5 AI Technologies that Transform CRM Value and Capabilities
- CRM software with Artificial Intelligence (AI) enables CRM to predict customer behaviors, create customer insights, recommend offers, content, products or next best actions, and perform other capabilities not possible without AI.
- Artificial Intelligence isn't a single technology.
- For companies seeking to leverage AI with their CRM, there are five AI technologies to understand and consider.
If you're thinking of adopting Artificial Intelligence as part of your CRM software application, understanding the capabilities, relationships and differences among the five most used AI technologies will accelerate your performance results.
AI is most simply described as intelligence delivered by machines. It is intended to mimic human-like abilities to investigate, reason, deduce, communicate and self-improve. It is frequently used as an umbrella term that may include several other cognitive technologies.
AI is commonly created from a programmatic algorithm, which is essentially a mathematical equation that uses Bayesian networks, decision trees, regression analysis other similar statistical models. Natural language processing (NLP) text and speech tools can make these complex models easily accessible to CRM software users.
AI adds value to CRM because its algorithms can sift through large volumes of customer data, identify important information and make recommendations that achieve outcomes important to the customer and the company.
Machine learning is a subset of artificial intelligence.
It's the ability of machines to learn by themselves and without the intervention of a programmer or other person. Machine learning mathematical models are built on data sets, known as training data, and learn to make improved decisions based on experience and without being explicitly programmed to do so.
The below model show the Machine Learning process most often used by Johnny Grow.
Here's an example to share the difference between AI and machine learning. To calculate what offer will achieve the highest conversion rate among customer segments, a business analyst or developer would create a marketing AI algorithm with stated rules that identify the data to consider and how each data element should be weighted in the offer conversion calculation.
With Machine Learning, the data sources or factors are still defined by the business analyst, a process sometimes called feature engineering, but the algorithm automatically chooses the optimal way to combine these factors. Machine Learning will experiment, learn and self-improve the factors over time. Machine Learning uses inferred rules which are learned from data and experience. More so than AI, Machine Learning can adapt to new data, features or challenges without coding or human intervention.
For complex calculations it is more effective to train a machine to develop its own algorithm rather than try to specify every instructional step that may be needed.
Arthur Samuel while with IBM actually coined the term, and defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed." Interestingly, IBM has moved to the replacement term of cognitive computing while most other vendors, including CRM software publishers such as Microsoft Dynamics and Salesforce continue to emphasize and bring distinction to their use of machine learning. Microsoft AI is built heavily on Azure Machine Learning while Salesforce AI uses AutoML and the closely related TransmogrifAI AutoML library.
Deep learning is a subset of machine learning.
The prefix of deep indicates the learning is derived from layered artificial neural networks, which are essentially layered networks of algorithms constructed in a hierarchical function and enable machines to process data in a nonlinear fashion.
This makes deep learning more self-adaptive and more closely imitates the workings of the human brain in processing data and making decisions.
We often deploy deep learning when we're looking for time-series patterns, antipatterns, or the absence of data. Deep Learning is ideally suited to find hidden patterns within data, and correlate them with other patterns, to produce more sophisticated decision rules.
Deep Learning is also ideal for unstructured data such as images, audio and video.
A CRM example for deep learning may be to calculate sentiment analysis from unstructured data to tag or classify customer cases, extract data from a social network to quantify customer feedback for innovation purposes, or measure customer affinity for customer segments or individual customers.
Deep learning is best used for highly complex models built on large data sets where recommendations may not be absolute. This cognitive technology can tackle ambiguous, uncertain and even conflicting data, questions and problems especially well. Machine Learning can weigh conflicting evidence and recommend an answer that is 'best' rather than 'right'.
Automated Machine Learning
CRM vendor Salesforce has elevated Automated Machine Learning, or the term AutoML, as its Einstein AI uses AutoML and the TransmogrifAI AutoML library.
AutoML is an end to end, data-to-insights AI asset portfolio that includes everything from raw datasets to packaged machine learning algorithms. It's intended to bring these technologies to less technical people and accelerate AI and machine learning deployment.
To simplify AI and machine learning implementation within CRM software, Salesforce makes TransmogrifAI (pronounced trans-mog-ri-phi) available as a packaged and semi-automated machine learning library for structured data, that is used in concert with the Salesforce Einstein AI platform. TransmogrifAI is an Open Source AutoML.
Natural Language Processing
And finally, Natural Language Processing (NLP) is the technology that bridges human-to-machine communications.
NLP enables computers to read text, hear speech and interpret human communication for processing by AI, machine learning and deep learning technologies.
Because NLP is a part of Artificial Intelligence that allows computers to read, listen, decipher, understand, and make sense of human languages, it is the most common interface for users to ask questions and AI to deliver responses. This makes AI extensible to the masses. Anybody that can ask a question can get a data-driven response.