How to Treat New Customers Like Loyalty

Hello, welcome to How To Treat new customers like loyalty. I'm Phil Davis, and I'm the Chief Business Officer for tower data. towergate is a technology company that provides solutions for marketers to help them with their people based marketing. Our typical solutions include email delivery, validation, email intelligence, identity matching, and email activity metrics, you can go to tower data.com, to learn more about our services. So today, we're gonna be talking about loyalty, and the new customer. And really the challenge here is how do we help your new customer move along the customer journey to become a loyal customer, we'll talk about what you need to do to make a great first impression to give them a star. And then the do's and don'ts of engagement before we review a few case studies. And throughout the presentation, feel free to use the chat function for q&a. So I think we all can agree that customer loyalty programmes and customer loyalty marketing have by fire, some of the highest brand engagement, and ROI, certainly, your loyal customers drive a lot of your revenue. And that's why we invest so much in loyal and loyalty programmes. As a consumer, we all have our brands that were loyal to. And so it's not surprising that nine out of 10, consumers professed loyalty to some brand, or brands, which is great for the brands, because loyal customers also spend 36%, more than non loyalty customers. So those customers in their loyalty programmes will spend more money than a customer that's not in their loyalty programmes. I think we're all we all understand. And that's our goal is to drive our clients into loyalty programmes. But what do you do with the new customers, they just come to your site, they might make their first purchase? How do you drive them to loyalty? Well, when you think about this early audience, you really don't know much about them. And so because of that, they get the short end of the stick, they're getting a lot of bashed campaigns, they're getting weak re confirmations of their existing purchases, that are getting put into wrong drip campaigns based on a very limited data set. So they're getting really off the mark off the mark marketing. And therefore, you're not driving your early audience into loyal affiliation, at least not yet. So customers, here's what they think. They think it organisations should make, do a better job of getting to know them, and make that their top priority. And frankly, I couldn't agree more, the more you can understand and know your customer, the better you can do a job in moving them through the customer cycle. So when it comes to email marketing, it's getting them in the right campaigns, right? It's actually maybe using the right channel to reach your customers. Right. But it's all about using the right data to make a great first impression with your early audience. And there's a lot of data out there. Right. And so when you think about your loyalty programme, you have a lot of data, you might have a very sophisticated artificial intelligence engine that helps you optimise and determine what offers and what messages and what images should go into a campaign. But how do you really do that with early stage? And you're in your new audience? So think about the data you have at hand, right? So if somebody comes and makes a purchase, at the very least, you have some purchase history right out the gate, they just bought tennis shoes, right? You also might have their user email. So Dave, oh, 484 at gmail, when you look at that email, having a whole first name and a sequence of numbers that makes sense. We could assume, frankly, that Dave is was born in April of 1984. He's got a Gmail account. And he's got his full name Dave in there. There's a likely that Dave is a relatively early adopter to Gmail, and has been using this account frequently. time of day, coming in at 7am to make the purchase. That's an early morning action. And as you look at some click history you might have you might see that Dave's been on your site early and late at night. And so that can help you give some idea as to when to communicate with Dave, but certainly not enough to understand About Dave and why Dave might have bought those tennis shoes. So, when we look at third party data, right, you can look at some demographic data. So you can see that Dave is 35 is single is a homeowner. So what can you learn from from that? And how does it you know, change your impression or contextualise the experience so one thing, you know, you might have an idea that that Dave's shops himself and is likely to have more disposable income and a steady job since he's 35. Seeing on a homeowner. From a behavioural perspective, you might have noticed that Dave was able to learn through third party data that he visits Kelley Blue books, and so easy they're likely in market for a car or very interested in automotive and, and sort of a car car lover and, you know, wants to keep up with what's new, in in the cars. And then from an email activity metrics, you get third party data to find out how popular that email is, how frequently it's used, and how often in recent, it's opened other offers. And so, you know, if they have a recent open date and high popularity and frequency, this email represents a very active shopper. And that can help you determine frequency. But when you combine this data set, what you're going to do is contextualised behaviour. And by combining the data, a data set, you can learn a lot about the user behind that email. Right. So by combining demographic behaviour and contact data, you know, you begin to put colour around the person. And you might learn that, you know, they they're connected often on their iPhone, they love to travel, they're modelling a home, their mother, who shops for who's a bargain Hunter. And with that information, you can help determine how you want to engage in what you want in your messaging. So some of the do's that we'd love to talk about in terms of leveraging personalization and leveraging data to drive that loyalty is to use demographics such as gender, income location. And throughout my years as a marketer, I have found age, gender, location, marital status, presence of children, homeowner status and income to be very useful. And when you can tie that data and leverage the purse the purchase and behavioural data you already have on a customer, you can begin to combine that data set to contextualise their behaviour. So, back in December, I was shopping for video games for my little niece. And frankly, none of those searches had anything to do with what I would do for about 11 months in two weeks out of the rest of the year. And so when the brand was able to take my behavioural data, I purchase data, but tie it with my demographic data, you know, my age, my gender, the fact that I'm a homeowner with no children in the house, they were able to better understand that as buying a gift. And now they could drop me into the right segment, to encourage me to make purchases for myself. So it's real important to understand that buyers journey also, and where they are along the journey and using the data to influence and and encourage next movement down that journey. In many cases, how do we move them down the journey towards loyalty. And then of course, using data to engage with them at the right time, and in the right channel, is what you need in order to boost loyalty and give you a chance to turn these early customers into your loyal customers. Now, we do a lot in email marketing, because it works and it's very efficient. But there's some don'ts from your subject line, you shouldn't have misleading subject lines, you know, there's recently an email from a travel site that often will tell me to click now for 50% off travel. But then when I discover and I go in the dates I want to travel are never available. And that's misleading. And, and really an unpleasant experience. So in terms of your design, you want your design to be clean, you want it to be mobile friendly. And you want your template to be a predictable, allow for predictable experience for your clients. You know, especially your loyalty programmes. They're used to, you know, a recommended offer maybe the top, some other exploration in the middle, right. And then, you know, maybe a call to action but sort of, you know, over time as as your customers engage with your loyalty emails, they expect to see certain things content in certain places, so have a really good template. When you're retargeting, right? Let's Let's not retarget for too long. So for instance, you know, if a consumer bought winter boots in November that retargeting offers should not if they're looking at winter boots in November that retargeting offer should not be following them around in April. So really retargeting for too long long is almost the same as out of context promotions. And out of context, promotions often result from personalization based on low frequency searches. So those those, those middle zones kind of go hand in hand. And then of course, be careful about being overly personal with your copy, you know, provide appropriate general engagement, but don't get overly personal. Certainly not until they're in your loyalty programme. And then in terms of mailing frequency, do not use the same mailing frequency for all right. So allow the customer response to help you model out ongoing frequency and then certainly, your most loyal customers are comfortable talking, you know, hearing from you more often, then you're less engaged customers. Additionally, mailing frequency should have something to do with the life of the product that they've been purchasing from your or the products you sell. So keep keep track of mailing frequency. Let's take a look at a few case studies. In this first case study, we were working with a customer that offers spa discounts. And initially, they sent the same offer to everybody in their database. And so people going on, and it was just discount was coupons for that with a discount. So people were buying these, these coupons, but they didn't know much about the user. And they continue to just push the same offer. And so one of the things they tested was gender and marital status that took two fields, and that was it. And they use them and applied them and dropped consumers into different campaigns including the control group. And so for a single woman, she got a message that was it was longer than this one. But this is the crux of what was said tired of holiday shopping for loved ones, you deserve something for yourself. And so, you know, treat yourself for for this discount for a married man. If they knew that their offers in this case, this was a Mani pedi offer that men were not responding to this for themselves. They have the data to suggest that so they use an offer that would encourage the man to come back and buy further coupons for later purchases. When they personalise the content using demographic data, it resulted in a 300% increase in revenue per month from the mailings compared to the control group. So it worked real well was a fantastic offer, and a great programme. And our next example, we have a retail client very boutique clothing. Think super preppy clothing, which I might have just dated myself. I'm not sure preppies. A typical term we use today. But that's how I describe it. And what they were doing was sending a one size fits all email to everybody. We call it blast and batch. They call it one size fits all. And so their hypothesis was that they would segment emails, they would provide good results and a one size fits all. So what they did was they use demographic data and married it with purchase data that they pulled out an agenda to build out segments. And they were using age and gender, marital status, presence of children, homeowner status and income as the fields that they were combining with their purchase data. And from the combination of data, they created various drip campaigns with personalised content, content, and graphics and offers for each segment. They measure and compare the segment campaigns to the control group. And they found over a five month period of 912% increase in site traffic, and at 22 times ROI by revenue when measuring revenue when you looked at the campaign's that used more data versus and segmented campaigns versus the want the control group. So as you can see, again, this worked very well for a midsize retailer. And I'm not sure that it really surprises any of us. Now, one advanced tip we'd like to add in there is as as you're all mostly working from home and Thinking about the next big campaigns you have, whether it's a back to school offer, or you want to prepare your database for next year's holiday season. And frankly, it's never too early to prepare for that. You have a lot of emails that are unresponsive. And some of those unresponsive emails are bad emails through emails you shouldn't be mailing to. But how do you know which of those emails you should use for an reengagement campaign? And so what we do with our clients is we look at that data. And we can append open data, we can let them know where this email has been active for other brands. And we can also append email activity metrics, let them know which emails have shown, frequency frequently come up, and which and how often how many sites they come up sets popularity and frequency of that email over the last three to six months. And when you bring that data in, you could identify the emails that might be gold within this database of responsive emails, and take those emails in a reengagement strategy to try to win back those customers. So additionally, you've learned as you learn a lot about how you can use additional third party data with their past performance and behavioural data, you can now segment them into more personalised and reengaged campaigns for these reengagement programmes. So that's just an advanced tip. Certainly can learn more about that at tower data.com. So, hopefully, throughout this email that throughout the presentation, I've also been able to answer some of your questions in the chat portal. But certainly feel free to add any additional questions and we'll hold for another, you know, 30 seconds or so so I could answer such questions. In the meantime, it's been a pleasure to spend time with you again, my name is Phil Davis. I'm with tower data and stay safe. Keep social distancing. And hopefully soon we'll be back to normal. Have a fantastic day. Thank you so much.