Virtually every business collects and buys data to assist salespersons and marketers do their job more effectively.
But sometimes it’s too much data.
How can we make sense of it all and zero in on the data that actually matters? Which data points – individually, or together in a “secret sauce” – predict buying behavior in B2B customers? And where does predictive intelligence fit in?
As a data company, we knew there had to be a quantifiable answer: “What prompts someone to make a purchase?”
We asked over 200 sales and marketing professionals about 78 predictive data points (and “secret sauce” combinations of data points) in a comprehensive survey, Breaking Open the Predictive Black Box: Which Data Points
Actually Lead to Higher Conversion Rates and More Sales?
Our study did indeed reveal the most predictive data points. (SPOILER ALERT: Across all categories, it’s Companies Comparing the Products of Other Vendors in Your Category. The least predictive is Age.)
But all predictive data points are not created equal. And they don’t exist in a vacuum.
The best predictive data lives in sort of Maslow’s Hierarchy of Needs:
- Basic Fit data is the fundamental layer. The most predictive data point here is Job Title.
- Intent data comes next. The most predictive Intent data point is Companies Comparing the Products of Other Vendors in Your Category. The most important takeaway here, however, is not the individual data point; rather, it’s that Intent data is meaningless unless it’s informed by Fit data. Companies can “compare the products of other vendors in your category” all day long … but without the proper Fit criteria such as Industry, Department budget, or complimentary technologies – a sale will never happen.
- Finally, the data stack is topped with more sophisticated Opportunity data. The most predictive data point in this category is Requests for Proposal / Pain Points. But once again, without Fit and Intent, that RFP will never turn into a winning proposal.
Indeed, the most significant finding from this comprehensive study is that “Buying magic happens at the confluence of 3 different types of predictive data: Fit, Intent, and Opportunity.”
For complete predictive intelligence results, download the new study.
The combination – and mutual integration – of these types of data can be difficult and expensive outside of an integrated platform like DiscoverOrg. Our offering includes Fit, Intent, and Opportunity data.
One of our account executives recently reviewed some numbers with a prospective customer, who wanted to compare costs. Her company had been spending about $73,000 per year on data, broken down thus:
- Audience/market data and data enrichment ($30,000/year)
- Predictive purchase intent data ($20,000/year)
- Predictive analytics ($10,000/year)
- Technology install data ($13,000/year)
By selecting a data provider that with a comprehensive data solution addressing Fit, Opportunity, and Intent data, she saved her company 40% in one year (…and no one had to worry about integration).
It’s the synthesis of each data point – ensuring a good fit, at the company and prospect level – and refining results with subsequent information – that allows sales and marketing professionals to predict those prospects who are most likely to make a purchase.
If you know which prospects are most likely to buy, you can get the first seat at the table. Because the data point we’re all really after is Business won.
To see the other study findings and which data points really affect purchase decisions, grab a free copy of the complete study.
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