Honest-ly: Personalization Boosts Conversions For The Honest Company

The Honest Company is a very personal service that tries to solve a real problem for busy moms: their busy-ness. Founded by actress and mom Jessica Alba, it delivers bundles of monthly basics to clients' doors, from beauty and wellness products, to house supplies and even diapers. “We can help the mom by saving a trip to the store and having products shipped,” says Susan Cho, Email Marketing Manager. Moms can customize their bundles with certain products, as well as deep discounts on additional special items.

But the technical execution of the messaging was not in keeping with the customizable service itself. The basic model for converting potential subscribers was a 7-day trial of standard discounts. Leads essentially included a name and an email. Personalization meant simply putting a first name onto the message. “For us the biggest challenge was how to know if you are a mom or not and what bundle you are interested in,” she says.



Company strategists worked with the company Retention Science to grab more information from its list to create profiles and scoring to determine who were the more active online buyers. Retention Science used data from Datalogix’s Connection Engine to start making predictions about whether a potential customer needed a 10% or 20% discount as motivation to book the subscription. This basic analytic tool helped the company save money by not wasting discount levels on customers that didn’t need them. “We don’t have to send a 40% offer. We can send a lower offer,” says Cho.

The company also started using the prospect’s browser behavior to identify her key areas of interest, to determine which bundle was the most likely match for her. Subsequent email content was then populated with content from the appropriate category. “Before using it I never thought of linking browser behavior to customizing emails,” Cho says. “But it is so valuable, with surprising insights.”

Retention Science created 20 variations of bundles and promotions in order to model the best one for a particular customer according to where she was in the customer lifecycle.

For instance, Cho could start seeing patterns in browser behavior that predicted a potential customer’s likelihood of canceling a trial. A higher number of logins during the trial usually indicated someone who would continue. The company could use this knowledge to fire off an offer to the people whose logins were flagging in order to capture them before canceling. Cho found that specific dollar-off discount offers performed better at converting customers than did percentage-off offers, even though the actual amounts of the reduction were the same.

By rolling these insights and personalized email content into the sales process, Honesty Company saw a 170% increase in conversions and an 80% jump in average order value. The AOV increased because the cost of trials, the standard method of pushing the sale until this program came along, was no longer needed in all cases. “We were able to push bundle subscriptions before trial depending on the kind of consumer,” Cho says. In some cases the profile of the prospect suggested she was already ripe for a bundle offer. The company also found that customers who converted immediately to a bundle offer without a trial were also retained as subscribers longer.

Cho says that the analytics and personalization piece of the business is something she is happy to outsource rather than bring in-house. “I would rather focus on the bigger picture,” she says. “This is most important for us: to activate the new buyer. I would rather outsource that and focus on the user once they become buyers.”       

1 comment about "Honest-ly: Personalization Boosts Conversions For The Honest Company".
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  1. Pete Austin from Fresh Relevance, March 26, 2014 at 5:59 a.m.

    Re: "The company could use this knowledge to fire off an offer to the people whose logins were flagging in order to capture them before canceling". I'm impressed. This is exactly how people should be using real-time analytics:

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