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Gainsight Account Research | How oper8r Creates Measurable Impact

Executive Summary

• Gainsight is doubling down on agentic AI, EMEA expansion, and CustomerOS-led growth under new CEO Chuck Ganapathi while integrating recent acquisitions CEO announcement, EU Datacenter, Skilljar acquisition, Community acquisitions.
• What oper8r can solve now:
  • Compress RFP/security questionnaire and enablement content cycles by up to 82% via batch/autocomplete workflows
  • Generate on‑demand sales collateral and research to support live launches and field execution oper8r.io, [oper8r Docs](https://w

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<script>
/* 
INSTRUCTIONS
To begin syncing this parameters with SFDC you will need to excplitly reference 
them as qualification attributes in your Intercom Qualification Data settings in the Intercom UI:
https://www.intercom.com/help/faqs-and-troubleshooting/capture-qualify-and-convert-leads/what-is-qualification-data
Then, you will need to map those qualification attributes to your SFDC lead fields in the Intercom UI.
See the "Map and sync your qualification attributes" section of this document:
https://www.intercom.com/help/apps-in-intercom/apps/salesforce-app
*/

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//does Intercom exist in this scope
if(window.Intercom){
  //track our event
  window.Intercom("trackEvent", "YOUR EVENT NAME");
  setTimeout(function(){
    //refresh our user, so we can take actions based off of the action above
    Intercom("update", {last_request_at: parseInt((new Date()).getTime()/1000)})
  }, 500);
};

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<script>
/* 
INSTRUCTIONS

To begin syncing these parameters with SFDC you will need to excplitly reference 
them as qualification attributes in your Intercom Qualification Data settings in the Intercom UI:
https://www.intercom.com/help/faqs-and-troubleshooting/capture-qualify-and-convert-leads/what-is-qualification-data

Then, you will need to map those qualification attributes to your SFDC lead fields in the Intercom UI.
See the "Map and sync your qualification attributes" section of this document:

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<script>
        (function() {
            var w = window;
            var ic = w.Intercom;
            if (typeof ic === "function") {
                ic('reattach_activator');
                ic('update', intercomSettings);
            } else {
                var d = document;
                var i = function() { i.c(arguments) };

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{
  "type": "notification_event_data",
  "item": {
    "type": "conversation",
    "id": "18255009678",
    "created_at": 1535647240,
    "updated_at": 1535952882,
    "user": {
      "type": "user",
      "id": "5b881d43dc78d66a0817eab9",

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//instantiate globally
//below is the global array where we'll record the last 6 steps
window.NewRelicDirectFunnelArray = ["", "", "", "", "", ""];
//and the function we'll use to manage the data structure
window.NewRelicDirectFunnelFunction = function(pageActionName){
  //capture a delimited string containing the last 6 pageActions since the initial pageload
  var previousSixPageActionsString = window.NewRelicDirectFunnelArray.join("||");
  //dequeue the oldest pageAction value out of the queue
  window.NewRelicDirectFunnelArray.shift();
  //push the newest pageAction value into the queue

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/*
New Relic GPT ad monitoring code
//
This snippet captures PageActions containing references to Google publisher tag ads
Should you have any questions about this, please reach out to me at [email protected]
Please note that you must be using New Relic's SPA browser agent.
//
*/
//
//first, make sure the newrelic object exists properly

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New Relic automatically discovers seasonality and evaluates the use of 4 algorithms for unique time series slices. Further, we've built an extensible ensemble algorithm that we continue to add to. In regards to seasonality, we've applied a technique common in signal processing, called Fast Fourier Transforms (FFT’s). FFT’s can be used to identify the underlying frequency in a time series. We use FFT’s to sniff out good candidates for 'seasons', then try the candidates on the baseline bootstrapping data and see if works better than the default. Currently in our ensemble approach we evaluate four options: triple exponential smoothing with the discovered seasonality, triple exponential smoothing with the default seasonality (Holt-Winters), double exponential smoothing (i.e. look only at recency and trend factors) and single exponential smoothing (just look at recency). Lastly, end users do not currently have access to modify these dynamic baselining algorithms.

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Utilizing New Relic APM inside of a Meteor application

This is a running log of information I plan to use to help me research, understand, implement and communicate why and how to utilize New Relic's APM suite inside of a Meteor application.

Why?

Meteor's rise to prominence (currently 30th ranked project on GitHub) has brought challenges for developers that choose to utilize the framework in a production environment. One of those challenges is application monitoring. With the downfall of Kadira, Meteor's defacto go to application monitoring service, developers have been relegated primarily to self hosting Kadira, or jumping into the larger Galaxy platform for insights.

Enter New Relic and it's industry leading APM suite.