What You Need to Know About Lead Scores and Predictive Marketing
Sales and direct marketing professionals often face a difficult and competing set of business priorities. On the one hand, these folks should be reaching out to as many potential leads and opportunities as possible – after all, every phone call to a prospect brings you one step closer to another sale, and one extra sale could literally be the difference between hitting your revenue targets or not. At the same time, there are only so many hours in a business day, with most employees clocking in around 9 AM, and leaving by 5 PM. This limited time frame presents a difficult challenge – how do you reach out to as many “good leads” as possible, without wasting time on “bad” leads?
This is the crux of the problem that lead scores aim to solve. In a nutshell, the goal behind lead scoring is to have a system that enables you to prioritize your leads. Intuitively, this is not hard to explain – an individual who has just read through your pricing page is probably showing more interest in what you’re selling than someone who has only visited your home page. To take the example one step further, someone who is browsing your pricing page at this very moment will be more likely to respond positively if you were to give him or her a call right now, than someone who visited last week.
So instead of just calling a list of customers from A to Z, lead scores allow you to spend your time reaching out to individuals who are more likely to follow through with a purchase. If you’re amazed at why successful sales people always seem to call the right person and make the right offer more consistently, this often boils down to having better information about their leads in the first place.
The challenge, of course, is around formulating a process for finding great leads. “Lead scoring” is the framework that provides a clear process for determining which leads are likely to close or make a purchase at a given time. By applying lead scoring to your business, you and your sales reps will be able to know which individuals should be contacted first – saving you time and money.
Types of Lead Scoring
Okay – so we’ve convinced you that lead scoring can be pretty useful for your business. Now how do you actually operationalize on this, and what are some ways of using lead scores? There are many different approaches to lead scoring, but here we’ll profile three common types: categories of leads, propensity scoring, and trigger-based approaches.
1) Categories of Leads
The simplest approach to lead scoring is dividing your leads into a few distinct categories. For instance, here at Canopy Labs we’ve worked with professional sports teams to categorize every one of their leads as “high”, “medium”, or “low” priority. Depending on their level of customer activity, an individual is automatically moved into one of these three groups. This categorization process is quite intuitive – for example, a season ticket holder who leaves a voicemail inquiring about new tickets would be considered a high priority lead, since they’re evidently indicating an interest to purchase. On the other hand, a low priority lead would be a prospect who has not responded to emails and who doesn’t read marketing materials very often.
Lead categorization allows your sales team to decide which group of leads should be contacted first. Sales reps should always call the high priority leads as soon as possible, and move down to medium or low leads only when the first list has been exhausted.
2) Propensity-based Lead Scoring
One of the downsides of lead categorization is that categories can still be large and unwieldy especially if you have a large customer base. Suppose that you have a large number of leads and end up with 1,000 of them in the high priority bucket. That’s a great problem to have – but it’s still a bit too much for your sales team to effectively prioritize. Unfortunately, there’s no clear way built into the categories themselves.
One solution is a propensity-based scoring approach, which removes set categories and instead applies a continuous set of numbers that ranks leads with differing scores. For example, a common approach would be to score leads from 0 to 100, with 100 being the best lead you could hope for. You can also set additional business rules that add or subtract points to an individual, based on criteria and qualifications that fit your industry or business.
For example, if you’re a B2B company, you might add 10 points to a lead that’s within your geographic target area, and another 15 points if someone at the company has requested a white paper. At the same time, they might lose 5 points for not clicking any links you send them as a follow-up to the white paper.
A second B2B lead could receive 50 points for requesting a pricing proposal. Now that you have two prospects with clear lead scores – one worth 50 points and another worth 20 – you can finally start to prioritize your day for outreach activities.
A more advanced version of the above would be using regression models to estimate the actual probability of a lead converting to a sale. Similar to Lead Scoring, this process takes different inputs (e.g., “did they request a white paper?”, “number of days since last contact”, etc.) and adds them into a statistical model. Unlike lead scoring, however, the regression model here will continuously learn which activities are most likely to result in a sale, and give those activities a corresponding score to the leads.
Propensity models are great because they typically result in a continuous score between 0.0 and 1.0, so you can be as detailed and nuanced as you’d like for your business. Picking the top 10, 25, or 1000 leads for the day or week simply becomes a ranking exercise, since every lead could have a different-but-comparable score to every other lead.
3) Trigger-based Approaches
Finally, a third approach to lead scoring is the use of specific activity triggers. In this case, you can create filters for specific activities that automatically signal to your sales team that they should reach out to the lead. For example, you might set up a trigger where someone requesting a white paper should get a phone call or email as soon as possible, regardless of the other leads within your list. Don’t discount the importance of reaching out in real-time – often that makes a big difference in whether sales close or not!
In some ways, trigger-based approaches are similar to propensity models or lead scoring, because you assume that a specific event (i.e., the “trigger”) has an extremely high score or effect on the model. The difference is that the trigger-based approach is more interactive in nature, telling you in real-time to reach out to customers just as they’re thinking about you.
Where Data Fits into the Picture
As you can probably tell, lead scoring is a great way of making your sales and marketing team more efficient. So what do you need to get started?
All of the strategies that we covered in this guide assume that you have the necessary data to track individual leads, aggregate the data, and actually score those individuals. Indeed, setting up such an approach requires the ability to collect data from multiple sources: your CRM system, your website, email marketing system, and call center.
The process also requires you to translate the large amount of data from these systems into a specific set of rules or activities. For example, it’s not enough to simply know that someone opens many of your email marketing campaigns – you need to set a threshold so that someone who reads, for example, 5 emails in a month, automatically gets a lead score of “10” added to their profile.
This process might sound a little daunting at first, but fortunately can be automated once the data is configured in your system. With the advent of new analytics tools like Canopy Labs, this process can even be automated through the use of apps that connect to your CRM system, online store, or email service provider.
Lead scoring is a great opportunity to improve the way you sell and reach out to individuals. Yes, it does require an up-front investment, and might seem a little scary to get up and going, but once configured it can yield immediate results with a relatively minimal amount of rules and data integration. Ultimately, however, the results speak for themselves – if you prioritize your leads and are diligent about following up, you will not only be more efficient with your time, but will also see an up-lift in funnel movement, and ultimately, revenue.
Learn more about Lead Scoring
An Intro to Lead Scoring and Optimization is brought to you by Canopy Labs, a predictive analytics company. We work with businesses of all sizes to better understand – and sell to – their audiences. If you have questions about this guide or are interested in learning more about customer analytics, feel free to get in touch!