Case History: Proflowers.com Markets to the Heart
Here’s a question for you expert data analysts: A Customer buys flowers on Valentine’s Day. Obviously, they are a gift, right?
Well, yes. But Proflowers.com couldn’t always connect those two dots to predict who would buy gifts on other occasions. But now it can. And that limited its ability to retain customers.
Founded in 1998, the online floral marketer was one of the “few pure-play online companies that actually was profitable,” says Sandra Gudat, CEO of Customer Communications Group Inc. But it was data challenged.
For one thing, disparate sources made it difficult for the startup to access the data on its 1.5 million customers. Also, it had limited purchase history and what appeared to be “a large number of one-time buyers and low category customer frequency,” says Gudat.
The Challenge
So it turned to Gudat’s company for help. It wanted to increase the conversion rate of first-time buyers to repeat purchasers, increase lifetime value and secure the loyalty of its best customers.
It started by appending third-party lifestyle and demographic data. It also analyzed purchasing patterns, buying motivations and seasonality.
And it defined buyer types based on analysis of when and why they made a purchase. For example, was it driven by emotion or an event? Product and promotion also played a role in this analysis.
In another advanced application, the company utilized an algorithm that crawled through the notes that were sent with flowers, looking for key words. And it examined source codes to determine the portals and media favored by high-profit repeat buyers.
In some ways, the analysis reinforced what Proflowers already knew – for example, that seasonality dominates the floral business. And some of the findings about customer segments seem obvious in hindsight. For example, men are less price sensitive than women because they tend to wait until the last minute to buy gifts. And single women are not Valentine’s Day flower buyers.
But it also found that some customers will purchase at any time during the year. And it identified five basic customer groups.
The Solution
These findings enabled the company to develop predictive models, and to develop creative that would reach customers when they were most ready to buy. Many of these efforts were based purely on an emotional appeal:
For example, this personalized e-mail was sent to working women:
Maribeth, I suggest it may be time to “smell the roses.”
I’ll tell you why I’m contacting you today.
Just on a whim, I sent my Mom a bouquet of flowers. It’s been a couple of months since we saw each other…so I sent her a bouquet.
She called me. She was crying…”What makes these so wonderful is that you didn’t need a reason.”…
Oh, one more thought. How about you? Celebrate anything ay all – even opening that bottle of Chateau Margaux you’ve been savings—by treating yourself to a bouquet.
Have a happy day!
Tracey Beneilli
Vice President, Proflowers.com
Clearly, that was directed at an “emotion buyer,” says Gudat.
The firm tried another approach for affluent couples with kids.
Hi Jules,
I’m contacting you personally because I have an idea to share with you. It may involve your spouse, or the two of you might want to team up on this.
The day before yesterday, just because of a burst of love, I sent my wife a bouquet of graceful Strawberry Gerbera Daisies, from your “loving family…”
I only know it’s two days later and I’m still a champion and so are our kids…
How about it, Jules? Want to e a champion at home? It’s simple, just click here: www.proflowers.com.
Regards
Bill Strauss
CEO, Proflowers.com
The Results
These efforts have been rewarding for both the customers and the company.
According to Gudat, the average response rate increased 35% as a result of the personalized messages. In addition, the Jules & Roz versioned messages yielded a 53% increase in response. Moreover, the average order increased $1.44 over control, and $3.12 for top segment.
As it turns out, Proflowers didn’t need Gudat’s help in sourcing product.
It grows its own.
