The Curse of Abundance: How Mindjet is Using Predictive Analytics to Improve Conversions


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For some companies, one of the main challenges for marketing is the “curse of abundance.” But wait, how can too many leads ever be a problem?

The curse of abundance stems from a flurry of inbound requests for free trials and demos, causing a surge of leads that the sales team can’t possibly manage. There simply aren’t enough sales professionals to actively engage all of the leads. And, of course, not all of the leads are actually ready to buy. The result of the curse is that your sales team is spending time on leads that will never close … and not enough time on leads that will close.

Mindjet Reverses the Curse

Mindjet, a leader in collaboration software, was face to face with the curse. In a given month, tens of thousands of requests for free trials pour in. Mindjet was asking itself: What leads should we try to engage? Where should we focus our efforts? Where do we have the best shot at winning?

Throughout the duration of the free trial, Mindjet’s sales team focuses on engaging all new leads with a five-touch model. Due to resource constraints against the huge volume of leads, it was nearly impossible to reach all the leads, let alone uncover the leads most likely to purchase.

Jascha Kaykas-Wolff, Mindjet’s CMO, wanted to reverse the curse. Jascha needed a way to separate the signal from noise: Which of the leads were worth pursuing and which weren’t?

To solve the problem, you could apply business rules based on intuition and judgment. That would be lead scoring. For example, you could score and prioritize leads from certain industries, or above a certain annual revenue threshold, or those that went to certain pages on your website. Jascha was already doing that. In fact, he was scoring more than 50 attributes. But his conversion rates weren’t improving fast enough to keep up with his growing mountain of leads. Jascha was looking for a big improvement in performance.

I have known Jascha for a number of years. He’s a very sophisticated CMO. He was an early adoptor of marketing automation and social media, and he relies on data to make most of his decisions. There had to be a better, analytical way to identify the leads worth calling. He reached out to Lattice Engines to help.

Predictive Analytics and the CMO

We worked with Jascha and his team to uncover more than a thousand attributes that we could model. Here are some of them:

Predictive Attributes Predictive Attributes

These account-level attributes are, for the most part, hiding in plain sight. They are discoverable, yet stored in disparate silos – the CRM, marketing automation platform, websites, social media outposts, etc. Once you gather all the data, you can develop predictive lead scoring models over the historical records for accounts that were won. Traditional lead scoring alone was not enough for Jascha to move the needle on his win rates. He needed predictive analytics that could accurately predict the accounts that were most and least likely to buy.

Amongst the attributes that were most predictive for Mindjet were:
• # of employees per location
• presence of high-end notebooks and tablets
• websites that use cloud-based applications
• # of independent trials
• presence of specific job postings

The chart below shows the result. You can see that the average conversion rate for all Mindjet leads is 6 percent. Data science allowed us to quickly divide the leads into deciles. The decile furthest to the left has a predicted win rate of about 35 percent, about 6 times the average. At the other extreme, more than half of Mindjet leads are only predicted to close 3 percent of the time. The sales team was spending most of their time with leads very unlikely to close.


Within minutes of the creation of a new lead, the sales team knows if the lead has a strong likelihood of closing (>20 percent level). This allows Mindjet to focus solely on the most valuable leads in real time. Mindjet is able to deliver its best leads to its sales team and ensure 100 percent coverage for the leads that are more likely to convert to revenue.

Note: some data have been masked to protect confidentiality

Brian Kardon
Brian is the CMO of Lattice Engines, a predictive analytics company that delivers data-driven business applications that help companies market and sell more intelligently. He is responsible for demand generation, thought leadership, and integrated marketing programs. Prior to Lattice Engines, Brian was CMO of Eloqua, where he led their explosive growth and leadership in the Revenue Performance Management sector.


  1. Great Post. With the amount of information that is going through social media channels it is great to see that someone is using that data to improve conversation. Its just like with IT strategy Social Media needs a strategy and it all starts with the data


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