There are different solutions for mapping customer data across regions include CDP, mapping software, data integration tools, and machine learning to match customer records.
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Customer data platforms (CDP)
We generate approx. 2.5 quintillion bytes of data daily. Businesses are continuously gathering and using data insights from websites, social media, mobile apps, email campaigns, and other sources. If one wants to use all the data efficiently and intelligently, customer data platforms can provide a way to do this.
These companies have been getting quite a bit of attention in recent years. The big data industry grew from $169 billion in 2018 to $348.21 billion in 2024. However, 61% of chief marketing officers believe they are underusing big data.
Customer data platforms give users a 360-degree view of their customers. Essentially, the software aggregates data from service software, marketing clouds, ecommerce engines, and other data sources and provides it to other systems. This, in turn, allows CDP users to develop customer profiles in one central hub and use those profiles for sales, marketing, and customer service purposes.
Mapping software
Mapping software doesn’t have to be complex. The best tools perform the analysis for the user, so no coding is necessary. When we speak of the best mapping software, it’s hard not to mention Maptive, a cloud and web-based mapping solution that doesn’t require downloads. The user always has the latest features at their fingertips and can access the tool from any device – mobile, desktop, tablet, etc. Making a map from your location data is quick and easy: you only upload a spreadsheet.
Maptive takes cold hard data and represents it visually appealingly, thereby getting various company departments on the same page. Generally, marketing and sales professionals are visual or creative types. When they see a heatmap, the business’s current financial or operational situation becomes clearer to them, and they also understand what improvements they need to make.
Data integration tools
The data integration market was worth $13.6 billion in 2023 and is predicted to reach $43.38 billion in or by 2033, growing by 12.32% a year in this forecast period. North America generated the majority of this value in 2023, accounting for more than 40% of the data integration market. In terms of business application, the marketing sector comprised over 26% of the total revenue share in 2023.
Data integration is the process of merging or combining data from different sources, displaying it in a unified perspective, and answering questions based on the combined data. Data integration streamlines and guides a business’s operations.
ETL (Extract, Transform, Load) tools are worth mentioning in this context. They can be configured to pull data from different regional sources, transform it to match a standardized schema, and load it into a centralized system. Commonly used, free ETL tools include Apache NiFi, Talend, and Informatica.
Many customer data platforms offer APIs that facilitate data integration from different systems, even if they operate across regions.
Machine learning to match customer records
82% of companies are currently recruiting employees with machine learning skills. The machine learning market is predicted to increase by more than 36% by 2030. Machine learning algorithms can match customer records across regions, even if the formats differ. These algorithms can identify and consolidate duplicate entries and minor variations in data, such as different name spellings.
Natural language processing can standardize and match unstructured data (e.g., addresses and names) in various languages across regions. The global NLP market is projected to grow from $29 billion this year to just over $158 billion in the next eight years.
Recap
- Customer data platforms provide a 360-degree view of customer info
- Mapping tools perform fast data analysis
- Data integration merges or combines data from different sources
- Machine learning algorithms match customer records across regions
- Natural language processing standardizes and matches unstructured data