The stakeholders on your sales and marketing teams disagree about which type of leads will convert into sales, so your advertising budget is inefficient. Furthermore, your sales team is wasting time on "bad leads" that don't close. In order to solve the debate, you'd like to use the patterns in your historical sales data to assign a probability of closing to every lead. Even better, you'd like to exploit the patterns in the lead-to-close data to advertise to the leads with the highest probability of closing.
Use a machine learning algorithm to score your leads based on certain characteristics. Each lead will now have a probability of closing that we can use to generate better sales projections. While we are at it, we will be able to zoom out and see which characteristics of the leads translate to a higher chance of closing so we can adjust our advertising budgets accordingly.
Good at defining and solving problems but not a yes man. Not a fit if you're looking for someone to check off tasks in your list.
If you're not afraid of brutal honesty, schedule a time - let's talk.
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Richard is obsessed with finding areas of opportunity for his clients by digging deeply into the analytics and focusing on what's working.
After years of working with vendors who simply turn the knobs and hand you a report, it was exceptionally refreshing to work with Richard who provides candid feedback and truly understands the full lead lifecycle from intention to conversion and how it impacts your marketing efforts.
Sr. Software Engineering Manager
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