Introduction to Lead Scoring
As with all Performance Marketing, the deciding factor in the success of your campaigns is the data that you feed the bidding algorithms in the platform.
For B2B Lead Generation brands there is a huge wealth of different techniques we can apply to enrich the data that we feed into the bidding algorithms. Lead scoring is just one of these techniques.
So what is lead scoring? Lead scoring is the practice of determining the value of individual leads based on certain characteristics and qualifiers. For example, for a business loans campaign, you would logically apply a higher lead score to a loan application from a business that has been turning over steady revenue for 5 years versus a business that has been operating at a loss for 5 years.
The Key Benefits Of Lead Scoring
Lead scoring also provides a number of massive benefits for your performance marketing campaigns.
It provides the bidding algorithm with a clearer picture on what a good lead versus a bad lead looks like, which helps the algorithm to prioritise finding higher quality leads when choosing which users to prioritise appearing for in the auction.
This helps lead generation brands in two ways. Firstly, it helps build more efficiency into their campaigns as spend is prioritised on high quality leads. Secondly, it helps brands to mitigate the risks of targeting bigger audience pools or more generic search auctions as you’re providing the algorithm with a more focused targeting system.
The purpose of this blog is to explain how integrating lead scoring into your Google and Meta campaigns can help you to elevate the effectiveness of your automated bid strategies and ultimately help you to capture a greater volume of higher quality leads.
Deciding How To Score Your Leads
The first step to delivering a lead generation system is in identifying the characteristics of leads that generally signal high quality. These signals can range from the very basic to the complex.
One of the most common and basic examples is scoring leads differently based on their email address. For example, many B2B sales teams will score a lead that has been submitted with a business email address as a higher value than those submitted with a personal address.
A more complex version of this might be an online course provider that scores leads based on the course title selected, existing qualifications and any funding the enrollee is entitled to.
A deep analysis of CRM can yield data and insight into which lead attributes signal higher quality. Sales teams can also be a really good source of insight for this purpose and will often have their own lead scoring systems based on the regular conversations they are having with clients. It’s important to reference both of these sources for insight, as the CRM will always be limited by the data fields you capture, but the sales teams will have a deep qualitative understanding of the customers needs and what makes a ‘good’ and ‘bad’ customer.
Once all of the relevant characteristics have been identified the scores then need to be placed on each characteristic. The most basic version of this would be to apply values based on the basic correlation between the attribute and the desired customer outcome. The more advanced version of this would be to create a machine learning algorithm that uses the lead scoring to also make an estimate for the lifetime value of those leads to the business.
Implementing Lead Scoring Into Your Performance Marketing
The next step is to determine which of these characteristics can be tracked in the sign up process using a tag management solution. If you have a simple sign up process, it can be worth adding in more questions or fields to surface these characteristics to the ad platform tags/pixels
Next, you will need to use your tag management solution to adjust the value captured when a lead is completed based on the value you have assigned to each of the characteristics you have investigated in your analysis. This enables you to assign a dynamic score for everyone that completes the sign up process based on your lead scoring system and pass a value back to the ad platforms.
Your conversion tracking will now be tracking against a variable value driven by your lead scoring as opposed to a fixed value. This enables you to onboard value-based bidding strategies such as Target ROAS or Max Conversion Value. Any leads that are of a high quality will have their value inflated by the lead scoring system, and any leads of a poor quality will have their value reduced.
Revenue oriented bidding strategies will naturally start to look for people that show similar characteristics to the high quality leads as they provide more value versus their low quality counterparts.
What Results Could You Expect To See?
A recent implementation of this for a B2B lead gen company helped us to increase the profitability of ad spend by more than 50%. You can read the case study here to learn more.
As mentioned earlier in this post, lead scoring also enables advertisers to mitigate the risks in broadening their targeting when it comes to audiences and target keywords. For another of our B2B clients, we used the lead scoring method to enable us to expand our paid search into cheaper but more generic keyword terms.
In Conclusion
Lead scoring is an excellent technique to help you to feed the algorithm with data that could change the game for your automated bidding. It can be used to dramatically increase the efficiency of your lead generation activities, help you to effectively scale campaigns and ultimately generate more revenue for your business.
