Trends change and customers can fall in and out of love with brands for many reasons. Staying ahead of customer preferences and understanding what drives behavior change or churn for your brand underpins your customer centricity strategy. Predictive or propensity modelling helps you understand what your customers are most likely to do next.
Models can vary in complexity. The more sophisticated models can predict future behaviour based on analysis of multiple historic features using a variety of different machine learning techniques.
Traditional, more descriptive approaches summarise past experience and look for key single variables that can explain a behaviour such as churn. Our approach, however, considers the relationship between 100's of variables to predict a customers likely behaviour.
By knowing what your customers are most likely to do you can optimise all areas of your business from acquisition, conversion and on boarding through to upsell and retention. The predictive nature enables you to plan for and execute actionable strategies that deliver on your growth or sales objectives, be it through launching sophisticated email sequences, online experiences or simply optimising your operations to ensure you have everything in place when the customer walks through your door.
The Data Science team at Beyond Analysis have a strong track record of developing and maintaining highly effective models for many business requirements from estimating share of wallet/potential value, likelihood to convert or to churn, next best product recommendations and predictive response models to power marketing and promotional campaigns.
Our modelling service
AI and Machine learning (ML) models play an increasingly significant role in the business decisions of companies today. As the use of these techniques grow in your business it is important that you also consider some key issues around governance of data and your AI.
In particular with predictive modelling you need to be cognisant of two important issues:
How do you ensure your models are delivering ethical and explainabel outcomes. Historical data can often contain the recipe for creating unintentional bias that unwittingly leads to your business tretaing people differently or unfairly. Our white paper, Bias in Machine Learning and AI tackles this issue.
The build and maintenance process of the AI models themselves is an additional important consideration. You need to consider what safety net you have in place to monitor the performance and degradation over time of your models.