Leads can’t exist in a vacuum. When you try to use leads that haven’t been verified with broader analytics in mind, you’re not doing anything much more advanced than opening a telephone directory. Why is it so important to include analytics when verifying and ranking leads? Understanding the journey of a lead helps your sales team to make the next right step.
Take a look at why lead validation that’s not informed by broader analytics could be behind your low sales success rate. What you’re going to realize is that lead validation processes that are highly subjective because they’re only based on your own data just aren’t going to cut it for much longer.
Why Analytics Matter With Leads
Lead analytics make it possible to separate mere leads from potential customers. The interactions that caused a lead to get on a list provide goldmines of information about a potential customer’s intentions. Did a lead get flagged for registering through a link? Did they opt in for an email list? Did they specifically request a quote for a product or service? Did they abandon specific products in their shopping carts? These are the questions that marketers at top-tier companies ask every single day. This same information can be used by sales teams at call centers to create the perfect “ask” when calling leads on the phone.
The simple answer on why analytics matter when handling your leads is that the right information helps sales teams to prepare the right pitches for customers. Your team is going to have a very different conversation with a person who simply signed up for an email list compared to someone who has specifically requested a quote for a product or service.
Categorizing Leads
Lead analytics don’t just help your sales team craft the perfect pitches for different types of customers. The right data can also help your team decide when it’s worth actually making the pitch at all. Generally, sales and marketing teams use analytics to decide where time and effort should be directed. It’s all about getting the best return on investment (ROI) for every minute spent pursuing a customer. Using broader analytics, teams can rank leads based on how valuable they are in comparison to how much time needs to be put into nurturing. Here’s what those categories might look like:
- Top Priority: Sales-qualified leads. These are potential customers who are ready to purchase a product or service. At this point, the sale is your team’s sale to lose. Sales-qualified leads may simply need an offer, an enticement, or an opportunity to sign up. In some cases, sales-qualified leads are past customers.
- Middle Priority: Qualified leads. Also known as qualified leads for marketing, these leads represent customers who aren’t quite there yet. However, they are certainly interested in learning more. In most cases, qualified leads are people who have interacted with a brand in some capacity. Unlike sales-qualified leads, qualified leads have never purchased the product or service at hand. However, they may have browsed a brand’s products, interacted with a brand’s website, or signed up for coupons or emails online.
- Low Priority: Unqualified leads. This is a user who has shared data at some point. However, they do not have an intention to make a purchase right now. There are several scenarios with this type of lead. In the first scenario, the customer is simply seeking product information because they intend to make a purchase somewhere within the next week to six months. In the second scenario, the lead is unsure if the product is something they want to purchase. In the third scenario, the lead has actually already purchased the product or service from another company. There’s also a final scenario where the lead has simply changed their mind completely.
In all of these cases, broader analytical data can help sales and marketing teams to identify where each lead on a list falls. What’s more, good data can actually help teams to identify leads that fall into categories within categories. Many sales teams that are stagnant are failing to get the results they need because they don’t have the information necessary to know their leads before they make the call.
Important Types of Data for Leads
While each marketing or sales department needs to define their own priorities for analytics, there are some universal points to know about when making a plan to start using data to make more sales. One key piece of information is lead origin. How a lead was obtained says a lot about a customer’s intentions and commitment level. What’s more, sales pitches can be tailored based on the nature of the first contact.
Engagement is another top factor. Was a lead obtained passively? Did the lead make first contact in order to learn more about a product or service? This type of data is essential for directing attention to high-value leads instead of giving equal priority to leads obtained from all origin points.
Get the Whole Picture on Leads
There is so much your team can get with the right data! Teams that use Pipes.ai’s free AI-based lead validation tool get to use data from all leads to inform their validation processes. Pipes.ai’s free lead validation tool lets you validate up to 2,000 leads per month in a snap with no cost. Why not schedule a demo today?
