Predictive modeling in a business context

I was reading this article by Piyanka Jain that is intended to be a guide to using predictive modeling in a business context. Overall, I think Jain gives fantastic guidance on how to be successful with predictive modeling to give your business an advantage. Predictive modeling can be especially helpful for revenue impacting teams like Sales/Sales Operations, Product Management, Marketing, Customer Success/Support, etc.

Jain makes the point that you can’t go into predictive modeling without having some defined questions, and a clear strategy for its use. I think this is really important, but developing that strategy can be difficult for businesses that don’t have experience in the causal AI / predictive modeling space. As an industry, I think a lot more can be done to businesses users down the strategy path to help ensure their success. Without a clear strategy, there’s the danger of the models not being used, or not being used in partnership with the rest of the business strategy. Whatever your goal with predictive modeling, it needs to align with your business goals. With that in mind, we think Dacture should play a role here and help users define their strategy for predictive modeling, both as a way to garner support within their organization, and to help keep it focussed. We’ll come back with a plan here shortly.

Scenario modeling is an important part of prediction, and Jain touches on the topic. We think this is one of the areas where the majority of organizations are missing out. In the conversations we’ve been having with prospective customers, most haven’t advanced to that point yet. We generally don’t even need to start talking about the benefits of scenario modeling before prospects are articulating use cases for their organization to help guide decision making. As an example, if you have a multi-tier or multi-product offering, you can do predictive modeling to drive more users to the next tier or adopt additional products, thus increasing your overall revenue. As part of that, you can even predict what would negatively impact further adoption, and address those concerns as well.

Another thing the article calls out, and I wanted to reiterate, is the importance of event type for prediction and scenario modeling. In this instance, you can think of event data as things like clicks in the product or on the site, usage metrics, etc. Event data helps give a richer view into what’s happening with users, and can create really interesting models/predictions.

If you’re interested in talking about predictive modeling strategy, we’d love to chat with you about it, or if you just want to talk about what Dacture is building and how it relates to the article and could help your organization, happy to do that as well!

You can schedule 30min with us or reach out at

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