Every day that passes, data becomes more and more valuable for businesses as its proper management can be translated directly into growth. Now that customer-centric approaches are the norm, and clients expect 1:1 interactions with brands that resonate with their unique needs and interests, scalable personalization has turned into the holy grail in many (if not all) industries, especially in debt collection. Here’s where data-driven engagement and AI come to play.
The latest advancements in AI have raised many eyebrows since they make mass personalization not only possible but more accessible than ever before. It’s no wonder, then, that 92% of companies are leveraging AI-driven personalization to fuel their growth.
But the journey to successful AI implementation isn’t as easy as chatting with ChatGPT. It begins by answering a vital question: "Is your data AI-ready?".
To answer that, first, we have to set the record straight.
What is Data-driven engagement?
As the name suggests, “Data-driven engagement” means approaching customer engagement strictly through the lens of data. It is about collecting, processing, and analyzing data to gain insights into customer behavior, preferences, and needs, and then using them to make better decisions when planning and implementing engagement strategies.
Data-driven engagement is becoming increasingly important in today's business climate, as changing technology, digital user behavior, and a rapidly shifting business landscape mean that brands have significantly more engagement opportunities.
What does AI have to do with it?
It is not that data became important overnight. When commerce was born (thousands of years ago), so was data and its collection. What’s different now is that we have the internet and smartphones.
Consumers create 328.77 million terabytes of data daily, and companies are stashing it like dollars under their mattresses, knowing what it's worth.
Efficiently managing these sizable datasets has become one of the most critical problems for 21st-century companies. Enter AI. Artificial intelligence and Machine Learning models can sift through big datasets while detecting risks and growth opportunities.
By analyzing vast amounts of data with precision, AI enables companies to turn raw data into meaningful relationships, by personalizing experiences, predicting preferences, and engaging with customers in real time; all for a fraction of the cost.
While AI seems to be a one-in-a-lifetime opportunity to offer personalized user engagement and experiences at scale, it also comes with its challenges, so let's look at what it really means to use AI for Data-driven Customer Engagement.
Harnessing AI for 1:1 Engagement: Benefits and Challenges
When it comes to scalable personalization, giants like Amazon and Netflix are the standard, using AI to create never-before-seen user experiences. Both companies have developed AI-powered recommendation engines that efficiently recommend products or content to users based on factors like time, zone, date, device, customer viewing data, search history, cart, etc.
But that’s just the result of hundreds of millions (if not billions) of dollars invested and tens of thousands of hours of pure research and development. While access to these types of strategies gets more and more accessible with time and technological advancements, it is still far from being easy, let alone cheap.
Benefits of AI Data-driven Customer Engagement
While expecting Amazon-level returns by investing only 0.1% of what they invested is far-fetched, there are many benefits that you can enjoy by implementing AI/ML into your data-driven Customer Engagement strategy.
The Machine Learning models we are talking about are designed for the primary purpose of identifying patterns in the four types of customer data:
- Identity data: refers to basic data from the name, birth, age, gender, location, email address, phone number, up to income, industry, social media profiles, household annual spending, etc.
- Engagement data: includes how users are interacting with your website, how often they interact with your social media posts, click-through rate, conversions, traffic, email opens, etc.
- Behavioral Data: tracks purchase history, subscription renewals, cart abandonment, free trial uses, average order value, browsing devices, etc.
- Attitudinal Data: data provided by the customer first-hand, like reviews, surveys, and comments from interactions with customer service.
These found patterns can be leveraged to improve many aspects of the business. Some examples are:
- Customer Experience
- Conversions and Sales
- Decision Making
- Lead Generation and Qualification
- Customer Loyalty and Retention
- Cross-Selling and Upselling Opportunities
If this sounds too theoretical, here is an in-depth review of the Top 5 Applications of AI in Debt Collection for improved customer experience and collections.
Challenges of AI Data-driven Customer Engagement
While implementing this type of technology for managing your data can be beneficial, three main challenges must be heavily considered.
1. Growing User Data Privacy Regulations
As a result, tech giants like Apple and Google are now pushing First-party data gathering: data obtained directly from customers during a payment transaction or when agreeing to terms and conditions during sign-up.
While it is an excellent way of protecting users and giving them more control over their data, the amount of data gathered will be significantly lower, inducing a new era of quality over quantity. It’s essential to watch how data collection evolves over time, as this is just the first step towards a more and more consent-based information-gathering future.
2. AI and Data Security
In order to comply with the increasing privacy regulations and also protect your users from malicious actors trying to sell their data, you need to establish and continuously update bulletproof security standards and risk tolerance.
3. With great (AI) power comes great responsibility
It’s necessary to understand the risks of the AI transformation and how paramount it is to do it responsibly. The main concerns are:
- Evolution: AI-based tools are designed to learn and adapt at a pace that exceeds human learning and understanding.
- Reliance: Businesses are entrusting AI with more significant and crucial decisions. This reliance can lead to efficiency but raises concerns about control and ethics.
- Ethic: It's not just about ensuring that AI makes morally sound decisions. It's also about how a business chooses, implements, and manages AI tools to ensure they are used safely and responsibly.
Is your data AI-ready? - A Checklist
While it may seem like it, Artificial Intelligence and Machine Learning models can’t do magic. They need data to work with (and a lot of it). Based on what you’ve learned in the previous section about the challenges when it comes to AI implementation, we suggest you use this 13-point checklist before pulling the trigger:
- Type: Are you collecting first or third-party data? Understanding the source is crucial for compliance and quality.
- Volume: Do you have enough data? AI and ML models often require large amounts of data to train effectively.
- Quality: Is the data accurate and relevant? Poor quality data can lead to inaccurate models.
- Consistency: Are there inconsistencies in the data? Consistent data formatting and structure are vital for training models.
- Accessibility: Can AI models easily access and use the data? Consider the format and storage of the data.
- Privacy & Compliance: Are you adhering to legal regulations such as GDPR? Ensuring privacy and compliance is essential, especially with personal or sensitive data.
- Bias & Fairness: Is the data representative and free from biases? Biased data can lead to unfair or skewed results.
- Security: How is the data protected? Ensuring proper security measures are in place is vital to prevent unauthorized access or breaches.
- Integration: Can the data be integrated with other data sources or systems? Flexibility in integration can enhance the model's effectiveness.
- Real-time Capability: Is the data capable of real-time processing if required? Some AI applications may need real-time data feeds.
- Maintenance: How will the data be maintained and updated? Regular maintenance ensures that the data remains relevant and accurate.
- Cost: Have you made the numbers to know which will be the costs associated with collecting, storing, and processing the data?
- Expertise: Do you have the necessary expertise to handle and analyze the data?
Back to you
AI applied to data opens a whole new and exciting world of possibilities. The road to AI readiness may be complex, but the promise of scalable personalization and unprecedented growth makes it at least a worthwhile challenge.
So, is your data AI-ready?
At Arrears®, we are pioneering a new era of collections by seemingly implementing automation, AI, Machine Learning, payments, communication channels, and more into an all-in-one, omnichannel, white-label collections and arrears management platform.
Ready to lead the charge in the new era of debt collection? Don't just follow the trend; set it. Try Arrears Now.