by Louis Gudema |
I recently studied 351 mid-market companies and found that, excluding those in software, very few use marketing technology. That’s unfortunate because various forms of marketing technology can provide a significant competitive advantage, especially for early adopters.
In a post for the Harvard Business Review, I described seven technologies especially good for companies to start with. These technologies can provide measurable improvements to lead generation and a rapid ROI.
In marketing automation programs (MAPs), lead scoring is central to segmenting leads for nurture programs, prioritizing leads, and better aligning sales and marketing.
Lead scoring often is a combination of demographic data (the contact in the right job in the right kind of company) and activities (e.g., the contact visited our website, downloaded content, attended webinars, opened and forwarded emails).
A lead-scoring matrix might look something like the following graphic, with priority leads in the upper right going to sales, those in the middle being nurtured, and those in the lower left being ignored.
However, lead scoring is more art than science. Only 68% of B2B organizations that implemented MAPs score their leads, and only 40% of those report that their sales team agree or strongly agree that leads scoring adds value, according to SiriusDecisions.
That’s not a very strong track record for marketing automation or lead scoring.
Going Beyond Lead Scoring
Predictive analytics, though, brings data science to lead generation and greatly improves on the results.
Consumer, financial services, and large B2B companies have been using predictive analytics (PA) for years. It’s the technology Netflix uses to make personalized movie recommendations to you and Amazon uses to suggest products that may interest you.
Moreover, PA is also used in many other fields beyond marketing and sales, such as human resources, healthcare, and agriculture.
Companies like Oracle, IBM, and Oracle have used and provided large-scale PA solutions for years. But now PA is available for mid-market and small companies through SaaS providers, with some solutions starting for less than $1,000 a month.
Data, Data, and More Data
In marketing, PA starts with the company’s data housed in its CRM, such as Salesforce.com, and its MAP. Some newer SaaS PA vendors have accumulated data from thousands of external sources with which to supplement the company’s internal data and sort through to find the best predictors of buyers. That data can range from public company news, such as new office space leased, patents filed, and job postings listed, to individual data, such as what a prospect is tweeting.
What Happens After the Data Collection
After all the data is collected, the PA firm uses its data science to determine the factors that best correlate with prospects and closed deals.
Sometimes, that’s easy… Companies with lots of job postings and new offices need office supplies.
Other times, the signals can be fairly deep in the haystack and not at all obvious to people at the company.
“We used to think our lead scoring models were great, but we came to the conclusion that it was basically automated cherry-picking,” Meagan Eisenberg, vice-president of Customer Acquisition and Marketing at DocuSign, said to Gartner. “Since we aren’t data scientists, we would never have been able to experience the ‘a-ha’ moments when we learned what was, and what was not, predictive.”
With PA, DocuSign experienced a 38% improvement in leads that converted to closed deals.
In time, through machine learning, PA vendors tune the algorithms and companies typically experience continuous improvement in the accuracy of the results.
PA can be very useful in separating the wheat from the chaff for companies dealing with thousands of inbound “leads”—people attending webinars and downloading whitepapers, etc., who may or may not be qualified. (For example, Cisco gets upwards of 30,000 new contacts a month through its website.)
For content marketers, PA can help with segmenting and producing better messaging. It can also help reduce churn by identifying existing customers most likely to be thinking about leaving.
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Like most marketing technology, PA has been in use for years. But its adoption rate is still low. Early adopters in a particular industry can gain a significant competitive advantage and, as there’s really no way for competitors to know who is using it, they won’t know what hit them.
Some of the leading SaaS predictive analytics firms serving small- and mid-market companies (and, in some cases, enterprise firms, too) include…
- Lattice Engines
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