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Acquiring new customers is critical to the success of your business. But how do you know which prospects to target? In this edition, Data expert, Steve Quast shares how customer acquisition models help brands answer this question and develop more effective customer acquisition strategies.
Customer acquisition models help marketers maximize acquisition budgets by focusing efforts on prospects that have higher conversion rates and are less costly to acquire. These models support all direct-to-consumer marketing channels, including direct mail, email, display and social. They are customizable to attract prospects who meet specific criteria once they convert, such as likelihood to:
Below are the three most common customer acquisition models:
Why It Works: We developed a look-alike model for a direct mail campaign to attract prospects that look like high value customers. We discovered that prospects who closely resembled the existing high value customers had 23% higher response rate than the rest.
Why It Works: When we built a response model for a large retailer, we discovered that the top 20% of prospects ranked by the model had 46% higher than average response rate while the bottom 20% had 32% lower-than-average response rate.
Why It Works: Prospects with the highest predicted future value are acquired at the same rate as other customer acquisition targets. However, revenue generated by these prospects in the year following conversion is significantly higher than that of other new customer segments.
Customer Acquisition models are largely dependent on 3rd party data, including:
Zero and first party data such as purchase activity, email engagement (opens, clicks), preference center data and other customer information may also be required.
We recommend revisiting models on a bi-annual basis to ensure they reflect current customer behaviors and attributes. The composition and behavior of a brand’s customer base changes over time due to shifting markets, business objectives and marketing strategies.
Response models are especially sensitive to changes in marketing channels or tactics (e.g. creative considerations and offers), so these models need to be re-evaluated (and potentially redeveloped) when such changes occur.
Models can and should be combined with other tools. For example, we create micro-targeting functionality by combining segmentation techniques (i.e. personas) with modeling techniques. The combination enables the marketer to target on two levels – relevancy and likelihood to respond.
A two-step process to creating accurate micro-targeting: