CRM can be a huge expense – implementation, updates and training all add up – does your CRM generate optimal return on investment?
With an expanse of data, originating from sales, marketing, customer support and product development, your CRM has the potential to deliver huge business value – but only if you can make sense of all that data. Using machine learning, making sense of that data, becoming more efficient and, most importantly, pleasing customers, can now be performed at scale.
An intelligent layer, sitting on top of an existing CRM system, can extract insight from your entire dataset, thus telling the complete story – it is comprised of three stages:
- Analyse the past to understand what actions led to great outcomes, such as high customer satisfaction
- Interpret each new customer interaction and make recommendations to influence a successful outcome
- Continually update learning based on the most recent set of outcomes, thus remaining relevant without the need for manual changes and inputs
Here are five areas where machine learning can help extend the value of your CRM investment – driving efficiencies without losing the personal touch.
Gain Future Insight
CRM systems are focused on aggregating historical data. However, one of the greatest strengths of machine learning is providing a future-facing predictive view. Machine learning looks at every interaction and makes recommendations on how to next engage with a customer and achieve the best outcome.
Continually Update Process
The world does not stand still and your entire dataset will shift – with new product releases, staff turnover and customer life cycle changes, machine learning will evolve alongside. By automatically interpreting past actions, machine learning eliminates the need to manually set up and maintain rules, continually learning and making recommendations beyond the static analysis typical of a CRM.
CRM can help gather all your data into a single pot for a unified view, but it still lacks the insight into why interactions happen in the first place. Even if the CRM flags a high-risk customer, you still need to spend time researching the underlying reasons. Machine learning can help decipher the prediction – by understanding fully why a particular prediction was made, a support agent is more likely to use the information to take the correct action and drive better outcomes.
Customer Level Prediction
CRM is beneficial at reporting on the general health of all customers. However, it starts to fall apart at the individual customer level, where there may be multiple people associated with the customer. Machine learning treats each component of any interaction as a separate data point and that is its power. It can render much richer customer engagement patterns and recommend the right message, for the right person, at the right time, delivering extreme personalisation.
Analysing Unstructured Data
CRM excels at handling structured data like revenue or customer categories, but that is only one piece of the customer jigsaw. Understanding the nuances of unstructured qualitative data, such as email, response templates or meeting notes can be the key to competitive advantage. Machine learning can convert unstructured text into solid data – adding new value to an otherwise elusive email conversation between a customer and support agent. Together with the structured data already captured in the CRM this additional unstructured data becomes a powerful data element, thus driving better outcomes.
With machine learning, you have an opportunity to transform your CRM into a predictive system of intelligence that improves productivity and helps create happy and loyal customers, all the while driving more return on investment from a system you already own.
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