Businesses are losing opportunities when they use static segmentation, which is not predictive of customer behavior and often fails to take customer context, into the account. Above all to deliver the right message to the right customer at the right time, organizations must adopt dynamic predictive segmentations. With dynamic predictive segmentation, businesses will be able to serve relevant content to their customers in real time and capitalize on their understanding.
Segmentation – A recent study revealed that the current state of customer segmentation fails to deliver on many fronts. In fact, 98% of the survey showed that static segmentation has lack of precision, unresponsiveness, and is deficient in proving actionable data.
Current Static Segmentation is no longer adequate:
- It fails to provide sufficient actionable details
- Segments do not get updated based on changing customer behaviors
- Segments lack precisions
- There are no tailored messages
- They do not serve customers in real time
- Do not take customer context into account
In today’s digital world, the customers have high expectations regarding for their experience in almost everything. Whenever they come across an improved experience, they expect it for all interactions to follow. Additionally, marketers need to up their strategies with improved intimacy and relevancy all through the interaction. So to deliver this type of service, marketers rely on segmentation to deliver what the customer needs.
Dynamic Predictive Segmentation
Artificial Intelligence enabled predictive segmentation solves the present days’ challenges.
Dynamic Predictive Segmentation is a unique and different kind of approach that accurately segments customers based on:
- Specific actions
- Predictive Characteristics
Even more segments and their associated characteristics change dynamically in real-time based on new data provided from customer interaction. Therefore advances in machine learning have made it a reality that represents a new level of relevancy in every customer’s interaction.
First of all, this solves the challenges that marketers are facing in terms of speed, granularity, and efficiency. In other words, to accomplish these challenges, AI methods are required in order to reveal the key factors that define the predictive customer segments.
Currently, there are four levels of advancements in customer targeting, from no segmentation to advanced recommended systems.
No Segmentation – This is targeting potential customers in the same way.
If a business specializes or involves companies with few customers then further segmentation is not required for a significant return. The requirement for it grows as the business upscale its services. Even with the narrowest segment, customers are not a homogenous group and their needs might be different.
- Cheap & Simple
- Initially Effective
- No cost to implement or maintain
Manual Segmentation – This is the most intuitive technique since the segmentation is done by human analysis.
The human analysts look at intuitive segments, such as demographics, geography, income, total purchase, or even other specific factors. Even with all the advantages that includes building groups of customers and doing analysis manually, there can be significant challenges. Such as Analyst processing the data might be biased, manual segmentation quickly becoming outdated, limited number of resources and so on.
- Intuitive & Simple
- Greater Efficiency
- Easy to Understand
Mainly for many small organizations, manual segmentation must be just enough. Including companies selling tailored products for demographics or using any other straightforward criteria or target audience.
Automated Segmentation – This involves machine learning to segment datasets and look for hidden patterns.
As a result, machine learning algorithms can predict behaviors for a product or service. However, this approach gets more challenging, if you need to cluster similar customers. K-means and hierarchical aggregation is the most widely used algorithm to cluster the database without any human supervision. Above all the algorithms can spot the most obscure, surprising and the least oblivious cluster within the datasheet.
- Finds hidden clusters in the datasets
- Maintains Productivity
Larger organizations can do the automation segmentation with too much data to handle manually.
Recommended System – Instead of building limited number of segments, developing an individual representation of each customer and product.
Recommendation engines offer the customer with a representation in the form of a multidimensional vector. Due to this system is able to leverage both official data and the less obvious information inferred from buying patterns.
- Taking care of every customer individually
- Constant update and evaluation
Large organization with data oriented culture that processes huge amounts of data must leverage this system.
To summarize, recommendation system is more flexible and sophisticated than segmentation. The tool, an organization should use, must suit the type of business they do. But machine learning is not a magic problem solver to provide an organization with “out of the box” solution, that works everywhere. Finally, where the issue of complexity, variety, and scale of datasets arises, machine learning can be the best tool.
Our previous blog post – AI Can Enhance Your Company’s UX. Here’s How!