Have you ever wondered about the quality of the data that you input in your lead scoring system?
In one sentence, there’s a quote that could answer your question:
You only get out what you put in.
Let’s go further: data quality is going to be crucial when you start implementing and running a lead scoring process.
That is because setting up a good lead scoring model isn’t just about coming up with the right elements to score on and having the automated tools in place to ensure you can process them.
Rather, it’s about the quality of the data that you push into your lead scoring model so that you can accurately identify and prioritize leads. Setting it right from the very beginning will give you an edge over your competition and speed up your revenue long-term.
In this article, we’ll analyze:
- how data quality will impact your lead scoring process;
- how you can standardize the data you capture;
- why you should only score what you have data on
Let’s get started.
The Impact of Data Quality on a Lead Scoring Process
Lead Scoring is the process of ranking prospective customers that come your way by using a numerical system (usually a 1-100 point system) to determine at which step of the customer value journey they are.
With this information, you’ll know their sales-readiness AKA whether it’s the right time to hand off the lead to your sales team.
If set up correctly, lead scoring will help you both identify your prospects’ intent and prioritize the ones that you really need to follow up with to close more deals.
In this sense, data quality and integrity become all the more important. You do not want your sales organization following up with the wrong prospective customers, and in particular those who are:
- prioritized improperly;
- not showing the intent you believe they had
There is sometimes a misconception that more is better when it comes to leads. That’s where the concept of data quality comes into play.
While the volume of leads generated may look good on your quarterly review slides, the truth is that if you can’t programmatically qualify these leads because the quality of the data collected is not consistent (or the data is just flat out missing), then the lead is all for nothing.
Wanna make best friends with your sales team? Then I would highly recommend focusing on data quality and NOT sending over every lead that takes a sniff at your website or opens an email.
But how you can make sure that this doesn’t happen?
In the next two paragraphs, we’re going to discuss two key practices you can enforce to ensure that the quality of the data you are putting into your lead score is good and that what you collect is clean, standardized, and scorable:
- Standardize your data capture
- Score what you have data on
Standardize Your Data Capture
When thinking about how to increase the number of leads that flow into your marketing automation platform, there are three primary ways that data is pushed in:
- Form Submissions – these can be forms on your website (i.e. contact forms) or landing pages that support your lead generation efforts.
- Inbound CRM Integration – this is data that is automatically imported into the MAP from your CRM.
- List Uploads – these are offline lists that are often provided from events, trade shows, or maybe even from your sales reps.
Data Quality on Form Submissions
With form submissions, you will often be capturing basic information on the customer (i.e. First Name, Last Name, and so on) but you also have the opportunity to find more persona-based information, like Title and Industry.
Moreover, to increase the quality of the data you collect, I’d recommend that your fields capture information consistently.
For example, data points like country, state, industry, and the number of employees can be set up in a drop-down menu where there are predefined values in place for the customer to choose from.
This will help to ensure consistency in the data flow back to the MAP, but also make the data much more reliable when it comes to scoring.
Data Quality on Inbound CRM Integration
As for forms submissions and data collected via inbound CRM integrations, it’s recommended that you use a consistent system to capture information.
In addition to that, in this case, you’d need the buy-in of different departments of your organization if you wish to ensure data quality on the information collected in this way.
That’s because this type of data is often maintained and manipulated by the sales team, but the structure for how the data is captured (and more importantly the validation that goes into ensuring the data is captured correctly) will oftentimes be maintained by Sales Operations.
Data Quality on List Uploads
The quality of the data when it comes to list uploads get a bit tricky. This is because they don’t always come from the same source, and every vendor will more than likely provide the data in a format that isn’t exactly how you want to capture it (nor is it standardized the same way from list to list.)
If you want to limit any manual cleanup you may end up needing to do when data do not match, setting up programs in your MAP to ‘normalize’ the data can be very helpful.
In these programs, you will want to build a list of different variations of a value (think of VP, V.P. Vice President) – when your MAP finds one of these values, you can write the standardized value to another field. In this way, you won’t overwrite what the customer gave you.
Although trying to accommodate for every variation imaginable isn’t practical, finding the big offenders will go a long way in ensuring that your lead scoring program isn’t missing any critical data to score on.
While the hygiene of your email lists is part of another – broader – discourse, you can make sure the data quality of your email list is good by running it through an email verifier – you can use ours for free!
Score What You Have Data On
Verify which fields you have data on is something that is oftentimes overlooked, but is certainly important when it comes to defining how you weight your lead scores as part of the overall model.
When reviewing the data that you want to score on, what you need to ensure first is that you have enough records that actually have the data you want to score against.
It sounds simple, but let me give you an example. You wouldn’t believe how many scoring models I have seen that were set up to score a field like Company Revenue only to find that:
- the information wasn’t being collected anywhere in the system
- there were so few records in the database that had any value that the lead scores were effectively lower than what they could be if the scoring was done using a more complete data point.
Now more than ever, time-to-revenue is extremely important when it comes to lead management. That is, you want to be able to predict the time it will take a prospect to become a customer – all the way down from their first interaction to the closing of the deal.
Critical processes like lead scoring are the oil that helps your revenue engine go – the companies that are better able to qualify and quantify the engagement of their customers are the ones that will hit their quarterly revenue targets faster and more efficiently than others.
Lead scoring is an important process for any marketing and sales organization. It can streamline the way that marketing pre-qualified leads and make your sales team more efficient by following up with the right leads at the right time.
Ensuring that your data as part of your lead scoring program is consistent, clean, and scorable will ensure that all the work you have put into the automation actually does make your life easier.
In this post, we have analyzed how data quality impacts your lead scoring process, how you can (and should!) standardize the data you capture, and why you should score what you have data on.
Keen on getting started with lead scoring for your business? Sign up for free with Breadcrumbs here to start improving the quality of the data you input in your lead scoring process today.