In the digital age, when the customer has so many distractions, It is more critical to be aware of the buyer's budget and segment, the devices and digital touchpoints he can access, his shopping and payment preferences, and his purchase timeline. These details will help define the product's price and channels to engage him, provide optimal messaging without annoying and provide a frictionless checkout experience. In composable commerce, customer data is used by marketers to understand the accurate picture of their customers and create personalized and seamless shopping experiences across multiple channels and devices. To provide a seamless experience, marketers require data from many sources, including eCommerce platforms, social media, marketing tools, and analytics tools. When marketers have a realistic view of the customer, they can create personalized product recommendations based on their interests and preferences and segment customers based on their characteristics and behaviors to create targeted marketing campaigns. They can improve their customer service by providing more personalized and helpful support. Also, they can identify opportunities to improve the user experience and make their website or app more user-friendly and effective, which can ultimately increase customer engagement and loyalty, sales, and revenue.
However, challenges and risks are also associated with using customer data in composable commerce. One of the main challenges is managing and protecting customer data responsibly and ethically. Businesses must ensure that they comply with relevant local laws like the General Data Protection Regulation (GDPR) in the European Union and obtain customer consent before collecting and using their data.
Another challenge is the need to ensure that customer data is accurate and up-to-date. As customer data can come from a wide range of sources, it is essential to ensure that it is consistent and reliable.
Customer data platforms should provide data ingestion capabilities, i.e., the ability to ingest data from owned data sources (first-party data) and online and offline sources.
CDPs should be able to consolidate profiles at the person level and connect attributes to identities. The profile data has to be linked from data captured from various devices to a single individual once that person is identified and deduplicated to customer records.
In digital marketing, marketers often need to group people within larger buyers who share specific characteristics or traits. Identifying and targeting particular buyer/audience segments can help businesses and marketers effectively tailor their messaging and marketing efforts to better resonate with particular groups of consumers. CDPs provide rule-based segments, automated segment discovery, predictive analytics and propensity models, and custom models.
Some CDPs have added advanced support for consent-based filtering, suppression, personalization, journey orchestration, A/B testing, and recommendations.
There are several factors you can consider when comparing customer data platforms (CDPs):
Ability to store, organize, and protect customer data.
Ability to integrate with third-party data providers
CDP should provide robust analytics and insights capabilities to make informed decisions about marketing and personalization efforts.
CDP should be able to provide personalized experiences to customers across channels, such as email, web, and mobile.
CDP should provide tools to help marketers map out the customer journey, enabling marketers to understand how customers interact with the brand and identify opportunities for improvement.
CDP can integrate with other marketing and customer-facing systems, such as your email marketing platform or customer relationship management (CRM) system.
CDPs should be able to support consent management.
Provide a data science workbench, so data scientists within the organization can build custom predictive models.
CDPs should integrate with Identity management platforms.
CDPs should be able to clean the profile data and establish an identity graph.
With CDPs, Marketers should be able to create organizational aggregations of contacts to support account-based marketing.
CDPs should provide a recommendation engine that can provide intelligence to nonmarketing channels such as call centers or live chat agents.
Overall, customer data is valuable in composable commerce, but businesses must manage and use it responsibly and ethically. There are many customer data platforms (CDPs), including, Segment, Tealium AudienceStream, BlueConic, Lytics, Treasure Data, and Adobe Realtime CDP. It's important to carefully evaluate different CDPs' features and capabilities to determine the best fit for your organization's needs.