What is Look Alike Modeling?
Look-alike modeling is an advanced analytics methodology that can be leveraged to identify and acquire potential clients who behave pretty much like your existing clients. This approach relies on look alike modeling algorithms and machine learning techniques to analyze customer behavior and buying patterns and leverages the insights obtained to find potential customers with similar attributes. In other words, it is the best approach you can adopt to ensure the effective use of your marketing dollars.
From a marketing perspective, our marketing analytics experts suggest that look alike modeling techniques can be of great help to businesses trying to sell their products and services as it is very likely that look alikes identified through such techniques will turn into long-term customers. This means the perspective of growing your business and driving sales using look alike modeling algorithms is quite impressive. Also, identifying and targeting look alikes can help businesses to optimize campaign budgets and improve marketing effectiveness by fine-tuning marketing strategies.
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How Do Look Alike Models Work?
Though it sounds like a herculean task, look alike data modeling is pretty simple as afar as you have the datasets and analytics capabilities to analyze your data.
Step 1: Integrating Customer Datasets
Data can be obtained from several internal and external databases, including CRM tools, customer touchpoints, websites, and other external sources. To make the most out of it, businesses must integrate data sets on a common platform and deploy data models to extract insights. Look-alike models do just that but from a perspective of identifying new customers to maximize the effectiveness of your marketing campaigns. Since data is the starting point, irrespective of the look-alike modeling techniques used, the more information you have, the more likely you are to build a better lookalike audience portfolio.
Step 2: Defining Customer Attributes
Once you’ve identified and integrated data from all relevant sources, you’ll have to leverage advanced look alike modeling techniques to analyze those data sets and identify potential customers. This might vary from person-to-person depending on the data sources being used. However, specific seed audiences can be created by combining customer attributes obtained from different data sets. The more precise your look-alike model, the easier it is to find your potential customers.
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Step 3: Building the Audience Database
The final task of building a look alike customer base is generally done in the demand-side platform (DSP) or the data management platform (DMP) and may vary depending on the platform being used. However, if your only focus is to drive brand awareness then being less strict with your attributes will help you generate a larger audience database that will most likely drive brand reach.
Eventually, the look alike modeling algorithms being used depends on the goals of your marketing campaigns and what you want to achieve by building a look alike customer database. But if your marketing initiatives are built around targeting specific people with a high-value proposition, then it’s crucial to use narrowly defined behavioral traits to maximize reach and marketing outcomes.