Look-alike modeling has become a game-changer in the marketing world. By identifying groups of people who resemble your most profitable customers, this strategy can significantly enhance your advertising efforts. In this article, we'll dive into three key pointers to help you leverage look-alike modeling effectively.
1. Quality In, Quality Out: Ensuring the Right Input Data
The success of any model hinges on the quality of its input data. To maximize the effectiveness of your look-alike modeling, you must start with the right data sets. You'll need two primary sources of information:
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First-Party Data: This includes data from your website and sales tools (like your CRM).
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Third-Party Data: Purchased information about consumer traits and behaviors.
Begin by identifying the primary data that will drive your model, based on your campaign goals. For instance, if your aim is conversions or sales, focus on data about people who have made purchases or filled out forms on your website. Conversely, if you're seeking brand lift, look at metrics such as frequent site visitors and their engagement duration.
Once your goals are clear, start collecting new data elements by setting up beacons on your site. These beacons capture valuable user information every time they're triggered, helping you build a rich user profile database. This database can then be enhanced with third-party data sets for a more comprehensive view.
"Look-alike modeling results in double or even triple the results of standard targeting, according to 30% of advertisers." – Exelate Study1
2. Integrating Data Sources: Creating a Unified View
After gathering primary and third-party data, the next step is to effectively cross-reference these datasets. Start with an ID sync to merge your data with the purchased data. For example, if you want to match website visitors with Amazon shoppers, an ID sync will help identify users looking for jewelry on your site who also bought artifacts on Amazon.
ID syncing is a common process and can be done with major companies like Amazon or through independent service providers. Another crucial aspect is taxonomy. Different sources might use varying parameters to define the same data type. Unifying these data fields ensures accurate comparisons across datasets.
3. Broader Horizons: Flexibility in Data Sets
When refining your data sets, remember to leave enough room for pattern changes. Narrowing your dataset too much can limit your audience. For example, if you only target people who have bought vintage jewelry, your audience might be too small. Instead, broaden your dataset to include users who added jewelry to their cart or inquired about it. This approach gives you more flexibility to uncover potential customers and adapt to changing patterns.
Bringing It All Together
Look-alike modeling is a powerful tool for audience targeting, offering the potential to uncover new insights and improve advertising outcomes. By following these three tips, you'll set a strong foundation for your look-alike modeling initiatives and move towards a winning strategy. Remember, the quality of your input data, effective integration of data sources, and maintaining flexibility in your datasets are key to success.
For expert guidance and comprehensive solutions to drive growth and achieve your business goals, reach out to MarketStar. We specialize in transforming your marketing efforts into efficient and effective strategies that keep you ahead in the competitive market. Let us help you turn your data into a powerhouse of success.
References:
https://www.adweek.com/creativity/van-gogh-bnb-takes-the-creative-effectiveness-grand-prix-at-cannes
/https://trailhead.salesforce.com/en/modules/digital-advertising-with-customer-data/units/use-your-own-data-to-acquire-new-customers
