In our last post, we walked through Austin Chang’s article on the importance of mapping out your growth channels every single year. We argued that mapping out your customer data plan is just as—if not more—important.
The reason is that most companies we talk with are in the process of mapping out (or re-mapping) their growth channels. But the main topic of our conversations is less about channels and more about the difficulties they face in mapping due to an incomplete or disparate customer data.
So, this is the first post in a series where we’re going to follow Chang’s steps, but with a focus on customer data. His first question is, “Who are your customers?”
Let’s dive in and look at some of the simple fundamentals that form the foundation of a solid customer data plan.
If defining who your customers are—or who you think they are—is important, then mapping data to those definitions is absolutely critical.
The most basic level of defining customers is grouping them into what we call “personas.” Sometimes these are called customer profiles or archetypes and often they are grouped into what marketers call “cohorts.” The basic concept is the same: customer personas are descriptions of the most important and/or most common types of customers your company serves.
You can have all types of customers, but growth teams generally focus on building personas for their most valuable customers—the ones they are trying to target with marketing. It’s worth noting that you don’t have to have personas to be successful, but most successful companies use them. Why?
Personas can be extremely valuable because they focus teams all around the company around solving specific problems for specific people. In the the specific context of growth, personas are the foundation for highly targeted and effective acquisition and nurturing practices. (If you want a great, simple example, we enjoy this past persona work from MailChimp.)
Building customer personas can be an involved process, so we’ll cover that in detail in another post. For now, we’ll assume you’re starting from the beginning.
We always start at the most basic level: a several-sentence description of each persona. Next, we build a short, bulleted list of demographic (and some psychographic) information that fits a majority of the persona (age range, job titles, interests, motivations, etc.).
Next comes the most important part: translating those descriptions and bullet points into actionable data points in your martech stack. But…
Ideally, you should be able to identify someone as a certain persona across your entire martech stack, from traffic and user analytics to lead capture mechanisms to your CRM and all of the tools you use for marketing automation, but that can be a significant, time-consuming and expensive challenge.
There is software that manages all of that, but it’s generally bloated, enterprise-level technology that is overkill and unaffordable for most companies. Data routing tools like Segment are beginning to offer persona functionality, but again, the price tag is often reserved for companies with deep pockets or lots of funding.
We’ve helped companies tackle personas at a large scale with enterprise tools, but the most common need we see is for businesses to simply begin utilizing personas in their growth activities.
Even though things can get extremely complex and technical, starting simple is almost always best. Here’s a simple guide to basic persona data mapping:
1. Contact records
At the most basic level, you need to be able to classify a contact record or customer identity as a persona in your CRM (or whatever system of record you employ to house and use your customer data). If that sounds as simple as adding a custom field, attribute or tag, you’re in luck, because it is.
If you have existing customer data, it’s almost always worth going back and tagging records with personas—that historical analysis can help validate your persona definitions and reveal which assumptions might not reflect your actual customer base.
2. Lead capture mechanisms
Next most important is updating your lead capture mechanisms to be able to classify incoming leads as personas. For forms, that might be a hidden field. For chat tools, that might be a conversation tag. For sales people, it might be adding a question to a sales script. No matter the intake channel, the eventual end result should be that a contact record is classified as a persona in your system of record.
3. Acquisition data structure and tagging
Once your lead capture mechanisms are technically able to classify leads as personas, it’s time to update your acquisition data structure and tagging to include personas.
For paid digital campaigns, that means things like adding persona information to your query string parameters (that ideally populates form fields or chat tags dynamically). For organic efforts, that means things like tagging lead capture mechanisms according to content that implies a certain persona. And so on.
4. Segmenting by persona in marketing automation tools
Classifying incoming leads where possible is critical, but personas are just as helpful (if not more) for nurturing activities. Grouping leads by persona in your marketing automation tools gives you the flexibility to communicate differently with each segment. Even more importantly, it allows you to test persona-specific hypotheses on subsets of each group.
In an ideal world, your CRM is integrated with your marketing automation tools and you can pass the persona classification directly. If not, using a tool like Zapier will most likely get the job done. (If that’s not an option for some reason, you can do daily exports/imports of .csv files, but that solution is prone to data corruption and isn’t scalable as far as the time involved.)
5. Set up persona-based reporting in your analytics tools
Last, but certainly not least, is making sure that you can see persona-level reports in your analytics tools.
If you’ve incorporated personas into your query string parameters for certain types of traffic, you should be able to easily expose that data in Google Analytics. It’s just as easy, if not easier, in data analysis tools like Mixpanel and Amplitude. And of course there’s nothing wrong with the tried and true method of exporting data into spreadsheets and performing manual analysis, though that won’t scale from a time and complexity standpoint.
At the end of this process, you’ve not only defined who your customers are, but you can now quantify those assumptions, report on them, build marketing activities on them and leverage all of that data to refine your personas build better customer experiences through things like behavioral analysis and dynamic content.
We’d encourage you to set up a testing framework that allows you to run experiments on each persona to see what converts best at each point in the customer journey.
It’s easy to go overboard defining your customers, so we always recommend keeping things simple starting out. Limit yourself to 3-4 personas and build some high-level reports that answer some basic questions.
When building a data-driven martech machine that will scale, knowing a few important things about many customers is far more powerful over time than knowing a huge amount about a few customers (which is data you should already be collecting from qualitative customer interviews).
If you’re reading this and feel like you already have a handle on the basics of using persona data across your martech stack, here’s a quick thought on where to go next.
Because you won’t know the persona of every visitor or user who interacts with your site or app, it’s important to think about behaviors or data points that can help you determine what someone’s persona is so that you can begin targeting the marketing and nurturing efforts towards them. Or, if you already know the persona, there may be data points that help you drive conversion.
This is generally where things start to get more complex, but here are a few quick examples:
• If you’re a B2B business ,you might try to find out what size of company someone works at so you can classify them as “enterprise” or “SMB” personas
• If you’re an eCommerce company, you might try to find out if a visitor has purchased a similar item from another company in the past to determine how much education they might need about the product
Whether it’s progressive profiling via cookies, through a chat tool, using surveys or an email campaign, there are tons of ways to collect data points from your customers that help you provide a better experience.
Eric was formerly CMO of The Iron Yard, which at its peak was the largest coding school in the world. There he grew the business 10x in less than 2 years by building out a data-driven acquisition practice and full-funnel attribution models across a dozen software systems. He is also a consistent lecturer in MBA programs and sought-after speaker on growth topics.
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