CRM

Advanced Analytics (Machine Learning)

Data has become a commodity. The more we store, the more challenges it brings.  However, one of the key strengths emerging from captured data,  is  what we learn from it.  Companies are surviving and experiencing continued, sustained, growth by applying the right tools to effectively manage the data captured. For instance, Dynamics 365 CRM “out of the box”,  meaning without any development effort, provides cross-selling and upselling functionality. It suggests a product, based on the product’s relationship structure stored in the system.  Hence, with applications like Dynamics 365, your agents will surely be asking: “do you want fries with that?”; shortly followed by: “do you want to go large?

On the other hand, Advanced Analytics,  is what puts your organisation leaps and bounds above its competition. To do this properly, you must have the right data set, and must also know the traits required to get the most value out of your data.  For example, Azure Machine Learning (ML) service, a part of Cortana Intelligence, can process significant amounts of prepared data, uncovering patterns and trends in your customer interaction history. To do this effectively, #ML uses techniques from statistical analysis to build a coded algorithm, called a Model. This model is used on data to identify traits and can be plugged into an application, like Dynamics 365, to provide some real insights.

 

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This form of advanced analytics, surfacing on your CRM system, delivers the right kind of customer-centred information, adding a new dimension to your decision-making process.

For example, banks may use machine learning to identify fraudulent transactions.  To achieve this, data is prepared to isolate traits such as location, transaction amount, time and type of transaction. ML  can analyse these traits from customers transaction history to build a model, that can learn the general trend or pattern of a normal transaction. This model can be  used to deduce the probability of a transaction being fraudulent.

#ML will not only increase the value to your business; it will also enable you to work more in line with your customers’ behaviour, understanding your customers’ demands  and reveal some interesting “Relationship Insights.”

 

 

Consider the amount of data harvested by companies like Google and Smart Phone providers, I shudder to think, what data do they hold? Moreover, What kind of insights can they gain about our behaviour, using this data? Who has access to that data and how are they sharing that information?

These questions raise further concerns around data protection,  data privacy and more importantly, challenge the authenticity for consent of data processing. This use of data also raises legal and moral debates surrounding data privacy, especially, when data is transmitted across international boundaries.

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