Sailthru Prediction Manager Delivers 175% Lift in Revenue Per Send for IR 500 Company, The Clymb
By Neil Capel | September 10, 2015
For most marketers, new technology implementations are bogged down by process, professional services and performance that does not live up to potential. The predictive analytics and intelligence space is the same. B2B technology marketers have adopted “predictions” and stretched and conflated the term to describe a wide-range of capabilities: personalization, recommendations and even segmentation. When every message seems to say the same thing, but manifests itself with varying degrees of the truth, it’s difficult for marketers to understand where to invest, which partners have proven results and the use cases that a specific technology can support.
We believe in letting results speak for themselves.
When we built and launched the Sailthru Prediction Manager beta program, it was with the knowledge that predictions are not new to the marketing world, but that there was tremendous opportunity for improvement. No single technology existed to measure both consumer interest and intent at the individual level. Better yet, nobody made it easy for marketers to apply those measurements to optimize marketing in real time. Saying you solve a challenge is one thing. Truth through proof is another.
Here’s a snapshot of what challenges marketers are solving with Prediction Manager and the results that they are producing.
Country Outfitter: easily advance segmentation to identify 70% more “best customers” and generate 72% more revenue
Yep, I just said segmentation when we’re talking about predictions. Here’s why: when you use predictions, like Country Outfitter did, to identify which of your individual customers will purchase within 24 hour and 7 day time horizons, you’re able to group them into a new best customers list, or segment. Prior to Prediction Manager, Country Outfitter was using RFM modeling to form their highest value customers segment; however, this form of reporting-based analysis restricted the number of customers that Country Outfitter was engaging. They were leaving money on the table. With Prediction Manager being able to accurately predict purchase behavior, this digitally-savvy team was able to:
• Increase email messages delivered by 70%
• Lift email revenue by 72%
• Increase purchase per click by 24%
All without driving an increase in opt outs and without spending time working RFM models to generate an inferior result. They’re saving time and making more money. It’s marketing done right.
The Clymb: optimize revenue through personalization and predictions to increase revenue per send by 175% and decrease opt-outs per sale by 72%
This Internet Retailer Top 500 company knew that they had the opportunity to increase revenue and loyalty by accurately matching specific buyers to specific products and optimizing their customer engagement by predicting which individuals would purchase and how much they’d spend. They chose Sailthru because of our ability to identify the unique interests of each of their customers – those who enjoy biking vs. surfing, travel vs. gear… even down to color preferences – across both web and email at the individual level. By applying this multichannel data set to email marketing, they experienced an 11.8% increase in revenue and 8.2% lift in purchases within 90 days of implementation. When they turned on Prediction Manager to match interest with purchase intent, the results were dramatic:
• 71% lift in email revenue
• 175% increase in revenue per send
• 72% reduction in opt outs per sale
What did they have to say about it?
“Sailthru isn’t just a technology company, it’s a competitive differentiator. Prediction Manager is just one example of how they make modern marketing easy. No one else can match this… You can’t buy multiple point solutions or a legacy marketing cloud and get the same results as what we’re seeing with Sailthru.” – The Clymb
Would you say the same thing about your marketing cloud, ESP or predictions tool?
—Neil Capel, Founder and Chairman of Sailthru
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