Defining Customer Value Tutorial

3.2 Introduction

This is Matt Bailey presenting marketing automation. In this module, we're going to look at how to define customer value. Many times in marketing automation, people aren't sure what to measure or how to look at their return on investment for the multiple campaigns or automated campaigns that they're running. So, we're going to look at many different ways that you can develop. Measurable goals so that you can provide a return on investment and a proper view of how successful or what you need to change in your campaigns.

3.3 Determine Value Through Data

The first thing I always point people to when it comes to measurement is context, you can't just go to your analytics and look at the general numbers. It's always important to look at things from the standpoint of assigning an activity to a specific group of people. So the first thing I want to look at to establish context is where people came from was it a specific campaign? Was it part of my overall content marketing or inbound marketing plan? And if so, what channel brought them? Was it a link on twitter, bringing people back to a white paper or something like that? Was it an email promotion? What specific campaign and what channel? That is were people came from and that is what I want to measure by, then I want to look at what they saw. Did they go to the specific landing page? And did they go beyond that? What exactly did they see based on the campaign, based on the channel? And then what did they do? Obviously, this is my conversion. How many people took the action that was intended based on the campaign? And then finally, what was it worth? What was it worth to have people take that action? And through that calculation, I can then go back and determine the success of that campaign and the success of that channel in order to find out, through context, was my campaign right? Was it successful in targeting that audience? You see, when you build context and target specific segments and measure according to that segment, it allows you to target more effectively and it allows you also to develop the proper correlations of channeling one message to a specific audience and then measuring the result of that channel. Without looking at those specific segments or those specific traffic sources or channel sources, you're left to make a generalized measurement. You aren't really sure which channel or which campaign is generating the leads of sales for your campaign. And so you've gotta make those proper connections and understanding what drew people there? What message was it? What did they see? And what did they do? And that enables you then to focus your attention in the right channels, in the right place to drive the right kind of visitor and that's the question when we come to analysis and we come to measurement is answering that first question. What is the ideal customer? What's the ideal lead? You see you've got to start your measurement with the end in mind and that end has to be an ideal customer. What do they look like? What do they act like? How do you treat them?

3.4 Dallas Cowboys Example

A similar situation came up and I highly recommend going to the ESPN website and watching a short video. It's about 15 minutes long called The Cowboys and the Indian. And this was a look at the Dallas Cowboys in the 1960s. They were a brand new football franchise, just starting out, they had no good players, they got the cast offs from other teams. And so they realized that the only way they were going to build a team was through the draft. Well, they brought in a brilliant computer programmer who was Indian by background, and recently immigrated to the US. And he worked for IBM, and he was a data scientist, a statistician. And the Cowboys called up IBM and said, we need to figure out a way to measure a good football player. Well, IBM sent over Mr. Qureishi and this started an incredible relationship because Mr. Qureishi had no idea what football was, he'd never watched a game. And he had no idea where to even start, except with the first question. The first question was, what makes a good football player? And that's when the Cowboys realized that up until that point there was no system of quantifying a good football player. A scout would call a coach and ask them, tell me about this guy. And the coach would say well he's fast, you know he's a team player and all the typical cliches. And that's how they measured talent. Well what Mr. Qureishi helped them do was come up with a systematized method of measuring certain talents. But what it came down to was answering, first the question of what makes a good football player. What traits? What behaviors? What skills? And that's when they realized it went far beyond just the skills. It also went into their attitudes in their ability to play with a team and get along with other players. So as you can see here, they came up with a system of a 1 to 9 measurement and they asked coaches to rank players on specific abilities and traits and behaviors from 1 to 9. The coaches would fill that out, the Cowboys would take that. And their programmer would put this into the system, and come up with a way of measuring talent and ability, as well as teamwork. Their goal was to find the slightly above average player, one who was slightly above average in skills and abilities but above average in the non-tangibles such as teamwork. They were looking for the middle percentage because everyone knew the top prospects coming out in the draft. So the Cowboys were looking for the later rounds of where they could pick up people. Now what was interesting is, because they took this data-centric approach, the Cowboys started drafting from schools that weren't traditional schools that had players in the draft. They started recruiting from all-black colleges. They were recruiting from Division 2 and Division 3 schools. In fact, they were even drafting basketball players. On one Super Bowl team, five players on the team had not played football prior to joining the Dallas Cowboys. They were basketball, or baseball, or track players. But their data system took away all of the preconceived notions of where to find good players. And they went to improbable places and drafted star track athletes, basketball stars. And what happened is that they had 18 consecutive years in the playoffs in the 60s and the 70s. They had 5 Super Bowl appearances and 20 consecutive winning seasons, which is still a record today. But they started by asking a simple question,. Which is, what makes a good football player? We take that same question and we ask, what is the ideal customer? What makes a good customer? What makes a good lead? You see, you start with that question and then you work backwards and it helps you make the proper decisions as to how to guide someone along the conversational path. By doing that, you can come up with ways to better your marketing automation. Because you'll know which conversations, which automated conversations are going to be more profitable than others. Because by that measurement, you'll know which conversations move customers to that next step.

3.5 Understanding Customer Value

The first item I like to look at as far as measurement and this is also a best practices, is to understand your lifetime customer value. It's a very simple calculation, because you look at the average value of a sale, the number of transactions in a year, and the average length that someone purchases from your business or for your company. That's your lifetime customer value. I'll put some numbers to this so that you can see. So, for example, I like to buy things from ThinkGeek. The average value of my sale at ThinkGeek is about $100 every time I buy. And I usually make about two purchases per year. I've been a member of ThinkGeek and buying things there for about three years so I'm just going to make that the average retention from ThinkGeek. And so in this situation I have a lifetime customer value of about $600. Now what you want to do is do that with your entire customer list. Look at the average value of a sale, the average number of transactions per year, and then the average length of time that someone is a customer of your business. That could be three years, five years. It just depends. You have to look at your customer records and that will help you develop your lifetime customer value. Once you have that, you can start understanding how much money you can spend in order to receive new visitors. So if you're developing a campaign to get new buyers into the system, that lets me know that a new customer is probably worth about $600. So I need to budget accordingly in terms of my marketing and my budget for that marketing. Because I don't want to exceed the profit of what that $600 lifetime customer value would provide. And so this helps me determine proper budget amounts for targeting new customers.

3.6 Calculating Campaign Value

Another level is looking specifically at the customers or the email list you already have in house, and that's developing your subscriber value. So for example, if we have a lifetime customer value of $600, and you have 100,000 subscribers. And of those 100,000 subscribers only 30,000 have fit that customer value, only 30,000 have bought in that past year. That means that 70 percent of your list are non-customers. Then from those 30,000 customers, with a lifetime customer value of $600.00. That's about $6 million per year, that's what your email list is worth. So, now you can start to look at your email list as an asset, that look at what my email list is producing in terms of average revenue based on customer lifetime value and active subscribers. This can also help you look at how to engage inactive subscribers, those that are on your list but don't often purchase. So right away you can look at different ways of segmenting your list in order to target different groups and develop non-customers into customers. To develop regular customers into high-level customers. But it starts with understanding your lifetime customer value and your subscriber value. Next what you can move into are your trigger values. So for example, if I have an abandoned cart campaign, and in one week I have 400 abandoned carts, then it will automatically send out 400 emails. Of those 400 emails, that results in 13 orders, which gives me about a 3.25% conversion rate, and sales of about $1,250. What I can then measure is that I am making $3.13 of revenue per email. And so I can compare that to other email programs and I can see where this stands as far as a revenue producer. But typically, cart abandonment campaigns are very high in value because they remind people that something has left in their cart. And so it's an automated trigger, it's an automated cart email that you produce once. Maybe you can do some testing of different messages. But ultimately, it's great to have this in your dashboard to know what the value of your triggered cart abandon emails are. Another trigger is maybe a browse campaign. People who come to your website and browse but do not purchase or do not even add to the cart, but these are registered users. And so, we're going to see is that the conversion rates going to be a lot lower. However you're going to be sending a lot more triggers. And maybe there's only a certain engagement threshold. Such as viewing ten or more products within the same category. That sends a trigger and then that email reflects the products that have been seen in that category. So in this campaign there were 3,000 triggers sent, 25 orders as a result, and merely a 1% conversion rate, but it resulted in $2,200 worth of revenue. So here we can see our browse campaign is worth $0.73 per email. You see breaking down these numbers and these campaigns allow you to see how you're campaign is performing. And numbers such as these, as far as revenue per email, give you a great indicator of the success of your campaign and of course, you can test, you can try different messages. But the ultimate measure here is making sure that you are developing revenue per email with your triggered campaigns. You can also look at timed specific campaigns. So for example Dunkin' Donuts does an automated birthday mailer. That's an automated trigger based on my birthdate which I entered when I downloaded the app and joined their loyalty program. And so, Dunkin Donuts can measure everything that is in that triggered birthday campaign, as far as triggers sent and how many were redeemed. And then based on that redemption, the value of that cup of coffee. That's their conversion rate, is how many were redeemed. They can look at the sales, and they can look at the value that was produced by that birthday campaign.

3.7 Drip Campaigns

Especially in business to business, this is a great way to measure. You see, typically what people are measuring is as soon as they get the lead, what is that lead to conversion rate, but you can also look at it in terms of a funnel. That, what is my lead to nurture rate? So if as soon as I get that lead and I start putting them in a nurture campaign and I have, maybe, ten scheduled e-mails within that nurturing campaign. What I want to measure is from the lead through the nurturing campaign what's my lead to engagement rate? What's my lead to nurture rate? How many emails to people typically take before the conversion, and then what's that lead value? And so it's asking for more calculations within the process, rather than just jumping from the lead to conversion rate. I'd like to see which nurture emails Are more productive in pushing people into engagement or into the conversion funnel. That helps me then, maybe I need to change around the emails as far as messaging, maybe I can move some out. I can see which nurture emails result in engagement and which ones don't, which ones lead to conversion, which ones dont. But that's just measuring the different funnels that you are using, and of course you can also measure your welcome campaign, any segmented campaigns, triggered campaigns, and looking for that lifetime customer value, and measuring according to that

3.8 Subscription Drip Campaigns

So in this example, for a subscription based business, the first thing you're going to want to measure is you're initial subscription rate. Those people that come to the site and subscribe for the service right away, maybe on the first visit. But initially what you're going to be doing is getting that email interest, someone who subscribes, but is not yet purchased the subscription service, whether its software, news, any type of subscription monthly or annual type of agreement. And so what you're going to be using is a drip campaign. You have that initial e-mail, your drip campaign is going to be used to continue to sell the benefits of that product or service. And so you’re going to look at all of your drip emails, and all the communications that will go out. So, your initial subscription rate is a great measurement. But than your drip campaign to subscription rate overall, is your drip campaign successful in moving people from the initial contact or the initial conversion of getting that email address, to getting them to the primary conversion of a subscription agreement. Also what you'll want to look at are your drip messages. Drip one to subscribe rate, or drip two, meaning your first email, or your first automated contact. What is the difference between those messages and the subscription rate? What are the offers being made? So if you have a drip campaign of four or six or maybe ten emails, what offers are you making in those? And can you draw a line between the offer and the conversion rate of getting that subscription? So, you want to look at, specifically, your offer or messages to the subscription rate within that drip campaign. Other things to look at in the measurement is the click-thru rate. Is the content that you are provided in each drip message engageable? Are people wanting to read more? So look at your open and click-thru rates for each message. It should tell you automatically what is registering with people, and what they want to see more about. Or which messages are offers they don't even pay attention to. As you move on you can look at the content interest by the demographic. So if you have more than just that e-mail address, maybe you have a job title or a region, and you can look at specific content interest by click-thru rate or even by the pages viewed on your site by specific demographics. You can also look at which groups are not clicking through. And so, evaluate the messages that are within the drip campaign. Just because you created a group of emails that will be automatically sent does not mean that they're the right message at the right time, or even If they're going to be engaged upon. So once you create that drip campaign or any other type of campaign, you want to measure to ensure that people are engaging and the interest is increasing, rather than the interest decreasing, and so you want to measure the campaign engagement. Is the open and click rate improving as people receive more drip emails? Or is it decreasing? At what rate are you sending? Is it too much too soon? Or is it just the right amount, and you're steadily increasing engagement, until you finally get that subscription. You can do these types of measurements with any type of campaign, just look for those primary engagement factors that let you know throughout each step of the process or each marketing message that is sent out. What is the engagement level, are people responding to it, and how does it rate according to the rest of the messages? Look for areas where you can test different emails or different offers. Test those against each other and continue to improve. That's the great thing about marketing automation. It starts with a specific set of messages that you're intending to send out to your recipients. You can measure them and then look specifically at which ones can be adjusted or tested, and you can do this with any automated series.

3.9 Loyalty Campaign Value

For retail loyalty campaign you can measure the amount of emails that have been sent. And of course divide that by the amount of app transactions that take advantage of that offer. That will immediately give you the amount of people that are engaged in the loyalty program, based off that specific promotion. One-time purchases, for example, if you have a $20 eBook and you offer sample chapter, maybe using a pop up form intended to capture contact information. Then, what you can measure is the amount of people that download the sample chapter to a future purchase. What you can look at is the amount of the value of that sample chapter. The value that was created by the number of people who bought afterwards. Let's say we use that 50% rate. So if you have 400 sales from that pop up offer, then that's $4,000 of generated value through offering that sample chapter. You can also measure versus a campaign value. So if you have a subscription or a membership that's worth $99 a month, and over the lifetime value of customers you have a 5% attrition rate every year, and the average lifetime customer experience is about 36 months. And so then know know that your lifetime customer value is about $3,500. If you do an email campaign to increase your membership, 100 new customers will be worth $350,000. And so in terms of working through your list, and then how you follow up with each of those new contacts, whether it's a drip campaign, that once you've got that email and that interest, and you continue to follow up with them. This gives you a target, as far as understanding what the potential value can be of just 100 new subscribers or members into your business. This helps you establish a value of the customers, an expected value also, of what you can put into the campaign. And it also can help give you a measurement as the campaign is going along, as to your success rate of moving people from the initial contact to the conversion of getting their email address. And then your automated follow-up campaigns to move them into the ultimate goal of that membership or subscription. Another way of doing this is also offering a trial or a demo. And so again, if you have a $99 membership or subscription rate, and you offer a free demo to 2000 people, if only 10% become subscribers, that is a value of over $700,000. And so this gives you some initial numbers that you can work with in developing campaigns. Making sure that your targets are broad enough In order to reach the right amount of people, so that you can look at what a 10% increase, a 5% increase, or maybe even a 20% increase would provide to you in terms of value. You see, when you start measuring your planned campaigns in terms of value, it gives you a realistic expectation of where you can move. It does rely on your existing measurement faculties, however, and understanding what is your customer lifetime value. How long do they typically stay? What is your attrition rate? And how many new customers you need each year to make up for those that leave or phase out of being customers or members. And then, how many do you need in order to continue to grow every year and be profitable? Taking a measurement of where you are and where you need to be helps you establish goals, as far as new subscribers, retaining existing ones, and what the audience value will be for those types of campaigns.

3.10 Recognizing Trends

It's all about understanding the trends that your business is exhibiting. When you're measuring each campaign, as well as measuring overall the purpose of what customers are doing or members are doing. It helps you understand where your attention should be placed in order to maximize your campaigns. So for example, when we start looking at customer trends, the initial three things that we're looking for are your total customer contacts, the size of your email list. But then, how many off that email list are buyers? How many are active buyers within the past 12 months? And then how many new customers have you received in the past 12 months? This gives you a picture of where you are right now. Then understanding your attrition rate will help you know how many customers you will lose in Year 2. And you can also look at your conversion rates in bringing new customers in, and will that balance out? You can take this even larger, and I would highly suggest that you look for the book Marketing Experiments by Kevin Hillstrom. Kevin outlines this customer migration trend of identifying, what is an ideal customer? What's the next level of customer, all the way down to, what are our customers that are in danger of leaving, and how can they be recovered? How can you take a D level customer and move them into an A, or maybe just move them into a B? And so Kevin develops spreadsheets that you can download and evaluate his information, but also put yours in as well. By looking at what makes an A level customer, what makes a A level customer move down to a B level? What makes a B level customer move up to an A level? And so, you can do that based on spend. Usually it's based on a 12-month buying cycle. And then how many of your A-level customers remain A-level customers? In this example, 90% of A-level customers stay in A-level, 10% go down to Grade-B. In this example also, at a $250 spend, 70% of B-level customers go up to Grade-A, whereas 30% will stay at Grade-B. 40% will drop down to Grade-C if they make no purchases that year. So you can understand here how we have our recency, frequency and then monetary goals to establish our best and most ideal type of customer. But also a plan as to how to move people back up into their previous level. And so we look at what defines a Grade-A customer or a Grade-B customer. And typically, it is the recency, frequency, and monetary equation/. Because you want to look at, also in your customer files, how many are migrating between each level, and what does it take to stay at a Grade-A? How much should be spent in order to maintain that same level? And then what are the typical pathways people take once they are a Grade-A customer? Are they only a Grade-A customer for about three years and then tail off? What can you do? When you start planning and measuring your marketing and your automated messaging like this, you can then start putting in plans and conversations for people, regardless of where they are at in the spectrum. If you have people that are in danger of dropping off, you can implement a plan to bring them back in. If you have, your Grade-A customers, how do you treat them differently, to keep them engaged? And also, to market to their friends, by word of mouth, or by invitation in order to get more Grade-A type of customers.

3.11 Projecting Trends

You want to look at the business trend. Where are you? How many customers do you have in what you would call a Grade=A level? Who are your best customers? How many are there? After one year how many customers do you have? How many Grade=B customers and where have they gone? And how many new people do you need to bring in every year? And so what Kevin Hillstrom illustrates in these spreadsheets and marketing experiments is how to plan for the trends so that you're not taken by surprise. Using your existing data and also some of your historical data you can predict where your business is going and where you need to focus your attention. Whether it is on your retention messages, on your loyalty messaging, or also from getting new customers into the system. By these types of measurements you can forecast where you will be at the current trend, and see then what attention is necessary, and measuring the messaging that you're giving to each level. That is the value of measuring, and knowing, and defining your best customers, and how you can move people back into being a best customer or using solid messages, to engage them again.

3.12 Segment from Data

You see, the beautiful thing about automated marketing, is that when you're gathering all of this data, you can do very complex database queries. So, for example, if you're looking for a demographic of a female demographic between 25 and 35. Who have made at least two purchases in the past 60 days and visited the website in the past 30 days, in a specific geographical location or a metro region. And you can automate a promotion specifically to them. You see, when you're gathering all of this data, it makes these types of targeted messages to specific groups of people based on demographics. Based on recency, frequency, and monetary spend, and then also geographical regions. You can target your marketing to specific groups of people with customized personal marketing messages that people tend to respond much more positively. Because it seems like you know them, you're aware of their situation, and also you're giving them relevant information. That's the power of marketing automation and also of defining your customer value. This has been Matt Bailey with SiteLogic.

  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

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