We cover the importance of cross-functional teams in effectively using data warehouses and CDPs to personalize customer engagement and enhance data-driven marketing strategies.
In our previous discussions, we underscored the significance of building a cross-functional team to harness the power of data warehouses and Customer Data Platforms (CDPs). By integrating the expertise of engineering, data, and marketing teams, organizations are able to foster a comprehensive approach to data management and customer engagement. The emphasis was placed on the importance of documentation, training, and continuous support, which empower teams to effectively utilize these technologies. We also highlighted the necessity of monitoring and measuring data-driven initiatives to refine strategies and enhance customer retention.
Now that we've established the foundational elements, it's time to delve into practical applications. Understanding how to leverage data from warehouses and CDPs for specific use cases is crucial for driving actionable insights and optimizing customer engagement. We cover much of this in-depth within our Customer Maturity Playbook, which we recommend as required reading, but we will cover broader strokes here for this final post..
The key is to work together without biting off more than you can chew. The end goal is that you may want to build an omnichannel marketing machine that is adaptive to every customer's needs, but we may be able to get a more incremental lift from setting our eyes on a single-channel or specific outcome.
What are the biggest areas of opportunity for the business? Is it reducing churn, increasing lifetime value (LTV), maximizing conversions on a particular channel, personalizing messaging to increase engagement, or understanding customer behavior to inform product development? Identifying these key objectives allows you to prioritize and focus your efforts effectively.
Here is a list of some use cases, based on business goals, to get started with:
Return on ad spend: Suppress converted users using real-time data from the CDP or time-out visitors after a set time frame of last seen on your website.
Increase monthly active users: Personalize communication based on user preferences, subscription status, products purchased, previous visit behavior and more.
Cross-sell/Up-sell: Target customers based on their previous buying behavior and/or use predictive traits to target customers based on their expected behavior to drive upsell or cross-sell of existing products and subscriptions.
Increase CSAT & Loyalty: Use real-time behavioral traits to route users who contact support or sales and surface accurate historical information for a user to support and sales in their preferred tools.
With data warehouses and CDPs at our disposal, we can segment customers based on various attributes such as demographics, behavior, preferences, purchase history, etc. Working together as a cross-collaborative team - first determine who will be doing this activation and how you will get them access to the data they need. First, engineering needs to ensure the profiles are set up correctly before enriching them with data from the warehouse. From there, engineers can choose to send the audience data directly to a downstream channel with Reverse ETL or give technical marketers access to build the audiences themselves with Linked Audiences on top of the warehouse. It all depends on the skillset and needs of the business.
Utilizing data enables us to deliver personalized messaging that resonates with different groups of customers. For example, creating targeted email campaigns for customers who have recently purchased a certain product can help drive repeat purchases
Once you have identified your key objectives and targeted segments, it's essential to continuously test and optimize your efforts. This means experimenting with different messaging, channels, and strategies to see what works best for your customers. By constantly analyzing results and making data-driven decisions, you can learn more about your customers' preferences and behaviors, leading to better engagement and ultimately, higher conversions.
Many businesses are incorporating artificial intelligence (AI) and machine learning into their segmentation strategy to take personalization to the next level. Technologies like Twilio Customer AI can help make sense of large amounts of data and identify patterns and trends without the need of entire data engineering teams. This enables businesses to deliver highly personalized experiences at scale, increasing the effectiveness of their marketing efforts. For example, AI-powered product recommendations can be tailored to a customer's individual preferences based on past purchases or browsing history.
Personalization is no longer a nice-to-have in today's competitive market - it's an essential component of successful marketing strategies. By utilizing data, continuously testing and optimizing campaigns, and incorporating advanced technologies such as AI, companies can see improved customer retention and business growth.
Our annual look at how attitudes, preferences, and experiences with personalization have evolved over the past year.