Machine learning is working smarter, using data to interact with customers in different ways. Machine learning analyses your data to help your business to respond using experience and recommendations.
I was reading my colleagues post on Machine learning CRM 2016 Prices + Machine Learning = Profits & Retention and this article – why salespeople need to develop machine intelligence
Companies are focused on capturing new business they often forget to look at existing customers, how they can improve the relationship and improve selling.
You already have a great relationship with an existing customer so a lot of the hard work is done, Machine learning rise prices or offer other products .
When ever I think of Machine learning I think of Amazon’s recommendations, you brought this book
An example of Machine learning is Amazon’s recommendations, you I brought
What is Machine learning?
Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible; example applications include spam filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), search engines and computer vision.
The description is why it can be difficult for people to understand what machine learning is and how it will be used in the future.
This blog post describes machine learning – machine learning theory an introductory primer
There are two main types of machine learning
- Supervised machine learning: The program is “trained” on a pre-defined set of “training examples”, which then facilitate its ability to reach an accurate conclusion when given new data.
- Unsupervised machine learning: The program is given a bunch of data and must find patterns and relationships therein.
These blogs show examples of Machine learning
- Practical machine learning problems
- Real world examples of machine learning
- A Gentle Guide to Machine Learning
Examples of machine learning
- Sentiment on tweets
- Adds on Amazon/Facebook
- Self driving cars
- Imaging tagging in Facebook
- Text analysis in emails
The examples help to understand what machine learning does, it can crunch large amounts of data looking for patterns you have trained it identify.
Machine Learning and Microsoft Dynamics CRM
- How Microsoft is transforming CRM with machine learning
- 6 Ways Machine Learning Can Generate More CRM Value
- Microsoft Dynamics CRM 2016 Bolsters Machine Learning Capabilities
- DYNAMICS CRM: USE MACHINE LEARNING TO CALCULATE HAPPINESS INDEX
How will machine learning change CRM
The most effective environment for machine learning is to analyse activities which happen thousands of time.
Machine learning can analyse the data and learn, the more data the better. The rules apply for Machine learning
- Good data in, good data out
- with machine learning think
- lots of good data in, useful analyse out.
Amazon recommendations learns from the buying patterns of thousands/millions of customers, the crunching of lots of data enables Amazon to make useful recommendations and not annoy customers with annoying recommendations.
Recurring activities and data do we get in Dynamics 365
Where will the opportunities for machine learning occur in Dynamics 365
Product recommendations – Lead generation
Customers who buy this product are usually interested in this other product.
One powerful selling points of Microsoft Dynamics 365is offering the potential for customers to use their existing data to increase sales and increase selling effectiveness. If a company could focus on quality leads, focusing on selling products to customers who are interested in those products, it can sell more.
This would allow sales teams to time effectively, selling to existing customers.
Machine learning could automate suggests on what customers would want specific products. Machine learning gives this knowledge to all the sales people not just experienced sale people
Customer service sentiment
Social engagement has machine learning in its core functionality. You can train the social engagement to recognise sentiment in tweets and blog posts. You can create cases based on tweets with negative sentimen
Lead Scoring + Marketing
Machine learning can do customer segmentation, sorting and organising customers. This could lead to targeted interaction with customers, working smarter not harder.
This article suggests three areas around products
new customers – machine learning can help you find the right product for the right customer. Machine learning to analysis who you sell to and make recommendations on the target audience.
Retain customers – Customer service
Respond quicker to tweets and emails by using Machine learning to identify problems with sentiment in communication (SMS, tweets, Facebook, email)
Sell to existing customers
Machine learning plus automation = Awesome
- Not forgetting to do part of the process
- users not needed to do part of the process
The potential of workflows to save time and add value is obvious, the difficult part is mapping out the processes. Processes having three paths
Don’t just test the happy path
Workflows need inputs to trigger them, this can change of status, an email coming in.
Machine learning is an informed assistant, it analyses repetitive actions and uncover patterns and successful strategies. This learning is what humans called experience e.g. learning what not to do and what to do. We can tell machine what’s good, what’s not and then it goes through the data and applies the criteria we have given it.
This could simulate an experienced person going through the data and advising what they think we should do. The benefit with machine learning is it can sift through data and advise what records to focus on.
The future of Dynamics 365 and machine learning
Machine learning in Dynamics 365 will need developers to understand what machine learning is and how it works. Most people have heard of machine learning but few people understand how to use it with Dynamics 365 or have experience of implementing it.
Once the Microsoft Dynamics 365 industry gets used to machine learning, gets practical and theoretical knowledge, Solution architects will become adept at seeing where machine learning can be used.
an opportunity for businesses to change and adapt their processes to use machine learning.
It will not be a smooth a ride, taking time for Microsoft Dynamics 365 professionals to understand machine learning and sell it to customers. Selling the business benefits of machine learning is difficult because customers can’t see what they will be getting. It‘s not a dashboard, a portal or something visible.
Marchine learning is a long term investment with potentially dramatic benefits
picture from here