What are the most effective segmentation variables for marketing analytics?

To effectively segment a market, it is crucial to first define the objectives of the exercise and consider the category of the product or service being offered as well as the industry. Some important variables that should be taken into account include demographics, consumption habits, drivers of consumption, and potential barriers. It is also important to consider variables related to the path to purchase and the shopping behavior of the target audience. By considering these factors, businesses can create more focused and effective marketing strategies.

The question "what is your target audience and why?" it is absolutely fundamental to understand how to use the information in decisions regarding policies related to advertising campaigns and the company mission. All decisions involving organizational aspects to achieve results then depend on this starting point

This is always a challenge. You might have "enough" #data. But do you have enough: A. Relevant B. Clean C. Actionable #data That offers insights you can actually work on and generalize? That's the big question. Also the next question is: how much of this #data can you organically generate without hurting consumer #privacy, #digitalcivilrights and #datacontrol?

Before starting any analysis activity, it is essential to choose the most appropriate way to obtain quality data in order to segment the reference market effectively using coherent and consistent data. The data must also be easy to find and archive with a minimum effort of time, resources and money, with an eye on how much this data is updated or updateable in real time.

Recognizing customer insights and preferences are pivotal segmentation variables for marketing analytics allows the formulation of highly targeted and efficient marketing strategies. Delving into specific customer behaviors and preferences facilitates the creation of personalized marketing messages and offerings. Such targeted communication significantly enhances customer engagement and loyalty. This targeted approach not only drives higher conversion rates but also provides a framework for anticipating and adapting to evolving market trends. Employing a customer-first mindset as a core segmentation variable will help craft data-driven marketing strategies that resonate effectively with distinct audience segments.

I'll maintain that zoning in on #qualitative variables is as important. The challenge is: How do you measure subjectivity? The answer lies in semantic analysis. Looking for keywords, constructs and contexts that repeatedly come up in user-generated responses. Marketing #Analytics professionals need to develop good #data #cleansing and #data #classification skills to make this happen. On top of that, they need to keep a flexible stance towards their prospects. Don't lose hope with the first refusals or high bounce rates.

You must always be aware of your customers' desires and what are the weak points that can push them to make a choice. Knowing what the reputation of your product or service brand is essential, because keeping in mind the psychology of the target audience will help to define ad hoc advertising campaigns. It is therefore always important to get honest feedback

I was introduced to RFV segmentation by one of the greats, Jim Fulton of Customer Metrics Inc. RFV segmentation seeks to classify a company's customers by Recency (time since last order), Frequency (order frequency), and Value (average order value). While effective for retention and reactivation, with its non-reliance upon statistical techniques adding to its popularity and longevity of use, adapting it for acquisition requires adjustments such as: using different RFV metrics (e.g. R = time since last contact, F = contact frequency, and V = geodemographic type); adding additional variables (e.g. lead source, previous online behaviour); and employing statistical techniques (e.g. decision trees) trained upon past acquisition campaigns.

I would first delve into the available historical data of the company, like sales data, customer demographics, customer feedback, reviews and other qualitative data available. My aim will be to understand the customer behavior, in terms of the core business value. Understanding the customer behavior be it purchase behaviour, useage patterns will help identify patterns and likewise help in putting customers into groups.