Role of Data Science in Market Segmentation Analysis
“Market segmentation could be a natural result of the vast differences among people”
-Donald A. Norman
When establishing your business plan or launching your next product, establish who your target buyers are. Once you have got the general marketplace for your products or services, break that larger group down into smaller sections, called segments. A market segment looks at the smaller groups to seek out specific consumer needs unique to it section. The globe has billions of buyers with their sets of needs and behavior.
Data has never been more accessible or essential to running a business. An increasing number of sophisticated sources, from social networks to web databases, provide big data on an unprecedented scale. Data scientists, who process and translate data, have emerged, enabling business owners to use this new, valuable intelligence to tell their marketing strategies. Understanding the worth that data science can augment your business is now incredibly important.
What is market segmentation and why can we need market segmentation?
The idea behind marketing segmentation is to spot different groups of buyers among overall target market. By breaking your target market into smaller segments, create specific marketing messages for every group. Given current competition within the market, it’s essential to know customer behavior, their types, and their interests. Especially in targeted marketing, categorizing and understanding customers could be a crucial step in forming effective marketing strategies. By creating customer segments, marketers can concentrate on one segment at a time and tailor their marketing strategies.
as an example, you have got a hotel business, and you’ll target couples who have upcoming anniversaries and offer them a special romantic package. Overall, customer segmentation may be a key for successful targeted marketing, with which you’ll target specific groups of consumers with different promotions, pricing options, and products placement that catch the interests of the target market within the most cost-effective way.
What is Data Science?
Data science is an interdisciplinary study of large volumes of data using modern tools. It aims to provide a holistic, thorough, and refined look into raw data. It enables your business to focus straight in on those insights that will directly influence how your business works, help you make practical predictions for the future, and enable you to make effective marketing decisions. New trends in the field of data science reflect the growing possibilities for businesses, be it in the realms of customer service, product development, or customer value.
Use of Data Science in Market Segmentation
Recognizing the various demands of your clients is critical to a successful marketing strategy. No two consumers are identical, their pain problems, wants, and aspirations can be classified in useful ways to improve marketing strategies and increase conversions. Customers can be divided into groups depending on factors such as their geographic region, previous purchase history, and how they visited your website. Specific machine learning algorithms can be used by data scientists to assess the potential worth of each ideal client group as well as which items are most likely to appeal to them. This information may then be used to guide your content strategy, channel optimization, and advanced lead targeting.
Main Techniques Used
How is market segmentation actually undertaken using data science?
There are two main approaches to plug segmentation in practical use. If a company has access to sufficient marketing research and database information, then they will use a statistical technique referred to as cluster analysis. Otherwise, the marketers involved will have to divide the market supported their knowledge and understanding using what’s referred to as a segmentation tree.
Cluster Analysis
An analysis designed to categorize objects to a pre-defined number of various groups, with each group being relatively similar on a spread of selected attributes.
The resultant groups are cited as clusters. If a firm has conducted an acceptable marketing research study, they’re going to have suitable data about the market. Using this data, they will use a statistical analysis referred to as cluster analysis. because the name suggests, this statistical tool ‘clusters’ the consumers into related sets, based upon the variables within the data.
When using cluster analysis, the analyst will experiment with the information searching for suitable segments. While using the software, the analyst can select what number segments they need the statistical program to spot, similarly as specifying the actual variables which might be decided from the survey which should be considered. Cluster analysis is on the market with software programs like SPSS software, which is usually utilized in universities round the world.
For example, let’s assume that a variety of image questions were asked within the survey. If it absolutely was a survey within the airline industry, then a number of the image questions may well be asked respondents to rate variety of airlines on a spread of attributes including: service, reliability, value, friendliness, convenience, professionalism, and so on. The analyst could then select a number of these variables to be included within the cluster analysis. Let’s assume three clusters to be created. The software would then produce an output where each individual responded is allocated to at least one of those for groups.
A potential output can be the primary two lines of the table below, where the three groups produced are listed with which respondents are allocated to every cluster. The analyst would then must further review the information and identify why the software has allocated these respondents to the identical group.
The third line, the segment’s similarity, would be determined by the analyst who would describe or name each cluster. during this case we’ve three segments, the primary being value based, the second being service oriented and also the third being having the requirement for reliability. Therefore, we’ve got constructed three segments employing a benefit sought segmentation base.
If the analyst isn’t satisfied that the clusters produced are logical, then they simply repeat the method using different variables or different numbers of clusters. This process of observing the output from different variables continues until the marketer contains a segmentation which helps provide a useful way of watching the marketplace for the firm.
Segmentation tree
A segmentation tree is a visual representation of the separation of a market into smaller possible market groups, similar to a decision tree. Even if you don’t have a lot of industry knowledge, the segmentation tree approach is straightforward to grasp and implement. When valid statistical or research data is unavailable, this strategy can be useful. The total market branches out like a tree in this method. The tree trunk represents the total market, which branches off into the first level of segmentation. The primary branches are then split into several types of segmentation, such as behavioral, demographical, and so on. As a result, the whole market has been split into many segments.
The following diagram may be a simple example for a segmentation tree.
For the breakfast cereal market example above, it’s been initially segmented by a demographic variable for age, it then has been segmented by a benefit, that’s the preference of consumer or whether the patron is seeking the enjoy cereal of health or diet, or searching for something great tasting, or searching for cereal and has fun shapes come, colors and packaging. During this example a complete of nine different market segments are constructed.
Tools Used
Machine learning-based segmentation
Machine learning-based segmentation takes the segments from the various types of customer segmentation discussed above and utilizes clustering algorithms like k-means to build new and distinct groups of customers. Because models like k-means use machine learning’s unsupervised learning process, they don’t require labelled training data or the definition of specific groups. Instead, simply give the k-means segmentation model previously segmented customer data and the amount of clusters you want it to construct, and it will automatically assign customers to clusters based on their resemblance to one another. It can disclose customer groupings you hadn’t explored before in your study or research. The actual clusters are entirely dependent on the customer segments fed into the model, hence the technique can be applied to practically any type of complex consumer segmentation. It’s most commonly utilised to separate clients into behavioural groups.
K-Means clustering
Customer segmentation using K-Means clustering allows data to be grouped by similar attributes, which is beneficial to the business. K-Means Clustering separates data into groups based on comparable properties.
Unsupervised learning is best used for customer segmentation. We can identify segments of customers in the dataset using clustering to target the potential user base. They classify customers into groups based on similar traits such as gender, age, interests, and purchasing habits in order to successfully promote to each group. Visualize the gender and age distributions using K-Means Clustering. Then we can look at their annual earnings and expenditures. It explains how we can split clients based on comparable features according to their wants using k-means clustering, which is an unsupervised machine learning classification.
For example, if I own a grocery mall, I have some basic information on your clients, such as Customer ID, age, gender, annual income, and spending score, thanks to membership cards. Spending Score is a number you assign to a customer depending on your stated factors, such as customer behaviour and purchase data. Using this data, I can understand which consumers are likely to converge, so that the marketing team can organise their strategy accordingly. The K means clustering approach can be used to do all of this. As I can divide my costumers by their age, gender, interest, spending score, etc.
How Industry use data science in market segmentation
1. Netflix
Netflix uses market segmentation technique to enrich their satisfaction of subscribers. They divide their costumers by taking information lie gender, age, language, interest of shows and recommend the program using that information. Also, Netflix’s recommendation engine serves exact recommendations of latest films and series supported the viewing history of users with similar interests. It segments the users and recommend those sorts of content. Though the first-hand effect for the user is enriching, helpful, and private, the final goal is to make users stay subscribed month after month.
2. Luxury Clothing Retailer and Clothing Websites:
Luxury clothing brands will often subdivide their customers into various categories supported how likely those customers are to shop for again within the future. This is often important, because some customers are going to be one-off consumers, they may buy a rich handbag as a gift or for a big day, but they will not be likely to be an everyday or semi-regular customer. Knowing who the massive spenders and repeat buyers are allows the corporate to cultivate relationships with those customers. The clothing maker offers promotions to them, hold private wine receptions to showcase new products and make follow up calls or send handwritten notes to them after their purchases.
If we consider example of online shopping website like myntra, ajio, meesho, etc. Firstly, they collect the information of costumer which is age, gender, preference of clothing like western, traditional, etc and divide the group as per those and recommend products from that category. This companies get track of user’s choice and target them using this segmented information. This all can be done using data of costumer.
3. Spotify
Similar to Netflix, Spotify aims to sustain its subscribers by providing new, interesting ways for them to get music. Spotify also track the interest and recommend those form of music playlist to costumer. For this also the costumers are segmented as per their regular or frequent listening of music. One initiative that aims to unravel this problem is that the Discover Weekly playlist. Every Monday, a playlist are tailor-made for every user, supported the listening habits of comparable users. All of those ideas keep each user’s account fresh, up-to-date, and interesting, with none action required from them.
4. Banks:
All organizations target their customers and segment it into groups for various reasons, including the banks. The group can be formed based on two factors: their behavior, or as we call it behavioral segmentation, or specific characteristics like age, gender, income, etc. which is called demographic segmentation.
Data Scientists use methods like clustering to accurately group customers. After they are done grouping customers, the banks will then use this information to predict Customer Lifetime Value (CLV) for different customer segments. CLV is what organizations use to measure how valuable their customer is. It is important for banks to discover high-value customers or segments as it helps them sustain beneficial relationships and retain profitable customers.
Benefits of Data science in segmentation:
Data-driven segmentation can increase income and reduce financial risk, and there are a variety of ways it can benefit your entire marketing operations.
1. Improve the effectiveness of your strategies. There’s no reason to confine yourself to a single strategy. Market segmentation allows you to use the most effective approaches for each type of customer.
2. Develop hyper-targeted message. Instead of trying to reach everyone with the same message, target your marketing efforts to the specific pain point of your consumer base.
3. Boost the response rate. Higher response rates result from better targeted and customized messaging. As a result, you’ll be able to maximise the return on your sponsored advertising while lowering the overall cost of your marketing initiatives.
4. Attract the correct kind of client. Prospects who are really interested in your product will respond to more tailored and impactful marketing communications.
5. Increase brand retention and loyalty. When you address more of your audience’s demands, they’re more inclined to stick with you.
6. Extend your reach. It identifies slot markets that would otherwise go missed if your complete target audience was addressed to as a whole.
7. Encourage the development of new products. Segmentation aids in better understanding the needs of your target market. Utilize this knowledge to create specific features or launch new goods that address their needs.
8. Assist with decision-making in other areas of the organisation. By analysing different groups’ preferences, one may better accommodate the audience in other areas like pricing, distribution, and design.
Conclusion
In business, knowing the proper target is critical since a company can only provide excellent service to clients if it understands who their customers are among a large number of people. Segmentation is the process of breaking clients into groups so that a company can focus on providing tailored products or services to a specific group of individuals. A three-step process of surveying, analysing, and profiling is used to segment markets. Geographic, demographic, psychographic, and behavioural segmentation characteristics for consumer markets can be employed separately or in combination. With the use of relevant business theories, data science methodologies can be applied to segmentation. Consumers were classified into various segment groups with specified criteria based on similarity in customer characteristics, and basic statistical findings were displayed. The use of relevant data may be a strategy to improve operations and help a company achieve its goals.
Published by- SNEHAL VARNE, Aadvija Medhekar, TUSHAR RANJAN, Shrutak Shende, Siddharth Sahoo
