‘CRM’ is an acronym meaning ‘customer relationship management.’ Customer relationship management describes the way an organisation administers its connections with various customers and is not, per se, a technological intervention; all businesses must manage relationships with customers. However, when we speak of ‘CRM systems’ we mean a specific kind of technology: a collection moving parts that does the heavy lifting of collecting and keeping data that relates to customers and how they engage with the organisation. This data is drawn from every interaction the organisation has with its customers and prospects, which ranges from clicking on a link on a webpage to actually making a purchase and beyond.

Data poisoning refers to an attack intended to corrupt the training data used to train a machine learning (ML) system. ML systems rely on the data they’re trained upon to generalise their responses to novel information. The training data is curated to present the model with an appropriate sample. When that data is compromised, the ML system is also compromised.

Large businesses across the globe have enjoyed productivity gains through their applications of machine learning and similar technologies. Lately, their efforts have turned to exploring the potential of generative AI. Unsurprisingly, small and medium organisations are keen to follow suit and reap these possible benefits — ideally before their competition does.

Different platforms will have an edge over others in certain areas. This makes a ranked list unhelpful, but matching CRM to business needs can help businesses prioritise which options to investigate. 10 of the most common CRMs we encounter, separated by use case.

While data driven operations have become more and more accessible to small and medium enterprise over the past years, that same ease of access has left a substantial number of organisations with stored personal data, serious privacy obligations and limited resources to keep up with the changeable privacy landscape.

High-quality data is vital for training any machine learning system. Data readiness is an ongoing process. Let us explain.