How customer lifetime value analysis is transforming partner marketing




  • Contributor Pete Cheyne explains how today’s smart marketers are employing technology and machine learning to optimize the results achieved through partner and affiliate relationships.

    How customer lifetime value analysis is transforming partner marketing

    A few years ago, direct response marketers primarily focused on cost-per-acquisition (CPA) metrics — how much it cost to drive a sale. In the partner and affiliate realms, that cost was reflected in the bounty that the advertiser was willing to pay for each sale.

    CPAs and bounties are still primary considerations in partner marketing. That’s not gonna change. But what is changing is the emphasis that data-driven partner marketers place on the revenue stream that happens after the sale.

    Understanding user profitability

    Did the customer buy again? And again? Did they keep subscribing to my service for three months? Six? Did they make related purchases through my e-store?

    While the specific questions that marketers ask vary based on the particulars of their businesses, what all of these brand questions have in common is a passion for understanding the relative value of a customer acquired through each of their marketing partners and programs.

    Partner marketing user quality analysis

    Lifetime value (LTV) analysis helps us understand whether customers acquired through partner marketing have the same buying patterns and loyalty as customers acquired through other channels.

    Partner marketing skeptics tend to think that customers attracted through CPA programs are fundamentally less loyal. A friend of mine at a massive US apparel retailer told me that he was absolutely convinced of that. But the data showed something very different.

    “Then my data team showed me that there was statistically very little difference between the relative value of these customers,” he told me. “They were just as likely to buy again, and their shopping baskets were within a hair’s breadth of the same value.”

    That’s not to say you will see the same results in your data — but it does reinforce the need to do the analysis rather than relying on hunches.

    Measuring partner marketing lifetime value

    Measuring LTV is relatively straightforward if you have the right kinds of integrations. A brand simply ties all of a user’s purchases to an anonymized user ID. From there, they can pass the data to their partner solutions provider through a secure transfer mechanism.

    The provider then consolidates the data and parses it by partner and program. They perform the necessary calculations and offer the resulting insights as a measure to help drive optimization decisions.

    Using LTV to optimize your partnership efforts

    Additionally, LTV analysis is also helping marketers compare the value of different partners and campaigns. By tying post-acquisition sales data to specific vendors and programs, marketers can determine which partnerships and offers drive high-quality users who stick around after their first purchases. Instead of simply optimizing to revenue or average acquisition cost, these brand leaders can compare and optimize to the most profitable partners and campaigns.

    In the enterprise end of the partner marketing industry, offering different rates to different classes of partner is now relatively common. Gone are the days when every partner was given exactly the same cash bounty or rate.

    Instead, sophisticated partner marketers sometimes have a number of different rates and rules for different classes of partner. Many companies base these rate differences on hunches about what kind of people each partner is likely to attract. But more and more, companies are basing their analyses on hard numbers and quantitative analysis.

    Proactively boosting LTV with machine learning

    Another way that LTV analysis is helping brands improve results is by enabling them to proactively grow total sales to consumers by identifying related items that are often purchased sequentially or in tandem.

    Using machine learning, brands can leverage association analysis to discover sales relationships between items, and then proactively market those goods together. By analyzing these patterns, brands can use partner marketing to remarket related products to shoppers who have already purchased certain items. This boosts average LTV.

    Conclusion

    With more and more granular data, brands can be smarter about partner marketing and where they invest their resources. They can cultivate direct partner relationships that ensure greater revenue and long-term profitability, and then focus resources and attention on those programs and partners that drive the best results.

     

     

     

    [Article on MarTech Today.]


    Opinions expressed in this article are those of the guest author and not necessarily Marketing Land. Staff authors are listed here.


    About The Author

    Pete Cheyne is CTO for Performance Horizon. The architect of Performance Horizon’s best-of-breed solutions, Pete oversees the technology strategy of the company. Prior to Performance Horizon, Pete was Head of Integration at Buy.at, where he was responsible for all integrations across a wide variety of EU-based companies.

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