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Experiment and Learn Tool
We’ve created a handy one-pager and accompanying digital tool (PDF and Microsoft Excel template; tips on using them here) to help you define and track experiments and start moving toward growth by experimentation today.
5 Principles: Using Data Science to Create Great Experiences
At Sprocket, we live and die by 5 principles that shape how we use data science to create better experiences. We’ve created a handy one pager to give you detailed tips and tricks. (If you want more details, check out our free webinar on the same topic.)
DOWNLOAD NOW (no email required) –> PDF
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We know everyone could use a thinking buddy on the road to innovation and growth. That’s why we’d like to give you a taste of what it’s like to work with Sprocket – at no risk or cost to you. Simply give us a problem and a good timeframe to schedule a session. We’ll contact you, get something on the books, and take it from there.
Skim some smarts.
Growth is a tricky business because you can’t analyze your way to the future. At some point, you need to pick up your shovel and start digging. Many firms, when they realize this, “bet the farm” and put themselves in a vulnerable position. Others do...read more
Food pricing at The Masters golf tournament makes no sense – unless you put your behavioral science hat on and think about why Augusta National may be doing what they’re doing.read more
Join the team at Sprocket at the 2019 Rev Leadership Conference in Minneapolis, MN and learn how to turn data into a better customer experience.read more
Domino’s gives you points for taking pics of ANY pizza. Crazy? Crazy smart we think.read more
As marketers, we’re often extremely focused on bringing new customers into the fold. And let me tell you there’s nothing wrong with that. In fact, it’s entirely necessary, especially during certain stages of your business lifecycle.
But here’s the deal – you’re most certainly leaving value on the table with customers that you already have.read more
Every loyalty program manager should be wrestling incrementality. But be ready for a tough battle. The questions are formidable. How much of the behavior in the program is incremental? What’s that behavior worth? Which members exhibit incremental behavior and what motivates them?
While not an easy pin fall – wrestling with incremenatly is a winnable match. By combing clever analysis tied to the use cases for incremental behavior with a mixed method approach using both quantitative and qualitative analysis, incrementality can be wrestled to the mat.read more
Most of us probably realize that there is incredible power in predictive models and machine learning models. These models can help predict our customers’ future behavior before it happens, create individualized customer experiences, and help us connect to our customers on a deeper level.
However, did you know that every predictive model comes with its own set of trade-offs? That’s right, many times, by maximizing predictive accuracy in one area, you can actually increase your error in another area.
Read on to learn how to balance these trade-offs and take advantage of predictive modeling.read more