Buyers are constantly inundated with messaging from companies trying to sell their products. With the average person seeing hundreds of advertisements every day, how can businesses more accurately target prospects to help encourage them to buy?

One of the best marketing strategies is to consider the customer journey that a typical buyer will make. This journey consists of the steps taken from having no awareness of your product, to eventually being a loyal repeat customer.

A cloud-centric data approach is one of the best ways to optimize that journey and to increase customer satisfaction as they proceed through the stages. This approach allows marketing teams to deliver granular personalized experiences targeted towards individual audiences- sometimes even individual buyers. Using cloud-centric data provides a single source of truth for all customer data, giving marketers a complete picture of each customer for precise, targeted messaging.

Cloud-centric data helps each step of the customer journey

Marketing takes a potential buyer on a journey from knowing nothing about a company, to making a purchase, to ultimately being a loyal customer and advocate for the brand. Although this journey may take different forms depending on the product, it typically follows a lifecycle like this:

1. Awareness
2. Consideration
3. First purchase / decision
4. Retention / loyalty
5. Advocacy

A cloud computing approach enhances the customer journey at every stage. Let’s look at the stages of a customer journey for one GrowthLoop client, NASCAR, for which we helped create a data-driven approach to their marketing.

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Awareness

During the awareness stage, having a single source of truth allows for rapid, real-time insights into what outreach is working and what is not. Paid ads, blog content, and social posts can be analyzed for how effective they are at getting noticed.

NASCAR used a variety of services for spreading awareness. One of its most useful was Facebook Ads, which was integrated with GrowthLoop for optimization. Customer data could be used to help find lookalike customers while suppressing advertising to already engaged customers.

Consideration

At the consideration stage, customers are evaluating your product critically, comparing your offerings with those of your competitors. Here, it is important to record what information they see, and what customer interactions occur. Whether or not a buyer goes on to make a purchase often depends on whether or not they were convinced that the features of your product would help alleviate their pain points.

GrowthLoop created more than 4,000 different audiences for NASCAR based on existing information from over 17 million customer records. This allows for both precise and consistent messaging across all platforms, ensuring that each customer is shown the ideal set of information for their background.

Before access to the data cloud, this operation was much more cumbersome for NASCAR. Data was scattered, and it was hard to create accurate customer personas, leading to inconsistent messaging for customers in the consideration stage.

Purchase

When a customer makes a purchase, their actions provide data about their experience: what was their source? Google ads? Email?  You can look at what add-ons people often buy, or what factors might have caused customers to return to an abandoned cart.

NASCAR customers may make different types of purchases, such as tickets, t-shirts, mugs, or other merchandise. All of this information is collected and stored on the cloud..

Retention and Loyalty

Over the long term, retention and customer loyalty can be measured based on how often customers return to your brand. This information can be compared with further engagement, such as customer support interactions or direct customer feedback, to determine what post-purchase activity is most effective.

After a purchase, NASCAR customers may interact with the company through several different entry points. Because the core product is an event, many customers return again and again to watch, sometimes at different venues. Additionally, customer service data is collected to enhance the image of customer loyalty.

Advocacy

Finally, whether or not customers are turning into advocates for your brand can be measured by looking at their tendency to promote it. Most noticeably, this is in taking actions like posting online reviews or mentioning your brand on social media. This information can act as a proxy for more private forms of advocacy like word of mouth.

The goal in every case is to produce actionable insights on enhancements that will improve the customer journey according to actual customer preferences. Ideally, these data analytics come from real-time data and are targeted at strengthening the customer relationship.

Specific benefits of cloud-centric data 

A cloud-centric data approach provides several benefits for the customer experience.

1. More accurate personalization and alignment with customer interests - Today, marketers can pursue a highly granular, customer-centric approach in their communications, to the point where every single customer can receive messaging unique to their own exact interests. This is increasingly driven by AI tools that can generate text based on prior knowledge of the customer's buying profile.
2. Consolidation of data from various sources - Having multiple different sources of truth impedes an organization's ability to make sense of their customer information, and therefore to make good data-driven decisions. By contrast, a single, consolidated source of truth presented in real-time maximizes the ability for key decision makers to do their job.
3. Enhanced targeting - More data points means more opportunities for audience segmentation, as well as more intelligent groupings of customers based on prior customer behavior.
4. Omnichannel personalization - Communications with individual customers can be automatically made consistent across channels, so that they will receive the same kind of messaging, whether it is on email, social, or SMS. This automation is important for ensuring customer engagement across platforms.
5. Personalization at scale - Using an AI or machine learning model on top of a single source of truth in the data cloud gives artificial intelligence access to the most complete, accurate, and up-to-date data for analysis. It improves scalability for meeting customer needs.

Case study: rapid, targeted campaigns for NASCAR

Before integrating GrowthLoop into their marketing stack, NASCAR had difficulty using their data effectively. Collecting and managing data in their information ecosystem was complicated and slow, resulting in frequent errors, poor data analysis, and mistargeted messaging.

GrowthLoop's composable customer data platform helped NASCAR activate their fragmented data and use it more effectively. The brand relied on the platform for better audience building and customer journey orchestration. Their digital transformation process helped connect disparate data silos and improve overall data management.

Ultimately, using GrowthLoop helped NASCAR’s team build over 1,900 distinct audiences- many launched in under a minute. This allowed for highly precise messaging, tailored to unique details of each customer profile. In their recent session at GrowthLoop Live, NASCAR reported its opt-out rates have decreased by nearly 15% since the switch, and their click-through rates have increased by 0.5 percent year over year. Needless to say, more personalized campaigns have had a huge impact on performance.

Are you ready to supercharge your results? Learn more about orchestrating cross-channel journeys with GrowthLoop. 

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