Acquiring new customers is critical for any business, however the methods available to companies have changed considerably in recent years. Artificial intelligence (AI) and in particular machine learning (ML) has become mainstream, especially since the Covid pandemic during which many advances were made. Businesses now have affordable and simple to use tools to attract, engage, and convert potential customers more effectively. Customer acquisition strategies are being revolutionised, to the point that businesses not using AI for customer acquisition will soon be at a considerable disadvantage to their competitors.
Understanding AI/ML in Customer Acquisition
Machine Learning models, together with modern processing power, enable the detailed analysis of vast amounts of data (available in most organisations) to identify hidden patterns and customer behaviour, and therefore to more accurately predict customer behaviour. A human can look at a small number of variables simultaneously and make sense of them. This is a key driver of “marketing segmentation” so that customers can be “boxed” into broadly homogenous and understandable groupings. Machines, on the other hand, can make sense of tens of thousands of variables simultaneously, to enable businesses to make the right product available to the right customer at the right time. This is the previously elusive concept of the “segment of 1”. I often use my own situation to illustrate this point. As a married man in his mid-fifties, I am unlikely to fall into a traditional segment for purchasing child diapers (not being a mom in her mid-twenties to mid-thirties). I am, however, the proud dad of a toddler and purchase large quantities of premium diapers as a result. ML models would be able to analyse my transaction and other data to predict this shopping behaviour.
Enhancing Lead Generation and Conversion
One of the primary applications of machine learning in customer acquisition is improving lead generation and conversion rates. By analyzing data from various sources, including demographic data, financial and payment data, social media, website interactions, contact centre interaction, purchase histories and similar, ML algorithms can identify high-quality leads and predict their likelihood of conversion.
For example, by focusing on the right set of leads to improve customer conversion, CyborgIntell enabled an insurance company to improve lead conversion by 65%, simultaneously improving the ROI of the marketing campaign by 45%. In another similar example the company was able to improve sales conversion by 35%. et audience.
Optimizing the Customer Experience
It is not only the business that benefits from ML, but customers benefit too. It is much more favourable for customers to only receive offers that are relevant for them, reducing the noise, interruption, irritation and inconvenience of irrelevant offers. ML can also significantly enhance the customer journey by optimizing various touchpoints, from initial contact to final conversion. By analyzing customer interactions and behaviours, ML models can identify bottlenecks and areas for improvement in the customer journey, enabling companies to implement targeted interventions.
For instance, companies can use AI-based application dropout scores to predict which customers are likely to abandon the application process. By combining this with signals of good quality applications, businesses can prioritize high-quality leads and implement strategies to reduce dropout rates. This approach not only improves conversion rates but also reduces customer acquisition costs.
For risk-based products such as lending and insurance, ML models can streamline the process by assigning risk scores in real time to applicants. Low-risk applicants can be approved automatically, further enhancing the customer experience, medium-risk applicants may undergo deeper underwriting, and high-risk applicants can be declined upfront without the time and cost of the application process for the business and for the customer.
Reducing Customer Acquisition Costs
Effective customer acquisition strategies are not just about increasing conversions but also about reducing wasted acquisition and administration costs. Think of the bizarre situation where a lender spends time, effort and marketing costs to reach out to a potential customer, the customer takes the time to respond to the offer, complying with the comprehensive process, only to be declined for the loan! We have come across campaigns where this can apply to 90% of loan applicants!
Modern ML models should have full explainability at a model and at an individual decision/prediction level. This enables the company to implement effective treatment strategies and to target clients that are more likely to be approved.
Managing and Mitigating AI Risks
Risks of leveraging AI/ML are well documented. Risk and compliance management are therefore a crucial aspect of leveraging AI/ML, particularly in industries like finance and insurance. As a basic requirement ML models should be fully explainable. At a model level this requires clear explainability of which data features are driving the model to ensure there are no unfair drivers such a person’s race, gender or similar (or proxies for these features). Individual decisions / predictions should also be clearly explainable in real time. It is not good enough to decline an applicant because “the machine made the decision”.
CyborgIntell’s platform also has a full risk management capability within a best practice risk management framework. The platform proactively measures 60+ KPAs in real time, to mitigate the risk of model failure. In addition, a key feature of the platform is auto-documentation, which creates a comprehensive document of everything the machine has done and can be provided to an auditor, regulator or risk committee in seconds.
Another risk is unconsented use of personally identifiable information (PII). In most cases ML models can be built without PII, or at least the PII is de-identified. ML models which are built without PII can still be very predictive and can skew the results of most customer acquisition positively. There are examples where the business should ensure that appropriate customer consents are in place. These include the case of personalised marketing or the use of credit scores or similar data.
Case Studies: CyborgIntell’s AI/Machine Learning Platform
CyborgIntell, a leader in AI-driven solutions, has implemented various use cases which demonstrate the transformative impact of AI/ML on customer acquisition. Some of the case studies include:
- Sales Conversion and Value Increase (India): Improved sales conversion by 9% and sales per item value by 9%, whilst reducing customer dropouts by 8%. The combined impact of these initiatives enabled the company to reduce acquisition costs by 50% whilst maintaining sales volumes.
- Lead Conversion (UAE): Company was able to increase lead sales conversion by 90%, whilst keeping marketing costs flat.
- Telesales Lead Conversion (South Africa): An insurance provider was able to improve lead conversion rates by 92%. In addition, the number of high quality leads improved which enabled the company to increase call volumes by 50%. The overall impact of these initiatives was a 300% increase in converted sales.
These results are above the norm that can be expected, but they illustrate just how transformative the use of AI/ML can be in improving customer acquisition, customer experience and overall business performance. Typically in our experience most companies, including those already using AI/ML modelling, can expect an improvement of 10% – 20% in customer acquisition which is still a significant boost to the ROI of the campaign.
Conclusion
Machine learning is a powerful tool that can revolutionize customer acquisition strategies for companies across various industries. By enhancing lead generation and conversion, personalizing marketing campaigns, optimizing the customer journey, reducing acquisition costs, and improving risk management, ML enables businesses to attract and retain customers more effectively.
