We’ve discussed segmentation in previous posts. Segmentation identifies valuable customers; lookalike modeling (LAL) helps you find more of them. LAL modeling is a predictive modeling technique used to identify prospects who resemble your top customers based on behavioral, demographic, or transactional characteristics. Marketers use LAL modeling to target people statistically similar to their most profitable customers.

Not All Customers Are Equal: The Cost of Targeting Everyone

Every DTC brand wants to grow its customer base, but our products and services are not one-size-fits-all. Your brand might have thousands of customers, but most are low and infrequent spenders. Recall from our discussions on RFM analysis that some customers are far more valuable than others.

The bulk of your sales often comes from a relatively small share of customers. This is an important – and valuable – concept to understand when targeting prospects. Paid ads are expensive and broad targeting wastes budget. By knowing the characteristics of this small cohort of high-value customers, you can focus marketing and advertising dollars more heavily on prospects who look like them. As a result, you will reach the audience most suited to your brand, achieve better conversion rates, and improve the efficiency of your customer acquisition efforts.

This is the core idea behind lookalike modeling.

Customer Data: Your Most Valuable Growth Asset

Every company collects data on its customers, but few use it to its full potential. Every transaction, web visit, inquiry, complaint, survey, or other interaction gives you information you can use to profile your high-value customers.

In addition, your DTC brand might collect other data about your customers through zero-party data collection methods or by purchasing third-party demographic, lifestyle, or spending data from companies like Acxiom, Experian, or Affinity Solutions.

When you know who your ideal customers are, you can determine what they look like demographically; the typical lifestyles they lead; and what they spend money on, so you can craft a winning customer acquisition strategy.

Using Lookalike Modeling to Scale Without Sacrificing Customer Quality

LAL modeling is valuable in preserving audience quality and expanding your brand’s reach strategically and intelligently.

Identify Your Seed Audience

Segmenting your customers is usually the best way to start with LAL modeling. Through either RFM analysis, K-means clustering, or other segmentation methods, you identify the distribution of your customers.

Select customers in the highest-value segments; they form your seed audience. Targeting your marketing to prospects who resemble these customers demographically and behaviorally is likelier than broader marketing tactics to generate more conversions, higher initial spend, and longer customer retention.

The quality of your seed audience determines the quality of your model. A bad seed audience – especially one created by targeting all customers – often results in a model that produces inferior lookalikes.

Examples of good seed audiences are:

• Repeat buyers
• High lifetime value (LTV) customers
• Loyalty program members.

Bad seed audiences include:

• All website visitors
• Discount seekers

Now, you’re ready to predict your best prospects.

Predict Lookalikes

Once you’ve identified your seed audience, you need to predict prospects’ propensity to belong to the seed audience.

Logistic regression, which we discussed in last week’s post, is one method companies use in LAL modeling, but there are many others, including:

• Random Forest
• Gradient Boosting
• Neural Networks.

After building, testing, and validating your model, score your prospects. The highest scores are the prospects most probable of being like your top customers and having the highest probability of conversion. These are the prospects you target.

Activate Your Seed Audience

Now that you know which prospects look like your best customers, you can target them directly.

In addition to direct targeting, you can also use platforms like Google and Meta Platforms to build audiences resembling your seed list. These platforms reduce complexity in LAL modeling for online marketing and are highly effective in creating lookalike audiences.

Insights only create value when they are acted upon.

Lookalike Modeling in Action: A Napa Valley Winery

The DTC channel is a key lifeline for most U.S. wineries, so LAL modeling is quite valuable, if not vital.

A winery in the Napa Valley might find that its most profitable customers share these traits:

• Orders at least twice a year
• Spends at least $125 per order
• Visits tasting rooms at least once per year

While the winery might then utilize any zero-party and third-party data on these customers to identify demographic, psychographic, and lifestyle similarities, LAL models typically use behavioral signals, which are often more predictive.

Then, the winery might upload its customer lists to ad platforms which build lookalike audiences automatically.

Lookalike Modeling: Solid and Strategic

Using platform lookalikes is easy, but building true predictive models requires data science.

Lookalike modeling can help you form a solid customer acquisition strategy that pays valuable dividends:

• More efficient customer acquisition
• Higher quality conversions
• Longer-term customer value

The key is a well-identified seed audience.