3 Lead Scoring Mistakes (And How to Prevent Them) – Part 2
In part 1 of our guide to lead scoring mistakes, we covered mistakes such as not scoring leads at all, not using clean data, and not separating demographic and behavioural scores. Today, we will be focusing on some additional mistakes, which include ignoring third party data, not having a consistent scoring model, and ignoring your rules.
While lead scoring is a highly effective tool for marketing automation, it goes without saying that mistakes happen. Even when processes are automated, there is still plenty of room for human error. Mistakes are far more likely to happen when processes are rushes or skipped altogether to try and save time and speed things up. The biggest challenge with lead scoring is that mistakes can end up ruining your entire email marketing campaign if you are relying on incorrect, incomplete or inaccurate data. Like many other aspects of digital marketing, taking a strategic, measured approach is always the best way to avoid mistakes.
To help you start off on the right foot, keep reading to read more about the biggest lead scoring mistakes that may be getting in the way of your success.
More Lead Scoring Mistakes to Avoid
In no particular order, the next three lead scoring mistakes to try and stay clear of include the following:
Mistake #1: Ignoring Third Party Intent Data
Like most marketers, you are very likely focusing the majority of your efforts on first-party data. Many marketers spend a great deal of time and effort incorporating behavioural data into their lead models, focusing on engagement with your own company. This includes visits to website, content downloads from your website, email clicks and opens, social media links and various other data that comes directly from your own channels. While this is important to capture leads who are already in the process of looking for your company, there is one flaw in the first party approach… you are not considering potential customers who have not yet been introduced to your business.
A large number of shoppers will do a great deal of research online before even thinking about making contact with your business/ What this means is that you will only be collecting data from leads that have already made contact. This data should not make up the entire portion of your lead scores. Instead, it should be used along with third-party data to create a bigger picture of potential leads.
What is third party intent data though, and how can it help you score leads more effectively? To understand the importance of not putting all your eggs in one basket, or putting all your efforts onto first-party data only, it is good to know that most people do not go straight to your website and sign up for your newsletter when starting their research. Many begin the buying process through an online search. This search could include reading industry publications, reading product reviews and even following industry leaders on social media. Essentially, third party intent data incorporates any potential actions that indicate purchasing intent. Third-party data can be found on social media, online searches, publisher websites, analyst reports, buying guides, review websites, company websites, video learning platforms, emails, print media and even events. Social media is especially important, as an infographic from Leadspace and published on MarketingProfs shows. In this infographic, the following results show much much more social media influences B2B purchasing decisions:
- 84% of B2B executives use social media to make purchase decisions
- 92% of B2B buyers use social media to engage with industry thought leaders
- 72% of B2B buyers use social media to research solutions
This data is an excellent way to determine pain points, while also gaining insight into the buyer journey. When used along with first-party data, the result is a far deeper score that allows you to target customers at the very start of the funnel.
Mistake #2: Not Having a Consistent Scoring Model
Consistency is everything when it comes to developing a lead scoring model. This strategy often comes down to intuition. As you develop your model, you will learn how to determine which actions are most worth scoring. Needless to say, you will not be right every time. You may be scoring specific content types more highly than other content types, without knowing for sure whether this is the best approach. If you are not looking at your conversion data when scoring leads, you will soon end up with an inconsistent model.
The best way to determine which actions are linked directly to conversion is to test and test again. Do a careful analysis to check how actions correlate to conversion. This way, you will be able to perfect your scoring model and ensure a consistent result. It is best to do these tests every month to make sure that you are always consistent. Things to look at include the number of times any given action was tracked, the opportunity win rate, the close rate, the lead to opportunity rate and the revenue produced. You could look at the number of email sign-ups over the last month, and how many of them went from a warm lead to a qualified lead.
A simple way to perfect your lead score so that it remains consistent is to consider the following tips:
- Determine the lead to conversion rate of every single lead
- Choose the attributes that seem to be shared by high-value leads
- Determine individual close rates for each attribute
- Compare these with your baseline close rate and assign points accordingly
- Consider a data mining technique for an even more accurate score
Mistake #3: Ignoring Your Rules
Rules are there for a reason. In lead scoring, you need to continually recalculate scores. To do that, you need to be certain that your rules are as close to perfect as possible. Mistakes happen when rules are not clearly defined. One wrong assumption can sabotage your entire strategy very quickly unless you are perfecting the process and ensuring that your rules are set up to give you the best results. If you have too many vaguely defined rules rather than clear rules that will allow you to determine accurate scores, you may end up with vague scores as well as a warped outcome.
Working with a great deal of data and a massive amount of leads can make it confusing. The best way to navigate this challenge is to create rule categories. Keep reviewing your data to determine whether there are any criteria you are not using. It can also be useful to include negative scores to ensure that icy cold leads are not in the system. Complacency is the biggest cause of mistakes and dirty data. With this in mind, it is vital to not forget or ignore your rules.
Looking for more help? Download our guide to lead scoring to find out how to get started today.