Marketing Automation For Dummies (2014)

---

Adding people to your list

There are a lot of ways to add people to your segmentation lists, so I’ve included the most common ways in the following bullets:

· Mining your database: Many times, you’ll need to make a list from people in your database. You already have the data, so you just need to find the people who meet your criteria.

· Segmenting in real time: Some marketing campaigns require real-time segmentation. For example, you may want to instantly segment someone who visits a web page into a campaign or list so that you can automatically initiate follow-up marketing campaigns.

· Adding people manually: Just because you have automation doesn’t mean you’ll always use it. For example, your salespeople may need to add people to a list manually. This situation usually happens when your sales team needs you to run a marketing campaign to a group the sales team identifies.

· Removing from lists: Sometimes when you add someone to one list, you need to simultaneously remove him or her from another list. Don’t forget to add this action to your segmentations if a segmentation change makes a former segmentation obsolete.

Exploring the Many Uses of Segmentation

Every marketing campaign should be relevant to the person you’re targeting. The good news is that targeting your message through segmentation is the fastest way to be relevant. That’s because segmenting your database based on activity allows you to find core groups of people with similar levels of interest.

The next sections show you common uses of segmentation and explain how to make your segmentations as relevant as possible in the context of your segmentation.

Segmenting from email actions

Email actions are a segmentation most marketers are familiar with. Email opens, bounces, and clicks are the standard email actions used for segmentation. Learning to use, or not use, the following email actions will help you understand how to segment correctly from these actions and increase the accuracy of your segments.

· Segmenting on email opens: Email opens are a very bad segmentation to use in most cases. Email opens are a false positive. It is better to segment on other email activity. This has been used mostly because there were not better metrics to segment from. Now you have better ones, so leave this one on the shelf.

· Segmenting on email bounces: Segmenting your list on email bounces is very helpful for cleaning your database. Many times, a bounce on an email requires a manual effort to determine the validity of the email address. Bounces may also signal sales to work other leads in the account. Segment your email bounces into emails that hard bounce (which denotes a bad email address) and emails that soft bounce (which denotes an out-of-office response) four or more times. For more information about what to do with bounced emails, read E-Mail Marketing For Dummies, 2nd Edition, by John Arnold (John Wiley & Sons, Inc.).

· Segmenting on email link clicks: Segmenting on email link clicks is the best way to segment on email actions. A link click is a direct action that is very accurate, revealing a specific interest in the content the link points to. Depending on your type of email, segmenting on email clicks helps you

· Segment leads by product interest

· Segment leads by level of interest

· Move leads into different campaigns

Segmenting from prospect actions

If your goal is to have a segment of people with a specific interest, you can easily accomplish this goal by segmenting based on one or more actions such as content downloads, pages visited, and emails clicked. Understanding and segmenting on multiple actions helps you understand your audience much better than segmenting based on only one action.

To segment from prospect actions, create one segment for product of interest and add people to that segment as many different ways as possible. For example, if you want to create a segment for people who are interested in your high level of service, you should add people to this segment based on search terms related to service, service links people click in your emails, and the service pages they visit on your website.

Segmenting from prospect inactivity

Inactivity can tell you as much about a person as the actions the person takes, because inactivity can give you insights into someone’s behavior patterns. Learning how to correctly segment on the following behaviors is key to your future success with marketing automation:

· Segmenting on time: Create segments for inactivity time frames. Consider using segments such as

· Date of last interaction

· No action within the past 60 days

· No marketing touch in past 60 days

· Segmenting on lead score: Scores should be calculated based on a combination of activity and inactivity. I discuss lead scoring in more detail in Chapter 12. Consider these segments:

· Leads with no scores

· Leads whose scores have gone up the most in the past 30 days

· Leads with the highest score over a period of time

· Leads whose scores have gone down in the past 60 days

Segmenting off of CRM data

Marketing automation makes it easy to access CRM data. Because CRM systems are usually central data repositories for a company, your marketing automation tool won’t be the only system putting data into your CRM system. Any data that is in your CRM system can be used for segmentations by your marketing automation tool. Consider looking at data sets that are helpful for more targeted campaigns:

· Segmenting on last purchase

· Segmenting on total purchase history

· Segmenting on lead stage

· Segmenting on opportunity stage

· Segmenting on a specific sales rep’s leads

Sales activity is also recorded in your CRM and can be used for segmentation. Segmenting on sales activity helps you pick up the slack when sales reps are too busy to reach all their leads.

Creating Personas for Personalization

A persona is a very effective way to segment your database. Personas are used to make sure your messages are as relevant as they can possibly be. Personas can be interest based as well as demographic based. There are two types of personas:

· Interest-based persona: This type of persona is based on actions people take, not just who they are. These may include the following actions:

· Page views (Number of or specific)

· Length of time on site

· Frequency of visits

· White paper download

· Lead score

· Demographic-based persona: A demographic persona is based on information about a person. This data usually lives in your CRM. Examples of these data points are

· Job title

· Company size

· Location

· Region

· Existing client

The next sections explain how you can use personas to make your list segmentation decisions more relevant to the people you’re marketing to. I also explain how to use personas to activate inactive leads in your existing database.

One person can easily have multiple personas. For example, a lead who is a VP can also be a decision maker and a lead in the early stage of a buying cycle. Every persona changes how you market to a person. I suggest starting off using buyer-stage personas. So you market to people based on where they are in the buying cycle instead of basing your marketing on their job title. If you want to use them both, run a few campaigns first so that you can see whether the additional work is worth the effort.

Creating demographic personas

Demographic-based personas help you create automated campaigns relevant to a data point such as a job title. To create your demographic-based personas, look at past deals that have closed and ask sales to help you determine how many people are part of the decision-making process on average. Do your best to determine common characteristics between people such as job title, company size, and so on. Try to do your best to keep this list to only three roles in the buying cycle.

· Decision maker: This is the person who has the final say. Usually this person signs contracts and is involved with only a very small part of the buyer’s journey and sales cycle. The decision maker is easy to identify by job title when you’re creating a behavioral segmentation. You can also identify this person by specific content engagements, such as which white paper she read or which blog post she found helpful. This assumes, of course, that you have content tailored to such a person.

· Information gatherer: This is usually a lower-level person on the team. This person is tasked with getting all the information for the team to then review and decide. The information gatherer is usually a main point of contact, but he can’t make any decisions.

· Champion: The champion can hold any job title in the organization. This person is a fan of your technology, company, or employees. This is your inside person who is fighting for you. Identifying champions is very hard by job title because they can be anyone. They are much easier to identify by their level of activity and engagement within the company. Your true champions are likely to be very active on social media and to frequent your blog.

After creating your demographic personas, create your segmentation rules to build your lists by persona. You should start with a list for each persona. For example, if you have three personas, you have three lists. This should leave you with three specific lists.

Creating interest-based personas

To create interest-based personas, you need to use a combination of segmentation and lead-scoring methodology. (I explain the details of lead scoring in Chapter 12.)

Use segmentation to track where someone is in his or her buyer’s journey and then use a score to measure the level of interest. Interest-based personas should be broken down into three levels (the following score ranges are based on a 100-point scoring model):

· Leads with low scores: 0–30

· Leads with mid-range scores: 31–75

· Leads with high scores: 76–100

Identifying existing personas in your database

You might not have enough information to create personas when you first set up your marketing automation system. Do the following to identify personas in a database with limited behavioral information:

· If you have a cold database, do some testing: You won’t know the interest-based persona of people in your database if they have no engagement. Set up special campaigns created to test different types of messages and content tailored to people at each stage. This will help you identify people’s personas based on their engagement.

· Go fishing: Because people typically engage with emails that are relevant to their interests, you can send a series of emails with content targeted to a variety of interests. The people who engage in each type of content can be grouped into personas based on the content.

· Use what you have: If you have data on the demographic persona of each person, start there. After you get people to engage, you can then switch to interest-based personas.

There are no “silver bullets.” If you think that you can have a 100 percent engagement by using a perfect segmentation and a perfect piece of content, you’ll find out differently. You will have much better engagement rates, but you can never hit perfect because you cannot control all the other factors surrounding a person’s engagement. You are just stacking the odds in your favor for when all conditions are right.

Reaching out again to old leads

Segmenting your database by persona allows you to reach out with marketing campaigns and identify leads. If you create your segmentations and nurturing programs correctly, reaching out to leads happens consistently without your having to lift a finger, and it helps you to generate more leads from your existing database. Figure 6-2 shows an automation rule for finding the leads and placing them on your nurturing campaign, with an automation rule identifying sales-ready leads and passing them to sales.

Figure 6-2: Use automation to find and reach out to old leads.

Use a single nurturing campaign to stay in front of leads you identify in your database. You can get more granular over time by breaking your campaign into many targeted campaigns for each buyer stage and persona, but a single campaign will suffice as a starting point.

Creating Advanced Segmentations

After you have the basics of segmentation down, you’ll quickly move into more advanced segmentations. Segmentations are automations. So understanding the key parts to advanced automations helps you to better manage your list and segmentations.

Each tool runs automations in a different way, so make sure that you are fully versed on how and where you create your segmentations and automations. Many tools can do them in the same place, but not all tools allow for this.

Understanding if-then statements

An if-then statement is an advanced rule that you can use to segment based on a true or false condition. For example, you can program your software to look at data in your database and apply the following if-then statement:

· If <condition> is true,

· then do <action>

Basic segmentations may look for a match on a single data point, as shown in Figure 6-3, without the use of an if-then statement. In the figure, notice how the statement is asking for all VPs in New Jersey and has no other conditions for the segmentation.

Figure 6-3: Basic segmentations often match a single data point.

If you want to make a basic segmentation more granular, use an if-then statement. Figure 6-4 shows an example of an if-then statement added to the basic segmentation shown in Figure 6-3. In this example, the if-then statement applies to all VPs in New Jersey who have also downloaded a specific white paper.

Figure 6-4: Use an if-then statement for more granular segmentation.

When using if-then statements, you need to know a few basic rules. Keep these in mind while building your statements to make sure that they work properly:

· Use an existing list. Depending on your tool, you may have to set up your list before you can build a segmentation.

· Use correct data. If you are running an if-then statement from a data point, there must be the correct data in the field. Make sure that your data is being populated correctly into your field.

· Strive for exact matches. If-then statements look for exact matches. If you tell your system that Job Title should equal “VP,” you must have “VP” in the Job Title field, not V.P. or Vice President. To avoid having to worry about data consistency issues, ensure that your forms use drop-down menus and pick lists on data entry forms. These options force people to always choose one of the standard options rather than create their own.

· Conditional statements. For very advanced segmentation, use conditional statements. A conditional statement is an if-then statement nested within another if-then statement, making it an if-then statement only for that data point, not the full segmentation. Conditional statements are very helpful when you have a very complex automation rule, such as when you’re trying to cover lots of scenarios with a single rule. The conditional elements allow you to place a more granular focus on a specific part of the automation rule. So, for example, the statement might first call for looking at the “VPs in New Jersey” and then to search all those records for which ones are not in “sales opportunities greater than $10,000.” Figure 6-5 shows a conditional statement for the segmentation of VPs in New Jersey who have read a white paper but are not in an existing sales opportunity at the moment.

· Test and check. After you set up your segmentation, make sure to test it and check to make sure that it works properly. Many times, human error causes segmentation to fail. If you notice any issues, check for exact matches and check your conditional statements.

Figure 6-5: Use conditional statements to refine if-then statements.

The importance of minimizing the complexity of advanced segmentations

Advanced segmentations can become unwieldy very quickly. When you start using multiple if-then statements in a single segmentation, you run a high risk of human error. Human error is the number-one reason that segmentation fails. So use the following advice to minimize your complexities and keep you sane:

· Minimize your risk of failure. When running very complex if-then statements, you need to monitor a number of rules based on the data in numerous different fields. Changing your database or a field in your database causes your segmentation to either work improperly or fail completely. Fixing these errors is easy. Finding which field to fix is more of a problem.

· Give yourself a hand. Use automations and related segmentations to help you, not to do everything for you. Some segmentations require a manual step or attention by a person. Overcomplicating your segmentation only wastes more of your time.

· Remove issues with turnover. You probably aren’t staying where you are at your company forever, and neither is everyone else at your company. If you and your team make complex rules, consider the difficulty for the next team in managing those rules when you leave. Using simple segmentations minimizes the risk of having to start over from scratch.