Yesterday we covered predicting how metrics for your business might change in the future. That helps at a high level, but what about predicting the outcomes of the activities you do everyday in your business?
One of the most common elements of any business are leads. These are potential customers who have either made contact with you or you have contacted them, but you haven’t yet started moving them through the sales process (or conversion funnel). The faster your business grows, the harder it will be to spend as much time with each lead as you would like. It would be great to have a technique to predict how likely a given lead is to convert into a customer so you can spend your time on those high potential leads!
There is and it’s called Lead Scoring.
Traditionally, Lead Scoring is a process that ranks all of your leads, ordering them from “hottest” to “coldest.” How does it work? It starts with data collection. Relevant data that helps score leads can include demographic information about the company / person to whom you are selling as well as their behavior metrics.
For example, useful company-level demographic data includes the company’s industry and size. Personal demographic information includes the person’s title and role at the company, particular does she or he have budget or authority to purchase what you are selling. Behavioral metrics include the number of times a person has opened an email from you or attended webinars that you offer.
After you’ve determined what you think are the best attributes that describe your leads, you can allocate points to each one, giving more points to the most important attributes (and you can even give negative points for “bad” behaviors, like unsubscribing from a mailing list). Once the points are tallied, sort your list in descending point value and you are left with the “hottest” leads at the top of the list. These are the leads that you should pursue now while others lower on the list need more cultivation.
How do I predict if the lead will convert?
Though the traditional scoring mechanism is helpful, it does not provide you a prediction as to whether the lead will convert or not. However, there are a number of machine learning techniques that can help you. One of the more basic approaches is something called a logistic regression. A logistic regression is similar to a linear regression except that the variable you are trying to predict is a binary outcome, will the customer convert or not in this case, as opposed to a continuous value.
No matter the technique, all of them will rely on high-quality historical data that is used to train your predictive model – the more of it the better! As you go score leads and process outcomes, you’ll want to continually re-evaluate which attributes you are collecting data, and how you weight your scoring, to see if you can better predict your outcomes in the future.
As with many of the things we cover this week, you’ll likely use tools to help you with lead scoring. My hope is that by understanding the techniques these tools rely on you will be a better customer when selecting them!
Quote of the Day: “A person often meets his destiny on the road he took to avoid it.” – Jean de La Fontaine