11_LeadScoringKickYourSalesFunnelIntoAHigherGear-BLOG

Lead scoring: kick your sales funnel into high gear

You have a strong inbound marketing strategy with a lot of valuable content and intuitive forms that bring in a lot of leads. Yes? Now what?

That's simple. We start with lead scoring. We assign a certain value, often based around a points system, to each individual lead. The more points a lead has collected, the more likely it is that he will become a customer. A user’s lead score is basically an indicator to define sales readiness. Based on how many points we’ve accredited to a lead, we can pinpoint his position in the buyer’s journey. By knowing the user’s position in the buyer’s journey, we can personalise our marketing communications and guide the user towards the information he needs and thus further down our sales funnel.

Setting up your lead scoring framework or lead scoring model will require input from both your marketing and sales teams. With flawless teamwork, your marketing communication will smoothly guide prospects through your sales funnel until they are nurtured enough for your sales team to close the deal.

Lead scoring is a profitable tool, especially in B2B industries, where service costs are generally higher, and decisions require more time. The longer the decision process, the longer we can track our leads and the more chances we get to allocate points. Building a ranking is also possible in a B2C environment if it concerns purchases over a longer period. If you want to buy a car, you will visit a website, download a brochure, think about it, come back a few weeks later, download another brochure… If the car company can track your route, they can nurture you in an optimal way and convince you to buy the car from them. This is, of course, only useful if it concerns the purchase of durable consumer goods.

Lead scoring along the buyer’s journey

Before we can start with lead scoring, we need to have a clear insight into the buyer’s journey. It is essential to map out the path your prospects travel before becoming customers.

Before there is any contact, every lead is a complete stranger to us. As soon as he starts looking for information, on our website for example, we can mark this stranger as an unknown visitor. Once we convince an unknown visitor to leave some basic information in exchange for valuable content, platforms often allow us to link historic data to this newly created contact record. This way we often have a firm basis for scoring fresh leads. After this visitor becomes a lead, he has probably gone through the first stages of our nurture campaign cycle and might show some sales potential, we can mark him an MQL or marketing qualified lead. After further nurturing, we want to look for signs that show this person is clearly interested in the products or services we have to offer, which means he or she is ready to make the leap to become an SQL or sales qualified lead. Our lead is now in the home straight: the purchase. This is where the buyer’s journey ends and the customer’s journey begins. With lead scoring in mind, we only need understanding in the lifecycle of the buyer.

(It is possible to continue to give points and thus distinguish ambassadors between your customers. But in order to do that, you’ll have to set up a completely new lead scoring system, aimed at a loyalty program.)

The more points a lead collects, the further he is located into the buyer's journey. Depending on where our lead is located, we can adapt our nurturing strategy and content in order to provide our prospects with the right information at the right moment in their customer lifecycle.

Our buyer gets himself on the scoreboard

So, we have leads and we can pinpoint their location in the buyer’s journey. Now how do we assign any points to them? Obviously, we must draw a points system or lead scoring model. But before we start drawing, we need to go back in time and take a look at our leads. Both those who turned into a customer and those who decided not to buy something. We must try to find patterns in both explicit and implicit data of our leads.

With explicit data, we mean all written down information that a contact is willing to share with us through webforms, such as: a name, address, email address, demographic information, job title, industry… This explicit data allows you to determine the sales potential of a lead based on his profile. Think carefully about exactly what information you want or need from a lead and how it should affect a lead’s score.

Implicit data is gathered by analysing (online) behaviour of our lead: what pages does he visit, how many times does he visit our website, does he open or click on our latest newsletter, did he download any content... You want to identify engagement with your content from which you can deduce a lead’s interest in your product or service.

By analysing both leads that converted and those that didn’t, we can figure out which data they have in common and where they differ from each other. That way, we can award (positive) points to common characteristics of successful leads and deduct (negative) points for aspects failed leads share. This approach allows us to pin down our ideal buyers who are at the top of our scoreboard. Perhaps someone from your sales team can look into them and provide a personal approach for that last push towards a sale.

Traditional vs Predictive lead scoring

Setting up the rules to identify qualified leads usually requires some manual input. Providing you have enough lead and customer data available, various inbound tools can already use algorithms or artificial intelligence for this process. Thanks to machine learning, some inbound tools can dig through your entire database in search of patterns. Once these patterns are found, we can switch to a predictive approach where we can anticipate our lead’s behaviour. Discovering these patterns is a never-ending job that we have to keep doing in order to keep our lead scoring technique up to date. So the more AI develops, the more patterns can be distinguished, and the more accessible predictive lead scoring will become. You can read more about AI in this article.

What does the scoreboard look like?

Depending on your business, there are different parameters to assign scores to leads. What’s relevant for you, might not be of any interest for another company. Creating a lead scoring model is therefore an exercise that every company must do for itself. Below, we will give some general examples that can serve as an inspiration to build on. As mentioned above, we generally distinguish between explicit and implicit data.

Put into practice, demographic information is often an important pillar if it comes to awarding points. If your company is active in a defined area, the people living there will be granted more points than someone living far away from your action area. Secondly, the information we obtain about the professional situation of the lead can also play a role in the awarding of points. If you are active in the B2B sector, it may be relevant to know how big the company where your lead works is, which function he has… Thanks to the explicit data, we can also find out if someone contacts us on behalf of themselves or on behalf of a company. If a lead leaves his email address, which is a Hotmail/Gmail/Yahoo address, he is most likely contacting us on behalf of himself. If a prospect fills in our form with a company email address, we can assume he’s active in a B2B environment. The same way, we can detect spam, by isolating leads who filled in our forms with “test” or “qmdlkfj”.

The online activity of the lead can also position him higher or lower on our scoreboard. We distinguish three major pillars here: his behaviour on our website, his email engagement and his interaction on social media. Starting with our website, we not only look at the number of times that he visited our website, but also which specific pages he consulted, and which forms he has filled out. If someone wants to receive a demo, he is in the final phase in the buyer’s journey and will therefore receive more points than someone who consults an informative whitepaper. High value forms, such as the demo download form, can yield more points than the form a lead fills in to see a presentation. Is the lead looking at the job offers for a suspicious amount of time? Then it's highly likely that he is on a job hunt, instead of looking to buy something, and should therefore be given negative points. A high value pricing page, on the other hand, can boost a lead’s ranking.

Did the lead subscribe to your newsletter? SCORE! We can now track engagement with your emails. The more opens and the higher the CTR, the more points we can award to our lead. If he clicks one of the social buttons in your newsletter and starts interacting -positively- on social media, he might jump up on our lead scoreboard, especially if he’s an #influencer.

 

Conclusion

Lead scoring allows us to manage our leads in a way we can better respond to his specific needs. Is he still at the very beginning of his buyer journey? Then maybe we can escort him to the next phase, for instance by sending him an informative brochure. Is he already nurtured enough, but does he still need a last bit of convincing? Then maybe we can have him called by someone from the sales team. By providing a lead with the right information at the right time, you can highlight your most relevant expertise and smoothly turn him into a customer. Lead scoring is the ideal approach to have less dropouts and more converted prospects.

Do you need some help drawing up your lead scoring board? Team up with RAAK, our specialists are real goal getters!

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Posted on
Apr 30, 2019