Customized and personalized service is a very crucial differentiator in retail markets, and it has become a key issue in developing customer relationships. Marketing gurus all agree that marketing dollars are best spent in initiatives that are targeted at specific customer cohorts with similar attributes and buying behavior. Marketing managers can develop long-term and lucrative relationships with customers if they can detect and predict changes in customer behavior. That answers the core question…
Why Segment Your Customer Data?
By segmenting your customers, you will be able to target specific groups of people with similar interests with a message that resonates with them, and so you will have a greater ability to cross- and up-sell your products or services.
In the past, marketers generally applied statistical surveys to study customer behavior. Over the last two decades, however, data mining techniques have been adopted to predict customer behavior (Chen, Chiu, & Chang, 2005; Giudici & Passerone, 2002; Song, Kim, & Kim, 2001). Current Data mining techniques can search through a database without any specific pre-determined hypothesis and still obtain implicit, previously unknown, and potentially useful information about groups of customers including knowledge rules, constraints and behavior patterns (Chen, Han, & Yu, 1996).
Data mining is a components of Knowledge Discovery in Databases (KDD) that involves the application of specific algorithms for pattern extraction (Mitra, Pal, & Mitra, 2002). Various successful applications of data mining techniques have been reported in areas as diverse as medicine, marketing, finance and banking. Applications in these domains generally involve the collection and storage of large amounts of data (“Big Data”).
The critical role played by data mining, and the significance of accurate customer segmentation to ensure a successful digital marketing campaign cannot be understated. When messages are customized to a specific target audience, it becomes more relevant to that consumer, and thus promoting and encouraging a far greater response rate, than could be expected with generic and non-personalized content.
When you send some customized and “personalized” content to a prospective customer, not only do they have an increased likelihood to buy your product, but they will also very likely develop greater brand loyalty for you, as they feel more appreciated and valued at being paid “personal” attention.
Standard customer segmentation can be completed manually, however this approach takes an inordinate amount of time to get done, and typically lacks precision and accuracy. Thus being able to leverage artificial intelligence (AI) and machine learning (ML) technologies to streamline the customer segmentation process would be a huge plus in order to get the very best out of your resources and data, and so help you achieve your business goals more effectively. These techniques can be used to segregate ‘segments’ by synthesizing and using complex variables derived from their past behaviors and buying personas.
Segmentation Using AI & Machine Learning
Your ability to effectively segment your customers is very dependent on the amount of available data you have on them and how well you are able to analyze that data. If all you have are names and email addresses, then at best you may be able to segment through gender (perhaps!). On the other hand, if you have a lot of additional data, including past browsing and/or buying behavior, you can use that information and AI and ML to delve deep into how to predict future behavior based on the customers’ past behavior as recorded in the data.
When segmenting using software, the development cycle comprises of an iterative three step process:
1. Data Preparation – Initial acquisition, cleansing and transforming of your data in order to make the subsequent process function. The data is often segregated into a “control” or training set, a testing and validation set, and the remainder – which is used to make future projections.
2. Data Modelling – Algorithms are coded and executed to identify what variables are important to your segmentation. These are then scaled in order of importance and applied to the “control” or training data set so the model can understand what properties are common for your segments.
3. Model Assessment & Refinement – The model’s effectiveness is tested by running it against a small set of existing data where the result is known, in order to assess the effectiveness of the predictive model developed in the previous step. This is used as a feedback loop to iteratively improve the model until you are satisfied with the “lift” provided by using the “intelligently” created segments. The tested model is then leveraged to segment the data based on predicted behavior.
Deploying AI and ML to generate the customer segments will yield far superior results, as compared to when the segmentation is done manually, as these advanced solutions offer numerous advantages that humans can’t match, such as the following:
- Ability to handle vast data volumes and thus have a virtually unlimited number and size of customer segments and a great ability to scale the approach that emerges
- Ability to operate in an unsupervised mode with minimal human intervention
- Ability to identify hidden patterns and trends (that might even go against prevailing notion or theories)
- Ability to automatically update segments to accurately reflect changes in the marketplace very quickly
- Ability to provide increased level of personalization
- Elimination of human bias and stereotyping in your modeling efforts
- Potentially better ROI and improved effectiveness of campaigns.
As an example, one study conducted by the Campaign Monitor website found that segmented marketing campaigns yield 760 percent uptick in revenue, compared to non-segmented campaigns.
Tips For Optimizing Your Campaigns using AI
While having increased levels of segmentation and targeting smaller more unique groups of customers may result in better conversions, but as the segments get increasingly specific, the task of marketing to them also becomes more complicated.
This complex task, however, can be tackled using machine learning technology to tweak and adjust numerous variables specific to the targeted segments.
These variables include things like delivery time, colors, images, and subject line.
Marketers have to only pick the initial set of variables, for example; a few options for images, set of headlines and process can be automated from there onwards, where ML algorithms experiments with multiple combinations of these variables.
Then, customers’ behavior could be monitored via specific metrics such as heat maps and click-throughs, and the personalized content can be tweaked for the next recipient accordingly in real-time, effectively optimizing the campaign for the individual segments with minimal human intervention.
Please contact us if you would like to discuss these ideas in greater depth.