Improving customer acquisition with hyper-personalized user experiences.
Personalization is a huge buzzword in the financial services industry right now. Organizations that continue to drive a non-personalized approach to customer acquisition are losing customers, market share and revenue.
The solution? To create hyper-personalized user experiences that deliver increases in topline revenue, profit margin and growth. But marketing teams that are looking to increase personalization and boost conversion rates while lowering acquisition costs need to maximize the utilization—and understanding—of their data.
Find out how it works
Book a meeting today and we’ll show you how you can make the most of your data. We can also use federated machine learning to securely evaluate your organization’s data, identify the best approach for monetization and improve your data science and AI capabilities.
Our step-by-step solution
At Nanogon, we’ve developed data science capabilities to build greater customer understanding and hyper-personalized user experiences. Through our step-by-step process we’ll build these new capabilities into your existing marketing stack to ensure you’re delivering the highest ROI and value for your organization.
1. Business understanding
We begin by evaluating your customer understanding, the state of your analytics, your ad targeting and how they align with your broader business goals. We’ll also evaluate your business strategy and assess your current goals and KPIs.
2. Data understanding
Next we assess the availability and utilization of your data. We work out where you’re capturing data and where you have untapped data sources. We’ll also lift the lid on your tech to see what it’s capable of and where it might need upgrading.
3. Data preparation
Now we have your data, we prepare it to enable data mining and feature engineering. We do this by merging your data into a central repository (BigQuery) and applying advanced statistical analysis and visualization techniques to maximize utilization and monetization of the data.
4. Modeling
Now we can begin modeling. We experiment rapidly through an advanced, highly automated MLOps environment to build, train and test many models at speed. enabling rapid experimentation of model architectures and data features.
5. Evaluation
With deep understanding of your business goals, we identify the model or ensembled models that provide the greatest business value. We evaluate how often the model should be monitored and updated to provide frequent incremental improvements in performance, account for bias and explainability in our evaluations and identify retraining frequencies that account for model drift.
6. Deployment
We utilize the latest software engineering technologies that best fit your business to deploy models that support resilient operations at scale. Your business grows and profits increase as you attract new customers.
7. Knowledge application
Our data strategy team advise you on ongoing business and workflow transformation and identify areas in your business and workflows capabilities that can be further integrated to boost ROI and business value.
Every year banks are investing more and more time and money into maintaining their legacy core. And new regulations, rising customer expectations and open banking are stretching these systems to the limit.
But upgrading isn’t easy; it’s complex, costly and risky. And most transformation programs fail.