Customer Segmentation Tools
Modern customer segmentation tools can be used to dynamically understand and activate insight that can transform a business. New big data sources mean a dynamic new way of understanding consumers is possible. But what is customer segmentation and how can businesses use this to improve ROI?
What is customer segmentation?
Customer segmentation is a method of dividing a customer base into groups that are similar in specific ways, such as demographics, location, interests and more.
When should businesses use it?
A business should utilise the process of customer segmentation when:
- Trying to understand more about who their customers are.
- Attempting to develop and optimise their communications.
- Looking to grow their customer base.
- Identifying new locations and new markets.
How should they use it?
There are a variety of ways to use customer segmentation, including:
- Personalising targeted communications to resonate with one specific group.
- Developing greater understanding of who specific customers are, in order to improve relations.
- Creating tailored customer journeys and content.
- Identifying the most effective channel of communication for a specific segment.
- Optimising your media spend by developing content that shows you understand your customers.
- Developing greater customer loyalty by rewarding those customers who interact most with a brand.
- Identify and engage with an influencer who resonates best with a brand.
- Discovering new customers and predicting buying behaviour.
- Boosting lead generation.
Is it effective?
There are a variety of forms that customer segmentation can take, such as surveys, focus groups and government census data, as well as transactional data. These are the most common sources, but are flawed in that the information that they provide can be defective, corrupted, out of date or invalid, resulting in ineffective segmentation.
However, new tools that use a variety of more dynamic, up-to-date and flexible data sources can provide a far more accurate and detailed representation of consumer segments, relying less on census and survey information and more on Big Data sources such as social and geodemographic data, which can reveal far more about the daily life of a customer. These revolutionary data sources allow brands to create a much more granular picture of their customers than ever before, by developing insights based on the passions and motivations of consumers, which can enrich a brands current transactional data.
Find out more about Starcount’s revolutionary Data Science Platform.
What makes these data sources so much more successful?
More than 2.5 quintillion bytes of data are generated every day – a number that is always steadily increasing. Meanwhile, over 90% of the world’s big data has been created in the last four years, meaning that this flow of information has developed rapidly.
This information circulates from new data sources, such as social data, and allows brands to develop new forms of segmentation, such as motivational segmentation.
Motivational segmentation uses connectivity to find patterns that accurately describe what really matters to consumers, identifying passions and mindsets in social data and spending behaviour in transactional data.
This means that brands can not only know what a customer has bought in the past but what they will purchase in the future, as well as what will motivate them to buy again. Starcount’s own data analysis platforms can analyse a passion graph of over 1.6 billion connections, creating a segmentation that spans the globe in real-time.
The benefits of customer segmentation tools
There are a multitude of benefits to using a customer segmentation tool using big data sources, such as social data, including:
- Accuracy: A far greater level of accuracy in the insight, which reduces the cost per acquisition.
- Dynamic: There is much more dynamic insight, allowing brands to see constantly changing consumer trends and, as a result, be far more relevant with communications.
- Predictive: Tools that incorporate emotional analytics are far more effective at predicting what customers will purchase and how their behaviour will evolve over time.
- GDPR-compliant: In accordance with GDPR regulations, Starcount’s customer segmentation tools aggregate all data to a small but anonymous geographic level, avoiding any potential compliance issues with personal data.
How is big data and data science transforming customer segmentation?
In this new technologically-driven world, consumers expect brands to recognise, predict and cater to their passions, needs and desires. Big Data is at the heart of transforming the methods of understanding and segmenting consumers and the digital lives they live. By activating this dynamic data, it is possible to understand how specific groups, trends and passions are evolving in real-time and predict purchases based on this.
However, building a complete view requires expert data science analysis to interpret the data, essential to understanding motivational segmentation. By applying data science to reveal the insights that big data can help to uncover, it is possible to convert this insight into practical action.
How do you use big data and data science in customer segmentation?
There are a variety of methods of utilising big data in customer segmentation. By developing large behavioural data sets, motivational segmentation doesn’t impose categories on the data – something which clearly differentiates it from past segmentation methods. Instead it will cluster by identifying a group of markers, or passions which are commonly linked, simplifying the data and taking it from thousands of markers per customer to a few key labelled clumps. Then, expert data scientists can granularize these clusters, developing the crucial analysis around each segment that will lead to actionable knowledge.
Take, for example, one particular case study where Starcount helped an automotive manufacturer to understand more about their customers, by segmenting them into passion groups based on their motivations and interests. This allowed the manufacturer to personalise its communications and optimise its targeting, leading to a greater increase in test drives of its new model.
Using a different example, Starcount worked with men’s grooming brand Lynx, to optimise their marketing and appeal to an older audience, by analysing the passions and motivations of their younger customers. This insight revealed why customers were halting using the brand at a certain age and what messaging might entice them to carry on with Lynx as they matured.
What industries is it being used in?
Customer segmentation is being used in almost every industry, from beauty and fitness to entertainment to transport. Here are just a few ways customer segmentation is helping to transform brands within different industries:
- Automotive: These brands are unlikely to have more than one interaction with a customer every 3 to 5 years. However, they can maximise the customer journey through motivational segmentation, understanding how and when to interact with consumers at the ideal time and create genuine loyalty, as well as greater interaction.
- Entertainment: With the lines between traditional media and the new disruptors blurring, only customer segmentation can help a brand get ahead: by helping brands to understand how consumers interact with their multitude of screens and what they are looking for when they turn them on.
- Fashion, beauty and wellbeing: Competition in these industries is always growing and evolving to move between each of them. By understanding how particular customers live their lives and interact between these quickly merging sectors, brands can talk to them in the right way, offering them the perfect product or service at the perfect time.
Are any big businesses using customer segmentation as part of their strategy?
Yes, and the list is growing. Here are just a few of the clients that we have worked with:
- Marks & Spencer
- Holland & Barrett
- Ordnance Survey
Find out how Starcount’s Observatory Platform uses Big Data to segment customers and identifies differences in seemingly similar looking individuals.