We're thrilled to announce we are launching Analyzr, our service streamlining machine learning analytics for sales and marketing teams!
Machine learning is undeniably useful. ML simplifies the computation of complex data outputs and extracts value from data that humans simply can’t achieve. However, just because a machine is executing the heavy lifting, doesn’t mean ML models are a “set-it and forget-it” type of activity. Like most things in life, data changes over time. Relationships between variables in the data pipeline can also change. These changes prompt what is called Model Drift, sometimes also referred to as Model Decay.
ML models don’t like change. They are trained to assume future data ingested will look like the data used to build the model, so Model Drift is a thing we want to monitor for and prevent. In the subsequent post, we’ll describe two types of Model Drift and outline a few ways you can identify and take action to prevent model degradation.
Machine learning is a better prediction technology, and with better predictions you can more easily optimize business outcomes. However, for many business users, the idea of implementing this feels unattainable. At G2M Insights we know that leveraging machine learning is possible for every company, no matter the size or maturity of the business. We know, because for the past several years the G2M team has been living this day-in and day-out, supporting our clients with building, training, and implementing unique-to-their-business analytic models in areas like propensity modeling and clustering. We’ve learned a few things along the way, and wanted to share some of those lessons with you.
A recent piece in the Harvard Business Review caught our attention: “Traditional B2B Sales and Marketing are Becoming Obsolete”. We agree and we see this every day with our own clients and partners, who look to us to help interpret what this means for their business and what they can do to address it.
Just as last year’s global shutdown changed the economic landscape, this year’s transition to the new normal will have an impact on many facets of business and life as we know it. So, what does this mean for data? And, more importantly, for the analytics driving our businesses that are supported by that data?
Pricing is one of the most important business levers impacting your bottom line. A small change in rate on a product or service can have a large effect on profit. It is imperative that your pricing strategy and execution leverages advanced analytics to ensure decisions are constantly optimized.
Now is the time to be making key decisions that will set your company up for success in 2021. Particularly in subscriber-based businesses, misses to Q1 revenue make your annual targets that much harder to make up. The impact you can have on your year-end revenue is the greatest it will be today.
Pricing is one of the biggest levers you can pull to affect business results. It is typically set by businesses using some version of the following planning cycle:
In a previous post we reviewed the macro trends underlying the AI disruption. In practice, how does it impact go-to-market strategy? Let’s review the three areas primarily affected by these drivers. In all cases the theme is the same: technology unlocks new capabilities. It enables leaders to operate faster and more efficiently, to identify new growth opportunities.