sooner and better than any survey or focus group.
While the incredible amount of data at our disposal therefore makes the job potentially more precise, the need to analyze this amount of data and create unique value propositions for each of these micro-targets greatly increases the complexity of the marketing job.
Automated marketing and dynamic segments
Automated marketing allows us to segment while taking into account all the complexity summarized above. It does this primarily by aggregating as many data sources as possible, thus trying to enrich the profiles of our potential consumers as best we can.
Among the data available to us, some are static data that do not require continuous updating (e.g., those in the proprietary database such as gender or age), while others are fast-changing such as interest of the moment, product sought, need, etc. (which can typically be gathered from online browsing or other touch points such as customer care).
Segmentation in automated marketing then accomplishes a further step: it adds and removes people from segments dynamically based on their behavior and, by managing the information in real time, constantly analyzes each user-or return user-to continue in enriching the profile and learning the main characteristics of that segment.These dynamic segments will then be indispensable for being able to propose the right thing, to the right person, at the right time. Marketing automation platforms independently know what product or service is closest to people’s need based on their membership in a segment, taking into account what is happening at that moment. Not only that. This personalized experience can be managed in multichannel, going to intercept people with the right timing depending on the stage of the customer journey they are in at that precise moment.
Segmenting with automated marketing to manage complexity
In this great complexity, automated marketing supports us in segmentation on three strategic objectives:
It helps us learn about our users to understand where to target investments .
Information-rich segments that are constantly updating have the virtue of letting us learn more about our consumers every day and, in turn, about our business. With automated marketing, we can continuously test new forms of engagement across multiple channels and thus quickly understand what works and what doesn’t. The systems learn the rewarding behaviors for a certain user and repeat them to all other similar users belonging to the same segment.
Usingartificial intelligence and big data means saving time
Proposal customization and the ability to automate the selection of the right products for each segment is the key to saving time and not missing opportunities. There is no point in continuing to work on manual, hypothesis-driven correlations. The data already contain the answer, and automated marketing, thanks to enormous computational capabilities and artificial intelligence algorithms, is able to return it to us with a margin of error that is certainly less than ours(to learn how they work, we talked about it here)
Investing “human” resources in what they do best
Segmenting is complex but it is undoubtedly also a fun challenge. If automated marketing is smart and manages for us the placement of users in certain segments-and plans all subsequent actions-we can devote ourselves to more strategic activities such as analyzing the results in order to develop new ways of interaction or even new business models, or more creative ones, such as choosing the content that can most stimulate our target audience. So let’s worry about creating interesting customer experiences and leave everything else to technology!
