Reducing Amazon Connect Telephony Costs by 46% while Improving Caller Experience

The “Call Me” concept isn’t new but it’s low-hanging fruit that many don’t take advantage of. Using Amazon Connect, we’ll create a simple UI to improve the caller experience while saving 46% on our telephony costs (assuming we’re making US-destined calls with a US East/West instance) by diverting inbound toll-free calls to outbound DID calls. This is an extension of the “Placing Outbound Calls Using Amazon Connect API” post I did a couple months ago. That post should be your starting point if the code examples below aren’t lining up for you.

The Benefits

The result of a “Call Me” UI is a streamlined caller experience whereby the point of conversion (whether that’s a sale, lead, support request, or other) is merged with a “Call Me” experience that allows you to control the population they speak to and how they get to that population. Beyond the caller experience side (where they benefit from not having to repeat their issue multiple times, not losing their self-service history once they contact, etc), there’s a financial benefit (at least with Amazon Connect). As the Call Me experience is outbound and DID dialing, the costs per minute are ~46% lower than inbound toll-free dialing:

Example based on 10,000 inbound TFN dials per day. This assumes US-bound dialing with US east/west instance types.

Beyond the immediate telephony cost savings and user experience improvement, there’s also the added benefit of transfer reduction and better staff tiering as you know the customer-selected issue before they call (and can route to the correct population/tier based on that issue selection). Additionally, there’s likely a reduction in caller identification, authentication, etc. It’s a win-win that takes very little effort to implement.

What we’re doing

  1. Creating a simple form to allow the customer to enter their phone number and also pass some basic contextual attributes that we’ll present to the agent.
  2. Setup a contact flow to deliver a custom greeting based on contact attributes we pass via the outbound call.
  3. Placing an outbound call to the customer.
  4. Surfacing the contact attributes to the agent via the Streams API (assumes you already have this installed).

You can download the full demo here.

Caller Experience Demonstration:

Agent Experience Demonstration:

Step 1: Creating the “Call Me” UI/Form

To make this look a bit spiffier than just generic forms, I’ll use the Cerulean Bootstrap theme.
We’ll include hidden fields to mimic the following attributes:

  • Last page the user was on before trying to contact
  • The authentication status of the user (if they’re authenticated, no need to go through this step on the call)
  • The VIP status of the user (are they searching for expensive items, a very loyal customer, etc?)

And we’ll surface the following fields to the user to verify accuracy and collect additional information up front:

  • Their phone number/the number we should call
  • The issue they’re calling about
<form id="callMeForm" method="POST" action="handler.callme.php">
<input type="hidden" value="" id="lastPage" name="lastPage">
<input type="hidden" value="false" id="authenticatedStatus" name="authenticatedStatus">
<input type="hidden" value="true" id="vipStatus" name="vipStatus">
  <div class="form-group">
  <label for="issueSelected">What can we help you with?</label>
  <select class="form-control" id="issueSelected" name="issueSelected">
    <option>Purchase Experience</option>
  <div class="form-group">
  <label class="col-form-label" for="phoneNumber">We'll call you at:</label>
  <input type="text" class="form-control" placeholder="+15555555555" id="phoneNumber" name="phoneNumber">
  <button type="submit" class="btn btn-primary">Call Me Now</button>

Step 2: Placing the outbound call

Starting with a blank contact flow, we’ll set it to:

  1. Detect the value of the “VIP” attribute we set
    1. If VIP=true, we’ll play a special prompt
    2. If VIP<>true, we’ll play a standard, more generic prompt.
  2. Locate the caller’s name (via stored attribute) and pass it to the contact flow to greet the caller by name.
  3. After greeting, terminate the contact.

The full contact flow:

In order to play the caller’s name as part of the prompt, we’ll reference the user-defined attribute we set in step 2 (see referenced code example zip file): “Hello VIP caller $.Attributes.CustomerFirstName“.

Step 3: Placing the Outbound Call to the Caller

Using the snippet from the “Placing Outbound Calls Using Amazon Connect API” post, we’ll simply add in an associative array of key/values which will be available for reference within the contact flow (ie greeting by name based on VIP status) and also stored in the contacts trace record:

//Include AWS SDK
require '/home/bitnami/vendor/autoload.php'; 

//New Connect client
$client = new Aws\Connect\ConnectClient([
'region'  => 'us-west-2', //the region of your Connect instance
'version' => 'latest',
'credentials' => [
  'key' => '', //IAM user key
  'secret' => '', //IAM user secret

//Capture form fields - should do some additonal sanitation and validation here but this will suffice as a proof of concept

//Place the call
$result = $client->startOutboundVoiceContact([
  'Attributes' => array("LastPageViewed"=>"$lastPage", 
  'ContactFlowId' => '', // REQUIRED
  'DestinationPhoneNumber' => "$phoneNumber", // REQUIRED
  'InstanceId' => '', // REQUIRED
  'QueueId' => '', // Use either QueueId OR SourcePhoneNumber. SourcePhoneNumber must be claimed in your Connect instnace.
  //'SourcePhoneNumber' => '', // Use either QueueId OR SourcePhoneNumber. SourcePhoneNumber must be claimed in your Connect instnace.
echo "<pre>";
echo "</pre>";

Step 4: Displaying the Connect contact attributes for the agent

For this, we’ll use the Streams API (assuming you already have this setup and in place).  Using the same styling from the Caller side demo, we’ll create an agent UI. I’ve plugged in the various API references below so I believe it’s pretty straight forward to follow:

<!DOCTYPE html>
<html lang="en">
    <meta charset="utf-8">
    <title>Call Me Demo</title>
    <meta name="viewport" content="width=device-width, initial-scale=1">
    <meta http-equiv="X-UA-Compatible" content="IE=edge" />
    <link rel="stylesheet" href="./style.css">
    <link rel="stylesheet" href="./_variable.css">
    <link rel="stylesheet" href="./_bootswatch.css">
  <script src=""></script>
  <style type="text/css">
  .ccp {
    width: 350px; 
    height: 465px; 
    padding: 0px;
  .ccp iframe {
    border: none;
    <div class="navbar navbar-expand-lg fixed-top navbar-dark bg-dark">
      <div class="container">
        <a href="../" class="navbar-brand">Call Me Demo</a>
        <button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbarResponsive" aria-controls="navbarResponsive" aria-expanded="false" aria-label="Toggle navigation">
          <span class="navbar-toggler-icon"></span>
        <div class="collapse navbar-collapse" id="navbarResponsive">
          <ul class="nav navbar-nav ml-auto">
            <li class="nav-item">
              <a class="nav-link" href="#" target="_blank">user</a>
    <div class="container">

      <div class="bs-docs-section">
        <div class="row">
          <div class="col-lg-12">
            <div class="page-header">
              <h1 id="forms">Incoming Call</h1>
    <div class="row">
      <div class="col-lg-4">
        <div id="ccpDiv" class="ccp" >
        <!-- your contact control panel will display here -->
      <div class="col-lg-8">
        <div id="connectAttributesDiv">
        <!-- the contact attributes will display here -->

<script src="../amazon-connect-streams/amazon-connect-v1.2.0-34-ga.js"></script> <!-- replace this with your streams JS file -->
window.contactControlPanel = window.contactControlPanel || {};

var ccpUrl = "https://<YOUR Connect CCP URL"; //Plug in your Connect CCP address here

//Contact Control Panel
connect.core.initCCP(ccpDiv, {
  ccpUrl: ccpUrl,        
  loginPopup: true,         
  softphone: {
    allowFramedSoftphone: true

//Subscribe a method to be called on each incoming contact; //

//The function to call on each incoming contact
function eventListener(contact) { = contact;
  updateAttributeElement(contact.getAttributes()); //
  contact.onEnded(clearAttributeElement); //

//Loops through attributes object and prints out the corresponding key:value
function updateAttributeElement(msg){
  for (var key in msg) {
    if (msg.hasOwnProperty(key)) {
      var connectAttributesDiv = document.getElementById("connectAttributesDiv");
      var newAttribute = document.createElement('div');
      newAttribute.innerHTML = '<strong>' + key + '</strong>: ' + msg[key]['value'] + '<br />';

      while (newAttribute.firstChild) {

//Clears the previous contact attrbitues onEnded (disconnect) of contact
function clearAttributeElement(){
  document.getElementById("connectAttributesDiv").innerHTML = "";


The end result is a “Call Me” framework that can be used to capture pass session attributes through to the contact experience:

Home Automation Dashboard – Version 3

Over the past two years, I’ve had a few iterations on my home dashboard project. All of the integrations for a “smart home” have been rather dumb in the sense that they’re just handling static transactions or act only as a new channel for taking actions. I wanted to change this and start bringing actual intelligence into my “smart” devices.

A major problem in the current smart device landscape is the amount of proprietary software and devices that are suffocating innovation and stifling the convenience and luxury that a truly “smart home” can bring to consumers/homes of the future — this means improving my standard of living without effort, not just being a novelty device (a “smart” lightbulb that can be controlled through another novelty device like Amazon Alexa).

In this vein, I’ve been connecting my devices (not just my smart devices) into a single product that enables devices to interact with each other without my intervention. This project has slowly morphed from a UI that simply displayed information and allowed on/off toggling to an actual dashboard that will take actions automatically. There’s not much special behind many of these actions at the moment but it’s a starting point.

Home UI: Version 3

In the prior two iterations of my Home UI product, I focused on two static aspects: device functionality and data collection. With V3, I’ve shifted focus to merging those two and bringing in proactive, intelligent actions and notifications.

Key features

  • Building Habits and Accomplishing More: Using my calendar, weather forecast, my entertainment preferences, and my to-do lists, the system will make scheduling suggestions to help me build positive habits or remind me to take take care of household tasks in a more timely manner. For example, the system knows that I enjoy going to the movies but also knows I enjoy doing things outdoors. The system will encourage an outdoor task if the weather is nice and suggest a movie when it’s raining/I have nothing else scheduled. Similarly, the system will suggest items from my to-do list based on their due date and priority.
  • Commute Planning: the system collects real-time traffic information from Google Maps; toll, traffic alerts (crashes, special events, construction, etc), and camera feeds from WashDOT; and road condition information, including subsurface temperatures from WashDOT, to compare against my calendar for the day and recommend a time for travelling to/from work. For example, if there’s a SeaHawks game in the evening, the system will recognize that and recommend an earlier or later departure to avoid sitting in traffic. Similarly, if I have an early meeting, the system will send me a push notification the night before to recommend setting an earlier alarm.
  • Device Event Bundling: a common use case in home automation, the system will take multiple actions across multiple devices based on a single trigger. For example: before leaving the house, I’m able to reduce my thermostat, turn off all lights, and set my security alarm with having to take each of those actions individually. This isn’t a new concept but it’s a nice implementation despite the various product types supported.
  • Neighborhood Awareness: police events around my home are pushed to me so I know when there was a burglary, car theft, or other concerning event near me. Others are stored and available in a map view.

Full List of Features

  • Pipes RTSP feeds from security cameras and save them to AWS S3 (30 days of storage for ~$1.50)
  • Detect motion in video feeds and triggers notifications
  • Push notifications for:
    • Motion detection from security cameras
    • Police events near my house
    • Traffic alerts that can impact my commute
    • To-Do list reminders and calendar reminders
  • SimpliSafe Security System integration
  • Nest thermostat API integration
  • Nest Hello doorbell camera integration
  • Police events, restaurant health inspection scores, building permit applications, and traffic information for my community are captured/plotted
  • YeeLight integration/control
  • Google Calendar integration
  • Stock price integration (for stock in my portfolio)
  • Amazon Echo Music integration (history only)
  • And a few other things I’ve shared before (such as my movie collection UI)

Hardware in Use

  • Nest thermostat
  • Nest Hello
  • Hikvision security cameras
  • SimpliSafe Alarm System
  • YeeLight light bulbs (I highly recommend these)
  • Raspberry Pi (handles some LAN things)

Software Used

The Underlying Logic for Expansion

The foundation of the system has three core components: 1) building and flattening a timeline for my persona so it knows what to recommend/do and when to recommend/do it, 2) data collection and transformation from a number of different sources, and 3) API/event handling for the devices I use (cell phone, Nest, security stuff, etc).

In order for the system to be most effective, it needs to know a bit about me – it needs data for intelligence. To enable this, I’ve integrated a ton of my day-to-day apps (calendar, note app, commute times, data from my android phone, etc.) so that it’s aware of what I need/want/plan to do. Using this, I can build a sufficient schedule on-the-fly and the system can accompany me by bringing relevant meta-data along the way.

When the persona and supplemental data are merged, higher-quality and intelligent recommendation are the result.


The downside to this approach is the obvious self-inflicted 1984 “big-brother” effect. I’m putting a lot of meta-data about my routine and my lifestyle into the system to effort to encourage the system to reduce the number of small decisions I’m burdened with day-to-day. It sounds crazy just writing that out…I know this.

I see this as inevitable, though. In order for us to achieve the next level of immediacy and convenience, we’ll have to get used to the idea that the next generation of smart devices (ie the next generation of Google AI, Alexa, Siri, etc) will begin using more of the information they already know about us to improve the quality and effectiveness of the convenience we told ourselves we’d get when we purchased the current generation of these devices. Accepting this, I’m okay with sharing a small amount of additional detail alongside what I already share today into a system I control end-to-end.

What’s Next?

I’m working towards extension of the personas concept through deeper integration. I want to focus on making the outputs surfaced to me higher value (ie more intelligent alerting and suggesting) while also concerning myself with less information.

In parallel, I want to continue shifting the system from primarily smart home to an intelligent assistance and entertainment console. I also see this evolving into hardware integrated into the house.

Lambda Data Dips within Amazon Connect Contact Flows

I’ve read many different guides on this but none seemed to provide end-to-end guidance or were cluttered with other noise unrelated to Lambda or Connect.

The power of Lambda function inclusion in the contact flow is immense – perform security functions, lookup/validate/store data, lookup customer data for CRM integration, etc. While learning this, I created a simple Lambda function to simply multiply the caller’s input by 10, store both numbers, and return the output to the caller – I’ll dive into querying Dynamo databases in the near future.

What we’re doing

Using Amazon Connect and AWS Lambda, we’ll create a phone number which accepts a user’s DTMF input, multiplies it by 10, saves the results as contact attributes, and regurgitates those numbers to the caller. The final experience can be had by calling +1 571-327-3066 (select option 2).

Step 1-Create your Lambda Function

Visit the Lambda console and select “Create Function”. For this example, I’m going to use the following details:
Name: “FKLambdaDataDip”
Runtime: Node.js 8.10
Rule: Create a custom role (and use the default values on the subsequent popup)

Step 2-Creating the Resource Policy

Now that the Lambda function exists, copy the ARN from the top right of the page:

Using the AWS CLI, we’ll create a resource policy for the function & Connect:

aws lambda add-permission --function-name function:<YOUR_LAMBDA_FUNCTION_NAME> --statement-id 1 --principal --action lambda:InvokeFunction --source-account <YOUR_AWS_ACCOUNT_NUMBER> --source-arn <YOUR_AWS_CONNECT_INSTANCE_ARN>

You can find your Connect ARN in the admin console and your AWS acount ID on your AWS account page.

Step 3-Granting Connect permission to invoke your Lambda function

From the Connect admin page, select “Contact Flows” from the left menu. Under the AWS Lambda heading, select your function from the drop down and click ‘+Add Lambda Function”.

You should now be able to successfully invoke your Lambda function via your Amazon Connect contact flow.

Step 4-Creating the Amazon Connect Contact Flow

I’m going to outline my high-level flow before finishing my actual Lambda function. We’ll come back and plug in all the variable names and details. Here’s the visual of my flow:

Step 5-Finalizing the AWS Lambda Function

As noted, our function will simply multiple the number entered by 10 and return it.

exports.handler = function(event, context, callback) {

var receivedCallerSubmittedNumber = event['Details']['Parameters']['callerSubmittedNumber'];
var calculated = receivedCallerSubmittedNumber * 10;

var resultMap = {

callback(null, resultMap);

Note that we’re getting to the “callerSubmittedNumber” variable via “event[‘Details’][‘Parameters’][‘callerSubmittedNumber’]”. This is because the json published from Connect to Lambda has this structure (where our Connect attributes are passed in the parameters section):

    "Details": {
        "ContactData": {
            "Attributes": {},
            "Channel": "VOICE",
            "ContactId": "4a573372-1f28-4e26-b97b-XXXXXXXXXXX",
            "CustomerEndpoint": {
                "Address": "+1234567890",
                "Type": "TELEPHONE_NUMBER"
            "InitialContactId": "4a573372-1f28-4e26-b97b-XXXXXXXXXXX",
            "InitiationMethod": "INBOUND | OUTBOUND | TRANSFER | CALLBACK",
            "InstanceARN": "arn:aws:connect:aws-region:1234567890:instance/c8c0e68d-2200-4265-82c0-XXXXXXXXXX",
            "PreviousContactId": "4a573372-1f28-4e26-b97b-XXXXXXXXXX",
            "Queue": "QueueName",
            "SystemEndpoint": {
                "Address": "+1234567890",
                "Type": "TELEPHONE_NUMBER"
        "Parameters": {
            "sentAttributeKey": "sentAttributeValue"
    "Name": "ContactFlowEvent"

6-Finalizing the Amazon Connect Contact Flow

Back in the Contact Flow Designer, we’ll edit the “Invoke AWS Lambda Function” module to plug in our Function ARN (again, copied from the Lambda function’s page). This is the same function ARN that you setup the policy for in step 2.

In the next “Set contact attributes” module, we’ll set the attribute “Destination Key” to “lambdaCalculatedNumber”, the type to “External”, and the “Attribute” to “sentLambdaCalculatedNumber”.
Lastly, we’ll edit the last prompt of the flow to play back the number by configuring it to “Text to speech”, “Enter Dynamically”, “External” as the type, and “sentLambdaCalculatedNumber” as the Attribute.

Save and publish your contact flow.

As the variable and key assignments can be a bit confusing and as the documentation provided by Connect on this is of poor quality, I’ve recorded what I’ve set each of my to in this demo. Connect’s own documentation actually has some typos in it that will result in errors from Lambda (at the time of writing this, at least).

Step 7-Testing

Once you associate your contact flow with a number, you can now test. Beyond dialing and hearing the response, we can see it recorded alongside the contact attributes:

I’ve setup a test number for this demo: +1 571-327-3066 (select option 2). Dial to experience the end result.