Data marketing: How to use model based customer segmentation


Data collection and the interconnectivity of the internet with daily life have changed the way businesses get to know their ideal customers. In order to identify who to sell to and how to sell it to them, companies have to understand the innate behaviours of these individuals.

Delving into this deeper realm of the consumer psyche requires constant acquisition and analysis of extremely large amounts of data and the right set of tools to uncover meaningful insights from that data.  

As incoming information streams increase and diversify, companies need to embrace the change and adopt new approaches to reaching, acquiring, and keeping customers.

Current trends are passing away

For a majority of companies, the prevailing method of creating targeted advertising involves looking at sharply segmented groups of customers.

Companies use basic metrics to sort potential leads and existing customers by gender, age, income level, location, and other basic demographical information.

Though this information offers non negligible details that are essential to optimise marketing campaigns and target offers, it only represents a fraction of the whole picture, a static snapshot that overlooks many important aspects of customer behaviour.

With the right systems in place, it’s possible to predict the actions and purchasing decisions of a subset of customers with incredible accuracy

Relying only on these details to inform decisions about potential buyers places strict limitations on the efficiency and ROI generated by marketing campaigns. In today’s data rich and connected world it is possible to access and collect data from a multitude of different channels (transactions, weather, social media, online activity, and so on).

After all, big data includes all the different ways customers interact with companies across platforms. To stay ahead of the game, companies can no longer afford to ignore these multitudes of data to enrich their existing customer data in order to uncover trends and characteristics that will enable them to better understand, segment, and intelligently target customers and prospects.

To understand the whole picture, to provide a comprehensive view of how customers act and what they want, companies and their marketing departments must move beyond traditional segmentation by integrating methods of advanced analytics into their segmentation and marketing strategies.

It’s all about the data

The problem with this classical approach towards customer segmentation stems from the elementary view it produces of one broad customer type or another.

One needs to focus on seeing customers as unique individuals with likes, desires, problems, and emotions driving their purchasing decisions. Customers are always sending signals to the companies they buy from, and interacting with these individuals is a big part of the modern purchasing process.

This creates an immense amount of data that requires careful analysis, sorting, and application. To handle the influx of data and discover underlying clues into customer behavior, organisations can turn to machine learning, the process by which computers can be “taught” to recognise and predict patterns through the application of specific algorithms.

The algorithms used to make sense of big data speed up the process of integrating and learning from continuously evolving customer behaviours and contexts. The output is information that can help, amongst many other use cases, marketing departments apply dynamic segmentation to better target their offers.

As opposed to static segmentation approaches that don’t change or adapt to a specific context, dynamic segmentation adapts to certain criteria enabling marketers to target campaigns based on specific needs and wants.

By continually analysing incoming information and providing feedback, machine learning takes the guesswork out of identifying target customer groups and can actually help to predict the actions these customers should be expected to take.

Moving into the era of prediction

Predictive analytics in marketing is dynamic, breaking down the walls between customer segments and data silos. Instead of looking at pieces of the marketing puzzle as separate entities, predictive analytics follows the 'digital footprints' customers leave behind as they search, browse, buy, and engage.

This provides information companies could never get by looking only at basic attributes.

The idea is to develop a 360-degree continuously evolving picture of each individual through comprehensive data analysis. With the right systems in place, it’s possible to predict the actions and purchasing decisions of a subset of customers with incredible accuracy. This can greatly reduce the amount of time and money that is often wasted when launching marketing campaigns.

This type of dynamic segmentation enables companies to promote any product or service to their client database while minimizing the risk of customer churn and optimizing likelihood (and size) of purchase.

New methods of customer targeting

With this amount of extreme detail, businesses can delve deeper into the modern realm of personalisation. Instead of making assumptions based on a handful of customer traits and ignoring the details of how they really behave as individuals, it’s possible to understand their wants and needs on a deeper level.

The goal of the modern company should be to delve deeply enough into its customers’ thinking processes to make predictions

Following customers as they move between platforms on their buying journeys sheds light on the external factors influencing choices and gives companies unprecedented insight into how to create the best possible marketing campaigns for optimised ROI.

Benefits for every industry

Companies can profit from the use of advanced analytics in marketing regardless of industry. Selling products or offering services to consumers and businesses becomes easier when predictive data can be visualised and used to create campaigns.

Companies using advanced analytics techniques can:

  • Develop better products and services
  • Improve pricing strategies
  • Better meet the needs of customers
  • Offer more targeted recommendations
  • Increase customer loyalty and retention
  • Improved organisation
  • Stay on track with business objectives

Adopting a new model

To benefit from this emerging trend in data analysis and customer segmentation, many companies will have to upgrade from outdated legacy systems that fail to ingest multiple data formats and sources.

The amount of information from different channels flowing in from multiple sources on a second-by-second basis makes it necessary to adopt tools and tech that enable teams to have access to the data on demand, no matter its format or size.

Software allowing for the use of machine learning in data analytics can take data as it comes and provide continual output so that no critical pieces of information are missed. This not only speeds data analysis but also creates the most comprehensive profile possible for each individual customer.

The goal of the modern company should be to delve deeply enough into its customers’ thinking processes to make predictions about what they’re likely to buy, how well products and services will sell, and the best times to launch targeted campaigns.

Proper use of predictive analytics in marketing saves money and time while increasing ROI. Creating the ideal shopping and purchasing experience for customers fosters increased loyalty and retention, improves trust and strengthens branding to improve growth and ensure stability for the future.

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