Over the last few blogs, we have moved through the process of building out and fueling your CDP, but how do you analyze high-volume data to derive and prioritize those most actionable insights to accelerate business outcome and maximize ROI? How do you determine your next best action? Which segments should you focus on? Which leads should you close first? Without utilizing a decisioning platform to analyze the data, you’re pretty much just flipping a coin.

While marketing automation platforms are valuable for taking immediate action when working within a single channel or a small data set, a decisioning platform envelopes multiple channels, firmographics, scoring models, and other real-time consumer behaviors to incorporate the insights that marketing automation platforms neglect.

A decisioning platform automates data insights, propensity scores, and other data-driven decision-making processes, putting your data’s power directly into the hands of the people who need it most – your sales and marketing teams. With the right CDP you can create complete buyer profiles and leverage them with decisioning models to quickly seek out closeable leads.

However, with numerous tools available, it’s important to understand that not all CDPs are the same, especially in terms of their decisioning capabilities, so you need a way to determine which is the most qualified to handle your data needs. Consider leading research and advisory companies like Forrester, who recently published The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021.

In Forrester’s evaluation of 14 leading B2B CDP companies, Forrester considers several factors to compare each CDP’s decisioning capabilities. First, Forrester wanted to know if each solution included AI/ML capabilities to automate next-best action decisions or to make recommendations for next-best actions for prospects and customers, and can it make recommendations for engagement by marketing, sales, and customer success throughout the customer lifecycle. Additionally, they looked at the range of data and insights used to make decisions and recommendations, and the range of decisions and recommendations provided (engagement channel, propensity to engage, timing, messaging, offer, pricing etc.). Finally, Forrester wanted to know if those predictions and recommendations are provided in real-time.

Some CDP solutions have native AI and custom modeling that can provide actionable insights to recommend next best actions where unification, company/person graph, intent, engagement scoring & look-alike capabilities are leveraged with AI/ML technology. The AI/ML-driven scoring models can be used to drive/trigger automated workflows & processes across sales, marketing, & customer success use cases.

This could include (but isn’t limited to):

• Enabling inbound leads to route directly to sales or specific nurturing streams based on propensity-to-buy attributes
• Utilizing AI-driven intent scoring to enable customer success to proactively improve customer retention and prevent churn
• Improving digital marketing conversion rates by utilizing AI/ML-driven propensity-to-buy and intent scoring models to more intelligently curate audiences and improve ad bidding strategies
• Recommending content stream by persona, or product best offering typically in cross/up-sell scenarios.

Additionally, some CDP solutions provide scoring predictions and recommendations are delivered in real-time and models are updated and refreshed on a quarterly or as-needed cadence.

In short, implementing a Customer Data Platform is an ideal solution to automating and pursuing a data-driven decisioning process. To learn more about the benefits of implementing a CDP, and to see how leading solutions compare in all 10 evaluated categories, check out The Forrester New Wave™: B2B Standalone CDPs, Q4, 2021. Take full advantage of an automated data-driven decision-making process to accelerate close rates and boost ROI with one of the B2B CDP solutions evaluated by Forrester.