Typically, company departments such as marketing, sales, customer service or finance use their own specialized systems that do not communicate with each other. This can create data silos - when information is stored in isolation within a company without an efficient connection to other relevant data sources.
Uncovering the Buried Treasure
In B2B marketing, data is like hidden gold, often going undiscovered because it is buried in data silos. This isolation leads to fragmented company data, causing efficiency losses due to the lack of a holistic view.
The problem isn't just about lost efficiency. Valuable insights from comprehensive data analysis remain untapped when data is inaccessible or cannot be combined. This hinders companies from reacting quickly and effectively to market changes or developing hyper-personalized marketing strategies tailored to customer needs.
To unlock this hidden value, data silos must be eliminated. Integrating data from various systems and sources into a central system, such as a data warehouse, enables more comprehensive analysis and use of the available information. With integrative systems, companies can fully understand and analyze their data in real time, allowing them to respond quickly and effectively to customer needs and market opportunities.
Eliminating data silos and creating a unified data infrastructure are essential for successful hyper-personalized marketing. This approach provides deeper insights into customer behavior and preferences, enabling the creation of precise customer profiles and more effective, targeted marketing strategies. The result is improved customer relationships, higher conversion rates, and competitive advantages in an increasingly demanding market environment.
Customer Data - The Gold Mine of Any B2B Company
Integration of different data
Integrating data from diverse systems such as CRM (Customer Relationship Management) , ERP (Enterprise Resource Planning) and PIM (Product Information Management) into a central repository is a critical step to overcome the challenges of isolated data silos. A data lake enables a holistic customer view and thus forms the basis for more precise marketing strategies and higher customer retention rates.
Difference between Data Lake and Data Warehouse
A data lake is a central data repository in which large amounts of structured, semi-structured and unstructured data are stored in their original format. This approach is fundamentally different from a data warehouse , in which the data must be prepared and structured before it is stored. The flexibility of a data lake makes it possible to store data from a variety of sources in its native form, which significantly speeds up and simplifies the integration process.
Centrally storing data in a data lake makes it easier to quickly access and manage this information. The data lake provides downstream applications with a single access point to all of the company's data. This not only promotes efficient data analysis and processes, but also minimizes the effort required for data maintenance and validation. This enables companies to make agile, data-driven decisions based on up-to-date information.
Data quality is important
In B2B marketing, data quality is crucial because it has a direct impact on the efficiency and effectiveness of marketing measures.
A concrete example of how data quality influences marketing effectiveness is the personalization of email campaigns and the resulting higher open rate. If a company is able to retrieve current contact data from its legacy systems and combine it with current interaction data, it can make its messages more targeted and relevant.
Optimizing advertising budgets is another example. Marketing teams can identify the channels and campaigns with the highest ROI (return on investment) by combining data from legacy systems with real-time behavioral data. They can then adjust their spend and invest more resources in the most successful channels to maximize effectiveness.
Keep data safe!
Data is the secret sauce of every B2B company. It is crucial to protect this valuable resource from unauthorized access or theft by competitors. Keeping data intact and confidential is therefore not only a compliance issue, but also a strategic imperative. This directly impacts a company's competitiveness and innovation potential.
There are three main types of data storage environments: public cloud, private cloud and on-premise.
Public Cloud
Here, the data is stored in a third-party cloud infrastructure. This can be cost-effective and improve scalability, but it poses potential risks to the security and control of the data as it is stored on external servers.
Private Cloud
A private cloud provides a dedicated environment that is either managed internally or provided exclusively by a third-party provider. This option combines many of the benefits of cloud technology, such as flexibility and scalability, with a higher level of control and security because data remains in a company-controlled environment.
On Premise
On-premise storage stores data on the company's own servers. This method offers the greatest data control and security because the company has complete control over the physical and network infrastructure. However, it can be less advantageous in terms of cost and flexibility.
Conclusion
Given the high value of the data and the potential risk of industrial espionage, it is advisable to store sensitive data either in a private cloud or on-premises. These environments offer a higher level of control and reduce the risk of sensitive information falling into the hands of competitors. They also allow for a tailored security configuration that is specifically tailored to the needs of the company.
View customer and prospect data in a differentiated manner
Basically, GDPR does not differentiate between existing customer and prospective customer data, which means that European companies have to implement stricter data protection standards
than companies in North America and Asia. This affects their flexibility in handling data and their international competitiveness. For existing customers with whom a business relationship already exists, data processing is justified by a legitimate interest of the company. As a rule, customers have already consented to the processing of their data as part of the business relationship, so that this data may be used for further purposes.
In contrast, the data of prospective customers (leads) who do not yet have a direct business relationship with the company must be handled more strictly. Here, particular attention must be paid to ensuring that data collection and processing complies with legal requirements, in particular the GDPR.
In order to minimize these competitive disadvantages, European B2B companies should strive for differentiated management of customer and prospective customer data. Such an approach makes it possible to achieve maximum marketing efficiency within the legal framework without violating legal requirements. This will enable European companies to run effective and competitive marketing campaigns despite the strict requirements of the General Data Protection Regulation.
| criteria | Customer data | Prospect data |
| Legal basis for processing | Contract execution or legitimate interest of the company | Consent required, especially for marketing activities |
| consent | Not strictly necessary for the performance of contracts or if there is a legitimate interest | Explicit consent required for most processing purposes |
| Personalized marketing | Possible under legitimate interest, as long as there are no objections from the customers | Usually requires explicit consent if no other legal basis exists |
| Data access and management | Data access under legally comprehensible requirements | Access and management of data strongly dependent on the consent given |
The focus is on the dynamic Golden Record
The "Golden Record" represents the ideal of a complete, accurate and up-to-date view of all relevant customer information within a company. This comprehensive customer view is achieved by integrating interaction, real-time and legacy data from different sources and systems. The Golden Record thus includes all essential information about the contact and forms the basis for targeted, personalized marketing campaigns.
The opportunities that arise from linking legacy and real-time data
Data from legacy systems typically includes historical transaction data, customer profiles, financial data, and other static information collected over years. This data is valuable for understanding long-term trends and patterns in customer behavior. In contrast, real-time behavioral data collected through digital interactions such as website visits, social media, and online purchases provides immediate insight into current customer behavior and preferences.Integrating data from legacy systems with real-time behavioral data in a centralized data lake presents great opportunities for marketing and pre-sales. This combined data view enables companies to understand both historical and current customer interactions, resulting in a comprehensive 360-degree customer view. By bringing this data together, companies can create accurate customer profiles and identify behavioral changes in real time, allowing them to respond quickly and specifically to customer needs.
Dynamic creation of the Golden Record by AI
The traditional method of data storage in CRM systems often requires static storage of customer data, which can lead to inflexible and often outdated contact information. In contrast, the use of Artificial Intelligence (AI) enables the dynamic creation and continuous updating of the Golden Record. This means that customer data is no longer permanently stored in a CRM system, but is analyzed and updated in real time by algorithms as new data arrives.
This dynamic processing eliminates the need for static segmentation. AI models can continuously learn and adapt, resulting in more accurate and up-to-date customer segmentations without the need for manual intervention or regular reviews. This flexibility enables companies to respond more quickly to changes in customer behavior and optimize marketing strategies in real time.
The dynamic creation of Golden Records by AI also reduces the problem of collecting and long-term storage of large amounts of personal data - a key concern of the GDPR. By allowing the data to be used for calculations only at runtime and then depersonalized or deleted, the risk of data retention is reduced. Companies can meet data protection requirements more efficiently by only temporarily processing the data needed for immediate use.
To fully realize these benefits, companies must ensure that their AI systems, data protection solutions and data processing protocols are designed to ensure anonymity through data masking.
