Empower Your Business with Sales Content Automation for Personnel

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Hey, everybody. Joseph here, welcoming you to another Stukent expert session. Today, we'll be hearing from Braydon Unsicker. Braden has a passion for marketing automation that can be traced back many years. He is currently an email marketing manager at Tinuiti, a full-service digital marketing agency. Tinuiti has managed over $1.5 billion in media spend for many well-known brands of which he is also a customer, like MVMT Watches and Converse. He likes to spend his money on golf and is not afraid to dabble in lesser-known sports, like dance skating. We're all ears, Braydon. Take it away. Hey, everyone. As was mentioned, my name is Braydon Unsicker and I'm really happy to be here. I'm a big fan of Stukent. I love what they're doing, and I'm glad that they've invited me to chat with everyone about leveraging data to optimize marketing automation. I think that marketing automation can be a very powerful part of any marketing strategy, but from my experience, I found that it is often underutilized or underperforms because companies are not leveraging data to inform their strategy. So I hope that we can change some of that today for the companies you work for or will be working for. Before we begin, I do want to throw in a short plug in for Tinuiti. Tinuiti is the largest independent digital marketing agency across the triopoly of Google, Facebook, and Amazon, and I'm really proud to work here. Tinuiti takes great care of their employees, and I think that's a big portion of why we have been so successful. We're always on the lookout for new talent and hiring for internships and other positions. So go check out our careers page on our website if you're interested. So, here's what we have on the plate today. I do want to define what marketing automation is; that can sometimes be confusing. And then I want to look at some of the data sources we can pull from, at different ways we can collect data. I have a couple of marketing automation examples from the wild that I want to share. And then I want to talk about what's coming. There's some big changes coming to the industry soon, and it's important to know what's coming so we can be prepared for it. And then I have some resources for everyone. So, let's get on the same page here. Let's define what marketing automation is. So, marketing automation uses software to personalize experiences for your audience by sending them relevant content at a time that makes sense for them to receive that content. It's a way to transform mass marketing and to personalize one-to-one experiences. So, I think it can be broken into two main functions, the first one being marketing operations and then the second being CRM and email marketing. So marketing operations would include things like lead scoring and lead management, while CRM and email marketing is where you set up triggered emails, set up nurture campaigns, and do lists segmentations to help your leads down the path to purchase and beyond. And so my presentation today is going to focus on how to use data to optimize the CRM and email piece of things. So, when we're talking about CRM and email marketing, I think that, at its core, it's retention marketing, it's a way to keep people coming back, coming back for more content, first purchases, repeat purchases, cross sales, reviews, time in app — and you do that by curating personalized communication through the data that we have. So, people don't always make the connection right away between their need and your solution. And that's where CRM and email marketing comes into play. It's a great way to continue the conversation and to fill in gaps and to educate them about your product and their needs and how to solve for it. And there are so many different ways to use CRM and email to get more out of your marketing campaigns, get more from your prospects or customers. So, I hope that my presentation today inspires you to think creatively and to come up with ways that you can use data to create these great experiences. So, let's look at some of the common sources that we can pull from to use in our marketing automation programs. So, depending on your business model and the sophistication of your martech stack, there are lots of different data sources that you can use. So, these are just a few general examples of sources that you can pull from. You have things like purchase history, browsing behavior, content engagement. We have, you know, how are they interacting with your ads, or how are they interacting with your product? We also have — you could pull from account data or persona profiles. These are all just typical or general things that you can pull from. Depending where you work, you may have some different data that you can use. So, a lot of email service providers out there, like Marketo or Klaviyo, Bronto or HubSpot, they keep track of some of these data for you and allow you to pull from what they're tracking to use in your programs. So, they'll often keep track of browser behavior and purchase history. But there's some things you need to go and collect yourself. And so, these are some different ways that you can go about collecting data. So, the first example I have here is an overlay or a pop-up. These are all over the internet. But it's very common to see a pop up only ask for an email address. I think this is a great place to get a little more data out of them so that you can personalize your messages. So, this is a great example from Brooks Running. They ask for your gear preference. And then here's another great overlay example from Electric. They're a sunglasses and apparel company out of California. So, they take it one step further than Brooks and ask for your birthday along with your interests. And so those are just two great examples that I liked. We can also use preference pages. And so, this is from Brooks Running again. If you go on their website, you give them your email address, it sends you straight to this preference page, and I love some of the questions that they ask. They're trying to understand your needs so they can provide that relevant and personalized content for you. I bet they have segments built off of each one of these questions. You can also use chat bots. These have grown in popularity recently due to their kind of instant gratification and conversational experience that they provide. Facebook Messenger bots kind of fall along the same vein. As always, traditional forms. These are still very common. And then, surveys are another great way that you can get more data. This is an example of an email I got from Brooks Running, again (you could tell I like how Brooks does things). But depending on what you click here, they know what your gender is and what you would use their shoes for, so they can speak to your needs and send you those personalized messages. But another example of surveys — I mean, you can be really creative with surveys and how you use them. So, I used to work for this tech company where we sent an incentivized survey to our closed-lost deals asking why they didn't purchase from us. And depending on how they answered, we sent them down specific nurture tracks targeting that objection. So, there's a few different ways we can collect data. Now I want to look at a couple different examples of marketing automation out in the wild. So, this is an experience I had with Udemy not long ago. So, this is a screenshot of the emails Udemy sent me right after I purchased the course. Udemy provisions all sorts of different online courses, and so I had bought a copywriting course from them. So, I purchased this copywriting course on January 10th, but I actually didn't start the course until January 26th. But you'll notice that I have 10 or so emails before January 26, so before I even started the course, but like 90 percent of these emails are promotional emails — they're asking me to buy more courses. And they just bombarded me with so many emails to buy more courses before I even started it. And you could tell I lost interest after, like, the third email; I didn't even open those last five. I'm still getting emails from these guys to purchase more content from them. So, what's wrong with this picture? To me, it's obvious. Their goal should have been focused on helping me consume their product and to get the most value out of it before trying to sell me anything else. And so, I mapped out what my user journey looked like for them. Notice the lack of data they're using. This is a very linear path built off of assumptions and not data. The one thing they got right was the related content part. They did send me related courses that I would be interested in, but I didn't buy any of them. And it's because an accurate recommendation or the right content at the wrong time is just as ineffective as anything else. And so, I took a little time to put together what Udemy could have done here had they utilized data. And so, some of the data I have in place where I'm pulling from is purchase history. I would only want this series to kick off for someone if they're a first-time buyer, like I was. And then I use product-usage data to determine timely messages to help them get through the course. And then, once I've helped them through the course, I could send them a survey at the end asking what they want to learn next. If they don't respond to that, then I can just start sending that related content that they were sending me before. But this would be a much better experience from the user side to help me consume their product, become a raving fan of theirs, help me enjoy their content, get value out of it. And then, once I consume that first course, then it makes sense to start selling the other ones. So, this is kind of a poor example of not using data and what they could have done. But I think there's a lot we can learn from that experience. So, the second example I have here is for a company called Bombas. So, this is actually a client of ours, and this is an example of some things that we did for them. So, for Bombas, they sell socks; they're a sock company. So for them, we use data from how their prospects and customers engaged with the brand. So, we look at things like browser behavior, email engagement, purchase history — a combination of those different things to segment their list and to adjust the cadence or the number of emails we sent them each week. So, for example, those who engaged more frequently with our emails by opening, clicking, and visiting the website, those people we would send more emails to, and more promotions and whatnot. And then those who engaged less, we would send emails less often. And by doing this, we decrease list attrition — so we kept more people on the list — while simultaneously increasing email engagement, so we were getting more from their audience and the list that they had by utilizing data to send them messages at the right time. So, in addition to that, we set up several triggered emails based off of site behavior, like a cart abandonment series. We did win-back emails for customers who have made purchases after a given time frame, and we did browse abandonment emails to entice shoppers to come back if they had left an item that they didn't add to their cart. So, as a result of these different things, we saw a 100% increase in email revenue, a 100% increase in subscribers, and a 56% increase in monthly conversions. So, this was a great example of ways you can use data to optimize your triggered campaigns and how you message and when you message your subscribers. So now, let's take a look at what's coming in the marketing automation world. So, artificial intelligence and machine learning are going to have a huge impact on the optimization and scalability of these programs that we can set up. So, ESPs and other marketing tools are going to be able to learn how and when to communicate with prospects and customers in a more personalized way than was ever before possible. So, for example, let's say that a customer named Alec is expected to purchase every 30 days, but another customer named Oakley is expected to purchase every 90 days. So, with AI, we can send them targeted messages that make sense to their unique timing. So we would send an email to Alec every 30 days and an email to Oakley every 90 days rather than sending a generic email to all customers every 45 days reminding them to purchase again. So, essentially, data that was difficult to capture before AI will now be much easier. So, things like predicting customer lifetime value, spending power, preferred communication channels will all better inform the automation programs that we set up and it'll be much more unique and personalized to that person rather than making some assumptions here and there. And already we're starting to see some ESPs and other marketing tools incorporate AI to power list segmentation, but I bet we'll start to see some new softwares pop up that have AI at the center of their software, and we'll start to see those replace the ESPs of today. And then, I do have some resources I wanted to share with you. So, I have a few guides linked here. They take a deeper look into how you can utilize CRM and email in specific scenarios and give you some ideas of some specific campaigns that you can set up with different sources of data. And then, I also have a link here to our careers page, if you're interested in that. So that's it. Thanks again for having me. I welcome anyone who would like to connect with me on LinkedIn to chat further. Don't be afraid to do that. Thanks.