Consumer businesses strive to manage and leverage customer data effectively for a competitive edge. These organizations often have vast quantities of customer data. Their databases can grow to tens or hundreds of millions of customer records. This is not only in number but also in the depth of information per customer. Amount of data is expanding due to growing storage capacities and technologies that track online and offline behaviors.
The challenge is converting this vast customer data into a profitable asset.
On example how to add value for business by utilizing customer data is to enhance online and offline customer journeys with personalization drawn from what we know about each customer. ‘Customer journeys’ refer in this context to the steps customers take across various touchpoints leading to purchases. By personalizing these steps with customer data and making completing them more attractive, we improve the likelihood of conversion.
However, it is important to note that collecting and utilizing customer data is not always straightforward. Despite having all the necessary technology to gather customer data, we are often unable to use this information for personalization purposes without explicit customer consent, due to privacy laws.
This is why customer data strategy should aim for a straightforward model that enables use of customer data securely to offer more personalized services while respecting privacy. It should cover different aspects of customer data and how to make the data readily available to various digital services. Systems such as e-commerce, marketing automation, and AI models need quick access to up-to-date customer data, including consent and preferences. This is why data strategy should focus on governance, compliance, security, integration, and data accessibility within the organization.
Generating Revenue with Data-Driven Marketing Strategies
Beyond journey optimization, other tactics can also boost revenue. For example, reaching target audiences through marketing activities can raise product awareness and increase sales. Marketing automation can orchestrate email campaigns to drive more shopping, both online and in-store. However, campaigns must be relevant and personalized, requiring detailed customer profiles for the marketing automation tools to craft engaging campaigns and targeting them correctly.
Omni-channel messaging is another effective strategy. Marketers can engage customers on social media with ads or send push notifications via SMS or in-app messages. The success of omni-channel messaging hinges on the right content, audience, and timing. High-quality customer data enables precise targeting based on past behaviors, such as displaying ads for products they’ve shown interest in on e-commerce platforms.
Generative AI offers another avenue for personalization. Achieving nuanced segmentation can take a lot of time from marketeers when creating content for marketing campaigns. With generative AI, content generation can be automated using large language models. Generative AI can create unique messages for each recipient. To do this effectively, generative AI requires setting up a context and input parameters to personalize messages for each customer. This context can often be sourced from the customer profile ultimately leading to hyper-personalized messages that contain content generated based on customer past purchases or clickstream. Therefore, having rich and high-quality customer data is vital also for leveraging generative AI in personalized communication.
The Importance of a Customer Data Foundation in Data Architecture
For revenue growth powered by customer data, a solid data foundation is a must. This foundation includes the frameworks and systems for data collection, storage, management, and governance.
Central to this foundation is customer master data management, holding a 'golden record' of each customer—a unified, authoritative source of their information.
Strategies for creating this golden customer record can vary.
One method is to pool all relevant customer information into a CRM system. Businesses have traditionally extended their CRM system capabilities to store different data dimensions about the customer and to support various customer data management use cases. For example, CRM systems may store information about customer past purchases or handle the business logic or processes for customer data management, such as consolidation, deduplication, quality reporting, and rules for data retention and deletion.
However, this centralized approach can be cumbersome due to the need for agreement on shared data fields and the challenge of incorporating complex or unstructured data like, for example, consent or customer preferences to customer record defined in central CRM system. Moreover, managing vast and diverse data sets in a single system is impractical.
An alternate strategy keeps the master data minimal in the core record, linking to detailed data in specialized systems. This modular approach, where the golden record contains only essential information like name, address, and phone number, allows for more flexible data management. In this model, data validation and updates can be partly delegated to customers themselves having access to edit their information in user-facing systems linked to the master record.
This kind of composable, modular architecture is ideal for companies managing large amount of customer identities, complex customer data definitions and having a need for scalability and near real-time data processing.
Building Composable Customer Data Architecture
In a composable architecture, the management of customer data is divided among specialized cloud solutions built on a shared data foundation. Underlying shared foundation ensures integrated and consistent data access, keeping the customer records consistent by blocking, for example, concurrent updates. At the same time data replication capabilities enable different systems to react to customer data changes instantly. With this architecture customer data is flowing between the systems in near real-time while it is kept uniform, coherent and accurate.
The following diagram provides a high-level view of cloud solutions offered by SAP for a modern and composable customer data architecture:
Above, the customer data foundation orchestrates the core customer master record for front-end systems. Updates are propagated in near real-time, so all systems have the same up-to-date customer information, facilitating an excellent customer experience.
Cloud solutions depicted include:
How to start building customer data strategy?
To get started with building a customer data strategy, few questions should be asked about the current state of the customer data:
Finally, determining the measurement of success through key performance indicators is needed. For instance, enabling access to customer data for marketing automation purposes is expected to boost sales, as more sophisticated and personalized data-driven marketing strategies are activated to improve conversion. Therefore, it is crucial to ask if there is a capability in place to evaluate the success of these initiatives.
In conclusion, raising these questions might help to plan for a data strategy that aligns with business goals and helps to enhance the customer experience through more relevant and personalized interactions.
The challenge is converting this vast customer data into a profitable asset.
On example how to add value for business by utilizing customer data is to enhance online and offline customer journeys with personalization drawn from what we know about each customer. ‘Customer journeys’ refer in this context to the steps customers take across various touchpoints leading to purchases. By personalizing these steps with customer data and making completing them more attractive, we improve the likelihood of conversion.
However, it is important to note that collecting and utilizing customer data is not always straightforward. Despite having all the necessary technology to gather customer data, we are often unable to use this information for personalization purposes without explicit customer consent, due to privacy laws.
This is why customer data strategy should aim for a straightforward model that enables use of customer data securely to offer more personalized services while respecting privacy. It should cover different aspects of customer data and how to make the data readily available to various digital services. Systems such as e-commerce, marketing automation, and AI models need quick access to up-to-date customer data, including consent and preferences. This is why data strategy should focus on governance, compliance, security, integration, and data accessibility within the organization.
Generating Revenue with Data-Driven Marketing Strategies
Beyond journey optimization, other tactics can also boost revenue. For example, reaching target audiences through marketing activities can raise product awareness and increase sales. Marketing automation can orchestrate email campaigns to drive more shopping, both online and in-store. However, campaigns must be relevant and personalized, requiring detailed customer profiles for the marketing automation tools to craft engaging campaigns and targeting them correctly.
Omni-channel messaging is another effective strategy. Marketers can engage customers on social media with ads or send push notifications via SMS or in-app messages. The success of omni-channel messaging hinges on the right content, audience, and timing. High-quality customer data enables precise targeting based on past behaviors, such as displaying ads for products they’ve shown interest in on e-commerce platforms.
Generative AI offers another avenue for personalization. Achieving nuanced segmentation can take a lot of time from marketeers when creating content for marketing campaigns. With generative AI, content generation can be automated using large language models. Generative AI can create unique messages for each recipient. To do this effectively, generative AI requires setting up a context and input parameters to personalize messages for each customer. This context can often be sourced from the customer profile ultimately leading to hyper-personalized messages that contain content generated based on customer past purchases or clickstream. Therefore, having rich and high-quality customer data is vital also for leveraging generative AI in personalized communication.
The Importance of a Customer Data Foundation in Data Architecture
For revenue growth powered by customer data, a solid data foundation is a must. This foundation includes the frameworks and systems for data collection, storage, management, and governance.
Central to this foundation is customer master data management, holding a 'golden record' of each customer—a unified, authoritative source of their information.
Strategies for creating this golden customer record can vary.
One method is to pool all relevant customer information into a CRM system. Businesses have traditionally extended their CRM system capabilities to store different data dimensions about the customer and to support various customer data management use cases. For example, CRM systems may store information about customer past purchases or handle the business logic or processes for customer data management, such as consolidation, deduplication, quality reporting, and rules for data retention and deletion.
However, this centralized approach can be cumbersome due to the need for agreement on shared data fields and the challenge of incorporating complex or unstructured data like, for example, consent or customer preferences to customer record defined in central CRM system. Moreover, managing vast and diverse data sets in a single system is impractical.
An alternate strategy keeps the master data minimal in the core record, linking to detailed data in specialized systems. This modular approach, where the golden record contains only essential information like name, address, and phone number, allows for more flexible data management. In this model, data validation and updates can be partly delegated to customers themselves having access to edit their information in user-facing systems linked to the master record.
This kind of composable, modular architecture is ideal for companies managing large amount of customer identities, complex customer data definitions and having a need for scalability and near real-time data processing.
Building Composable Customer Data Architecture
In a composable architecture, the management of customer data is divided among specialized cloud solutions built on a shared data foundation. Underlying shared foundation ensures integrated and consistent data access, keeping the customer records consistent by blocking, for example, concurrent updates. At the same time data replication capabilities enable different systems to react to customer data changes instantly. With this architecture customer data is flowing between the systems in near real-time while it is kept uniform, coherent and accurate.
The following diagram provides a high-level view of cloud solutions offered by SAP for a modern and composable customer data architecture:
Above, the customer data foundation orchestrates the core customer master record for front-end systems. Updates are propagated in near real-time, so all systems have the same up-to-date customer information, facilitating an excellent customer experience.
Cloud solutions depicted include:
- SAP Customer Data Cloud for managing customer identities and consents.
- SAP Customer Data Platform to consolidate customer interactions into a unified 360-degree profile.
- SAP Emarsys for mass outreach via emails and other messages, augmented by generative AI.
- SAP Service Cloud to enhance customer service with comprehensive profiles.
- A generic Customer Data Foundation layer to ensure data integrity across digital and non-digital channels. SAP offers SAP Master Data Integration service to provide consistent view on master data across a hybrid landscape.
How to start building customer data strategy?
To get started with building a customer data strategy, few questions should be asked about the current state of the customer data:
- What customer information is essential for our business processes and goals?
- What additional data sets about the customer are already available? What is the structure of these data sets?
- Which platforms already collect or will collect customer data in future?
- How will we comply with legal regulations on data collection? How will we secure the stored data?
- How scalable is our data architecture? Can it store growing amount of information about the customers?
- Can we activate and utilize customer data for marketing, AI models, or service improvement?
Finally, determining the measurement of success through key performance indicators is needed. For instance, enabling access to customer data for marketing automation purposes is expected to boost sales, as more sophisticated and personalized data-driven marketing strategies are activated to improve conversion. Therefore, it is crucial to ask if there is a capability in place to evaluate the success of these initiatives.
In conclusion, raising these questions might help to plan for a data strategy that aligns with business goals and helps to enhance the customer experience through more relevant and personalized interactions.
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