What's in this article?
Getting a lead is only the first step. And getting lead data is actually fairly simple. From there, how do we use and manage that data to work leads successfully?
There is so much data inundating marketing and sales teams that we can’t use it effectively. Instead, it easily becomes mismanaged lead data.
You need innovations that allow for working leads successfully using accurate data insights while creating good customer experiences and accounting for the human elements involved in the sales process.
Is artificial intelligence and machine learning, or AI/ML for data management able to guide these decisions? Can machine learning lead management data models improve your conversions?
This could be why more than 97% of organizations are investing in big data and AI.
Let’s dig into 10 ways mismanaged lead data is hurting your sales conversions and how data management solutions improve lead management through data that is complete, accurate, up-to-date and relevant to converting leads — giving those who use it a competitive advantage necessary for navigating today’s lead management world.
What is mismanaged lead data and how does it happen?
Mismanaged lead data happens within sales and marketing operations fairly easily, unfortunately. And 95% of organizations see impacts in their organization from poor data quality.
Logistically, lead data is mismanaged in a range of ways, from basics like how contact fields are collected and maintained for each lead, to duplicate entries, to how data is interpreted for qualifying and working leads.
In a practical sense, poor data quality causes you to miss out on obvious advantages over your competitors and productive strategies like what makes a good lead source and what factors impact whether a sale will close.
Believe it or not, getting lead data is the easy part. How you feed that data into your CRM and then what you do with that data can take a dive that shifts it into mismanaged data.
This wastes time and resources and leaves you with ineffective sales ops and missed opportunities to close deals.
Get ahead of the competition with the right support. Start with our Simple Guide to Optimizing AI/ML for Business Operations.
10 ways mismanaged lead data is hurting your business
Letting mismanaged lead management data slide by is hurting your business.
Don’t ignore these problems and assume your lead data is working for you. Become aware of what you’re working against through these examples of poor data quality in business.
And keep reading to learn the one solution that can solve many of these issues.
1. Not collecting the right lead criteria
If every time a lead enters your CRM system, it has errors in contact fields, duplicate information, typos, a lack of needed fields, or fields that are mapped incorrectly, you’re off to a rough start.
This makes life difficult for your sales and marketing teams — and limits any software you’ve invested in to optimize their efforts. They’re immediately stunted in effectively converting leads because they can’t even begin to score leads.
These issues only worsen over time if the mismanaged lead data doesn’t have ongoing maintenance to remain accurate and workable.
2. Inconsistent data leads to inaccurate actions
If you aren’t accurately collecting each lead’s data using consistent and strategic criteria, you’ll end up with a mess of data points that can’t relate to each other.
Without being able to spot behavioral patterns of leads or even categorize them by location, you can’t take simple actions to work those leads.
This creates more opportunities for false assumptions to be made and for the data to be further mismanaged.
3. Sales team is uncertain about how to prioritize leads
If your sales and marketing teams don’t get actionable insights on how to work with leads as they flow into your system, the leads fall flat and sales opportunities are missed.
This stems from inaccurate or confusing lead data. It could be a product of too much data flowing into the system without the proper support for how to make sense of it.
Without a consistent strategy across the entire team for working leads, lead management overall becomes overwhelming.
4. Leads get distributed to the wrong sales agents
With proper lead management, lead data reveals what makes leads most likely to convert. One factor that impacts their conversion rate is the actual salesperson they connect with.
There are many nuances to how salespeople perform when engaging leads. Relying on assumptions or blanketed systematic distribution like round robin won’t get true results.
Whether it’s product expertise, region, approach, etc. there are many different skills your current sales team can wield. Their strengths are optimized when synced with various lead data.
5. Quantity of leads distributed is out of whack
Sales ops commonly assume that top sales performers should get the most leads. In other cases, lower performers will take more leads and burn through them trying to find one that will convert.
This spectrum of lead distribution can take the very next step with leads — initial contact — and turn it into instant failure.
With the right lead management, various criteria are analyzed to intelligently distribute leads to the entire sales team, equitably, all with the main goal of finding what makes each lead most likely to convert.
6. Leads are left untouched or get duplicate contact
Before even thinking about more sophisticated lead distribution, you may need to understand whether leads are being distributed at all and who is reaching out to them. Mismanaged lead data can cause confusion for the leads and sales agents.
Leads may get duplicate initial outreach or possibly worse, go completely untouched because it isn’t clear what leads are available and what leads are assigned to which salespeople. Or because the lead’s data is in the system more than once because of improper data maintenance.
7. Data ages out without ongoing maintenance
Lead data changes over time and can become less accurate.
A lead may drop out if they’re no longer in the market for your product or service. Their job title could change or they could switch companies. Or maybe they move to another location and the sales agent in that region is no longer able to serve them.
To be managed correctly, data needs to be cleaned and re-verified on an ongoing basis. This is difficult to do without the right support for managing your lead data.
8. Sales team morale declines with lack of support
You can see how all of these poor data quality factors could make for a frustrating work environment. Salespeople can’t reach their sales goals if the leads they’re given aren’t qualified and they don’t have a clear path to prioritize and work leads.
Without proper tools and support, you’ll see poor sales performance and whether it’s because they leave or you begin letting people go, the churn of the team will cause other organizational challenges beyond managing lead data.
9. Lead’s experience is poorly managed
With all of these examples above, we can’t expect each lead’s experience to go very well when making initial contact with your organization.
Whether they weren’t contacted at all, were contacted by an unhappy or mismatched sales agent or were contacted without a clear understanding of their need, they likely won’t connect well with you.
There are probably others competing for their business, so they’ll not only want to ignore your efforts but will likely become annoyed by them if they aren’t optimized with the lead in mind.
10. Inaccurate trends and predictions are made from the data
Nailing down basic lead data logistics is challenging enough but this is made even worse when it’s assumed that lead data is managed well. And from this data you begin making predictions and spotting trends within the data.
In many cases, without ongoing efforts from skilled data scientists, these trends and predictions are inaccurate, based on mismanaged data and determined with some amount of human error, bias and assumptions.
And these predictions only get worse over time if data doesn’t have ongoing assessment and re-evaluation, especially as shifts occur in the market or within your organization.
There’s an easier way: Automate Moving MQLs to Sales Qualified Leads with AI/ML Solutions.
The ONE data management solution you need to solve all of these problems: ProPair
With these mismanaged lead data mistakes in mind, getting lead data on track doesn’t have to feel overwhelming when you have the right solution.
ProPair’s sales experts and data scientists work with your lead data as it exists today, mismanaged or not.
We not only clean up the data to optimize it for analysis, but we also run it through our lead management decision support software for you.
ProPair uses production-ready artificial intelligence and machine learning lead management data models to automate data analysis and provide ongoing, actionable insights within your CRM in real-time, running in the background as leads flow in.
Make sure you’re getting the right lead management support: Guide to Predictive Lead Scoring and Why Most Lead Scoring Models are NOT Predictive.
ProPair’s insights use predictive decision support to improve:
- Your overall lead management system
- Lead scoring
- Lead distribution
- Sales agent performance
We’ll pull your lead management data and show you opportunities to improve lead scoring, distribution and conversion using AI/ML for data management solutions.