Today’s buyers increasingly expect a customer experience tailored to their unique needs and preferences. According to a 2022 report from Insider Intelligence, 88% of consumers say the experience a company provides is just as important as its products or services, up from 80% in 2020. And 73% now say that they expect companies to understand their unique needs and expectations, up from 66%.
Companies that can meet expectations enjoy higher customer loyalty, but it’s difficult to provide an exceptional customer experience since the buying journey is now incredibly complex. A modern marketing data stack can help companies overcome this challenge by providing detailed insights into the needs and wants of customers, automating for speedier service, and improving the accuracy of success metrics.
How the Modern Data Stack Supports a Better Customer Experience
The modern marketing data stack is a constellation of tools and systems that collect, store, transform, and analyze customer data. Because today’s buying journey involves so many touchpoints, companies now have myriad data to leverage, drawing on sources that include company websites, social media pages, POS systems, third-party data providers, and more. With so much data being generated so quickly, traditional data stacks are no longer sufficient. Here are five ways a modern data stack can help companies deliver a better customer experience.
1. Shifts focus from maintaining IT infrastructure to data insights
Traditional data stacks rely on resource-heavy infrastructure for data storage and processing. These systems require frequent maintenance and upgrades and make expanding to accommodate growth difficult. Their finite computational power and storage capabilities place limits on the volume and speed at which data can be ingested, processed, and analyzed, making it challenging for marketing teams to take advantage of time-sensitive opportunities. In contrast, modern data stacks are cloud-native, freeing organizations from ongoing maintenance obligations, offering the needed agility to make necessary resource or tooling changes and allowing them to focus on gathering insights from their data, rather than the infrastructure.
2. Unites in-house and third-party data
Since it runs in the cloud, a modern data stack can join first-party data with data from third-party providers quickly so teams can work from a robust customer 360 and get the insights they need when they need them. Marketing teams can now gain a comprehensive view by working with a platform capable of combining demographic, behavioral, and other types of data into a single view of the customer and the enterprise.
3. Eliminates data latency for faster insights and more responsive service
Modern data stacks benefit from elastic, high-speed compute power and near-unlimited storage, eliminating the data latency issues that plague traditional systems. With no resource contention issues, companies can gather the insights they need quickly and are empowered to provide faster solutions to customer service issues. Furthermore, by having all data in a single source of truth, organizations can reduce data latency by eliminating the need to maintain updated copies of information across multiple data marts and data silos.
4. Unlocks AI and ML-powered tools
The incredible compute power and storage capabilities of the modern data stack enable marketers to deploy artificial intelligence (AI) and machine learning (ML) tools. AI and ML can quickly analyze massive data sets to identify patterns that would be impossible to recognize otherwise. AI can also be used to develop and deliver highly personalized messages and special offers to individual customers. And ML can dramatically improve clustering for customer segmentation, grouping customers based on relevant interest variables.
5. Enables accurate measurement of customer experience KPIs
Digital marketing campaigns generate mountains of data, making it challenging for marketers to gain a clear view of which initiatives are generating the most value. Modern data stacks enable organizations to execute multi-touch attribution and capture an in-depth view of how individual marketing campaigns are performing across channels.
How Data Improves the Customer Experience
The more data marketers have to work with, the better they can understand their target audiences and what they value most. Data can also be used to deliver a highly personalized experience and help companies be more efficient at the same time. Let’s take a deeper look at how companies are using data to improve their customer experience.
Deepen customer insights
A customer data platform (CDP), whether it is a single application or leverages the enterprise data platform, brings together data from various sources to create a unified profile for individual customers. With a CDP, companies can analyze data from customer relationship management (CRM) platforms, transactional systems, ecommerce websites, marketing emails, social channels, and more. This data provides a 360-degree view of each customer, with a holistic, real-time picture of customer behavior. Once that data is unified, harmonized and all needed modeling is done to create marketing segments, CDP can activate that data and put it in the hands of your business teams and into marketing and advertising channels.
By combining historical and real-time data, marketers can serve customized offers to individual customers, building brand loyalty and creating new opportunities to upsell or cross-sell relevant products or services. Marketing teams may use this data in a variety of ways, such as informing email marketing and powering a product recommendation engine.
Provide a more consistent omnichannel experience
Today, customers interact with brands across numerous channels including in-person, web, email, online chat, mobile apps, and social channels. The modern data stack unites these disparate interactions, creating a holistic view of customer behavior that enables marketers to provide a consistent, seamless experience with the brand across all touchpoints.
Reduce customer acquisition costs
Data can also help teams identify where marketing spend will be most effective. Customers can be segmented based on demographic, location, and behavioral data to more precisely align messaging and channel choices. Data can also be used to identify and exclude customers who are unlikely to respond, helping reduce marketing costs.
Boost customer lifetime value (CLV)
Maintaining current customers is often more cost effective than spending to acquire new ones. Predictive analytics can help marketers identify which promotions or product suggestions would resonate best with which customers. It can also trigger an alert when a customer exhibits behavior that typically leads to churn so the team can reengage the customer.
Enhancing the Customer Experience with Snowflake
With Snowflake for Marketing Analytics, marketers can deliver holistic insights faster, better know and serve their customers, and increase marketing and advertising ROI. In addition to gaining deep customer insights by unifying fragmented data, the Snowflake Data Cloud makes it easy for marketers to access and activate data to power segmentation models, recommendation engines, and other artificial intelligence and machine learning investments.
See Snowflake’s capabilities for marketing and customer experience. To give it a test drive, sign up for a free trial.