Our client is a global leader in consumer packaged goods and faced challenges managing and leveraging its growing customer data. With a vast product portfolio, including food, beverages, and household products sold through various channels (retail, direct-to-consumer, and e-commerce), the brand needed help unifying and analyzing data from multiple touchpoints. Using advanced data analytics, they aimed to improve their understanding of consumer preferences, enhance customer engagement, and optimize supply chain decisions.
Challenges –
- Data Fragmentation: Data from different channels (in-store, online, mobile app, social media) were siloed, making it difficult to form a 360-degree view of their customers.
With data stored in separate systems, the client couldn’t easily track customer behavior across different touchpoints. For example, they might know what a customer purchased in-store, but they lack visibility into that customer’s online shopping habits or interactions on social media. - Inefficient Campaign Targeting: Marketing campaigns lacked personalization and precision, leading to low engagement rates. Generic marketing efforts often fail to resonate with customers, leading to lower engagement rates. Without personalized messaging, customers were less likely to open emails, click on ads, or respond to promotions. Inefficient targeting meant that marketing resources were not optimized.
- Supply Chain Inefficiencies: Lack of real-time demand forecasting and sales data visibility affected production and inventory management. Without real-time demand forecasting, they either overproduced certain products leading to excess inventory and wasted resources, or understocked others, resulting in stockouts and lost sales. Overstocking increased the company’s inventory holding costs, including warehousing, spoilage, and discounting excess stock to clear shelves. This negatively affected profit margins.
- Difficulty in Scaling Data Insights: They wanted a solution that could handle large-scale data processing, delivering actionable insights in real time. Their data analytics processes were slow, meaning they couldn’t respond quickly enough to emerging customer trends or operational issues. For example, analyzing purchasing behavior or customer feedback often took days or weeks, by which time the data might no longer be relevant.
Solutions –
To overcome the challenges, we implemented Salesforce Data Cloud and integrate it with Service Cloud, Marketing Cloud and Commerce Cloud to unify data across all the consumer touchpoints, gain deeper insights into customer behavior, and improve operational efficiency.
1. Data Unification
- Data Sources: Salesforce Data Cloud integrated data from their CRM Retail transaction, mobile app interactions, social media platforms, and third-party data sources (such as loyalty programs).
- Unified Customer Profiles: Data Cloud aggregated the data into a single, unified customer profile, offering a comprehensive view of consumer interactions across all channels.
2. Customer Segmentation and Personalization
- Behavioral Segmentation: With unified profiles, they used Data Cloud’s Einstein AI to create segmentation to group customers based on purchasing patterns, brand interactions, and preferences.
- Personalized Campaigns: Marketing teams used these insights to create personalized campaigns. For instance, customers who frequently purchased organic products were targeted with promotions related to new organic lines or health-focused recipes.
- Journey Builder Integration: Data Cloud was integrated with Salesforce Marketing Cloud, enabling automated, data-driven customer journeys (e.g., cart abandonment reminders and new product recommendations based on past purchases).
3. Enhanced Demand Forecasting for Supply Chain Optimization
- Real-Time Analytics: They enhanced their supply chain decision-making process by leveraging real-time consumer demand data from both digital and physical stores.
- Predictive Forecasting: Salesforce Data Cloud’s predictive analytics capabilities allowed them to forecast demand more accurately, adjust inventory levels, and prevent stockouts or overproduction.
- Supplier Collaboration: Data Cloud also improved collaboration with suppliers, providing real-time insights into inventory levels and consumer demand, leading to more agile supply chain responses.
4. Scalability and Real-Time Insights
- Scalable Data Infrastructure: Salesforce Data Cloud provided them with a scalable infrastructure capable of processing vast volumes of data, allowing them to expand their data analytics as customer and operational data continued to grow.
- Real-Time Reporting: Data Cloud’s dashboards and real-time reporting tools, like calculated and streaming insights provided them with immediate insights into campaign performance, customer satisfaction, and sales trends.
Benefits
- Unifying data across multiple touchpoints helps companies create a 360-degree view of each customer. It leads to relevant marketing efforts.
- The companies can proactively engage with customers who are at risk of churning via targeted offers and loyalty programs.
- Businesses can track consumer demand across different regions and channels. It will help resolve the stock-related issues.
