Start growing your business with existing customers.

Download some tools.

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.

 

DOWNLOAD NOW (no email required) –> PDF and Excel template.

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

Get free ideas.

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.

5 ways marketers leave customer value on the table (and what to do about it)

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.

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Wrestling with Incrementality

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.

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Confusion Matrices Hold the Key to a High Performing Predictive Model

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.

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