The more data-driven marketing becomes, the easier it is for CMOs to attribute closed deals directly to their marketing programs.
But with so much information available they encounter a new challenge: knowing which tactics and strategies to prioritize when each bad decision can cost thousands in missed opportunities.
Predictive analytics is surging in popularity among marketing leaders. It combines several components of artificial intelligence (AI) to predict which prospects are most likely to become customers.
This technology eliminates a great deal of manual and redundant work from marketing and sales analytics, letting reps spend more time on high-value outreach and lowering chances that a calculation error will cost the company important deals.
You can use predictive analytics to identify your most promising prospects, build hyper-targeted segments, and personalize outreach at scale—often resulting in significantly increased conversion rates on inbound and outbound campaigns.
But not all predictive technology is equal. As more companies adopt it for marketing, the competitive edge shifts from whether you’re using it to how.
Predictive analytics lets you take large sets of data and mine them for actionable insights using specific types of AI. These models are created by data scientists and software that use machine learning algorithms to produce the most accurate predictions as possible to aid businesses in their decision-making.
Top uses of predictive analytics for marketing include:
Deciding to use predictive analytics is the first step, but effectiveness varies from vendor to vendor. Be prepared to do some comparison shopping before you find the best fit.
Once you are sold on the idea of predictive for sales and marketing, you still need to navigate the market and pick the best option for your organization. These tips will help you make the right choice:
Predictive marketing tools find relationships between the behavior and traits of your customers and those of your prospects. But simply layering more and more new tech onto your existing marketing stack isn’t economical or scalable.
The real value lies in finding a predictive platform with open architecture — one that integrates with your applications for things like CRM, marketing automation, or business intelligence (BI) and uses them to make accurate and actionable predictions.
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