Too much data? Or missing the right tools?

How a Tools Gap Can Make Marketing Data Seem Overwhelming

Have you ever looked at those charts showing what happens in a minute on the internet these days, or checked out live internet stats? With the mass amounts of information being generated online in the modern era, it’s not surprising that many marketers feel they’ve simply got too much data.

However, when we probe a little more, it’s not usually the data itself that’s the challenge. It more often is about having that data available in the right place with the right, marketing-friendly tools to turn it into actionable insight and modeled segments.

Data overload—sound familiar?

Picture the situation for marketers in many companies: Every time they want a piece of insight or modeling, such as to improve campaign targeting, they have to brief internal specialists or external consultants. That builds time and cost into their campaign process—no wonder so few do it as often as they’d like.

That approach might be right for your ‘strategic models,’ but does it really make sense when you want to use models in every campaign?

Don’t worry, there’s a better way.

There has to be a better way. What if, rather than data being in silos and difficult to reach, it sat in one data environment? Then, rather than needing modeling tools that only a high brow statistician can operate, there would be modeling designed especially for non-technical marketers to use.

Finally, rather than having to then move those models into a campaign system, they already exist within the same framework as your data. You could just drag and drop the results into your campaign selection and segmentation.

Sound too good to be true? It’s not! That’s what our Adaptive Customer Experience Platform, Chameleon enables—10x faster model building.

Delivering Results

We know from our clients that using the breadth of data in their campaigns delivers better results—2x better response rates, and here’s just one example.

When we went to one of our retail clients and started talking about our modeling, they were initially skeptical and challenged us to do a live test. Within ten minutes, we’d built a model predicting potential purchasers of golf equipment. Just five minutes later we’d included this model in test cells as part of an email campaign, and then pressed the start button. It was great fun to start seeing the results come in, and sure enough, a clear pattern emerged: The best performing segment, by a factor of more than two, were those our model had identified as the best to target.

What impact would it have on your campaigns?

Just imagine doubling your campaign response rates. What uplift what that deliver to your marketing return on investment? Surely exploring the potential of using modeling in your campaigns is worth investigating a bit further.