This blog explores the essential components to ensure a robust AI strategy and also covers the role customer data platforms (CDPs) play in ensuring your data is AI-ready.
Artificial intelligence (AI) and machine learning (ML) are reshaping the way we interact with customers, make data-driven decisions, and unlock new avenues for growth.
But, the road to AI maturity is dotted with roadblocks. Simply adopting AI without a thoughtful strategy might be more harmful than not adopting AI at all. Taking the internet by storm is the example of AI sharing a poisonous recipe. But a poor AI strategy can still lead to detrimental, albeit less lethal, outcomes to your business like wasted opportunities or sending incorrect information to customers.
This blog explores the essential components of a robust AI strategy.
We also share the role customer data platforms (CDPs) play in ensuring your data is AI-ready and discuss how businesses can leverage AI to drive customer engagement and propel their growth.
Whether you are embarking on your AI journey or looking to refine your existing AI strategy, we’ll provide you with valuable insights and practical tips to navigate the exciting yet complex world of AI. So, buckle up and let's dive into the future of AI together.
We’ve come a long way from the days where the only customizable parts of marketing emails were the customer’s first and last name.
Personalization driven by AI now leverages customer data to understand individual behaviors, preferences, and patterns. This deeper understanding enables businesses to tailor their offerings and interactions with each customer, enhancing the customer experience and improving engagement, loyalty, and conversion rates.
AI can process vast amounts of data and identify meaningful trends and patterns far more efficiently and quickly than a human ever could. It can analyze a customer's browsing history, purchase history, and interactions with your brand across various channels to generate personalized recommendations, targeted promotions, and personalized content.
Take the example of an online retail business. AI can analyze a customer's past purchases, items they have viewed, and their responses to past promotions. It can use this information to recommend products that the customer is likely to be interested in and tailor promotions to their individual preferences.
You can learn how to leverage AI-driven predictive traits derived from past customer behavior to pinpoint users with a high propensity to convert in this full recipe.
You know the saying- garbage in, garbage out.
Data quality is one of the most fundamental factors in the success of any AI initiative. If your data is incomplete, inconsistent, or inaccurate, your AI model will produce unreliable results. High-quality data, on the other hand, leads to accurate and effective AI solutions.
Data quality is not a single characteristic but a combination of several critical aspects, including:
Data Accuracy
Inaccurate or incomplete data can mislead AI algorithms, leading them to make erroneous assumptions or predictions. For instance, if an AI model for predictive maintenance is trained on inaccurate machinery performance data, it could fail to predict an impending failure, leading to costly downtime.
Data consistency
Inconsistent data can impair the performance of AI algorithms. If data about a single entity (like a customer) is inconsistent across different systems, an AI model may fail to develop a complete or correct understanding of that entity. This could result in missed opportunities or wasted resources.
Data timeliness
Moreover, outdated data can hinder the effectiveness of AI. AI algorithms need to be trained on the most recent data to make accurate predictions about current or future situations. Using outdated data could lead the AI to make decisions based on conditions that no longer exist.
Investing in data quality should be a priority for every organization embarking on an AI journey. This can involve adopting data quality management tools, establishing data governance policies, and fostering a data-driven culture that values and understands the importance of high-quality data. By prioritizing data quality, organizations can lay a solid foundation for successful AI initiatives.
Just because you implement AI doesn’t mean you’re immediately going to send top-tier, personalized content. After all, AI is only as good as the data you share with it. And it is high quality data that bridges the gap between receiving personalized content and ensuring that said personalization is indeed accurate.
Unfortunately, poor quality data is all too common. Let’s take a look at the havoc it can wreak:
Different teams within your organization rely on a different source of truth for their data. The marketing team could use a customer relationship management (CRM) system, analytics may use a data warehouse, and customer success could file all customer information in tickets.
Disparate systems make forming a single view of the customer incredibly difficult. This is time consuming to try and resolve and becomes harder to derive meaningful insights and make reliable decisions.
To bridge the gap, engineering teams often pull lists manually to share with marketers. Unfortunately, this information gets stale quickly, and creates a lag between insight and action.
Having data in multiple forms can:
Result in poor quality data because it introduces inconsistencies, redundancies, and inaccuracies.
Become challenging to ensure data integrity and uniformity as inconsistencies in data formatting, naming conventions, or data types can lead to confusion and errors during analysis.
Be redundant, creating duplication and making it difficult to identify the true and up-to-date information.
Ultimately, bad data can lead to poor customer experiences, loss of loyalty, and a loss of revenue. In a world where we rely on AI to improve customer experiences and drive growth, we must strive for high-quality data as the cornerstone of accurate predictions, personalized interactions, and informed decision-making. Without it, businesses risk compromising the effectiveness of their AI initiatives.
Good data requires significant time and resource investments in order to cultivate and maintain.
There are many ways to ensure your organization has access to high quality data for a more consistent and reliable AI approach, outlined below:
Take the time to look through the existing data that your company relies on. Bring in company stakeholders who can explain what data they rely on and where this data comes from. Then, map out where your data is stored and run your report.
Search for duplicate data, spelling errors, conflicting naming conventions, and other issues that might disrupt your operations, analyses, or campaign performance. Then, designate a team member to implement these changes.
Try to audit your data twice a year, or once a quarter, to ensure that it remains accurate and manageable.
A tracking plan is a “source of truth” document used across teams to help standardize how data is tracked and align teams around one strategy for data collection.
This plan consists of a list of events (i.e., user actions) that are paired with a description for each event. These events are used to map the most important steps of the customer journey, from free trial sign-up to recurring subscription to churn. Here’s an example of what a tracking plan looks like:
This document allows every member of your team to understand what data you’re tracking, where you’re tracking that data, and why to ensure your data remains clean and compliant.
Tracking plans also allow you to prevent data that doesn’t meet your standards from ever entering your downstream destinations.
What does this look like in practice? Say you implemented an event that had a typo in it: “Product Viewed” instead of your approved naming convention, “product_viewed.” Using Track Plan, you could prevent that event from making it to your downstream tools (like your CRM, ad platforms, and more). Instead, you have the option to forward this event to be cleaned and replayed later on so no data is lost.
Segment is granular enough where you can just block the properties that don’t match the spec, or block the entire event. This allows you to rest assured that all of your data remains high quality so you can continue building campaigns knowing you’re using the most up-to-date and accurate customer data.
Now that you understand how your business collects data, it’s time to ensure you standardize naming conventions. Standardization ensures all data entries are uniform and that one event isn’t being counted multiple times. It can also help your business automatically block events that don’t adhere to the tracking plan, protecting data at scale.
You can take a deeper dive and see more naming convention examples here.
An analytics database is a scalable data management platform designed for efficient storage and retrieval of data, typically integrated within a comprehensive data warehouse or data lake. It enables rapid analysis of large datasets, allowing you to identify patterns, trends, and anomalies more swiftly than manual exploration.
These factors collectively contribute to ensuring high-quality data and promoting reliability, accuracy, and trustworthiness in the analytics and insights derived from the data.
Once you’ve cleaned the data, you need to keep it clean. We recommend that you host a meeting with your team to outline your new schema, share the tracking plan, explain your naming convention, and discuss the process for tracking new events. This sets expectations and puts everyone on the same page.
CDPs, software that collects and organizes customer data across various channels into a single, unified customer database, play an increasingly important role in preparing data for AI applications. They offer a solution that addresses multiple data-related challenges, all under one roof and provide a holistic and real-time view of the customer, making it a valuable resource for AI applications focused on enhancing the customer experience.
One of the primary benefits of a CDP is data unification. As we've discussed earlier, data silos are one of the significant barriers to AI-readiness. With a CDP, customer data from different sources like websites, mobile apps, CRM systems, and even offline sources, can be consolidated, thereby breaking down data silos and ensuring that the data fed into AI systems is complete and coherent.
Data quality is another area where CDPs can make a significant impact. In fact, a CDP like Twilio Segment has built-in features to cleanse and standardize data, removing duplicates, correcting errors, and ensuring consistency in data formatting. By doing this, it ensures the data ingested by AI systems is of high quality, thereby improving the accuracy and reliability of the AI's output.
CDPs also enable real-time data processing, which is vital for AI applications that rely on instantaneous insights and actions. With a CDP, data collected from various sources can be processed and made available to AI systems in real time, thereby enabling them to react to changes as they happen.
In addition to these, CDPs also play a crucial role in data compliance.
With stringent data privacy laws like GDPR and CCPA, it's essential that the data fed into AI systems is compliant with all relevant regulations. CDPs can help businesses adhere to these regulations by providing features for data governance, including tracking consent, anonymizing data, and enabling data rights management.
In short, CDPs are invaluable in the journey towards AI-readiness. By centralizing, cleaning, and real-time processing of data, they provide a robust foundation for businesses to leverage the full potential of AI.
Adopting AI/ML technology requires investment of time and effort to work effectively. Companies must ensure they are using clean, high quality data to train their AI systems to get the best results. In this blog, we outlined four steps to prepare you for AI. We also explored how a CDP like Twilio Segment can help companies get a higher return on their AI investment.
Our annual look at how attitudes, preferences, and experiences with personalization have evolved over the past year.