Segmentation in 2023: Strategies, Tips, & Tools for DTC Marketers

This is a guest post from our partner, Simon Data.

Introduction

The promise of personalization is alluring: imagine a complete one-to-one experience for every customer, completely optimized and driven by every detail and data point about that person - who they are, their interests, their needs and previous actions or inactions.

This picture has long been a collective pipedream among marketers, achieving it has been nearly impossible or many organizations.

At Simon Data, we define personalization as any experience that is delivered to a person based on known data about them. By that definition, personalization strategies can exist on a spectrum: they can be one-to-many, one-to-few, or one-to-one. For this blog, we focus on one-to-few personalization strategies—and it all starts with segmentation.

There are many solutions designed to make personalized marketing easier—but the most valuable among them are audience segmentation tools. With these, marketers are able to cut through the complexity of their markets and develop personalized messaging strategies that lead to revenue.

Make data simple

You’ll also learn how a customer data platform (CDP), like Simon Data, can help you unify your customer data from any source, and make it easily actionable for your marketing team.

How to build a personalization strategy from scratch

The companies that are seeing the most ROI from personalization know two secrets: First, successful personalization relies on your ability to use data to answer questions about your customers. And second, in order to see value from personalization, the strategy has to be inherently customer-centric, rather than business centric.

Here’s what we mean by that: Sure, your business-centric goal may be something like “increase the number of second purchases among new customers,” but a customer-centric approach requires you to work backwards to understand the different segments that exist within that new customer audience, and the different reasons they aren’t or are making another purchase.

Put simply: Building a customer-centric personalization strategy begins with obtaining a deep understanding of the different types of consumers that are interacting with your brand, and identifying how and why they differ from one another.

There are reasons why some people take one action, and others don’t. Your job is to uncover why those differences exist, and by doing so, you can use those insights to create relevant, personalized experiences that provide value to those segments and drive the action you want them to take.

And if you’re feeling overwhelmed by this mandate, we totally get it. There are, after all, a myriad of channels and campaigns that offer opportunities to personalize the customer experience for your key segments.

But instead of personalizing all the things, or focusing on only what’s easiest, (we see you, %First Name!) our advice is to use your customer data to identify the largest or most valuable customer segments, as well as the problems that matter most to them.

If you can focus on solving those customer problems first, then, as personalization is used to solve one problem, then the next, and so on - you’ll eventually end up with a net different experience that’s personalized for a large swath of segments. This leads to all the metrics that marketers love to see: lower acquisition costs, increased LTV, AOV—and ultimately increased ROI for longer periods of time and across all marketing efforts.

Step 1: Determine your key customer segments

The first step, of course, is to uncover which of your customer segments and experiences are fit for personalization.

Most e-commerce marketing tools provide some basic, pre-built customer segments created using a combination of behavioral or demographic data like high LTV, cart abandoners, shoppers based in a specific location, category-specific shoppers, discount shoppers. The list goes on.

And while you can start with these out-of-the box segments (we offer them too!), you may find more benefit from starting from scratch. After all, your brand and your customers are unique, and the way you define your most valuable customers may differ from other businesses.

Consider the following 30 questions about your customers. The answers to these questions can inform the way in which you build your segments, and illuminate the experiences for which you want to personalize.

Step 2: Design personalized experiences for each segment

Of course, it can be difficult to turn the responses to the previous questions into an actual strategy.

Many of our clients find that organizing their thoughts in response to so much data can feel overwhelming.

Here’s a little template we put together to show how we approach the task of ideating personalization campaigns, and an example of it in action using our previous case of driving second purchases within a new customer audience.

This is a simplified approach. In reality, each statement should be aligned with quantitative and qualitative data.

For instance, if you’re using data to define your segments, then the key differentiators should be reflective of the factors that define each segment or include additional metrics to further profile each group.

Once you have your list of target segments or sub-segments, and have come up with viable personalization strategies, you can then prioritize them by comparing the perceived level of effort required to launch the desired experience for each group against the estimated return on investment (ROI).

Step 3: Identify the data you need

When marketers are grappling with building a personalization strategy, they often need to ask themselves some very hard questions pertaining to data. Questions like:

1. What kind of data do I need?
2. What data am I missing?
3. Do I have enough of it?
4. Where do I find the data?
5. How do I even know if I have enough?
6. Can I access this customer data today?

Because you’re building a personalization strategy that’s based on the idea that different customers need different experiences, the data you collect should help illuminate the differences that exist between customers.

Identifying the exact data points or attributes you need to drive these differentiated experiences depends greatly on your business, your products, and your customers. They generally fall into a few categories

The importance of contextual data for segmentation

Contextual data is information that provides a broader understanding of an event, person, or item.

Customer segments are often built around high-aggregation data attributes—such as whether someone is a new or returning user—but the more contextual data you gather about your customers, the more effectively you can target and time your personalization campaigns.

Contextual data can influence which segment a customer falls into, the timing or frequency of your messages, the channels you use to deliver the message, and the context of the message itself. This type of data can include things like weather, traffic location, seasonality, past purchases, preferred channels, and more.

How to build a technical foundation for personalization

A solid set of customer data is only the beginning. You can’t create customer segments with rich contextual data without the right technology in place. You need the right tools to be able to collect and make use of that data in order to bring your strategy to life.

Your technical foundation for personalization depends greatly on your company’s data infrastructure, available technology and configuration, budget, revenue goals, the way your internal teams are organized, and data governance.

In order to run sophisticated segmentation and personalization strategies, you need a tech stack that allows you to do several things:

1. Connect your customer data: It’s essential that your tech stack be able to connect and centralize your customer data, and make it easily accessible to your marketing team. By doing this, marketers can more easily recognize patterns in behavior, create custom segments, and deliver personalized messages. In short, it will help you gain a comprehensive understanding of your audience and act on those insights.
2. Manage customer profiles: Creating unified customer profiles enables you to see a detailed view of user activity, preferences, and interests, so you can engage your customers with highly targeted and personalized touchpoints. The right tools will offer user profile management features that allow you to track user progress. This will help you optimize your campaigns and adapt your messaging as needed to deliver the best customer experiences.
3. Orchestrate and deliver personalized experiences: By combining user data, segmentation, and predictive models, marketers can create experiences that are tailored to different customer needs and preferences, with the right message at the right time. This leads to increased customer engagement and loyalty, driving more conversions and revenue.
4. Access real-time reporting for quick analysis and segmentation: One of the greatest competitive advantages in any market is being able to react to real-time information as it comes. By responding quickly to changes in customer behavior or interests, you stay at the top of customers’ minds and continue to create valuable brand-to-customer touchpoints that lead them down the lifecycle journey.

With all this potential value, the next question is: Do you have the right layers in your tech stack to facilitate the end-to-end segmentation process?

Layering tools to build unified, actionable customer profiles

The best way to approach the segmentation and personalization challenge is by combining a cloud data warehouse (CDW) and customer data platform.

Cloud data warehouses (CDWs) are cloud-based databases that store valuable data from nearly every part of the business, including first-party customer data. CDWs were primarily constructed as a business intelligence tool, and access to the data is oftentimes limited to IT, engineering or other technical gatekeepers. To put it in laymen’s terms, think of your CDW as your dad’s garage. It’s completely chock full of valuable stuff, and a total mess - rendering it completely impossible for anyone who’s not him to find anything. So while CDWs provide a centralized source of customer data, supporting activities like personalization and segmentation requires brands to invest in technologies that integrate directly with a CDW, like Simon Data, to enable marketers to access and use the data within.

Customer data platforms are designed to integrate zero-, first-, and third-party data to create unified customer profiles that are easily accessible and usable for marketers. As a customer data platform unifies data across digital touchpoints, it provides a more comprehensive view of your customers, making it a great addition to any tech stack.

CDPs provide marketers with a centralized source of customer data that can be used to create targeted campaigns and deliver personalized messages to customers. Additionally, customer data platforms allow for real-time reporting. This is great for marketers who want to pivot quickly as changes happen.

While the platform type is important, it’s the features that make or break a CDP’s ability to carry your segmentation strategy to the finish line.

Achieving data fluidity

Ensuring data integrity when moving it between sources is challenging for even the most sophisticated marketing teams. This is because data-driven marketing involves many moving parts.

Data captured from one source may be formatted in a certain way, but it might need to be formatted differently when sent to another channel. The data in your data warehouse could require continuous hygiene.

A major value-add of Simon is that we are schema-less. No transformation needs to occur before the data comes into Simon.

Orchestrating these moving parts requires a tremendous amount of coordination across business groups that may not have the same resources or skills to make it happen. When your tech stack isn’t unified, it can mean constant issues with SaaS sprawl and siloed data.

And staying compliant with relevant data security regulations, such as GDPR and CCPA, adds an extra layer of complexity that can’t go ignored.

So how can marketers handle all this? Defining the parameters for your data integration is a good starting point. Here are three primary considerations:

How you plan to handle identification and unification across channels: Identifying and unifying data across channels is critical for achieving data fluidity. This is because customer data captured from one channel (such as website visits), must be connected to customer data captured from other channels (such as email activity).

This process of linking data from different sources to create a single customer view is called data unification. Data unification helps create more accurate customer profiles, which can be used to personalize marketing campaigns and provide more relevant experiences for customers.

Which unique identifiers will be used to stitch the data back together: Marketers need to pinpoint identifiers to stitch their data back together.

Using unique identifiers to match your customer data across different systems and sources (such as website visits, purchases, and email activity) allows you to create accurate customer profiles. Without them, it’s virtually impossible to create unified customer views or optimize your marketing strategies.

These essential identifiers also help you ensure compliance with relevant data privacy regulations.

How granular your data needs to be in order to take action on it: Knowing how granular your data needs to be is paramount in data-driven marketing—particularly when it comes to targeted content. Data points such as users’ behavior across different channels and their history with your products or services can help you decide how to approach content personalization and marketing campaigns.

But consider how granular your data actually needs to be in order to take action.

Point-of-sale data may enable you to access every single transaction a user has made on your website—but do you really need that level of granularity for sending targeted email content? Or do you simply need to know what types of purchases the user typically makes?

5 points to consider when evaluating a CDP

When you add a new customer data platform, it’s important to consider the technical complexities of the implementation. Successfully building your tech stack will require a combination of data engineering, cloud infrastructure, and marketing insights to ensure the platform is configured to serve both your current and future needs.

Here are some things to keep in mind:

Use Cases

Consider your use cases: The most sophisticated features won’t do any good if they’re not the ones that will work best for your business model. Think about how you will actually use the platform.

Say you want to send emails to your customers based on which products they’ve been browsing on your website. Or maybe you’d like to have an expert reach out with a tailored email or call based on the content a user is engaging with.

Starting with your use cases, you’ll be able to uncover which customer data platform features are must-haves rather than just nice to have.

Questions to ask:

1. What does your personalization / segmentation practice look like today?
2. What needs to change?
3. What are the key customer experiences you want to prioritize?
4. How will you measure success?
5. What data sources or channels need to be integrated to power your segmentation strategy?
6. How will you define segments, develop rules, and ensure they remain up to date over time?
7. What will you do with the insights the platform provides?

Future-Proofing

Always think ahead: When building your tech stack, don’t limit your thinking to current needs. Consider investing in tools that handle emails, call center data, or any other channels you may want to build into your marketing and personalization strategy over time.

Being proactive about knowing where your current strategy is headed, as well as which way your target market is moving, will prove invaluable in planning out a layered tech stack that’s built for the long term.

Questions to ask:

1. Which channels can the platform support?
2. Which channels do you use now?
3. Which channels are you likely to use in the future?
4. Will you need additional integrations or services to accommodate these channels?
5. What are the limitations or challenges specific to your use cases?
6. Is there a way to support scalability if your customer base grows significantly?

Integrations

Ensure total integration: To get the most out of your tools, make sure you can integrate these technologies across all your digital properties. This includes being able to create a unified profile for each customer and enrich that profile with additional data sourced from multiple channels.

Questions to ask:

1. Which channels can the platform support?
2. Can you integrate the platform with any third-party tech, such as a customer relationship management platform (CRM) or analytics system?
3. Does the tool allow for webhooks or other notification systems to trigger actions when certain events occur? Think about user sign-ups, purchases, support tickets, etc.
4. Can it integrate with third-party platforms or applications, such as Salesforce or Shopify?
5. How does the tool bring in outside data if there is not a pre-built integration?
6. Does it perform real-time data updates or batch data updates?

Security & Compliance

Consider security and compliance needs: It is essential to recognize the importance of customer privacy and regulation compliance when selecting and implementing a customer data platform. Above all, ensure that the platform you choose is compliant with GDPR and other regulations, and be sure to understand what kinds of customer data it is working with.

Questions to ask:

1. What security protocols are in place to ensure the safety and privacy of your customer data?
2. Does the platform comply with relevant regulations, such as GDPR or CCPA?
3. How is the customer data stored and accessed?
4. What processes are in place to update the platform and customer data?

Machine Learning

Machine learning algorithms: ML algorithms are often used to deliver personalized content “automagically.” Their output may look similar to segment-driven campaigns, but these algorithms are used to predict the optimal content for each visitor by evaluating customer data in real time.

If you want to get sophisticated, look into whether the tool can be used for a multivariate approach, to give you more control over which data the algorithm uses and help you fine-tune its application.

Questions to ask:

1. Does the tool have an automated personalization algorithm (that is to say, an online learning model)?
2. What type of model underlies the algorithm?
3. Can you customize which data attributes are used by the algorithm?
4. Can you ensure certain attributes are always included or actively excluded?
5. Can you choose between different algorithms and/or test them against each other?
6. What information is provided about the performance of the model over time?
7. What information is provided about data attributes in relation to the model’s performance?
8. Data technology can be a powerful tool in creating efficient, personalized customer experiences—but only if it’s used correctly.

Data technology can be a powerful tool in creating efficient, personalized customer experiences—but only if it’s used correctly.

Thank you for reading!

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