Speaker 0: Alright. Uh, thank you, everybody, for joining us. We have an amazing speaker today. Um, Jeff Stollenwerk is going to, uh, discuss his session using Einstein AI within account engagement. So with that, I’d like to introduce you to Jeffrey Stollenwerk from Salesforce.

Speaker 1: Yep. Thank you, Catherine. So, hey, everyone. My name is Jeff Stollenwerk. I’m a lead solution engineer at Salesforce specific for account engagement, the most tenured SE on the team. I have seven years under my belt covering every segment from SMB through to enterprise, which I support today. And seeing the evolution of the tool, and I can’t be more excited to present on such a hot topic right now, which is using Einstein AI within account engagement. And before I jump, um, into the session, wanna quickly give a special thanks to all the sponsors. Um, you know, this this event wouldn’t be possible without your support. Now like I was saying, Einstein AI within account engagement is a hot topic right now. Fun fact, for the last two years, I’ve been, um, doing the presentation for, uh, the Gartner Magic Quadrant. And they recently just came out with their report, and they highlighted our enhancements within the AI, uh, across all all different parts of the platform as as one of our strengths. And, uh, for today, I’m gonna cover the evolution of Einstein, give you a little bit of background of how it made its way into account engagement, speak to some of our competitive advantages with the AI with marketing and sales data, uh, the expected benefits that a lot of our customers experience when they start using the Einstein AI, what you’ll need to get started on this and and to get optimal insights, how each feature works, some of the values there, and some Einstein life hacks and pro tips of how you can really maximize the value in creative ways. So with that, let’s jump into the evolution of Einstein.

Speaker 1: And if you were around in in the in the industry or at Dreamforce in 2016, that’s when Marc Benioff announced that we’d be using Einstein with artificial intelligence and machine learning to make Salesforce a world class smart CRM using your own data to serve up insights to help you do your job better across various departments. Then we started for the next few years, we focused on enabling this within our core platform, within marketing cloud, commerce. And then in 2019, we introduced Einstein AI for account engagement, first by launching the Einstein lead score, the behavior score, and campaign insights. Then in 2020, we enhanced the behavior scoring piece with its own dashboard within b two b marketing analytics to uncover what’s fueling the Einstein behavior scoring, and then we released the dashboard for the Einstein data driven attribution, which was a game changer for our customers. Then last year, we introduced the send time optimization and key account identification, which is my personal favorite piece of the Einstein features. And then just earlier this year, we released the engagement frequency. Now these are all features that I will be covering in-depth. Um, but before jumping into it, let’s just speak to what I Einstein AI really is within account engagement.

Speaker 1: So in a nutshell, it’s a growing set of turnkey AI powered features across multiple objects that continuously monitor and analyze your data from account engagement and Salesforce for marketing and sales insights. Now we can break this down really quickly. So like I showed you in in the evolution, it is a growing set of, yes, I said, turnkey, uh, AI powered features. Now that doesn’t mean necessarily turnkey for every customer that we have at Salesforce, but turnkey for customers that are leveraging, you know, standard processes and the standard objects the way that they’re intended to be used. And then across multiple objects, we surface these within Salesforce campaigns, leads, contacts, accounts. We leverage it within the, um, CRM analytics and, you know, making that visible also to the sales reps to help them do their jobs better. That and it continuously monitors and analyzes your data with that ongoing machine learning to pick up on new trends within closed deals and new engagement trends to keep you up to date so you can act on those. And then with account uh, using account engagement and Salesforce data to provide marketing and sales insights, that here is really the big differentiator against our competitors. So we can leverage all of that Salesforce data so we can cover all of the data from every stage in the life cycle, from prospects to marketing qualified leads, um, you know, opportunity data, closed deals, so we can leverage it to pick up on the insights and and share those with marketing and sales throughout the life cycle. And you can see below in the orange where some of these features fall into the life cycle. Now not only can we leverage the entire life cycle, we can leverage all sorts of data. So, for example, at the bottom right here, the key account identification. That’s helping to identify key accounts for, um, you know, things like ABM. Now what does that leverage? It’s leveraging all sorts of data from Salesforce. So it’s looking for fields on objects like the account, the contact, currency type, events. Further down, you could see price book entries and tasks. So this is the type of data, that rich depth and breadth of data from the CRM that is really gonna augment, um, you know, our marketing data and kind of guide, uh, and and and set the direction of what is really moving the needle to help drive pipeline and close deals. So, again, we can leverage the full life cycle data as well as Salesforce custom objects and other ancillary data out of the box. That is that is the big differentiator that we can offer.

Speaker 1: So some of the luxuries that our customers experience when they start using Einstein AI and account engagement, So this is kind of high level. I’ll be speaking sort of some of the the luxuries and values per feature. But, overall, turning on these features is extremely easy. It’s really just a flip of the switch. And like I mentioned, if you’re using Salesforce as it’s intended, you’ve already been collecting most of this data in the background without even knowing it. Also, it automates time consuming tasks like building out a scoring model or refining the scoring model and eliminates annoying guesswork such as, you know, how many points should a certain form submission be worth, or how frequently should I be emailing my customers. And it brings the insights directly to you embedded within all of the Salesforce objects. So in providing that additional context to help you understand the contributing factors behind it, and it leads to more accurate and insightful reporting for your ROI, um, for your campaigns and what assets have been moving the needle to get people to an evaluation. So, basically, in a nutshell, it’s just helping you do more with less less time and effort. And I I haven’t heard anyone complain about that.

Speaker 1: So what you’ll need to really start leveraging account engagement and make the most and get those optimal insights is leveraging account engagement advanced or premium edition, the Salesforce Lightning, either enterprise performance or unlimited edition, and then ample data. So for each Einstein feature, there is a a a recommended amount of data to have in there, uh, in order to to have optimal insights. And as a follow-up resource, at the bottom of this deck, which will be shared after this session, I’ve included what is required and suggested for each Einstein feature. And then lastly, just like with everything in Salesforce, it’s all about data integrity, garbage in, garbage out. Einstein’s gonna be analyzing patterns within your data, and so keeping that clean is gonna be really important.

Speaker 1: So take a look here at the Einstein menu. I’ve kinda broken this down into different operational buckets. So I’ll be starting first with marketing optimization, then we’ll jump into marketing and sales prioritization, and then go into marketing insights.

Speaker 1: So when we’re talking about marketing optimization, this is one of those areas where if you were to ask a marketer if they thought that, um, you know, the times in which you send emails and the frequency in which you send to your prospects, if they were important, 99% of them would say yes. But when your day gets going and you have certain KPIs to hit and you have deadlines, oftentimes, these sorts of things will be brushed to the side. And what we’re gonna be able to do here is automate this for you, take it off your hands, and these, you know, quick little wins that are low calorie going on in the background really add up. So let’s go ahead and jump into the send time optimization. So this is a feature that really helps you send your emails at the right time for each recipient. So this actually you can see on the right in the visual, when you’re sending a list email, you can choose a window in which you want it sent between certain amount of hours or a certain amount of days. And then for each recipient, it’s gonna look at their historical email engagement data and pick the optimal time for them specifically. So you can beat your inbox competition. That’s gonna be at the top of the inbox when they’re usually checking their emails. And just that overall visibility is gonna really optimize the level of engagement that you get. And the pro tip here is, um, this helps really to overcome the challenges when you’re sending to different time zones and also with email throttling limits. So if you’re sending an email to a lot of individuals at one company, this is gonna help to stagger those emails so it’s less likely to get flagged.

Speaker 1: Now the Einstein engagement frequency here, this is to help you identify the optimal send frequency for marketing emails to identify that sweet spot to avoid unnecessary opt outs. Right? So this is an AI driven engagement frequency status that is stored on the prospect field and is unique to each prospect based off of their email engagement behavior. It uses email engagement from list email sends, emails through engagement studio, as well as, uh, sales emails and alerts formerly known as engage. And a pro tip here is, you know, you can use this as criteria anywhere. So we recommend creating a dynamic suppression list that’s automatically updating with your oversaturated prospects to avoid email fatigue and unnecessary opt outs. And to show you this, we’ll jump into this this demo here. Well, we’ll take a look at Ron Abel and one of our prospects. You’ll see here under insights, we have the engagement frequency status. He’s saturated right now, suffering from email fatigue, at risk of opting out. So here we’re creating a a dynamic list automatically updating for anyone’s current frequency that is saturated. Right? And we wanna avoid having them opt out, and we could use that suppression list here in an automation. Here’s an engagement studio nurture program where we’re gonna add those saturated prospects list to be automatically suppressed, again, um, so that we we reduce the opt outs. So, again, the whole idea of optimization, and this really does apply across the board with the other features I’ll be talking about, is optimization is really just doing more with less. Right? So, again, send time opt optimization is choosing the right time to send to these people, which results in higher engagement rates. The engagement frequency is finding that sweet spot as far as the volume of emails, and that’s gonna reduce the opt outs and increase your prospect database. And so the combination of those two really helps helps you to do more. And to enable it, you’re really just flipping a switch. When you send a list email, you pick your time frame and for the send. It eliminates the guesswork for as far as the frequency or the send time, and it’s all automated. So we’re doing more with less here, and that’s really, you know, how how you get those wins and and boost your marketing results. So one of our customers can attest to this. So Zeno from Elrond said that account engagement Einstein saves each member of his team fifteen to thirty minutes a day just on segmenting leads by using the Einstein optimization features. And, you know, when in this day and age with busy marketers, fifteen to minute fifteen to thirty minutes a day is huge.

Speaker 1: Now coming back to the menu here, we covered marketing optimization. Now we’re gonna jump to the marketing and sales prioritization features of Einstein. So we’ll start off with lead scoring, then go into behavior scoring and key account identification.

Speaker 1: So the Einstein lead scoring piece here, this is specific to the lead object, and it’s helping you to target leads who are likely to convert. So oftentimes, this is, uh, you know, similar to the rules based grading. So this is gonna help sales to prioritize their leads list for those profile fits, the type of people they wanna be talking to. And this can be used in smart workflows. So you can use automation rules to automatically assign tasks, for example, to sales reps for those high persona individuals or use it in an automation. So you could say, you know, if someone is not engaging with email and they’re they have a high profile fit, maybe we’re gonna try to put them in in an advertising audience. And then you you this provides the predictive insights. You’ll see here in the visual that you can see the contributing factors leading to this high or low profile fit, and that additional context is always appreciated by sales reps and really helps keep that marketing and sales relationship healthy. And one of the nice pieces about this is you have the flexibility to tell Einstein, hey. These are the fields I want you to consider within the pattern recognition. Please ignore these dirty, uh, fields and duplicative data.

Speaker 1: Now with the Einstein behavior score, this this is really nice because it really represents this person’s intent to buy and their current level of engagement with all the the tracked assets in your website with account engagement. And the other nice piece about this is this starts right when somebody becomes a prospect all the way through to being a contact. So it’s that continuous machine learning. It’s a unique scoring model for your organization based off of your data and the patterns within, uh, closed opportunities, open opportunities, the engagement patterns. It’s automatically adjusting. And the more data that you’re collecting, the more accurate it’s gonna get, the better insights it’ll serve. This has enhanced key factors similar to the Einstein lead score providing that um, additional context to the sales reps as far as why this person is showing intent to buy currently. You’ll see in the visual here, you could even call out specific assets that others in the past who got to an evaluation interacted with. Like, this person has viewed the pricing page five times in the last seven days. That’s that’s some helpful information. And this also will help with improving prospect targeting. So you can use the Einstein behavior score as the threshold to automatically pass over leads at the right time when people are showing current intent signals, and you can leverage it steps within engagement studio or some segmentation lists. And this also has time degradation built into it, which is huge.

Speaker 1: So a lot of, uh, you know, people ask me, but what are what’s really the real differences here between the rules based scoring and the Einstein scoring? We covered a little bit of this, but with the rules based scoring, there’s no silver bullet. Right? There’s a lot of guesswork involved to think of, okay, how many points should this form be worth versus another one? And a lot of that work, oftentimes, it’s it’s not guaranteed to drive results. It’s also lacking that additional context for sales reps to help them better understand why this is a good profile fit or why they’re showing intent to buy right now. And those rules based scores could get really top heavy, like 350, 500. And what what is that, you know, uh, is that good or bad? And then it’s also very time consuming, building out, um, a model from scratch, but then if you’re getting feedback from sales that it’s not accurate, trying to refine that. And every time you create an asset, you have to choose the weighting for it. So the Einstein scoring helps to alleviate a lot of those pains. So it takes away that guesswork. You’re leaving, um, Einstein to use the patterns in your historical data to determine prioritization. It also provides those valuable insights, which the sales reps really appreciate as far as the contributing factors leading to this high or low, um, profile fit or intent to buy. And then it’s automated and continuous. So, again, this is always gonna be keeping up with new engagement trends, new closed data, so it could serve you the most up to date insights and get smarter over time.

Speaker 1: So customers can use both. They’re not mutually exclusive. So, for example, some of the use cases I’ll see is customer might use both the behavior scoring and the rules based scoring specifically for the scoring categories if they offer lots of products and services. So So that you’re using the behaving behavior scoring specifically for things like you the threshold of when to pass over a lead because it’s based off of current engagement levels and intent to buy or using them within automation rules to flag an alert to a sales rep to see if they can drive an opportunity right now. But then the rules based scoring add is adds that, um, additional value for, like, a marketer to be able to automatically take people down nurture paths around where this person is showing engagement with specific products and services. It’s also helpful context to include on leads and contacts as well for the sales reps to know what to talk about.

Speaker 1: Now the Einstein key account identification is one of my favorite pieces here, and this is going to, uh, give you two things. The assesses fit of this account for your products and services as well as intent to buy. And so it’s looking at your historical, um, you know, accounts that have purchased, and it’s looking at their profile traits. Now one of the nice pieces here is we actually have our own proprietary data repository that we we give you access to use within the pattern recognition here. So it’s based off of normalized and accurate up to date data. You can even push that data into your fields on your account so you can use them within segmentation and automation. And those profile traits and and a lot of the patterns there is helping to serve up the tier fit, the like, um, the the fit as far as your for your products and services for this account. And it’s using the engagement traits of this account and how the those compare to people from the past that purchase our accounts, uh, to tell you how likely we think a deal will close in the next six months, if at all.

Speaker 1: So what else can you do with Einstein scores? Well, it’s not just a lightning component or an iframe like like our competitors would offer. You can use this as criteria within your automations or control your dynamic content variations. It’s visible on the prospect record and also within list views for sales reps to use and see the contributing factors, um, in a list view to drive you know, to be more efficient. It’s included, you know, in Salesforce flows and reports or list views so you can get really creative with it. It also helps to drive a data driven content strategy, for example, with the behavior scoring dashboard, which I’m about to explain in a moment here.

Speaker 1: So a little bit of a a demo here for, um, we’re gonna be jumping in to our ABM account targeting campaign. We’re here. We’re gonna see our ABM targeted account list that we’ve been previously using, where we are using industries that we think we work well with and some named accounts based off of their domain. But now we can supplement the amount of the the accounts that we’re targeting based off of the Einstein account tier for for the fit. So for any accounts with an a or b b tier, the good fit for our products and services, they will be added to this list to supplement what we’ve been using before. And now we can, uh, use this with also within engagement studio. So here for this ABM nurture, if somebody’s not engaging with emails, right, this is where we can use the Einstein lead score as a step to see, okay, are they a good profile fit? Because if they’re not engaging with the emails, well, let’s go ahead and maybe place them in an advertising audience, um, using the external action functionality because maybe we can drive engagement on that channel. We can also use this in the the automation rules, kind of the quick and dirty automation. So if at any point, any lead or contact has a behavior score showing intent to buy that is high, we can flag that to a sales rep by notifying them or creating a Salesforce task so they can follow-up in a timely manner and try to drive an opportunity. And also for the sales reps here in sales sales cloud, they can sort their list by the behavior score, hover over it to see the contributing factors to help help them, um, understand the why, and also for the profile hit fit here with the Einstein lead score.

Speaker 1: So back to the menu, we just covered the marketing and sales prioritization Einstein features. Now let’s jump into the marketing insights.

Speaker 1: So we’ll first start off here with the campaign insights. So this is gonna help you unearth hidden campaign insights as far as the key factors that drive, um, engagement from an individual campaign level or all of your campaigns across the board. It also serves content engagement insights to call out significant, um, engagement from specific audiences and also help you to optimize your target audiences because it gives you the the attributes of individuals that have been engaging a lot. So you can build list for for future sends and future engagement programs to try to repeat that success.

Speaker 1: Now with iSign attribution, this was a huge release because this is really gonna give you a a more accurate representation of your campaign ROI. So, historically, the marketers would have to rely on sales reps to take a manual step and add contacts to the opportunity contact roles and opportunities in order for marketing to get credit for the work that you’ve been doing, and that would drive marketers bonkers. And so this is an out of the box AI data driven model that overcomes that challenge. It uses AI to scan all of the engagements from contacts at the account where the opportunity closed. You can even set the time frame window in which it, um, searches for that engagement and campaign membership. And it also looks at things like the sales task and activities being logged against contacts at this account to then automatically add those contacts to the opportunity. So marketing gets credit where it’s due. It’s more inclusive of the buying committee and not just the the decision maker, but everyone else. Their research is gonna also gonna be included. And then we’re gonna distribute the campaign waiting based off of the the level of meaningful engagements, uh, around each campaign, which is which is a huge improvement upon things just like an even distribution model.

Speaker 1: Now the behavior scoring dashboard, this is unveiling what is fueling the behavior scoring to understand key, uh, engagement patterns with your asset types and assets that are moving the needle, getting people further in the life cycle to an evaluation. So understanding, really, the factors behind purchasing behavior. So you can see asset types that are most influential such as your webinars, and I’d be able to click in to see specifically which webinars were most commonly engaged with for individuals that got to an evaluation. And I could see those those those, um, contributing factors and assets within different time buckets as you can see there at the top of the visual. And then this is gonna, um, also to help you just have a data driven content strategy going forward based off of these insights.

Speaker 1: Now, again, just wanted to do a high level review of what we covered with the different features with Einstein for account engagement. We covered the marketing optimization, really easily be able to send emails at the right time to improve the engagement. The in, uh, the engagement frequency, which is automated, so you find that sweet spot, so you’re not over communicating or under communicating, avoiding opt outs. Prioritization for the Einstein lead scoring as far as who’s the best profile fit, behavior scoring showing the intent to buy currently based off of the current engagement patterns, key account identification to help marketing and sales, uh, target the right accounts and understand their the likelihood to purchase. And then lastly, with the insights, understand what’s fueling campaign performance, the why, the Einstein attribution, which is much more accurate representation of campaign ROI, and And then lastly, downside behavior scoring to understand which assets and asset types are really, uh, beneficial in driving people to an evaluation. Um, and so that was everything I wanted to cover here today. I wanna thank you all for your time and joining today’s session. Um, I hope this was helpful.

Speaker 0: Thank you, Jeffrey. We really appreciate it. Um, there were some questions in the chat, um, but it looks like we are a little bit low on time. So what we’re going to do is we’re going to capture those questions, and then we will be following up with you for some answers. And just wanted to thank you all again for joining us, and, of course, a special shout out to our sponsors for their support. Without them, Marjorie, it wouldn’t be possible. So please make sure to pop over to the sponsor booths sometimes during this event to show them some love. Alright. We hope you have a good rest of your day.

Speaker 1: Awesome. Thank you so much.