Azure IoT Hub Streaming Analytics Simulator

Azure IoT Hub Streaming Analytics Simulator is an application written by Manny Grewal. The purpose of this blog is to explain What, Why and How of this application.

 

What?

Streaming analytics is a growing trend that deals with analysing data in real-time. Real-time data streams have a short life span, their relevance decreases with time, so they demand quick analysis and rapid action.

Some areas where such applications are highly useful include data streams emitted by

  • Data centres to detect intrusions, outages
  • Factory production line to detect wear and tear of the machinery
  • Transactions and phone calls to detect fraud
  • Time-series analysis
  • Analogy Detection

 

Data used by streaming analytics applications is temporal in nature i.e. it is based on short intervals of time. What is happening at the interval TX can be influenced by what happened 2 minutes ago i.e. at the interval TX-2

So the relationships between various events are time-based rather than entity based (e.g. as in general Entity Relational Database based systems)

Take the scenario of a Data Centre which has two sensors that emit a couple of data streams – Fan Speed of the server hardware and its temperature.

If temperature reading of server hardware is going high, it could be related to the dwindling Fan Speed reading. We need to look at both the readings over an interval of time to establish a hypotheses on their correlation.

 

 

Why?

In order to model and work with streaming analytics it is important to have an event generator that can generate the data streams in a time-series fashion.

Some example of such generators can be vehicle sensors, IoT devices, medical devices, transactions, etc. that generate data quickly.

The purpose of this application is to simulate the data generated by those devices, it just helps you setup quickly and start modelling some data for your IoT experiments.

 

 

Main benefits of this app

1. Integrated with Azure IoT Hub i.e. the messages emitted by this application are sent to the Azure IoT Hub and can be leveraged by the Intelligence and Big Data ecosystem of Azure.

2. This app comes with 4 preset sensors

a. Temperature/Humidity

b. Air Quality

c. Water Pollution

d. Phone call simulator

3. Configure > Ready. App can be easily pointed to your Azure instance and can start sending messages to your Azure IoT Hub

4. Can be extended, if you are handy with .NET development. I have designed the app on S.O.L.I.D framework so it can be extended and customised the link to source code is below

 

How?

 

App and source code can be downloaded from my Github

 

A quick tour of the app is below

IoT Hub

 

 

Configure

The app needs to be configured with details of your Azure IoT Hub account.

The following files need to be configured

1. App.Config

2. If you are registering Devices in the Hub, then keys for the devices need to be stored in the SensorBuilder.cs

3. You may need to restore the Nuget Packages to build the application

 

Once the above three steps have been completed, you can build the application and the EXE of the application will be generated.

 

Sensor Tuning

Sensors can be tuned from the classes inheriting IDataPoint e.g. in the FloatDataPoint.cs

The following properties can be used to tune the sensors

Property Name Tuning
MinValue The minimum value of the sensor reading e.g. for climatic temperature it can be -40C
MaxValue The maximum value of the sensor reading e.g. for climatic  temperature it can be 55C
CommonValue This is the average value of the sensor e.g. for warmer months it can be 30C
FluctuationPercentage How much variance you want in the generated data
AlertThresholdPercentage When should an alert be generated if the reading passes a certain threshold e.g. 80% of the maximum value

 

Azure IoT Hub

The messages sent by the sensor simulator can be accessed in the Azure IoT Hub. Once you have configured your hub and related streaming jobs. The messages can be seen in the dashboard as below

image

 

The messages are sent in the JSON format and below is a structure of one of the messages emitted by a sensor located at Berwick, VIC


{
"IncludeSensorHeader": 1,

"MessageId": "949a3618-c4a4-42bc-9c2a-39da86aa9191",

"EmittedOn": "2017-06-30T11:13:45.3543200",

"SensorDataHeader": {
"Sensor":
"Berwick",
"DeviceId":
"G543",
"Lat":
-38.0309,
"Long":
145.3461
},
"SpecialMessage":
null,
"Readings": [

{
"ReadingValue":
27.9943523,
"MetaData":
{
"Name":
"Temperature",
"Unit":
"C"

},
"Level":
"Normal"
},

{
"ReadingValue":
49.6043358,
"MetaData":
{
"Name":
"Humidity",
"Unit":
"RH"

},
"Level":
"Normal"
}

],
"EventProcessedUtcTime":
"2017-07-01T11:26:53.1434112Z",
"PartitionId":
0,
"EventEnqueuedUtcTime":
"2017-06-30T11:13:48.4340000Z",
"IoTHub":
{
"MessageId":
null,
"CorrelationId":
null,
"ConnectionDeviceId":
"G543",
"ConnectionDeviceGenerationId":
"636297589019910475",
"EnqueuedTime":
"2017-06-30T11:13:47.5760000Z",
"StreamId":
null
}
}

Can Dynamics CRM understand images? Yes! Using deep learning.

Machine Learning is quite a buzzword these days and we have witnessed how quickly Microsoft and other vendors have made progress in this area. Just couple of years back Microsoft had no product or tool in this space and today they have closer to a dozen. Recently Microsoft has integrated Machine Learning into SQL Server and Dynamics CRM, it is slowly becoming core to its product line.

I would not be surprised if machine learning becomes a mandatory skill for most of the development jobs in the next decade.

How Image Recognition can help CRM?

Attaching documents is a common feature asked for in many CRM projects where customers can complete an application form and then upload scanned copies to support their application. Think of invoices, receipts, certificates, licenses, etc. As of now there is no way that Dynamics CRM can detect if the scan that a customer is uploading is a picture of a license, or beach or a car.

What if Dynamics CRM can detect and recognise the scanned image and tell the user that it is expecting a license not a Dilbert on the beach.

clip_image001

Source: Ol.v!er [H2vPk] – Flickr

Wouldn’t it be great?

Although there are some Image engines that can tell you what an uploaded picture contains but there isn’t any engine or tool (as per my knowledge) that can tell whether an upload document is a license or not. This is because there are only subtle differences between scanned copies of various documents.

In this blog series I will build and demonstrate an approach to have this kind of image recognition capability with our favourite Dynamics CRM and we will use a branch of machine learning called Deep Learning that is very good at tasks related to Computer Vision. I would not be delving into the concepts of Deep Learning (there are numerous posts and videos on the internet) but will try to cover the major building block in this whirlwind tour.

Australian Identity Documents

I will take a real business case which is ubiquitous in many online applications in Australia where a customer is asked to provide a scan of their Australian ID as a proof. For our blog we will use the following Australian IDs

1) Victoria Driver’s License

 

clip_image003

Courtesy: VicRoads

 

2) Australian Visa

 

clip_image005

Courtesy: http://www.thejumpingkoala.com/

3) Medicare card

 

clip_image007

Courtesy: Medicare

Note: Because of their sensitive nature I would only be exposing sample documents in this blog

The expectation is that the system can tell if the user is attaching a scanned copy of their Australian Visa when the record type is Australian Visa. So we will validate the image based on its content.

Good thing about deep learning based systems is that the detection algorithms do not rely on exact colour, resolution and placement but rather on pattern and feature matching. I got pretty good results when I built this system which I will share in later posts.

Technical Setup

Deep Learning based systems use a concept of neural networks to train themselves and to perform their tasks. There are many kinds of neural networks and the one that does the job for us is the Convolutional Neural Network. CNNs are good at image related tasks.

In order to train a CNN from scratch you need lot of hardware and computing power and I do not have that. So I will be using a partially trained network and customise it for our specific task i.e. to identify the images of those 3 types of Australian IDs.

Let us cover the building blocks of our solution

TensorFlow TM

TensorFlow is an open source framework for Deep Learning and we will be using it to train our engine.

Python

TensorFlow comes in many platforms but we will use its Python version.

Dynamics 365

Once our model is trained we will deploy it online as web service and CRM can query that. I would not be posting the integration code here as I have already posted code to integrate Dynamics CRM with Machine Learning web services in my other blog

 

Let us start by training an image recognition model that can classify an image e.g. a scanned copy and tell if it is an Australian ID e.g. driving license or visa scan, etc.

Approach

We will use an approach called Transfer Learning. In this approach you take an existing Convolutional Neural Network and retrain its last few layers. Think of it this way that you have already got a network that can detect differences of aeroplane from a dog but you need to retrain it to pick more subtle differences i.e. the difference between a scanned invoice and a scanned passport.

TensorFlow is based on the concept of a tensor which is a mathematical vector that contains the features of an image. We will grab the penultimate layer of tensors and retrain it with some sample images of a Medicare card, an Australian Visa and Victoria’s Driver license.

Once the model is trained we will use a simple Support Vector Machine classify and predict the likelihood of the uploaded image to be an Australian ID. The output of the SVC classifier will a predicted class along with a likelihood probability e.g.

(Visa, 0.83)

Model thinks 83% the image is that of an Australian Visa

(Medicare, 0.89)

89%, it is a Medicare

(License, 0.45)

45% it is a license

If the confidence percentage is low it means that image is not in the class of our interest e.g. in the last example the uploaded image is most likely not a license. As a rule of thumb, a probability of 0.80 is good mark for the prediction to be reliable.

Training Pool

Below are the screenshots of the samples that I used as a training for my image classification model. As you can see images differ in terms of angles, positioning, colours, etc. system can still learn based on important properties and disregard irrelevant properties.

Australian Visa

Training Set

clip_image008

Medicare

Training Set

clip_image009

Victoria Driver’s License

Training Set

clip_image010

Training Phase

The training procedure involves categorising all the training images into a folder which is a named after their class. As you can see in the screenshots above, the windows folders are named after the class i.e. DriversLicense, Medicare and Visa

We then iterate over all these images and pass them to the penultimate layer of TensorFlow which gives us a feature tensor (a 2048 dimensional array of that image), we then label the image with its respective class.

Support Vector Machine

Once we have the feature tensor and label of every image, our training dataset is complete and we feed it to a Support Vector Machine and train the model. To save time, I pickled the model so that it can be reused for all predictions.

I know some of this terminology may be new to you but in the next post I will explain the architecture and some sample code that generates the predictions. Then it will start falling in place. See you then.

Part 3

In the previous two instalments I have been explaining the image recognition system that I built to recognise Australian IDs and discussed how our traditional CRM can benefit from such intelligent capabilities.

In this post I will cover the Architecture and share some sample code

Architecture

clip_image012

As you can see above there are basically two major pillars of the system

A) Python

B) CRM ecosystem

Python is used to build the model using TensorFlow, then the compiled version of the trained model is deployed to an online webservice that should be able to accept binary contents like image data.

On the CRM ecosystem side, user can upload the image in a web portal or directly from CRM based on the scenario, then we need to pass it to the model and get the score.

Source Code

Below is an excerpt of the source code from one of the unit tests that will give you glimpse of what happens under the hood on Python side of the fence. This is just one class for introductory purposes, not the entire source code.

import os

import pickle

import sklearn

import numpy as np

from sklearn.svm import SVC

import tensorflow as tf

import tensorflow.python.platform

from tensorflow.python.platform import gfile

model_dir = 'inception'

def CreateImageGraph():

#Get the tensorflow graph

with gfile.FastGFile(os.path.join(

model_dir, 'classify_image_graph_def.pb'), 'rb') as f:

graph_def = tf.GraphDef()

graph_def.ParseFromString(f.read())

_ = tf.import_graph_def(graph_def, name='')

def ClassifyAustralianID(image):

nb_features = 2048

#Initialise the feature tensor

features = np.empty((1,nb_features))

CreateImageGraph()

with tf.Session() as sess:

next_to_last_tensor = sess.graph.get_tensor_by_name('pool_3:0')

print('Processing %s...' % (image))

if not gfile.Exists(image):

tf.logging.fatal('File does not exist %s', image)

image_data = gfile.FastGFile(image, 'rb').read()

#Get the feature tensor

predictions = sess.run(next_to_last_tensor,{'DecodeJpeg/contents:0': image_data})

features[0,:] = np.squeeze(predictions)

clear = '\n' * 20

print(clear)

return features

if __name__ == '__main__':

#Unpickle the trained model

trainedSVC = pickle.load(open('Trained SVC','rb'))

#Path to the image to be classified

unitTestImagePath = 'Test\\L5.jpg'

#Get feature tensor of the image

X_test = ClassifyAustralianID(unitTestImagePath)

print("Trying to match the image at path %s.....",unitTestImagePath)

#Get predicted probabilities of various classes

y_predict_prob=trainedSVC.predict_proba(X_test)

#Get predicted class

y_predict_class=trainedSVC.predict(X_test)

#Choose the item with the best probability

bestProb = y_predict_prob.argsort()[0][-1]

#Print the predicted class along with its probability

print("(%s, %s)" % (y_predict_class, y_predict_prob[0][bestProb]))

The purpose of the above stub is to test the prediction class ClassifyAustralianID with a sample image L5.jpg which is below. As we can see it is a driving license.

clip_image013

Running this image against the model gives us this output

clip_image014

It means the model says, it is 93% sure that the input image matches the Driving License class. In my testing I found anything above 80% was the correct prediction

i.e. the confidence percentage for the below images was low because they do not belong to one of our classes (Drivers License, Visa or Medicare), which is the expected output

clip_image015

Closing Notes

Image recognition is a field of budding research and getting a lot of attention these days because of driverless cars, robots, etc. This little proof of concept gave me a lot of insight into how things work behind the scenes and it was a great experience to create such a smart system. The world of machine learning is very interesting!!

Hope you enjoyed the blog.

Use Machine Learning to predict customers you might lose – Part 4

So far we have seen how a Dynamics CRM integration can be connected to Azure ML to receive the predictions. Once we got the integration going there is no dearth of possibilities. You may like to build an alert / flagging functionality that can alert a Customer Service rep to contact a customer if their predictors are indicating that they might churn. You may incorporate predictions into exec reporting so that the execs are aware of the churn trends and make decisions to minimise churn.

Insights

One of the things I discussed at the start of this series was to be able to get some insights into the key drivers of customer churn e.g. how do you know which features are most likely to cause churn. Answering such questions begins with analysing your data, few starting points can be

1. From your data find out what fields change with respect to the Churn variable e.g. does the churn rate increase as the income of the customer goes up or is it dependent on their usage?

There are measures like correlation, covariance, entropy, etc that can help you answer such questions.

2. Find the distribution of your data and identify any outliers e.g. check if there is a skew in the data or if the classes are unbalanced. You may need to apply some statistical techniques like variance, standard deviation to have a better platform to delve into some of these insights.

Azure Machine Learning does provide some modules straight off the bat that can make the job easier e.g. it has the following modules

Compute Elementary Statistics

Compute Linear Correlation

Getting advanced insights can be tricky based on your algorithm or setup of the experiment (project). But there are ways e.g. with bit of a Python code you can produce a decision rule tree below. The last label in the box class= {LEAVE, STAY} tells us if the customer will churn based on what path they fall under

clip_image002

Above is the automatically generated insight that tells us that overage is most important variable in deciding customer churn. If overage exceeds 97.5 then a customer is more likely to churn, this does not mean that every customer whose overage is more than 97.5 will churn nor does it mean that whose overage is less than that will stay. It is just that Overage is the strongest indicator of churn based on our data.

We can even derive decision rules from insights like these e.g. customers with overage less than 97.5 and Leftover minutes less than 24.5 minutes are most likely to stay. On the contrary customers with overage more than 97.5 and average income more than $100059.5 are most likely to leave.

Here is another one that shows the impact of House Value, Handset Value and other features on the churn

clip_image004

Once decision rules have been identified based on the above insights, policies can be made to retain such customers who are at risk of churn e.g. give them discounts, offer them a change of plan, prize them with loyalty offerings, etc.

Where to from here?

Hopefully by now you appreciate the potential of machine learning and recognise the opportunity it provides when it is complemented with traditional information systems like CRMs, ERPs and Document Management systems. The field of machine learning is enormous and sometimes quite complex too as it based on scientific techniques and mathematics. You need to understand and lot of theory if you need to get into the black box i.e. how machine learning does what it does?

But great thing about using Azure Machine Learning suite is that it makes entry into machine learning easier by taking care of the complexities and giving you an easy-to-understand and easy-to-use environment. You have full control over the data structure and algorithms used in your project. It can be tuned as per the needs of your organisation to receive the best possible results.

For example you can tune the example I provided in the following different ways

1. Rather than going with Random Forest you can choose Support Vector Machines or Neural Networks and compare the results.

2. You are not restricted to Javascript, you can call the web services from a plugin. That way in a data migration scenario, while you are importing data you can set the prediction scores as the data is being imported

3. You can also change the threshold of confidence percentage to ignore the predictions score where confidence is less than a certain amount.

So there are lot of possibilities. Hope you enjoyed the series.

Happy CRM + ML!!

Use Machine Learning to predict customers you might lose – Part 2

Continuing our journey from the previous post where we defined the issue of churn prediction, in this instalment, let us create the model in Azure Machine Learning. We are trying to predict the likelihood of customer’s churn based on certain features in the profile which are stored in the Telecom Customer entity. We will use a technique called Supervised Learning, where we train the model on our data first and let us understand the trends before it can start giving us some insights.

Obviously you need access to Azure Machine Learning, once you log into it, you can create a new Experiment. That gives you a workspace designer and a toolbox (somewhat like SSIS/Biztalk) where you can drag control and the feed into each other. So it is a flexible model and for most tasks you do not need to write code.

Below is a screenshot of my experiment with toolbox on the left

image

Now machine learning is something which is slightly atypical for a usual CRM audience, I would not be able to fit full details of each of these tools in this blog but I will touch on each of these steps so that you can understand at high-level that what is going on inside these boxes. Let us address them one by one

Dynamics CRM 2016 Telecom

This module is the input data module where we are reading the CRM customer information in the form of a dataset. At the moment of writing the blog, there is no direct connection available from Azure Machine Learning to CRM online. But where there is a will, there is a way i.e. I discovered that you can connect to CRM using the following

1. You schedule a daily export of CRM data into a location that Azure Machine Learning can read e.g. Azure blob storage, Web Url over Http

2. You can write a small Python based module that connects to Dynamics using Azure Directory Services, the module can the pass the data to the Azure using a DataFrame control

From my experience having an automatic sync is not important from Dynamics to Azure ML but it is important the other way round i.e. Azure ML to Dynamics.

Split Data

This module basically splits your data into a two sets

1. Training dataset – The data based on which the machine learning model will learn

2. Testing dataset – The data based on which the accuracy of the model will be determined

I have chosen stratified split which ensures that the Testing dataset is balanced when it comes to classes being predicted. The split ratio is 80/20 i.e. 80% of the records will be used for training and 20% for testing.

Two-class Decision Forest

This is main classifier i.e. the module that does the grunt of the work. The classifier of choice here is a random forest with bootstrap aggregation. Two-class makes sense for us because our prediction has two outcomes i.e. whether the customer will churn or not.

Random forests are fast classifiers and very difficult to overfit, rather than taking one path they learn your data from different angles (called ensembles). Then in the end the scores of various ensembles are combined to come up with an overall prediction score. You can read more about this classifier here.

Train Model

This module basically connects the classifier to the data. As you can see in the screenshot of the experiment I posted above there are two arrows coming out of Spilt Data, the one of the left is the 80% one i.e. the training dataset. The output of this module is trained model that is ready to make predictions.

Score Model

This step uses the trained model from the previous step and tests the accuracy of the model against our test data. Put simply, here we start feeding the data to the model that it has not seen before and count how many number of times the model gave the correct prediction Vs wrong prediction.

Evaluate Model

The scores (hit vs miss) generated from the previous modules are evaluated in this step. In Data Science there are standard metrics to measure this kind of performance e.g. Confusion Matrices, ROC curves and many more. Below is the screenshot of the Confusion matrix

clip_image002clip_image004

I know there is a lot of confusing details here (hence the name Confusion Matrix) but as a rule of thumb we need to focus on AUC i.e. area under the curve. As shown in the results above we have a decent 72.9% of the area under the curve (which in layman terms means percentage of correct predictions). Higher percentage does not necessarily equate to a better model, more often than not a higher percentage (e.g. 90%) means overfitting i.e. a state where your model does very well on the sample data but not so well on the real-world data. So our model is good to go.

You can read more about the metrics and terms above here

In the next blog we will deploy and integrate the model with Dynamics CRM.

Use Machine Learning to predict customers you might lose – Part 1

“Customer satisfaction is worthless. Customer loyalty is priceless.”

Jeffrey Gitomer

Business is becoming increasingly competitive these days and getting new customers increasingly difficult. The wisest thing to do in this cut-throat scenario is to hold on to your existing customer base while trying to develop new business. Realistically, no matter how hard it tries, every organisation still loses a percentage of its customers every year to the competition. This process of losing customers is called Churn.

Progressive organisations take churn seriously, they want to know in advance that approximately how many customers they are going to lose this year and what is causing the churn. Having an insight into customer churn at least gives an organisation an opportunity to proactively take measures to control the churn before it is too late and the customer is gone.

Two pieces of information help the most when it comes to minimising the churn

1. Which customers are we going to lose this year

2. What are the biggest drivers of customer churn

The answers to the above questions often are hidden in the customer data itself but revealing these answers out of swathes of data is an art – rather a science called Data Science. With recent advances in some practical Data Science techniques like Machine Learning getting these answers is becoming increasingly feasible even for small scale organisations who do not have the luxury of a Data Science team. Thanks to services like Azure Machine Learning which are trying to democratise these advanced techniques to a level such that even a small scale customer can leverage them to solve their business puzzles.

Let me show you how your Dynamics CRM can leverage the powerful Machine Learning cortex to get some insights into the key drivers of customer churn. In this blog series, we will build a machine learning model that will answer the questions regarding churn. I have divided the series into four parts as below

Part 1 – Introduction

Part 2 – Creating a Machine Learning model

Part 3- Integrate the model with Dynamics CRM

Part 4 – Gaining insights within Dynamics CRM

I will take the example of a Telecom organisation but the model can be extended any kind of organisation in any capacity and from any industry.

Scenario

Let us say there is a Telecom company called TelcoOrg which uses Microsoft CRM 2016 and they have an entity called Telecom Customer that stores their telco profile. Such profile may include some data regarding a customer mobile plan, phone usage, demography and reported satisfaction.

Understanding the features

In data science projects, it is crucial to understand the data points (called features). You need to carefully select those features that are relevant to the problem at hand, some the features also need to be engineered and normalised before they start generating some information gain. Below are the features that we will be using in this scenario of our Churn problem

Let me quickly explain the features so that we can understand the information contained in them

Feature

Information

Has a College degree?

If the customer has a college degree

Cost price of phone

Price of the customer’s phone as per the plan/contract with TelcoOrg

Value of customer’s house

Approximate value of customer’s house based on Property Information websites like RPData, etc.

Average Income

Yearly income as reported by the customer

Leftover minutes per month

Average number of minutes a customer normally does not use from monthly quota

Average call duration

Average duration of calls made based on call history

Usage category

The category customer’s phone usage falls under as compared to other customers e.g. Very High, High, Average, Low or Very Low

Average overcharges

Average number of times a customer is usually overcharged per month

Average long duration calls

Average number of calls a customer usually makes per month that are more than 15 minutes long

Considering change?

How customer responded to TelcoOrg’s survey when asked if they are considering changing to another provider e.g. Yes, considering, Maybe, Not looking, etc.

Reported level of satisfaction

How customer responded to TelcoOrg’s survey when asked if they are satisfied with TelcoOrg’s service e.g. Unsatisfied, Neutral, Satisfied

Account Status

Current Status of the customer (i.e. if they have left or are currently Active)

Predicted Churn Status

This is the predicted status returned by the Azure Machine Learning Web Service

Prediction Confidence Percentage

This field means how confident Azure Machine Learning Web Service is regarding its prediction. A threshold can be set to only consider the predictions above e.g. we can say, take only those predictions where WS is 70% confident.

The screenshot below shows the information from the Telecom Customer entity. The section highlighted in blue are predictions based on Azure Machine Learning web services. Whenever any of the fields on this CRM form changes, the WS updates its prediction scores based on the record’s data. I will provide details later during the series as to how I built this integration.

clip_image001

Below is a screenshot of some of these records

clip_image003

We will achieve the following business benefits using Azure Machine Learning

1. Customers who are predicted to be at a higher risk of leaving (churn) can be flagged, so the customer retention teams can get it touch with them to proactively address their concerns in a bid to retain them

2. Find what factors affect churn the most i.e. out of all these fields we will determine what fields are more likely to make a leave than others

3. We will also get insights into some business rules that dictate churn i.e. the drivers

I hope you understand the problem now and find it interesting so far. Let us meet in next part of the blog where I will show how a machine learning model is created.

Dynamics CRM – Prediction based routing

Imagine a day at work of a front desk staff who is handling the support mailbox or reception of any mid-size organisation. It is not uncommon for them to receive hundreds, if not thousands of email and phone enquires everyday.

If this organisation happens to be using Dynamics CRM, then every enquiry is usually handled as below

  1. Read the description of the enquiry
  2. Understand
  3. Determine what team/department the query belongs to
  4. If there are multiple members in that team, then find who is best suited to answer it
  5. Assign it to that person

Move to the next enquiry. Repeat 1 to 5 above…. hundreds of times.

Now imagine the time spent on every enquiry to perform steps 1 to 5. A fair guess – it can easily take 10 minutes to grasp, digest and route the query.

Realistically, for most queries there is often a support rep matching ritual, back and forth, something like

Hey, who do you think this should go to?

Oh sorry! so it was meant for Helen, no worries I can assign to her

Have you worked on this kind of stuff earlier?

When will you get free to look at it, customer needs an answer today

 

Capture

Courtesy: http://www.glasbergen.com

We have already spent 20 minutes and the ticket has not even landed on the support rep’s desk yet !!

Well – time is money. If we can find a solution to save this time, its a great return on investment.

Supercharge your Tier 1 Support

Through this blog series, I will try to explore a solution to this problem using Machine Learning. We will automate steps 1 to 5, full automation.

A machine algorithm will predict

  • Which team does this query belongs to?
  • Which agent will get free first and which agent is best-skilled to answer this query

And machine would not take 20 minutes to decide, it will take 20 seconds

 

Scenario

Let us layout a scenario

A big advisory firm that uses Dynamics CRM  offers many kinds of services to its clients. They have professional advisors on their team who can answer queries across of the range, no matter if they are tax enquires, investment or even medical.

Each team – tax, investment and medical has a range of support reps available to handle enquires.

Traditionally, Tier 1 staff created support cases upon receiving customer enquires and assigned them to the relevant support rep by following steps 1 to 5 described at the start.

Upon assignment, Support Rep gives an ETA before working on the query and system tracks how time support rep actually spent.

We will also track some other parameters which we can be leveraged by the ML engine.

 

 

Machine Learning Algorithm

The Machine Learning approach will tackle this situation as shown below

1. ML engine will train itself by synthesising content and correlating parameters that belong to a category

2. ML engine will be deployed as a web service (compiled model) to be consumed by Dynamics CRM

3. It will start predicting what category the enquiry is after reading, tokenising and tagging the content

4. Once it has known the category, it will then find who is best suited to answer the query using parameters like

  • Which customer support rep will be the earliest to get free to look at this
  • Which customer support rep is generally good at these kinds of queries

 

Techniques

We will see and the use the following machine learning techniques to build the smarts

  • Tokenisation & Semantic Analytics using Natural Language Processing
  • Support Vector Machines
  • Inverse Document Frequency (TF-IDF)

 

See you in the next post

Dynamics CRM – Find similar customers using Machine Learning

In the previous blog, we used Machine Learning inside Dynamics CRM to add value to our customer records by getting a quick health check of how customers are doing based some measurable data points. We used supervised learning, a technique that involves training your machine first, and then deriving your predictions based on the trained model. In this blog, we will use another technique – unsupervised learning. This technique is often used to determine similarity between records, categorise them into clusters and other scenarios which involve correlation of records. We will use unsupervised learning to solve a shortcoming that had existed in Dynamics CRM for a decade i.e. to match (and detect duplicates) records based on a semantic match.

 

This is a very common requirement in Dynamics CRM when you need to cleanse your data and get rid of duplicates with similar sounding names. CRM does have a duplicate detection wizard but that doesn’t address this problem because it cannot do fuzzy match or a semantic match. I have seen many situations where hundreds (even thousands) of records are distributed among various team members for them to fix by identifying duplicates manually. Sounds familiar?

Capture

Courtesy – boredportal.com

 

Let us put some intelligence in Dynamics CRM to save us from the wrath of the painful manual work.

 

Problems Solved

 

We will solve the following problems when it comes to matching records

  • Juxtaposed word sequences e.g. it can match Manchester University to University of Manchester and Socceroos Australia to Australian Association of Socceroos
  • Takes are of little punctuation and abbreviation tidbits e.g. match Manchester University to Manchester Uni or Manchester’s Univ or Man. Utd. University
  • Covers spelling mistakes, similar sounding names e.g. match Scot’s And Christina to Scott & Kristina Corp
  • Phonetic match and verb forms e.g. match Richtie Rich to  Rishi Richest

 

 

Matching Accounts

So this is how the solution works inside Dynamics CRM.

A web resource is added to the Account form called Similar Accounts  that lists other accounts with similar names and their matching score e.g. 100 for a perfect match and 60 for partial match. The threshold can be adjusted to pick only closer results. Below are some of the screenshots from my Dynamics CRM where I have applied this algorithm. I have kept it simple as the focus is to demonstrate the matching engine rather than look and feel.

 

 

Similar Account2

 

 

Similar Account3

 

 

Similar Account4

 

Similar Account1

 

Powered by Machine Learning Algorithm

This solution is built using Python and uses a Machine Learning algorithm called Levenshtein Distance to determine the similarity between two records. I have built a package around this core Python library and integrated it with Dynamics CRM. The package is hosted as a Flask web service that communicates with Dynamics CRM using Json.  More details of the Python package are here