6.4 How do I score leads? - Video Tutorials & Practice Problems

<v ->Let's take a closer look at lead scoring.</v> So if you have an e-commerce business, you don't really care about lead scoring. But if you have a business where you are trying to figure out how to move people offline, eventually to a Salesforce, not only do you care about lead scoring, but you are also going to have a CRM system, a customer relationship management system, and that will store in it data about your leads. It'll have data about your prospects. It'll have also data about your customers, people who actually followed through and bought from you. It'll have their contact information. It'll have all the ins and outs of each deal that they have been part of. It'll know the size of the deal, whether it was won or lost. And it will also know about what products they eventually purchased if they did follow through with the deal. Now, a marketing qualified lead is the term that most companies use, an MQL, when they're talking about a lead that is being passed from marketing to sales. And the purpose of lead scoring is for you to do a good job of sending the best leads over to sales as a marketer. So how would you identify your ideal customer? What are the characteristics that you can be looking for that will help you to know that this particular lead is better than others? Now, you might try answering those questions on your own, but I don't recommend it. I recommend you spending a lot of time talking to the sales team and finding out what they think a good lead is. And in fact, you can use data to do this. You can look in the CRM system, and you can see what leads were passed over for marketing. And you can figure out what the characteristics were of the ones that actually closed the most frequently that had the largest deal size. And maybe even the ones that closed faster. Because if they close faster, that means you get money sooner. And also means that the salesperson had less to do with that client and could work on more clients at the same time. So as you're looking at those characteristics, there's a couple of different types of characteristics to look at. Explicit and implicit. So let's look at some explicit data points. These are characteristics of your prospects that you can use to score leads. So you can think about the fact that you might have some demographics. Maybe you know, their title or job role, or purchasing authority. But you might also know firmographics, things about the company. So the size of the company in terms of employees or revenue, how fast it's growing, how long it's been in business, where it's located, what industry it's in, whether they have budget defined for this deal or not. That's something you might know if you asked a survey question about it. There's all sorts of things that you might know. You might also know if you have a prior relationship with this company. Is it an existing customer or has it been a customer at some point in the past? These are all explicit data points because they have answers to the questions that you have gotten either from the customer themselves or from a sales person or someone who knows the customer well. The questions have been explicitly answered. What about implicit data points? Implicit data points mostly have to do with your conclusions about the importance of the data that is harvested based on behavior. And you can see all sorts of behaviors that are listed here. It's probably true that you don't care for your marketing analytics problem about all of these different behaviors. But the question is, are there some that you do care about? And that's a good question to ask for the explicit data points as well. What are the explicit data points and the implicit data points that matter the most for you to solve the marketing analytics problem that you chose. When you're doing lead scoring, you can add points to the lead score when you see a characteristic that you think is good, is positive. But you could also reduce the score. You can downgrade the score when you see something that you think is a negative indicator. So if they unsubscribed to your content or maybe they don't look like customer prospects at all, maybe they were visiting the career pages of your site, that doesn't look like somebody who's likely to buy. Or the investor pages of your site. Or maybe they made negative comments about your company on social media. Or there's just been a lot of time that's gone by where either they haven't come back to your site or you haven't seen any progress in the buyer journey. These are just a few of the possibilities for things you might downgrade on, but any data that you can collect that could be positive is something that you can add points for. But don't be shy about taking away points for something that you think could be negative. So let's look at an example of how one client of ours scores leads. And so, this is a client that is actually looking for companies to buy their travel services. And so, if they're in the travel industry, well, that actually seems like a pretty high value because this is someone who might buy those types of services that we're offering to the travel industry. If they're a franchiser, that's even better because they might be buying these travel services for hundreds of outlets, not just for a small chain or a single hotel. And if you look at the rest of these, you might want people at a higher level in the organization. You might want companies that have higher revenues. But for this particular company, once they get over $50 million a year, they find that they don't do a lot of business with those companies. There are competitors do that business. And so, they didn't really add anything for over 50 million. They might be okay for that, but their sweet spot is really over 15 million. And that's what they really scored for. They're based in the US, and they're on the east coast of the US so they added points for that. You can see there's a few behavior things here if they downloaded a white paper. But here's a characteristic that if they're a student or they're a startup, that reduces points. And maybe if they have a non-corporate email address, they are gonna reduce points. And another behavior characteristic: every week that they fail to return to the website, they start to reset the lead score. They start to cool it off by a couple of points. Now, again, these are very specific examples for this client of ours, but this is an idea for you. What are the things that you look for in those hot leads? And have you talked to your sales team? Have you researched which deals actually close in the CRM? And have you checked to see what the characteristics are of those customers who close those deals? This is how you can develop a lead scoring algorithm that works for your business.