Editorial
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.
What Predictive and AI Can Do for Your Marketing Team
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:
- Defining your ideal customer profile and identifying prospects that match
- Providing a common framework for decision-making between sales and marketing
- Creating personalized content, offers, and campaigns for high-value customer segments
- Increasing conversion rates, closed deals, and deal size
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.
Choosing the Right Marketing Technology
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:
1. Look for Integrations With Popular Marketing Apps
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.
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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.
Reliable first-party data on wins and losses is especially important for the success of predictive models. To understand which leads will convert to customers in the future, predictive tools have to pull data from a system of record like Salesforce, Microsoft Dynamics, Insightly, Marketo or HubSpot and identify the characteristics and behavior of prospects that closed.
An open architecture also means you don’t have to disrupt current workflows adding more complexity to daily activity. Your marketing team can see predictive insights like lead or account scores and compare them to campaign conversions, right in the tools they are accustomed to using.
2. Demand Transparency on How Predictions Are Made
You will only feel comfortable trusting predictive software to replace manual workflows if you understand how the insights were formed. The vendor you choose should clearly explain how they built their predictive models and why they chose to include some signals while excluding others.
A predictive platform needs a diverse mix of first- and third- party data sources to build models that everyone can trust. This helps create alignment among other teams, especially sales.
When sales buys in to predictive analytics, both teams can adapt their behavior around the new insights and launch cohesive, high-conversion campaigns. Be wary of any models heavily dependent on one or two external sources—if access is cut off to any of them, predictions are rendered useless.
3. Choose a Vendor That Understands Your Use Case
The real differentiation among competing predictive analytics platforms is in how well they address customers’ most important use cases. As you decide on a predictive vendor, evaluate their experience deploying solutions for various company sizes, workflows, and industries.
Regardless of your company’s size, examining each vendor’s customer community will show you if its solution is right for you.
Ask yourself a few key questions about any predictive platform you are considering:
- Who makes up its core user base?
- Do those customers look like you?
- Are they using the platform in a way that is relevant to your business?
Make sure the software can produce results at scale, adjusting models and adding customization as you grow.
When CMOs invest in predictive technology they aren’t just planning for the next quarter or year. They’re setting a direction for the future, building a go-to-market strategy around predictive analytics to drive revenue for years to come. In a truly data-driven approach like this, the success of your business is directly bound to the technology you adopt.
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