dedicated to the strength of the nation proudly we hail he fits Gerald in the local Calarney United States Army and United States Air Force presentation now here is our producer the well-known Hollywood showman CP McGregor thank you thank you and greetings from Hollywood ladies and gentlemen welcome to your theater of stars where each week your favorites from the world's film capital join us for your entertainment that lovable and salty actor Barry fits Gerald is our proudly we hail star and portrays a typical Breumann hand Irish janitor in our comedy the luck of Calarney when an efficiency expert tangles with Barry and attempts to retire him for being over age he learns how the Spanish war veteran depends his job and how things were handled back in 98 the curtain for act one of the luck of Calarney in just a moment here now is Wendell Niles with an important message everywhere in the world the uniform of the United States Army and the United States Air Force is more than a uniform it's a badge of courage a symbol of freedom and democracy this fact has been true for more than a hundred years true since the first American soldiers set foot beyond our borders as the representative of our country and the principles for which we stand no other armed service holds the respective millions everywhere as ours does think of this and you will have every reason to be proud of the men who wear the uniforms of the United States Army and United States Air Force now back at the microphone our producer the curtain rises and act one of the luck of Calarney starring Barry Fitzgerald as Tom O'Reilly there was a touch of Calarney green in Kings a branch of the well-known department store chain that stretched for Maine to California a touch of Calarney and the person of Tom O'Reilly veteran of the Spanish-American War who has been a maintenance man in the store for more year-all as our story begins we find this venerable old gentleman broom in hand sweeping out the office of Brighton Pell the store's efficiency expert who has long been emulating a comb getting into everybody's hair as O'Reilly sweeps Pell's charming secretary Edith Good morning Mr. O'Reilly and how are you this fine morning oh pretty good you I couldn't be finer if I was made of gold you're always so chipper well I suppose you've heard the news mr. Adams is gone transferred to Jacksonville yes we're to have a new general manager and in the meantime if the genius takes over not my hog in the head yes mr. Pell's well it could be worse you don't have to work all day in the same office with that breathing Hulk of efficiency no but it's only for a bit child as I was telling a few of the boys the other night who went through the Spanish-American War with me well we once had a filly in our outfit called Fleetworth Cassidy the scourge of Chickamauga Bloot now yeah that was one of the more considered names we had from he would out snore 10 man well we thought we'd never get him out of our bags there Chickamauga but finally finally we got him moved over with the other snores did that help whoa no end but Fleetmouth well he was so low he even woke up the snores so they put him in the bags of himself well I suppose that fixed it but everyone concerned it did Fleetmouth started waking himself up developed insomnia and as far as I know hasn't snorged since oh mr. O'Reilly some of your stories of course I shouldn't really mention Fleetmouth in the same breath with mahogany head but the same thing it's oiled we'll move mr. Pears out of these backs when the right time comes you know mr. O'Reilly I like to be around you well thank you Chad where do you get such a sunny disposition but how can you be so happy just you mean just leaning on the broom well I'll tell you child it's just like I have me own little corner of the universe to keep clean it's me responsibility I enjoy it good morning miss Winston good morning mr. Pears I'm three minutes late I'm sorry I missed two traffic signals don't quite know the route chat oh well O'Reilly morning mr. Pears come here O'Reilly yes sir what is it sir are you in the habit of cleaning this file oh yes I am then clean it now but I just did it thoroughly only this morning dusting isn't enough get yourself a nail file or some other such tool a nail wrap around the end of the nail file one thickness of cloth but what for what for the centers of these screw heads are a disgrace oh boy must be inside but you certainly must do it the first time and do it right now remember that is our by word and don't go there's something else I wish to speak to you about let me see what escapes me at the moment oh miss Winston yes mr. Pears take a memorandum will you yes sir attention all sales personnel subject sharpening of pencils it has come to the attention of the acting general manager that certain personnel have been negligent in the matter of sharpening pencils breaking points callously and indiscriminately please exercise care in this operation in the future sign breaking pearls acting general manager will that be all mr. Pears just tell the department managers by telephone to check the pencil sharpener reservoirs for broken points I want to be sure this memorandum effects an improvement very good mr. Pears oh yes oh yes now I know what I don't want to talk to you about yes and it isn't very pleasant yeah I thought it was probably something nauseating yeah your psyche karate mm-hmm you see I've had the brooms of all janitors checked for where during the last month and yours already is a quarter of an inch longer than the rest a quarter of an inch less where than the rest you see you're already I have my ways of finding out who leans on the broom all day oh mr. Pears I'm surprised you should make such an allegation that you can stand here accusing me of laziness when you should be complimenting who is that so well then how do you account for the fact that your broom showed being a master of me trades I take fewer stokes per square foot of pace space covered oh is it so well oh my well I haven't thought of that yes I'd be glad to teach me techniques of the others yes it's a good idea yes well we'll have to try that yes but I you know I think I'll go down to the beverage bar and count noses so much time is wasted there I'll be back shortly mr. Oh good for you mr. O'Reilly he thought he had me for a minute didn't know he sure did I say I'd better empty the pencil sharpener before mr. nosey gets back you too yes I think I broke a point a couple of weeks ago morning mr.  O'Reilly you're the only one in the store to know it Waldo and I've we've been married a year but what congratulations she gave it to me on our anniversary we couldn't afford it when we were first married and we couldn't afford it now give it to me wall I eat thank you I told you never to wear it at the store you know mr. Pell's rule about married couples he says they're inefficient he would why only last week he fired a couple who got married oh Waldo have you been wearing this ring all morning oh yes somebody surely saw it and at a time like this when we both need our jobs more than at any time before what is me do what is it you tell mr. O'Reilly Waldo I'm about to become a father that's very beautiful mr. O'Reilly you don't think anybody saw the ring on Waldo do you well yes mr.  O'Reilly how do you like the new company restaurant mr. Pell started well the food's all right dad but they don't give you enough of it portions are pretty small sure you know what he has in the kitchen there no what he is a p-counter counts out exactly 30 ps per plate no more no less excuse me what is waiting you've used two paper napkins please make one do next time yes paper is still one of our scarcest commodities you know come on Waldo I can't stand it another minute that pairs making a sweet little girl prattle that sort of thing to you when you're trying to fill your stomach oh I sure hope nobody saw that ring and told him about it I was crazy to wear it I guess at a time like this so you're going to be a father I am and believe me I'm plenty nervous ah there's no reason that way fathers have been having children killed down through the age not my child they haven't well regardless of me boy you have many great things to look forward to and one in particular oh what's up mr.  O'Reilly I just hope everything works out all right and nobody reports that ring to mr. Pell's Edith gets very tough with me sometimes aren't does she throw things at you yes and just her wind up is something fierce well don't worry let her feel it in me bones everything is going to be fine well good morning Edith you me yes you what's happened to you oh everything mr. Pells knows all about Waldo and me being married he's gone down now to get Waldo yeah but I didn't tell anyone did somebody spot the ring that day oh no no no yeah but then how how you had to talk to Waldo about electric trains that's how yeah but this makes no sense to oh yes it does you like the fire so Waldo has to go down and buy one they don't sell trains here but Waldo finds out where they do he has to go out and buy an electric train for the baby I'm not even gonna have for five months yeah but how did mr. Pells find out Waldo couldn't pay cash for the train had to buy it on time so the droop fills out a questionnaire his name name of his wife where he works he fills it all out never dreaming they'd call over here to check up that's Waldo well they do call I'm not in your office mr. Pell talks to them no say no more me dear this is calamitous mr. Pells just now stormed out to get Waldo shouting in for me perfidy and two other words I I didn't even understand well mr. O'Reilly what are you gonna do about it what am I going to do yes you got us into this you did yeah I suppose I did well I'll get you up me dear now don't you worry didn't I save the boys from the scourge of Chickamauga flute Mount Cassidy well you said you did anyway oh but you only had me colonel to talk to now he passed from our story the luck of Kalani starring Barry Fitzgerald to bring you an important message from our government let's face it men day after tomorrow as history develops a man with wings will be the man with a future many young men of today who are aware of this have taken a step that will give them wings these young men have signed up for the United States Air Force aviation cadet training at the close of the course when they have completed the training successfully they will receive their pilot's wings and a commission as second lieutenant in the US Air Force reserve and they will be ordered on active duty at once outstanding graduates will receive regular commissions in the air force immediately while others will have the opportunity to qualify for a regular commission while they're on active duty here then is a future for most young men with our eyes on the sky the requirements are that you be between the ages of 20 and 26 and one half that you are single with two years of college or the equivalent that you are physically fit as an aviation cadet you'll be on your way to a great career as an aviation cadet you'll be opening the door to a bright future find out now if you qualify get your application today at your nearest air force base or u.s. army and u.s. air force recruiting station the curtain rises on act two of the luck of Kalarney starring Barry Fitzgerald is Tom O'Reilly there is considerable suspense in the office of Brighton Pells efficiency expert and acting general manager of kings department store one of the branches of the national chain for mr. Pells who was unanimously voted heel of the month by kings employees has discovered that his secretary Edith Winston has been secretly married to Waldo the mail clerk a marriage which violates mr. Pells strict edict no man and wife employees in the store in Pells office Edith and the venerable Tom O'Reilly look up as mr.  Waldo has been loyal and efficient and sometimes how can a fragile woman withstand brute man now listen easy easy Waldo mrs.  O'Reilly's right I'm coming well at least one he is still working yes and it's a good thing too but when mr. Pells finds out I'm going to have a baby yeah there midday now don't you fret everything will work out I'm sure you always say that well that's my philosophy that's what kept me spirits up when flute both Cassidy was blasting us out of the bags with the snoring and we were trying to quiet him well it's the best way to look at things all right sure oh but it'll give me great pleasure like Waldo to punch him on the nose just once but sometimes it's better to retreat with valor than to advance when you know you can't win or check with you tomorrow either well morning what's cook well quite a bit mr. O'Reilly mr. Pells hasn't filled Waldo's job yet that's good news isn't it and something else mr. Pells may go to the Toledo store no really yes well enough on our side soon I hope well right away according to the teletype the front office wants it they're just waiting for confirmation from Toledo no isn't that great you see what I tell you everything works out it always does check with you tomorrow has he left yet no and he isn't going he isn't Toledo won't have him oh I don't blame them a bit well then what's more he's interviewing men for Waldo's job oh dear me isn't there a speck of good news well our new manager's coming his name is mr. Olson Olson really well maybe he's a square shooter if so he'll sure see it out away well that's something to hope for well see you in the morning has he come yet oh you mean mr. Olson the new manager that's right he arrives tomorrow well we've got a fighting chance then I'm afraid not mr. Pells is delighted he says he and mr. Olson have worked together before and they'll surely have Waldo's job filled in a day or so has Waldo tried elsewhere to line up something yes but you know how things have tightened up and you know Waldo yes I know Waldo well it's all better only a challenge to us but it's almost more than I can bear he's after you now mr.  Pells is inaugurating a new plan retiring everyone at 65 well let me tell you he was furious when there wasn't even an application blank on you when I was hired there wasn't even a stall well anyway he's heard you talk about the spanish-american war he says if you served in the spanish-american war you'd almost have to be over 65 uh-uh maybe you better tip off your buddies to keep mum huh yeah I'll do that by all means better be prepared for mr. Pells he's really gunning for you you sent for me mr. Pells yes so rather back in the dark ages when you were first hired by this company they didn't keep complete records I wonder if you'd give me the date of your birth me uh me birth that's right well no I don't rightly recall oh come now Riley yeah but I don't but surely you must remember but yeah but I don't and you'll be unfair to ask it oh I am now you're asking a man to remember something that happened back before the dark ages and now already I'll stand for no flippancy however if you want to know how old I am really mr. Pells yes well that's better well a man is as old as he feels and I feel about 27 now listen all Riley tell me this are you or are you not a veteran of the Spanish-american war that question sounds entirely around oh it is well then listen I call for the roster of veterans of the Spanish-american war and they won't send it I don't understand you listen to me already with my own ears I've heard you talk about the barracks at Chickamauga of embarking at Tampa for Cuba mr. Pells amongst me many past accomplishments I was once a writer of fiction the habit sometimes takes over me oh you bother me oh excuse me sir but there's a man here who insists on seeing mr. O'Reilly well upon me war day up and found you I did Tommy O'Reilly you son of a gun sure I'm glad to see you oh well as I live and breathe it's fluke mode capacity and did I have trouble finding you why the buyers here wouldn't tell me a thing the boy where you got them all buffalord like you used to have them at Chickamauga did you see Chickamauga no I flew but if you please please if they please what don't these people know you won the war a 98 single handed you always used to say oh did hi hi hi hi oh dear fluke mouth if the boys here in town wouldn't tell you where I am how did you phone me why the national roster's got you listed time why I was carry one when I had to do a play helps out looking up the buys fluke mouth he was a pen only like 50 years ago and you haven't changed a bit since take him out he's just I want to speak to mr. Pells Well, Tom, I didn't mean to be butthin' it at anything, of course. Well, O'Reilly, then you did serve a hitch in the army.  I cannot countenance prevarication in any form. I didn't prevaricate, Mr. Pells. I merely evade you the question. One is as bad as the other. You can be assured I'll rush your retirement papers through. I know, please, Mr. Pells, and pointy-gears, I'm an old man. I know, but I'm sufficient for me job. Statistics prove, O'Reilly, 65 is the optimum retirement age. And you are well over that. Yeah, yes, I am, but, Mr. Pells, listen to me. Never before in my life have I obeyed a man for anything, but I'm beggin' you to give me me job. It's really what keeps an old man gone. And I'm sorry. I'm sorry, O'Reilly. It's always difficult to turn a firehorse out to pasture, but we'll make it as painless as possible. I think I can have you ready to leave by tomorrow night. Very well. I have no more arguments in there. Tomorrow's me last day. Oh, Mr. O'Reilly, I'm so sorry. Where's Flutemouse? He's waiting down the hall. Oh, I can't wait to leave you get me hands on him. Well, he'll see you in the morning. Well, good morning, dear. Why, Mr. O'Reilly, you sound so happy. Well, I'm quite kind of man. Be happy. Well, yes, but after all, this is your last day. Ah, no, I think I'll stick around a bit. Stick around? As a man of faith, he had just called Waldo and told him to report back to his old job. Waldo? Mr. O'Reilly, did you and Flutemouse have a fight last night? Were you hit in the head? Flutemouse? No, not with that wonderful lad. Oh, wonderful lad. Yeah, I had dinner with him last night. Ah, he's a lovely boy. Yes, he moved to town with his daughter, a sweeter girl as Cassidy ever produced. I don't understand. Well, she married a sweet, and he fixed us up a lovely dish. Oh, you smorgasbord. Good morning, Mrs. Waldo. I'm two minutes late. I'm sorry. I had to stop at a drugstore for an airspin headache. That is an only headache you're going to have, Pell. What's that, O'Reilly?  I got to sweep this floor. Why, ma'am, are you insane? Mr. O'Reilly. Well, you said out of me way, Pell's, before I smack you with the business end of this room. This man's crazy. I'll have the store police eject him. I'll report this to our new general manager, Mr. O'Reilly. Wait a minute, Mr. O'Reilly. Flukemouth's daughter, you say, is married to a sweet? Our new general manager, Mr. O'Reilly. Oh, Mr. O'Reilly, that's wonderful. Yes, I've met the lad last night. Had a long talk with him about certain affairs of the store. Pell, in particular. Needless to say, we were in perfect agreement. Hello, darling. Somebody called and told me to report to work. Oh, darling, it's true. You're starting back to work. Oh, Mr. O'Reilly. What do you think Mr. O'Reilly will do to Mr. Pell? Well, I made him promise not to commit ma'am. But I don't understand. Mr. Pell said they'd worked together before. Yeah, correct. That's why Mr. Alston swears they'll never work together again. Oh, it's a lovely day indeed if you look at the sunshine. The curtain falls in the final act of the luck of the clarity. Our star, Barry Fitzgerald, will return for a curtain call after this timely message from Wendell Niles. There are thousands of men in the United States to whom the term triple-A has a special meaning. They are the veterans of the anti-aircraft artillery of the armed services. Their communication is specially for them. You see, you triple-A veterans, of all the armed services, your skills are needed now by the anti-aircraft artillery outfits of the U.S. Army. Enlistment now means you will help bring the triple-A up to its proper strength. And to you, personally, it means a secure and exciting future. Experienced triple-A veterans will be enlisted in grades according to skill and length of service. In many cases, you'll be able to get back your old grade to rejoin your old outfit. And more, the peacetime United States Army offers you benefits possible only in an Army career. Interesting, exciting work at good pay. Food, clothing, quarters, and medical care. So think this over, you veterans of the triple-A. Your old anti-aircraft artillery outfit has a vacancy for you now. Your special skills can help keep our country at peace. An Army career is open for you. Visit your local United States Army recruiting station today. Now, once again, our star Barry Fitzgerald and our producer. Motion picture audiences have come to regard Barry Fitzgerald as one of the deans of Hollywood character actors, a lovable man, pipe in mouth, heart of gold beneath a salty disposition. I'll tell you, C.B., it's my golf game that does it. Oh, I had the disposition of a saint before I took it up. I know exactly what you mean. Well, Barry, it's a real pleasure to have you back on another proudly we hail. You know, a character from the old sod, lovable, high integrity. Yeah, yes, if that's anything to do with knocking off a couple of strokes here and there off my scorecard, well, yeah, I'm there. Sure, what's a few strokes between friends? Oh, nothing just costs for me, him, if you knock off as many as I do. You should go a couple of rounds with Crosby and Hope. Yeah, I should, but I won't anymore. You won't? Why? They won't let me. What did you say a while ago, integrity? Integrity? Hope and Crosby? Oh, no, me. And I won't play unless I can keep score. I think I'm beginning to understand. I could show those two whippersnippers a thing or two. I don't doubt it.  But plain enough, I'm mine. Why, they never made a three in their lives. Well, that's what I came on telling them. But you made yours fairly. Oh, as honest as Patty's being. I imagine they didn't get a little upset. A little upset, now, CP. Now, you know that I'm the mildest tempered man in the world. Sure. Look, those two act like a couple of dervishes if you never offer stroke or two in there. They take their golf seriously. Oh, it's life and death of the man. You think they couldn't afford to pay me a few dollars a hold? Well, Barry, you come out to my club with me tomorrow. There won't be any argument. Yes, sure, I know that. You're a sensible man. But I keep score, man. Just in time to see who's playing here in your theatre next time. Next week, Barry and ladies and gentlemen, the highly talented actor Barry Sullivan will be our star in a comedy titled The Martins and the McCoys. It is a bright story of the New York Theatre and the elements that comprise that world of entertainment, their feuds and their jealousies. Sure, and I'll be listening. Goodbye, CP. Goodbye, Barry Fitzgerald. Be sure to join us next week, ladies and gentlemen, when we bring you Barry Sullivan in The Martins and the McCoys. Until then, this is CP McGregor saying thanks for listening and cheerio from Hollywood. Barry Fitzgerald appears with the courtesy of the Hollywood Coordinating Committee with the arranging for the appearance of all stars on this program. Scripted by Rich Hall with music under the direction of Eddie Scravan. The program is transcribed in Hollywood for release at this time. Wendell Niles speaking.


Hello. I'm Machi, and I'm a data scientist at LIST. We're a fashion company. We basically go all around the internet and we get all sorts of fashion products from all sorts of retail and all sorts of designer. Put them all in one place so that, as a user, you can just go to one website and follow your favourite designers and browse all your favourite fashion products and buy them from us. So that's the principle. I'm going to be talking about nearest neighbor search. So nearest neighbor search is very simple in principle. Basically you have a point and you want to find other points that are close to it. So the most obvious application is obviously maps. So that's something we use every day. You've got, let's say, your location on a map and you want to ask Google or Bing or whoever you want to ask. What are the nearest restaurants to me or what are the nearest cafes? And that's what it does. It figures out where you are. Rather, you give a location and then it looks up other points on the map that are the restaurants that you're looking for and then calculates the distances between where you are and when the restaurant is and tries to give you the closest ones. So that's basically the essence of nearest neighbor search. Given a point, give me other points that are close to it. Now, this is the most obvious application, but even if you're not building a mapping application, which you may well not be building, we certainly aren't, you may still find it very useful. So what do we use it for? Well, we use it for image search and we use it for recommendations. So image search, how does it work? The principle of image search is, well, you've got an image. Let's say we had a user and they submit an image to us of a dress and they want us to give them images of similar dresses or something that's a good substitute for the dress that they've submitted to us. So, as programmers, it's sort of hard to find similar images just by looking at an image. So the first step you want to do is to transform an image into something that you can more easily work with. So one very naive idea would be to, let's say, well, we've got an image which is RGB, like red, green and blue, and these are three numbers, and for a given image we could average the values of each color and then try to find other images in our database which have sort of similar color values, and that's a very naive approach and it's definitely not going to work, but it illustrates the principle that after transforming the image into a numerical representation you've got a point. In this case you've got a point in a 3D space and that's the nearest neighbor search. If you want to find similar images to the image the user has just given us, we look at other images, other points, which are close in that space. So that is the nearest neighbor search. The way we would actually do it is something more complicated. So recently the most fashionable way of doing this and probably the most effective is deep learning. So this is a very simplified diagram of a convolutional neural network. So basically the black square, the far left end of the slide is the image that you start with, and then you basically build a machine learning model which successively detects more and more interesting features of a given image. So let's say at first you detect just simple edges. Is there a line in that part of the image that goes from left to right, that sort of stuff? But then as you progressively build better and better representations you start to learn more and more about an image. So maybe in the first layer you just detect edges, in the second layer you detect shapes, is it a square, is it a circle, and in the following layers you detect high level concepts.  Now the nice thing about it is that in the final layers you've got a long list of numbers, an ordered list of numbers, a vector which represents a point in space, in a high dimensional space, and the nice thing about it is the images of cats in that space are going to be close to other images of cats and the images of bridges will be close to other images of bridges. So that's how you can do very good, very good image search. And this is indeed what we do at LISP. We sort of process, take images and process them into this point representation, high dimensional space, and then we want to use nearest neighbor searches to find similar images. That's useful for two things. One thing is search, you give us an image and we can find you similar images, or maybe you type in a text phrase and then we convert your text phrase into a point in the same high dimensional space where the images are and suddenly we can find images which are similar to the text you typed. So that's really cool and that's something we can do. Are there useful applications, the duplication? So we've got two images and we have no metadata associated with those images, but we know that these are the same products, like the underlying truth is this is the same product and we can use this nearest neighbor search to find out that these are the same products and present them just as a single thing rather than two disparate things on our website. So that's really useful. Another application's recommendations. So this approach is known as the collaborative filtering approach. So basically you have your products and you have your users and you represent both products and users as point in space. So let's say we take our products and this is a handful of points and we cast them at random into this high dimensional space and we do the same for users. These are our users and we represent them as points and we cast these points at random into a high dimensional space and what we do is well if a given user, a point in our space interacted with a given product we draw the points together but if a given user did not interact with a given product we push them further apart and the nice thing about it is at the end of this pushing apart and pulling together process users end up close to the points to the products that they will like and far apart from the things that they wouldn't like. So when we want to recommend things to a user well we look up the point that represents them in the high dimensional space and then we use nearest neighbor search to find the products that they would like. So that's also very, very useful. So I'm a data scientist and you know data scientists are very excited by these sorts of things and we spend a lot of time thinking about them and implementing them and you sort of go away and you work for six months and you come up with this amazing solution where you translate pictures of products into this high dimensional space with similar products close in space and you say well as a data scientist I've done this amazing thing and it now works and it's going to be great let's just deploy it on the website and make a user really, really happy. Now this is where you are as a data scientist you've got your beautiful child which is going to be great and you sort of go and let's just deploy it let's make it work. So how would you do it? You are given a query point you take a user and you want to find all the nearest products and that's simple, right? And you find all the points representing products and you compute the distance between your query point and all the points, all the products in database and you just order that and you give the closest ones simple, right? I mean it couldn't be simpler. So yes but no. The problem is at least we have 80 million images and we have about 9 million products so if we wanted to do the simple solution i.e. you know calculate distances with all the points and then return the closest users would be very, very bored by the time we finished. It would take literally minutes so that will not work. Okay so how do we make it work? Look how it is sensitive hashing to the rescue so we all know about hash tables or dictionaries in Python so we are going to build a special hash table so we are going to pick a hash function which unlike normal hash functions it maps points that are close together in space to the same hash code which is very different than normal hash functions are supposed to map things uniformly over the hash space so this is sort of special it picks two points that are close together in your space will map to the same hash code and you just build a hash table a normal hash table using that so you take your points and you take the hash codes and then you put your points in the hash packets corresponding to your hash codes and then magically all your points are close together will end up in the same bucket and when you are doing search you just look up the bucket that you need and you just search within that bucket which is really nice so to do this at least we use random projection forests and I am going to tell you how it works so this is our imaginary space of points so we have got about 100 gray points and one blue point which is the query point the point that we want to find the nearest neighbors for and this is how we do it so if we didn't do locality sensitive hashing we would have to calculate the distance between the blue point and every other point which takes too long, we cannot do that so to make it faster we draw a random line at the beauty of it we just take a random line and draw it it has to go through the origin but otherwise any line will do and the nice thing about it is well if we look at the picture most of the points that are closer to the query point end up on the same side of the line and the points that are not close to the query point end up on the other side of the line so just by drawing a random line we managed to create two hash packets and suddenly we only have to look through half of our points to find nearest neighbors so that's already a speed up factor of two just with one random line and we didn't have to do anything intelligent to draw that line, it's just a random line so that's the nice thing about it if this speed up is not enough for you you draw another random line again completely random and the points that end up on the same side of the line end up in the same hash packet if this again, the speed up is not enough for you you keep drawing lines until you've got few enough points in your hash packet and that's your speed up so you draw enough lines to have small enough hash packets and then when you need to perform a nearest neighbors query you take a blue point and you calculate which hash packet it should end up in and then just compute brute force distances between the query point and the points that are in the same hash packet so that's the principle if you can think of it as building a binary tree as well so we start with all the points and then we have a split and the points that are on the left side of the line go into the left subtree the points on the right side of the line go into the right subtree and then we follow the right subtree in this example and do another split into a left subtree and right subtree and another split and another split and you can sort of with a query point you can start the root of the tree and then follow the splits until you end up in the right hash packet so that's the principle of how it works now it works really really well in some cases but in some cases doesn't really work very well so the way we started this at least we thought will we going to draw a fixed number of these lines and hopefully that will give us speed up and hopefully will be accurate enough so what we did is we decided on the dimensionality let's say we want to do 100 random splits and then after 100 random splits we stop and then things that end up in the same packet are the nearest neighbours and the rest we discard that works reasonably well if your points are fairly uniformly distributed in your space because all regions are of fairly equal density wherever you draw the lines hash packets are going to end up to roughly the same size so the same number of points and the splits are going to be good enough but in spaces where some regions in problems where some regions of your space are of high density but other regions are of low density where you're going to end up is with some buckets having lots and lots of points and some buckets being completely empty neither of which is very good so if you have a packet with lots and lots of points you're not going to get a good speed up but if you have a packet with very few points in it you're not going to get any results back both of which are horrible so the first point is keep splitting until the nodes are small enough so you don't take a fixed number of splits you just build a binary tree and you split and you split and you split until you've reached a stopping criteria in which this bucket contains X number of points and when that happens you stop splitting and you take that tree so that's the first point the second point is use medium splits random lines you can end up with highly unbalanced trees so the left subtree will be very short because for some reason there are very few points there but the right subtree will be very deep because there are lots of points in that part of the space that's not really horrible but it's not great either because you spend more time traversing the deep part of the tree and you will be traversing the deeper part of the tree more often because that's where more points are so medium splits you take a random line and then you calculate the median distance from the point to that line and then you split on that distance so it's guaranteed that half of the points will always go to the left tree and half of the points will always go to the right subtree so that gives you nice balanced trees and faster traversal times and the final point is build a forest of trees so you don't just build one tree you build lots of trees what's the reason for that well the random projections algorithm and locality sensitive hashing in general is approximate algorithms they don't give you an exact answer they're probabilistically correct but in some cases they will be wrong so if we look at this picture if you look at the query point well maybe there are some points to the left of the line that are closer to the query point to the points on the right side of the line but if you build just one tree we're never going to surface these points because then in a different part of the tree so that's a mistake that this algorithm makes and that's not great we want to have as good results as we can giving the speed up so the way we get around this problem is we build lots of trees and because in each tree the lines are chosen and random again each tree will make its own errors but they will not repeat each other's errors so when you combine the predictions from all the trees they will end up correcting for each other and the accuracy score of the aggregate will be higher than the accuracy score of any single tree so that's why you build lots of trees another nice property of this approach is if you build more trees you're going to get more accurate results at the expense of having to traverse more trees so it's going to take slower but this is something you can control you can pick the trade-off between the number of trees you build it draws your performance curve and you can pick a point on that performance curve speed versus accuracy that's appropriate for your application so that's very nice if you want pure speed, build few trees you get pure accuracy but you'll be very quick if you want something accurate but don't care about speed that much build lots of trees, it'll be accurate maybe not so fast so that's the principle of the algorithm does anybody have any questions at this point because I'm happy to clarify obviously it works well for two dimensional points how do you generalise it to higher dimensions like you assume you will use hyperplanes but how do you split the feature space with a hyperplane right so the algorithm actually one of the main reasons of the existence of the algorithm is high dimensional spaces and the reason why it's in 2D on the slides is because 2D is easy to visualise and high dimensional spaces aren't so that's why it's in 2D but basically what you do is you run a hyperplane whatever your dimensionality is and then you do the same calculation so it's exactly the same principle which is just in higher dimensions and it works very well for very high dimensions we can talk about that afterwards so that's the principle of the algorithm how do you do it in python at a python conference it's useful to give an idea of python packages so that several python packages for doing this one of them is ANOI which is a very cleverly named package A and N very cleverly named package from Spotify it's a python wrapper for C++ code it's pip installable it's very very fast it's actually very nice another package is LSH Forest those of you who are data scientists if you play with ML you probably already have scikit learning in computers so it's really easy to use it because it's already there and it's also quite easy to use and then you've got Flan which is I believe C++ code and it's sort of gnarly and hard to deploy the nice thing about it is you give it your data and then it takes a long time to train but it figures out the optimal structure for your problem and there's a python wrapper for it which works I'm told there are some bits that we don't like as much so LSH Forest itself isn't scikit-learn you can read the source, it's fairly readable but it's actually very slow so if you want to develop a high performance application maybe not the best solution Flan is a pain to deploy it's C++ code, you need to have CMake all sorts of dependencies ANOI is great, I recommend you use it but for us it didn't really fulfil two important requirements, one is it doesn't allow, once you build a forest of trees you can't add any more points to it which for us was a no-no we need to add new products to the index as they come in so this is something we needed and secondly you cannot do this out of core you have to keep all the vectors in memory all the time so like any engineer we wrote our own it's called RP Forest which speaks to the algorithm it's available on GitHub and it's pip-installable as well so please go forth and try it out and break it in all sorts of novel ways and I'll try to fix them it's quite fast, it's not as fast as ANOI but it's fast enough for us it's certainly much much faster than LSH Forest which is built into scikit-learn allows adding new items to the index and does not require us to store all the points in memory which is really really very nice so how do we use it? we use it in conjunction with PostgresQL so basically we have a lightweight service that has the ANM indexes in it the RP Forests so we send a query point there and what it gives us back is these are the product IDs or these are the image IDs you are going to be interested in so we get these IDs back and then we push them to Postgres and go dear Postgres here are the IDs please apply the following business rules all your worst statements and so on and then do final distance calculations also in Postgres using C-extensions so we store the actual point locations the actual vectors as arrays in Postgres and we've written some C-extensions to Postgres that allow us to do the distance calculations in Postgres which is quite nice side note Postgres is awesome if you're doing all sorts of numerical stuff you have arrays in Python arrays in Postgres and you can write custom functions to do anything you want in C so if you really want to you can write your stochastic gradient descent machine learning algorithms in Postgres and run them in Postgres I'm not sure you should do that but it's definitely possible so the whole combination the algorithm and the implementation and Postgres as a data as a backing store gives us a fast and reliable ANM service with a 60% precision at 10 if we get 10 nearest neighbours we get the 6 out of 10 actual nearest neighbours using the approximate approximate approach at 100 times speed up so I think that's reasonably good speed up over what? speed up over brute force not particularly demanding speed up baseline but that's where we start so we've got that and the speed up that we gained it allows us to serve real-time results so we don't have to pre-compute we can just serve it real-time which is also very nice and it's all built on top of a real database so stuff like concurrency and updating the database that's all taken care of by smarter people than us so it works well anyway thank you and be very happy to take any questions yeah hi I've just did something like that for chemistry with distance and I'm doing brute force but I would like to ask you for an estimate of what number of entries this is going to be hard so when is my application going to need trees like in the future I don't know when I mean it's sort of an empirical question right so it depends on your requirements as well so let's say if you're doing offline processing and you want to you don't really mind that it's taking 10 seconds or 20 seconds with the data you have but when let's say one lookup takes 10 seconds but you need to do your lookup 100,000 times then maybe that's that's the point where you really want to look into these solutions if it's fast enough for you for what you're doing you don't need it so what was your pain point in your application what was too long for you so let's say if we want to serve web requests like 100 milliseconds is too long for us and doing this brute force would take anywhere up to 3 seconds and it would completely destroy the database so getting from 3 seconds to under 100 milliseconds that was the difference for us hello and thanks I have a question about clustering so are there any algorithms like kayamins and so on which could be fast and precision have nice precision thanks yes so the question is can you use clustering algorithms to achieve the same sort of effect and yes you can and there's an approach called hierarchical clustering which again is sort of building a let's say a binary tree for example where you have all your data points and you put them into two clusters and you build two clusters and then these are your two clusters on the first level of the tree and then you go down into each cluster and you recursively build more clusters and keep splitting the tree that's also an approach it's not something I have investigated so I cannot give you a good answer on what the performance trade-offs are have more questions thank you


Hello, everyone. I am Hershey, like the Hershey's Chocolate. I head up the strategy for digital customer success here at Geinsight, and I'm super excited to be joining all of you and sharing some of my experiences with driving personalized digital experiences that can help lockdown user loyalty. Please feel free to put any questions as they come along in the chat. I would love to have an active conversation and learn from all of you and from your experiences as well. So thanks for joining today to kick things off. Here's the agenda. Let me know how it sounds, starting with the why, the what, and how the digital user journeys. Secondly, really sharing how at Geinsight, we are driving some of these personalized digital experiences, especially at scale. And lastly, going beyond some tactical and execution examples, but going into what could be the key strategic considerations any organization should be making when thinking about overall long-term success of digital experiences. Hope that sounds like a plan. So let's get started. 70% of the consumers are actually expecting personalization today. However, 76% of those consumers get frustrated when they don't receive it. Why is that? While the technology has actually evolved in the last few years, especially with AI today, the customer experience has actually devolved. This is what the digital user experience looks like today. If you were to put your shoes in the, if you were to put your foot in the shoes of your users, this is what the experience would look like today. If they were navigating your product and needed help, they are basically confused. Should I go to my self-service portal, or should I go to my knowledge base? Should I go to a learning management system? Should I ping the CSM or the support or the salesperson? Probably I can get answers from community, but oh wait, like I might not get a response immediately. So it's, we, all of us are doing digital experiences, but it's not orchestrated, and hence leads the customer's experience poor and frustrated. But digital experiences need to be orchestrated. And there is a world where the experience can look like this and not a devolved, poor, noodle, and confusing experience for the user, where it's orchestrated across multiple channels with the central digital success hub. So here at Gainsight, we have developed this maturity model to help organizations organize their efforts around digital customer success and digital user journeys so that those experiences can be very orchestrated across the omni-channels, multi-channels, experiences for the users. So if you think about the reactive phase, most of us have been doing some digital experiences for the users, but they've been quite reactive, ad hoc, unorganized, using multiple platforms, not very orchestrated. That causes poor experience for the users, but also causes inefficiencies internally within the team. And now is the time to make that leap to digital. So think of these three P's as starting with a proactive journey where we are empowering users to self-serve with centralized resources and guides, almost like a one-stop shop of where they can find all their resources, like maybe with a federated search, onboarding checklist, their support tickets, usage metrics, so on and so forth, and even peer-to-peer networking. From there, thinking about more personalized user journeys, like really using data to drive those distinct, contextual user journeys for our customers. And lastly, predictive, using intelligent experiences, AI technology to power the omni-channel digital experiences for the customers. Now, they don't have to be one after other each of these stages. They can be very much in parallel. And I'm sure if I were to pause today and ask each of you, where are you today? My question is, are you mostly proactive? Are you mostly personalized? Are you mostly predictive? And as you're thinking about that answer, you're probably thinking we are doing a little bit of everything. So don't think of this as going from one to stage two, but things that we can optimize in parallel and really drive those personalized experiences and journeys with digital in hand. And once you do that, want to share an example of what that orchestrating customer experience could look like. And it's important to remember that digital experiences are not really replacing humans, but they are to elevate the humans and also very much to go hand in hand. And humans on the other side are actually starting from sales, marketing, customer success, product. Each of these teams actually have touch points with the customer's journey at various several points. So really orchestrating that digital experience, both for the customers, but also for our internal teams to scale. So here's an example. If you stay with me, and there's a lot going on on this slide. So if you stay with me just for a bit, on the y-axis, you will see expansion on top and churn risk at the bottom. This graph represents the most high value activities that we want our humans to be spending in a customer journey. And on the x-axis, we have renewal towards the end, which talks about churn or retention of our customers, which is driven by our user loyalty. So when we talk about expansion and churn risk, that's where we want to surface the right opportunities with data to our customer-facing teams to make sure that that's where they're spending most of their time. Everything else in between, all motions, all engagement activities, all value action activities for users that are repeatable in nature should be taken care of by the digital experiences like in-app experiences, email community webinar, and to a large extent, even productize some of those experiences as much as possible. So if you were to take example of onboarding here, onboarding in the first, let's say, x-axis, why x-axis days? Because the onboarding journey and experience differs product to product. For some, it's within first two days, first seven days, 15 days, 30 days. So it really depends on where your product is. So if I were to take example of onboarding, we can start with a simple welcome email with the very prescriptive approach of, hey, here are first steps, user you should take to set up the product, and then adding a link to the self-serve resources for the user to go and ask and answer questions that the user might have as they're going through implementation. It gives them a link to go to the product and start setting the product up. Once they are in the product, it can serve them an in-app onboarding checklist. And this is where data is important, where because we are tracking the usage data, we are tracking the activation data, whether license have been activated or not, whether the email was opened up or not. So now if the license was not activated, if the product was not set up, that's where we send them a follow-up, and that's where the first red flag happens. Now, even after a couple of follow-ups and few days have passed by, the product has not been set up, or the initial onboarding actions have not been taken up. That's where the risk intervention happens, where the data triggers risk CTA for your customer-facing teams, implementation team, where they are now reaching out to the customer and making sure that we are helping them get set up. The other workflow here is when the customers are set up and they have activated the product and taken their first few value actions, that's where we want to send them a sentiment CSAT survey, understanding how their onboarding went. And from there on, they move into the next lifecycle stage of adoption and outcomes. So that's how you should be thinking about orchestrating your digital customer experiences between your humans on your side of the company, who are customer-facing, and your digital experiences for the customer. So it's really orchestrated on both sides of the world. So now I'm going to share live examples of some of these personalized digital experiences that we've been driving at Gainsight. Starting with the example of onboarding. Like I briefly explained, starting with a prescriptive welcome email. So this is really an example that talks about how to use omnichannel customer onboarding to accelerate that time to value, but really at scale. So here in this example, we're starting with a simple, one of our products, Gainsight PX, which actually is, by the way, is a product usage and in-app tool. For that product, our customers, we started with a prescriptive welcome email of, here are some config steps. Here is a link to your resources. Once our customers logged into our product, we serve them an in-app onboarding checklist. So as you can see, this is an in-app within the product onboarding checklist with top three to four onboarding workflows that we know typically drive success for our users and customers in the initial onboarding phase. And it also has linked to key resources, which also allows them to chat with an expert or reach out to a customer-facing team, go through universities, so on and so forth. And at the top of the bot, as you can see, it has a federated search as well. So if they wanted to search a specific topic on how to get something set up, it'll actually serve up resources to them across all the articles, guides that are hosted within this bot, but also which are connected to our knowledge base. So it also connects to our documentation system and community, and it basically serves results based on federated search across these two or three channels that are connected with each other. Pretty cool, right? One-stop shop for the digital experiences of the users. And think about how to really accelerate time to value for your users, where they're not having to go to multiple resources to find answers. And lastly, this is a link to our community, which, again, we serve up in our in-app onboarding checklist, is where they can actually go and do peer networking. It's a community. There are knowledge bases hosted here. We post office hour updates here. There is peer networking, everything. So this is, as you can think of it, a very omnichannel customer onboarding experience for our users to set up at their own pace to self-serve and really get the help in a very orchestrated way. Next example I have here is capturing goals and outcomes at scale. So as they say, KYC in your customers, in product world, we talk about personas, making sure we know and understand our personas very well. So here's where we, on a quarterly basis, serve up a two-step survey to our users. The first step in the survey is, what are your goals for the next six months? And these are standard four or five goals that we know typically our product gains that BX can solve for. And that helps us capture the goals for the next six months. And based on that, we understand the persona and personalize our one-to-many digital experiences based on these responses and the use cases selected by the personas and the users. And the second step to the survey is what outcomes did you achieve in the last six months with our product? And again, these are standard outcomes that we know typically are achieved by our users and from our product. So this helps us not only understand the goals, but this also helps us understand outcomes which are verified by the users, which then typically are used during the newer conversations where with decision makers, we can have that conversation of, hey, here's where your teams have been driving value from the product and where there are no responses. It helps us, the data knows and sends triggers to our customer-facing teams to say, hey, there's a potential risk here because they don't have an outcome in the last six months or in the last one year. So very data-driven personalized experiences at every level, both for our customers and also our internal customer-facing teams to surface the right and the most valuable activities for them to go spend time on. And we actually usually get 75% response rate. We run it on a quarterly basis and it has been a huge success hit and very, very useful to us in driving a lot of those personalized journeys and also making some of the product decisions around which areas to invest in and which areas to de-pratize. Another example I want to share is improving end-to-end admin experiences. This is implementing voice of the customer which really helps develop that user loyalty. When your customers know that their voice is being heard, there's nothing else that drives that loyalty that trust in your brand, in your product and wanting to work with you. So here's an example of, we knew that for one of our flagship products, Kinsight CS, we had poor admin experience and responses. So we started, took this as a project, started with a multi-channel survey. So we did both email and in-app. And as you can see, in email, we had about 17% response rate. But when we combined email and in-app, our response rate jumped by 70% and we had about 30% response rate to our admin survey. We really wanted to hear out our admins, understand their sentiment, their experiences, what were the areas of improvement for our product and then go and work with product to influence the product roadmap. And really bring that voice of the customer in the product prioritization. So once we collected that data, based on the responses, the sentiment analysis, we identified the top trends and drove proactive digital journeys for our admins. We also worked very closely with our product team to make sure that the top two or three trends that we observed were also being prioritized in the product roadmap. So this together, where there was a stopgap of driving digital journeys for admins, but also investing in the long-term success of the product and working with the product team to making sure we are actually making some of those improvements, so that the UX increase in the NPS of the admin. So that was our highest record ever. So this is a great example of, again, multi-channel digital experiences at scale implementing voice of the customer and working cross-functionally very closely with the product team, customer-facing teams and driving some of these digital experiences hand-in-hand cross-functionally. One more example I want to share with you that we've really done fantastic job with digital experiences and seen great success is release experiences. I'm sure as product teams, you all are thinking about this all the time. And gone are those days where you ship a feature, ship a whole sprint feature release and your job is done. In today's world, the job actually begins then where we need to socialize the features. We need to enable our users with those features. We need to make sure that we're driving adoption and most importantly understanding if the release went well and what was the customer sentiment around that. So this is an example around that where we did the release experience in three parts where we started with specific releases which were targeted just for our admins. So this release experience was just for them to showcase the feature of grades required for new releases. This helped admins plan their work better and plan the enablement for their respective teams better. Then the second step around this was release previews and highlights for all users. So features that were relevant and applicable to all users, there was a separate release experience where there were key highlights from the releases and a preview with in-app video and guides for all users there. And then lastly, validating and confirming how the release went. So capturing that sentiment of the release from our users and tracking that quarter over quarter. That helps understand the quality of the release and the sentiment of the customers. So this really helps drive the release experiences in a very orchestrated way, but at scale and through digital experiences and journeys. And all of this again in a very orchestrated way across multiple channels and collecting data so that further insights and actions can be taken from that. So hopefully some of these examples of onboarding, of adoption of release experiences were helpful and got your wheels spinning on what you can go and implement. I now want to switch to talking about some strategies that you could be thinking about. So those are some examples, tactical examples, which hopefully you can go and start implementing today. But if you were to think about the overall profile, investment profile for driving some of those personalized digital experiences to drive user loyalty, these are some strategies that you should consider. So think of the scope for digital customer success. And this is again, not owned by any one team. This is integrated across the entire customer journey, hence integrated across multiple teams, cross-functional teams in the organization. So starting with the overall strategy, data and the impact that we want to drive. Starting with like, hey, here's a digital experience we want to drive. So making sure we are understanding what's our key goal. Is it effectiveness? Is it efficiency? So on and so forth. Starting with baseline metrics around customer experience, sentiment scores or efficiency within the internal teams. So on and so forth. So then from there, driving cross-functional alignment on what are my key moments of truth to drive those digital journeys, digital user experiences? What should be the strategy? What tools are we using? And really developing an aligned point of view and partnership between product sales, CS and marketing. Thinking about data tracking from the get go. Like you've probably seen data so critical in driving digital experiences, especially when it's contextual and personalized, really tracking that data. And starting with like simple contact hygiene CRM so that we can drive very personalized journeys for various personas and roles, tracking that product usage. So when there's a drop in usage, the customer facing teams can intervene for a potential risk. Or if there is increase or uptick in usage, expansion opportunities can be surfaced and highlighted. And lastly, making sure that we are analyzing the impact of some of these programs and investment and measuring some of the results from there. Then from there, thinking about what are user journeys that we want to start? Everybody wants to do everything, but it's good to understand what are our top priorities? What are the top pain point areas of optimization and really driving those value-based adoption aligned to the goals of the users? So a few ideas to think about is designing those journeys and campaigns which could be around onboarding, adoption, outcomes, so on and so forth. Then around tech automation, planning resources, planning your right tools and technology, which can actually automate some of those journeys or campaigns for your users. With email, with in-app, with community, with office hours, with webinars. So basically all in every channel that you have at your hand to use to drive those digital experiences. Obviously, you also, if you're considering a platform, there is a lot of benefit to moving to one platform versus using five, six different tools to do it because with one platform, you can orchestrate the journey and the insight does that really well where it's orchestrating across multiple channels. And lastly, thinking about scaling some of those experiences. Now, events, community, all of these you might think, what has this to do with digital user experiences? This is pretty much a part of every organization, which it is, but it also has a very special impact on driving digital experiences for the users. It helps them bring, it helps bring the users together virtually in a digital way via virtual events, via community, whether it's peer-to-peer networking, helping each other out, where there is customer education happening. We are bringing voice of the customer, like the example that I shared with you, via surveys, via, and driving those advocacy journeys at scale. So again, these are things, programs, the overall scope of digital customer success that you should be thinking about. You could start with a small experiment throughout the value and then think about expanding to all these various programs and buy cross-functional and leadership buy-in. Like I mentioned, driving digital experience is not any one team's responsibility, it's very much a team sport. And why is that? Because all the teams are really focused on driving, their lagging indicators are actually retention and expansion, which is driven by a leading indicator of product adoption. All companies are today, number one priority is customer retention. And that is everyone's, every team's job. It's product team's job to deliver great products, great features, and productizing some of those flows. It's customer marketing teams to making sure the customer experience and user retention and education is really happening and helping them drive value. And it's customer success teams to making sure that product adoption, those value realization conversations are happening. And in making sure that we are delivering a world-class digital experience, all these teams need to come together because we need help of documentation and education teams to make sure our documentation is clear, concise, and accessible and searchable. We want to make sure our UI UX analytics teams are working on improving the user experiences of our product. We want to make sure our support and implementation teams are on top of their game to resolving customer issues on boarding process in a very healthy and a fast way. So all of these teams need to come together. So think about making, driving digital user experiences as a company priority. Obviously, starting with a small experimentation, proving out value, working in a steerco manner, tiger team manner is great. But once you've done that for longer term success, this is where you should be thinking about at the company level and driving that, making it a team sport and driving for all the, driving with all your cross-functional partners and teammates because all of these teams have a touch point with your customer, either digitally or live. So that's the experience that we need to replicate and orchestrate to eliminate inefficiencies across the journey for your teams and also drive fantastic experience for your customers. So this is how you can think about, when I talk about bringing all those teams together, this is how you can think about orchestrating that customer experience, both for your customers, which is on the left side of the diagram, digitally and both for your customer facing teams, which is represented in the right side of the diagram. So just to take an example here, digital customer experiences, as some of the examples I shared, you can use in-app onboarding checklist, you can drive user nurturing campaigns and journeys, you can develop and direct them to unified self-serve destination where your knowledge-based community, everything is housed under one unified hub. You can track product telemetry, product usage, capture that voice of the customer feedback and get roadmap inputs and really drive that cohesive journey and send those product telemetry and triggers on the right to your customer facing teams. So they know where there is a risk intervention need to happen, where there's an expansion opportunity, where there's a value realisation conversation, so on and so forth. And of course, there are now air-driven recommendations, federated search and whatnot. And on the right, you want to think about how to replicate that and scale your humans. So what technologies can we use to do shared success planning? So sales can use a platform and a tool to capture goals, which is then digitally passed on to your post sales team like services, customer success, and they exactly know how to drive those digital experiences and journeys based on that success planning goals captured during the sales process. Again, all of this can be automated and orchestrated in a very digital way. You can scale the release awareness process as the example that I shared. This really helps scale your customer facing teams, where they are not answering questions from your customers about release awareness, how to use a product, how to use a feature, so on and so forth. And using data, again, to drive those identification interventions as I shared in my previous example. CT is in playbooks for your customer facing teams. Customer 360 and health score, where your product usage, voice of the customer, everything is available to your customer facing teams. So this is how you should be thinking about orchestrating digital customer experiences between your customers and customer facing teams. And lastly, this is overall, think about it as essentials or key elements for digital experiences. You can consolidate all the various building blocks. Now, this might seem a lot, but take a pause and I'm sure each of you have lots of these elements already going on in your organization. It's just about consolidating all of them so that the personalized experiences can be driven. So look at this diagram at the top. Our end goal is driving those personalized digital end user experiences, which is the right message at the right time and meeting the customer where they are, whether they are in the product, whether they are in the community, whether it's one-on-one. Now, how do we get to that goal? So go to the bottom of this diagram and let's start there. Data is the key to this for automation, for personalization. That is the source of truth for our customer health and driving automation. Some examples are CRM data, tracking user attributes, product telemetry, so on and so forth. Once we have that, focusing on different building blocks, which again, you probably have a lot of these blocks already, resource centralization, right? Like a simple knowledge base or using a community to house all of your customer-facing content together, tracking success metrics, baseline and target metrics, quarter over quarter, driving those cross-functional alignment as I just discussed between marketing, sales, CS, and product teams and making sure that everybody has a shared and aligned goal and view of the customer journey. Once you have that, identifying the triggers for driving those digital experiences, the personas, the key milestones and moments of truth, so making sure there's an alignment on that point of view for the customer journey. And lastly, feedback loop, bringing that voice of the customer from your product and incorporating that in our product roadmap and our customer engagement model. So we are really driving that fantastic world-class customer experiences. And you want to make sure you're using those right channels and the right platform to bring all of these together and meeting the customer where they are, whether in the product, community, one-to-many, webinars, so on and so forth. And here there's an example of GINSIDE because that's probably today the only platform in the industry who's driving those superior experiences at scale across all these channels whether it's knowledge-based product elementary, in-app guidance, email, community education. So the key really is data and driving those experiences in a very, very orchestrated way across all of these channels so that we don't want to be that noodle experience of our users. We want to be the superior experiences for our users. And if this feels too much, I want to leave you with last one simple slide. If you're not ready to or that feels overwhelming, these are three simple things that you can get started for your digital experiences today. Test it out, see the impact, see the small win, and then learn from it and go and think about the larger investment. You can simply start with centralizing your key customer resources. It can be even in a simple word doc which you can bookmark and share with your customers. Start with a simple welcome email, post your first value action in your product and link them to your resources. And lastly, do an in-app release highlight, whip up a quick in-app communication highlighting your key release features. So signing off there, thanks for tuning in today. I'll be taking some questions and answering some questions in the end. Would love any feedback, thoughts, ideas, something that has worked for you, something that has not worked for you. All of you on LinkedIn. So thanks for tuning in. Until next time. Bye-bye.


Okay, we're going to try something a little bit different that I have never done before. And for those of you that have the download link for this, hopefully it's going to be helpful. And if you have any feedback, I will certainly take it and we'll see where this goes from here. So what we're going to do here is we're going to do a little three minute exercise together. And I'm going to teach you what it actually looks like to relax and surrender into anxiety and panic. I get asked to do this all the time. People want to know like, well, what does that look like? I know you say to float. I know you say to surrender, give up, let it all happen. What does it look like? So I'm going to try and actually teach you what it looks like. You hear me use three things all the time. Relax your body, breathe from your belly and focus somewhere other than on the thoughts and sensations. So we're going to learn to relax, breathe and train our focus where we want it to be. And this exercise is going to use our breath, but it could be anything. So what is this not? This is not a rescue tool. This is not designed to like save you from panic. It's not going to stop a panic attack dead in its tracks. It's not going to make your anxiety go away. It might not even make you feel any better. It's not supposed to do that. What we're doing here is we're learning a skill and building a tool that you will use as part of this new way of dealing with anxiety and panic that you're learning.  I hope this is something that you will ultimately practice multiple times every day because this is not something that you do once and like know how to do it. You have to actually practice it over and over, learn how to do it, get good at it, just like learning a new language.  So if this is all new to you, ultimately you will be able to use this tool even while you're up walking around working at school, even driving. But for now, when you're just learning it, you're going to learn it in a stationary position. So I want that to be either laying down or sitting in a comfortable chair somewhere. So find a comfortable place to either sit or lay and we're going to start off by scanning your body for tension and releasing it. We're going to go from your head down to your toes. So when you're comfortable, try and get into as neutral a position as possible and let go of all the tension in your body, all of it. All right, so we want no tension anywhere in your body. Just relax, let go of all of it, all of it and now let's start from the top of your head. Check the top of your head, the muscles in your scalp. Is it tight? Now those are involuntary muscles, but do you feel any tension there? Do you feel any tension in your forehead?   Let's do this with your eyes closed. But gently close your eyes.  Let's move down to your jaw. Your jaw should be slack. Open your mouth if you have to. But the preferred position that we want to get to is mouth closed, jaw unhinged, tongue on the roof of your mouth just behind your teeth. Then a nice relaxed position. So take the tension out of your jaw. Now let's go to your neck. Is there any tension in your neck? Are you holding tension in your neck? Your neck should be in a neutral position, relaxed. Not craned forward or back or off to the side or twisted. So take the tension out of your neck. Then we're going to go to your shoulders. Wear your shoulders. Drop them down. They do not need to be up around your ears. That's tension. So relax. Let your shoulders fall to the lowest position they will go to naturally. Now move up to the front. Is there any tension across the wall of your chest? There might be. This is really common. You cannot relax your chest wall muscles. But do your best to let go of whatever you can that you feel there. Then I want you to go back down to your tummy. Is there any tension in your abdominal muscles? Are you tensing? Are you tight? Like you're ready to take a punch in the stomach. Let go. Let go completely. Especially if stomach upset is an issue for you. Just let it be upset if it's upset. But let go of the tension in your stomach muscles. Now let's go back to your arms. Open your hands and let your hands be open in their natural position. No flexing. No fists. Just let them fall open in a natural position. No tension. Keep those shoulders down. Release the tension in your arms. Now let's move back to your legs. Is there any tension in the top of your legs?  Let go. If you're sitting, go into a neutral position where the chair is supporting you. You don't have to grip the floor.  Let gravity do its job to keep you in the chair. So release all the tension in your thighs and your quads. Make sure that you are not clenching your glutes. Very common. Let go.  Make sure there is no tension there. Let your feet just be in a natural neutral position. You don't have to flex or exert any sort of muscle control there. And make sure your toes, just give them a little wiggle. No tension. You don't need to be gripping the floor or the inside of your shoes. So let's go quickly back up to the top of your head.  Down your arms into your hands. Your chest and stomach especially. Through your legs all the way down through your calves and your feet. Make sure that there is no tension anywhere there. And every few seconds just make sure, if any comes back, you're just going to re-release it. That might happen. It's okay. You're learning. You're learning this. It's not going to be perfect. Now that we've got the tension out of your body, now let's look at your breath. So I want you to start breathing into your belly. Not your chest and not your shoulders. There's no reason to have your shoulders moving. There's no reason to have your chest moving. When you inhale, your belly should expand. So inhale into your belly. Pause for a second and exhale. I want the inhale to be through your nose. A very brief pause.  So your exhale should be longer than the inhale. This will keep you from over-breathing and hyperventilating. So breathe into the belly. Expand your belly. Hold for a second. Gentle exhale. Longer than the inhale through your mouth. And just let that come naturally. Relax all the tension that's in your body again and again and again. You may have to continually do that over and over. That's totally fine. If you find any tension, let it go and just breathe in and out. This may be incredibly uncomfortable for you right now because you are not tensing and you're not doing anything to react to what you feel. So you're going to feel it very intently. So now I want you to take a few more of those breaths. No tension. Just let it be there. You're safe. In through the nose. Quick pause. Out through the mouth. Always in and out of the belly and the abdomen. Never the chest and the shoulders. So now what I want you to do is take your focus and put it at the tip of your nose because when you inhale, you will feel the sensation of the air entering your nose. You'll feel it running across your nose. Just focus on that. And if you have to, gently say to yourself, breathing in. Then a pause. Breathing out. Focus on the sensation of the air entering your nose and then leaving through your lips. Into the belly. No tension in your body. So now that we know where we want our focus to be and when those thoughts, anxious and fearful thoughts are coming and sensations in your body are coming, it's okay. Let them come. The object of this game is to just bring your focus back to your breath. So stay relaxed. Stay relaxed. No tension. Just breathe the way I taught you. We're going to do three minutes. I'm going to be pretty much silent while we do it. Ready? This is going to be scary and uncomfortable for you. If it is, that's okay. It's supposed to be. This is practice.  All right, ready? We're going to do our three minutes starting right now. Breathing in and breathing out. Breathing in and breathing out. You do it without me. If you're tensing, release it again. If you're having thoughts and you wind up engaging with them, that's okay. Just bring your focus back to the sensation of your breath. Gentle breaths in and out. Excellent. You've already done a minute. That's a minute of not tensing and not reacting against the anxiety. Let's keep going. Releasing the tension. When you feel bracing, when you feel tension, just re-release it. Keep breathing. When those thoughts come in your head, just let them come. You don't have to stop them. It's not bad for you to think them.  And through the nose, hold out through the mouth. You've done two minutes.  You can feel it around the rim of your nose. Notice what it feels like when it leaves through your lips. Purse your lips a little bit. Just focus on that sensation. No tension, no bracing, no fighting. Just breathing and just putting your focus where you want it on your breath. No fighting. That was three minutes. You did it. You did it.  And it may have been scary and it may have been uncomfortable and it may have been very foreign and awkward for you to do this, but that's okay. That's okay. So while you're listening to me now, just keep the tension out of your body. Keep re-releasing the tension. Focus on the breath when you need to. It's totally fine. You're not trying to stop the thoughts. You're not trying to stop the sensations. You're just learning to be non-reactive when they come. And what we're doing here is we're actually speaking to the fear center in our brains. It only understands body language. It doesn't understand words. It doesn't understand logic. It only understands this. So when you do this, you are sending signals to your brain that say, this is uncomfortable, but I'm totally okay. And over time, the fear center in your brain begins to adapt and say, oh, I don't have to sound an alarm when I have those thoughts or have those sensations. So I hope you're still laying or sitting. Just keep re-releasing the tension. Scan from the top down. Breathe into the belly, not the chest and the shoulders. And bring your focus back to your breath. So what I'd like you to do is schedule some time over the next few days to practice this again and again and again. Take a little three-minute breaks. Get comfortable. Scan for tension. Check your breathing. Go into your belly breathing. And then just focus on your breath. Give yourself three minutes at a time. If you want to do more, excellent. You go as long as you want. And the more you practice this, the better it is you're going to get at it. All right. So that's our little three-minute exercise. You did really well. Thanks for giving a shot at this. I know it's new for me, too, to teach this. So let me know what you think. Your feedback is welcome. And we'll modify it from there. OK. See you soon.


Hi everyone, Joe for jasmiescasebrakes.com coming at you with 2020-2021 upper deck SPX hockey five box Random team break number three from a fresh inner case the second inner case from the master that we popped open a little while ago Very big thanks to all of these people for getting into the action. Thank you one spot gets you three teams So let's triple you up. You'll see Michael P's last spot mojo star next to his name three times and then Ducks and Jets combo it up. So it'll be 30 on each list. Let's roll it. Let's randomize it four and a three seven times lucky seven for names and teams one Two and seventh and final time after seven got Adam all the way down to William four and a three seven times for the teams one six and Once again seven one more time seven and final time After seven we got Buffalo Down to the Boston Bruins. All right Adam with the Buffalo Sabres Peter with the Oilers Dan with the Wild mark with the Canadians Jack with the Rangers Adam with the Blackhawks and the Predators William with the Devils Michael with the flames in the abs Dan with the Hurricanes Adam with the Blues mark with the Toronto Maple Leafs Daniel with the Florida Panthers Dan with the combo ducks and Jets mark with the Senators Michael with the Caps Jack with the Blue Jackets Peter with the Kings Adam with the Sharks Dan with the Flyers Jack with the Golden Knights William with the Red Wings Dan with the Islanders and Coyotes Daniel with the Stars Adam with the Lightning Daniel cats with The Canucks Peter with the Penguins and William with the Boston Bruins Let's put this all on one screen here. There we go. Let's sort by column B by team alphabetically and While you're so you can trade if you'd like and we'll pause in just a second But we're gonna select the five boxes first. All right, so there's five boxes on the left side five boxes on the right side Let's select a die. Let's select that one So we'll go one two three for the left side four five six for the right side, and it's one So we'll do the left side here this side. We will save for This is a random team three random team four marked all those boxes are T4 and I'll post the next one of these and I think there's another master case after this one, too So if you want to keep running back this spx, I'm definitely down to I'm gonna pause the video We got Vegas up for trade by the way I'm gonna pause the video. We're gonna come back and when we come back We'll see if there's any trades then we'll have the break stick around All right, welcome back folks. So no deals so the list remains the same here. It is hot off the presses Thanks, everyone for making this happen Appreciate it a little bit of trade chatter, but in the end no deals were done And let's see what we got it's a small example of what of what we're looking for good luck everybody First one is Matt Matthew Kachuk to 299 that's for Calgary that'll be for Michael Anthony what's going on every break? That's not sold out when you click on it says page not found No, but it works for me. I Just clicked on select football and it takes me right to select football Is there a specific page you're trying to look at all right next one is rookie jersey and autograph Ali ULV For the Canucks 129 out of 375 Yeah, it's working for TJ there's Daniel Katz with Vancouver Anyone else having issues with the website with the page not found Yeah, I think this guy's pretty good, right Corill Capers off SPX rookie jersey Going to Dan and Minnesota Right works for Adam Cofferman. He's saying that when he clicks on select football reminds him that hey He wants to sell out that select football or halfway there on that break. It's only a three-boxer. We can get that done fairly quickly People jump in on it We've got an Islander. We got Kiefer bellows Finite rookies that's going to be for Dan and the Islanders Yep, HP and Jack are saying this guy's probably gonna be a rookie of the year All right. That was the first box next box first one is an autograph. It's an island It's the Kiefer bellows again 172 out of 299 It's another one for Dan and the Islanders and another autograph this time for the Dallas Stars Tyler Sagan Stars Either way, it's gonna go to Daniel Katz the Daniels Hitting so far. There you go next up is Is Nick Robertson for the Maple Leafs SPX rookie jersey Toronto It's Mark in the mix now and the last one out of this box is gonna be Shadow box Alexander Barkov for Carolina Check that for that's the Carolina Panthers. This is the this is the Florida Panthers This one goes to Daniel Katz box Second half of this inner case in the store. So if you're watching live if you want to run this back Only 10 spots pretty easy to don't need too many people Yeah, the shadow box cars do look pretty awesome. I think sometimes they can be autographed maybe You got a city in Jersey Vitek Venisek for the Capitals Michael P last Bob Mojo and that's 32 out of 399 kind of hard to see right there, but 32 out of 399 autograph Dylan Cousins Obsidian scripts silver ink autograph for the Sabres Go to Adam cuppermen There's our Temi Panarin for the Rangers The we have Contenders figure teams. Yes, Jasper's case rates comm got contenders basketball figure teams available TJ saying this is a good one Dylan Cousins Yeah, I don't know too much hockey. I'm relying on everyone to tell me who's good. Who's not all right So Mike Towers saying cousins was number seven in the 2019 draft I'm gonna base card here obsidian rookies Brandon Hagel 341 out of 349 Chicago Adam with the Chicago Blackhawks two more bucks. I've got sixers at wizards About to tip off we got a jersey spx rookie jerseys Ryan McLeod for the Oilers Peter with Edmonton. I know right Bunch of very excited kids at the shop Jeremy Jasper's we're just we're just setting up a new generation of collectors That's what we're doing. There's Liam Fowdy Columbus for Jack Actually, I think it was like two or three different families here. The store is really busy for a second there Josh Norris. Oh No, I you just said contenders pick your team So I wasn't sure what you were talking about but no no contenders football pick your teams. It's Josh Norris Ottawa Mark and kind of a thicker card here. It's super scripts seven out of 25 Philip Denalt For Montreal for mark. That's right TJ There is a 15% off Automatically applied at checkout for court Kings pick your teams. Oh Nick saying Sean pulled a one-on-one printing plate auto last night as well out of contenders draft We have a hot case. Yeah, let's fill up the last two boxer Here's the last box of SPX hockey Second half of this is already in the store Jasper's case breaks.com if you want to run this back Remember no late night tonight. So get your spots in early and often You got a piece of the Begney Malkin's jersey for the penguins. That'll be for Peter. Let's see what we have here Is that an autograph? Like it should be autograph. It's a pretty good-looking card right here. What is that number two? It's kind of hard to see actually Putting some purple up against the black background, but that's 10 out of 99 That's a Vitelli Kravstov. You can kind of see the purple foil against the against the black there New York Rangers that's gonna go to Jack B And we've got Pierre-Luc Dubois for the blue jacket. It's also for Jack and let's see Last one here is autographs and it's it's Anze copetar for the Kings Nice try to watch a little more Kings hockey this year Copetar Having a nice nice season as well Doesn't look like it's numbered, but that's a nice one for Peter and the Los Angeles Kings And that is that ladies and gentlemen. Thank you very much. That was 2020 2021 upper deck SPX hockey Five box pick your team number check that random team number three from Jasper's case breaks calm Thanks for breaking with us. Thanks for watching and I'll see you next time for the next one