Data driven customer experience Salesforce reference architecture for automotive sales and service

Architecture Landscape

Customers are increasingly looking for personalized communication, and with data captured across multiple systems and platforms, it’s important to find a way to bring the data into a single view to understand your customer.

As a Salesforce partner, Salesforce Data Cloud sits at the heart of our reference architecture for Data Driven Customer Experience (DDCX). Our reference architecture shows our end-to-end view of the technologies that may be applicable to implementing DDCX within this industry. Whilst this can be different for each company, the desired end-to-end architecture below is designed to show that DDCX can be implemented to scale and utilize various technologies to enrich customer experience and communication. It’s worth noting that this architecture should not be considered a big-bang change but is a progressive introduction of new systems to enhance the user journey at multiple points.

With this architecture, the sales and service team could benefit from the unified customer profile when serving and tailoring offers for customers based on the interactions with the business and vehicles. Furthermore, Data Cloud could connect to not just Salesforce but various other systems for activating the segments via MuleSoft, such as notifying your SAP system when a vehicle order is made by a customer and triggering a series of communications to inform customers what the next steps are.

Figure 1 – End to End Salesforce Reference Architecture powered by Data Cloud

There are various systems in play when we review the architecture above, and the key is to leverage the right system for each client when implementing DDCX, but what are some of the key components of this architecture?

Key DDCX Components of the Landscape

There are multiple systems in play to deliver DDCX, and sometimes different terminology is used interchangeably when discussing the systems. To clarify this, we have listed the 3 fundamental components below along with their characteristics.

Salesforce Data Cloud

Data Cloud provides a real-time data platform which helps unify all your customer data. Data Cloud harmonizes and stores your customer data at massive scale, and transforms it into a single, dynamic, source of truth.

Some features of Data Cloud include:

• Real-time data – ingest data in real-time to create experiences seamlessly.
• Built-in connectors – built-in connectors to bring data across multiple systems.
• Unified customer profile – create unified profiles across customer touchpoints.

But what can you essentially use Data Cloud for, and what scenarios should you avoid using it for? We have compiled some key points to help understand the key strengths of Data Cloud.

Figure 2 – Salesforce Data Cloud features & misconceptions

Marketing Cloud Personalization

Personalisation delivers real-time decision-making and personalisation at scale, providing you with the opportunity to personalise the customer experience whilst they browse on your website, engage via email/SMS/push, and offer the next best action in store.

Some key features include:

• Real-time web personalisation – Use powerful real-time segmentation and AI to determine and deliver personalised web experiences that inspire customers.
• Campaign attribution – Assess how much revenue has been driven from online and email campaigns actioned via personalisation.
• Product recommendation – Use the power of Einstein to recommend the most relevant products, content, categories and more based on each customer’s unique characteristics.

Figure 3 – Marketing Cloud Personalisation features & misconceptions

Data Lake

Data Lake is a data warehousing platform that allows users to store, manage, and analyse large amounts of data in a secure and scalable manner. Some features include:

• Single Fully Managed Solution – Analyse data sets which were previously impossible to obtain and store and access your structured, semi-structured, and unstructured data in one location and gain seamless access to external data with similar scale and speed.
• Near Unlimited Resources – Its multi-cluster shared data architecture separates compute from storage, enabling customers to elastically scale up and down, automatically or on the fly. Users can apply dedicated compute clusters to each workload in near-unlimited quantities for virtually unlimited concurrency without contention.

Figure 4 – Data Lake features & misconceptions

Summary

In conclusion, our series has emphasized the vital role of data-driven customer experiences in the automotive industry. We’ve showcased the importance of personalized communication, real-world examples, and the centralizing power of Salesforce Data Cloud. We hope you have gathered some key takeaways from this series to ignite impactful dialogue and embrace the ever-changing landscape to enhance customer experiences. Together we can harness the potential of data and cultivate long-lasting relationships with our customers.

To read the first two blogs in our Data Driven Customer Experience series, please see quick links below.

1. Introduction to Data Driven Customer Experience (CX) within Automotive Sales and Service
2. Exploring Use Cases for Data Driven Customer Experience (CX) within Automotive Sales and Service