On Identifying Leads, Predictive Analytics, and the Future of Marketing

Filed Under Mindjet

Old mechanism
jascha kaykas-wolff

October 22, 2013

Recently I had the opportunity to speak with Forbes’ Louis Columbus about using predictive analytics to vet and understand sales leads. In an environment like Mindjet’s — where thousands of potential leads come through each week — the usual lead-scoring approaches just weren’t cutting it. It’s one of my imperatives to ensure that the sales team gets quality leads that have an exceptional chance of becoming customers. Without business processes, systems, and tools in place to make that happen, it’s akin to asking our salespeople to play roulette with their opportunities.

Choosing a Catalyst

To give us an edge, we chose to implement Lattice Engines, a tool that helps reduce the uncertainty we face when prioritizing leads through big data aggregation and analyses. From the article:

“Understanding which attributes are most predictive of an account buying also serves as a catalyst for advanced reporting and follow-on data analysis. These attributes are used in the context of data science algorithms to segment sales leads into quintiles as is shown in the following dashboard. Having this insight into which leads have the highest probability of conversion saves valuable selling time, increases the potential for up-sell and cross-sell, and leads to higher Average Contract Values (ACVs) and greater customer satisfaction.”

We’re very pleased with the initial results. As purveyors of innovation and collaboration processes, it’s good to know that there are companies and tools out there that not only share our vision, but that can help us achieve it. Read the full piece here, and as always, let us know what you think in the comments.

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>