Data is a powerful resource for any business. Any decision that a business takes, be it pertaining to marketing, sales, or customer service, is data-driven. Businesses generate enormous volumes of data through various sources. However, poor quality data or even unorganized data is of little value to businesses. They need accurate data to be grouped according to appropriate criteria in order to provide clarity and valuable insights. This process of grouping data into sets depending on common characteristics is called data segmentation.
The Need for Data Segmentation
The way in which brands collect data has undergone a tremendous change over the years. With the advent of social media, analytical and data collection tools, the data collection process has become simplified. So, it is only natural that the influx of data with the brands is huge. This calls for data segmentation.
Let us understand the need for segmentation with an example. Consider a business selling footwear. It might have an audience consisting of different demographics. An ad for heels might only appeal to a section of them, thereby leading to lower click-through rates, and hence negatively impacting sales. If the brand segments its audience data to send targeted ads that align with customer interests, it will have higher success at driving sales. Thus data segmentation helps brands to tailor their marketing efforts to create a personalized experience for customers and generate more revenue.
According to salesmanago insights, through data segmentation, businesses can gain 60% more significant insights about their target audience. The research also suggests that, businesses that leverage data segmentation to provide personalized experience to their audience via targeted campaigns can increase their ROI by 77%.
Data Segmentation Approaches
Brands can approach data segmentation in two ways
- Market Segmentation: It groups the target market comprising of prospective and existing customers depending on metrics like demographics, geography, psychographic traits, and behavior. Company size or firmographics is also considered for B2B companies. It helps to device the most effective marketing strategy for increased revenue.
- Customer Segmentation: It groups an already existing customer base based on their interaction with the brand in addition to the metrics used in market segmentation. It is beneficial to improve customer loyalty, customer service, and overall revenue generation.
Example of Data Segmentation by a Business
Insight to action created a customer segmentation model for a B2B insurance company- Colonial life & Accident Insurance Company based on firmographic segmentation. It considered factors such as financial structure of the companies, ownership, number of employees, etc.
Benefits of Data Segmentation
1. Chalking Out an Effective Marketing Strategy
Data segmentation gives valuable insights into customer behavior and interests to develop an effective marketing strategy. For example, while choosing social media as a marketing channel, data segmentation according to demographics, behavior, etc., helps in deciding the type of content to be created, choice of platform to market, etc.
2. Identifying New Business Opportunities
By segmenting and analyzing customer data, brands can identify customer pain points, preferences, niche markets, and emerging trends. These insights present them with a scope to innovate and create new products or solutions to cater to such segments of customers.
3. Ensuring Customer Satisfaction and Loyalty
Grouping data into segments according to users’ needs, interests, and other characteristics helps businesses create a personalized experience, like offering appropriate product recommendations to specific user segment. Enterprises can also provide effective customer service as they have a better understanding of different segments. By identifying their most valued customer segments, brands can earn their loyalty by prioritizing their service requests, customizing products, and special offers for them.
4. Effective Targeted Communication
Data segmentation helps to identify the most suitable ways to reach out to different segments of audience. It could be in the form of advertisements, emails, live chats, etc. For example, targeted ads on social media platforms like Facebook, YouTube, etc., devised according to customer interests and behavior. With targeted communication, brands can enhance their engagement prospects with quality audience, thereby generating leads and sales.
5. Streamlining Endeavors and Reducing Cost
Data segmentation helps enterprises identify the most promising audience segments so that their budget and resources can be utilized effectively. By offering their services or products specifically to these audience segments, businesses can streamline their endeavors, witness positive outcomes and gain higher ROI. With segmentation B2B firms can avoid allocating manpower resources on products and services that are not profitable.
6. Growth and Revenue Generation
Since data segmentation makes it possible to direct marketing efforts to the most profitable audience segments, success rates are high leading to increased revenue generation. Identification of new opportunities, scope for product improvement, increased lead generation, and subsequent conversions are all benefits of data segmentation that ultimately result in revenue growth. Brands thus grow not only in terms of their revenue and operations, but also as a reliable entity.
Disadvantages of Data Segmentation
- Certain segments of the audience may be left out or neglected as the focus will be on high performing segment.
- Initial costs for data segmentation are high as brands will have to analyze many sets and curate a different marketing strategy for each set.
- When audience fall under multiple data segments, it becomes difficult to cater to their interests effectively
- Some customers might not comply with their personal information fearing privacy invasion.
- Mass production of products might not be possible due to small size of segments.
Challenges to Data Segmentation
Even though data segmentation offers immense benefits to businesses, it comes with its own set of shortcomings. Following are some of the challenges faced by companies while segmenting data.
- Ensuring quality and accuracy of data: Data has to be cleaned and verified before it can be segmented.
- Updating segments based on the changing dynamics of the market: Brands must constantly monitor the changes in the market and update segments.
- Data security and privacy concerns: Brands must consider regulations like GDPR (EU), CAN-SPAM, CCPA, and adopt cyber security measures
- Delay in gathering insights: When segmentation takes a lot of time, it can delay benefits.
Automating Data Segmentation Using AI and Machine Learning: Emerging Trends
Automation of data segmentation using Artificial Intelligence and machine learning makes it possible to process large volumes of data in real-time quickly. It has the ability to uncover hidden patterns in audience data to make accurate predictions. This enables AI to personalize services to different segments of the audience while keeping the segments updated due to its adaptive nature.
Here are some emerging trends in AI that can further enhance customer satisfaction and conversion rates:
- Hyper-personalization: It aims at creating a unique experience for individual customers.
- Explainable AI: It gives a better understanding about the reasons chosen for making predictions in a certain way while segmenting data
- Focus on ethical aspects: Consideration for ethical aspects like data privacy, bias, etc., while using AI for segmentation is gaining importance.
Wrapping Up
Data segmentation strategy has revolutionized how brand utilize data. With data segmentations, brands have been able to get a great deal of clarity regarding market potential, customer interests and behavior. The insights thus gained are helping B2B businesses focus on tailoring their marketing efforts according to market sentiment and customer needs across different segments. Such a targeted approach ensures high conversion rates for brands and also leaves customers feeling valued due to their personalized experience. Thus brands gain the loyalty of customers, in addition to revenue. Even in terms of cost, and effectiveness of resource allocation, brands tend to gain with data segmentation.
Segmentation does come with its own set of challenges, like data accuracy, privacy issues, changing dynamics of segments, etc. Brands must work on overcoming these challenges to effectively use data segmentation and derive its benefits. Overall, data segmentation is the need of the hour. When done right, it can boost growth and revenue, putting the brands’ at the pinnacle of success.