Dividing up the data into relevant segments supports marketing operations by grouping information, for example, into customer personalization parameters and sales prospect insights. Data can be divided up in myriad ways, such as lifestyle, demographics, gender, age, buyer personas, location and ever more specific categories, such as personal identifiers, individual engagement, personal finance and more. There is generally a lot of customer data to take into account.
The benefit of such segmentation is that it gives companies, “more personalized datasets. With a strong understanding of who customers are, and the experiences they value, comes a stronger understanding of how to best communicate and connect with them,” states Sagacity in its blog, “What is Data Segmentation? Why brands should be using it.”
Data segmentation, notes Sagacity, can further help support identifying new opportunities by breaking up data into more meaningful and more detailed insights. Companies would be able to better target communications, tailoring sales and promotional messages to specific sub-groups, for example. These practices can lead to the potential to increase revenue. Ultimately, data segmentation can help build trust with customers on a more personalized level.
Questioning Data Quality
Of course, there are challenges with data segmentation as well. This could include not having enough data, or perhaps having too much data. Data quality and accuracy is an issue facing the data science community, especially as technology advances the discipline. A lack of internal resources may hinder data segmentation and other data science practices.
The All Things Insights and marketing analytics and data science community completed an extensive survey, the H1 2023 Analytics & Data Science Spend & Trends Report, covering what executives are thinking, how they’re spending and the issues and opportunities they face. Data quality was clearly an issue on survey respondents’ minds. When asked, “Which are your organization’s top challenges in using data to inform decision-making?,” the majority of respondents (55.9%) said that data quality was the top challenge. In the blog, “Data Quality Challenges Raise Questions as Community Eyes Future,” we explored the data quality topic with several experts in the field.
In the blog, “Solving the Data Bottleneck,” the flow of data into an organization has increased as technology such as AI has been developed. Big data is here. Some companies may feel pressured by just what to do with all that data. There’s a lot of untapped value there. Some in the analytics and data science community have likened the flow of data to that of a firehose on full power. There’s just an issue, however, of effectively directing that flow of information. This has led to more of an emphasis on data governance and data democratization, which is a process of shifting the mindset and responsibility of data analytics from the data scientists or IT, and moving beyond the outdated silo process, to all users across the organization.
Improving the Data Flow
Data segmentation, the process of dividing a dataset into smaller, more specific subsets based on certain criteria, offers several benefits to organizations in various fields. We asked ChatGPT for some insights into the key advantages of data segmentation:
- Improved Targeting and Personalization: Segmentation enables organizations to target specific groups of customers or prospects with tailored marketing messages, products, or services. This leads to higher relevance and better engagement, ultimately improving conversion rates.
- Enhanced Customer Understanding: By segmenting data, organizations gain deeper insights into customer behavior, preferences, and needs. This understanding can inform product development, marketing strategies, and customer service improvements.
- Higher Marketing ROI: Targeted marketing campaigns based on segmentation tend to yield better results, reducing wasted marketing spend and increasing the return on investment (ROI) for marketing efforts.
- Reduced Churn: Segmenting customer data can help identify at-risk customers. Organizations can then proactively address their concerns or needs, reducing customer churn and increasing retention rates.
- Optimized Product Development: Data segmentation can guide product development by highlighting specific market segments’ needs and preferences. This leads to the creation of products or features that are more likely to succeed in the market.
- Effective Resource Allocation: Segmentation allows organizations to allocate resources more efficiently by focusing efforts on the most promising segments.
- Customized User Experiences: For digital products and services, data segmentation enables the customization of user experiences. Users receive content, recommendations, or features that align with their interests, improving overall satisfaction.
- Improved Customer Satisfaction: Personalized experiences and offerings resulting from segmentation often lead to higher customer satisfaction levels. Satisfied customers are more likely to become loyal and advocate for the brand.
- Market Expansion: By identifying new market segments through data segmentation, organizations can uncover growth opportunities that they may not have previously considered.
- Risk Mitigation: Segmentation can help organizations identify high-risk or low-performing segments early, allowing for proactive risk mitigation strategies.
- Cross-Selling and Upselling Opportunities: Segmentation can reveal opportunities for cross-selling related products or upselling higher-value offerings to specific customer segments.
- Compliance and Data Privacy: Segmentation can help organizations comply with data privacy regulations by allowing them to manage and protect customer data more effectively. It also enables better control over data access and permissions.
- Better Inventory Management: In retail and supply chain industries, data segmentation can optimize inventory management by identifying demand patterns within specific customer segments.
- Enhanced Predictive Analytics: Segmentation can improve the accuracy of predictive analytics models, making it easier to forecast future trends and customer behaviors.
Data Defining & Refining
To realize these benefits, organizations should invest in data quality, analytics tools, and segmentation strategies that align with their specific goals and objectives. Regularly updating and refining segmentation criteria based on changing market dynamics and customer behaviors is also essential to maximize the advantages of data segmentation.
At the beginning, we mentioned that data segmentation is a standard that very well could be overlooked internally. As Qualifio puts it in its blog, “5 ways to improve your data segmentation,” “Data segmentation is a major strategic goal to implement relevant, personalized and impactful marketing campaigns. A goal that can be difficult to manage internally and for which external expertise will bring freshness, innovation and differentiating value.”
Video courtesy of Hurree