Are you helping your reps or perhaps – slowing them down instead?

Here’s a common myth debunked:

“The more we know about our prospects, the better our chances of converting them”. Why is that completely WRONG?

Too much information (or data) will drive your reps in circles, unable to pause and think, unable to derive the necessary conclusions and make up their mind about their next steps with the prospect.

Here’s a common pitfall I see too often:

Reps are instructed to “decipher” on their own huge amounts of data such as prospect’s activities on the website, traffic data coming from SEO engines, list of clicks and gated content consumed, etc.

Expecting reps to digest all that data before a meeting with a prospect is absolutely ridiculous – every minute sellers spend consuming useless data hurts your business, hurts your sellers’ motivation and keeps you behind your target.

Sellers need to decide on their next steps with minimum effort from their end, using short & simple guidelines.

To achieve that, Marketing usually puts together a Marketing Qualified Lead Score, A.K.A MQL. If you’re hearing this for the first time or suspect you’re not leveraging your data to full effect, here’s a list of common parameters you can factor in when designing your own tailored MQL:

• Pages visited
• Time spent on each page (or specific content)
• Gated Content consumed by the prospect
• Gated Content shared by the prospect
• GEO
• Any info the lead has provided through common practices such as Contact Us etc.

For example, when visitors land on your homepage and then navigate to read more about your use cases, pricing or competitive info, they get a higher score than random visitors who merely spend time on your solution pages etc.

To wrap up – instead of instructing your reps to use multiple plugins, chrome extensions, iframes within Salesforce, etc., factor in all the data using your own tailored MQL formula and share it with your reps as one simple Score (e.g. Low, Med, High).

MQL Scores are usually referred to as Intent Score. By looking at the score sellers can determine if they’re dealing with a Window Shopper or a prospect with real and tangible intent to buy.

So far so good, no big news here. So where’s the catch?

A real and strong intent to buy does not guarantee a fast conversion. Let me rephrase that –

Not every customer is a good customer (from the supplier’s point of view).

In simple words, they may love your solution and potential but if they don’t have what it takes to leverage your solution to full effect, you will end up losing them during the sales cycle, or later during the Customer Onboarding journey, or during the first year of their contact. Both sides lose.

Qualifying in and out based on Intent score can work well but it comes with a high risk of inserting noise (or garbage leads) into your funnel

To improve your seller’s ability to qualify in or out, sanitize their pipeline as early as possible and increase their conversion rates, your need to transition your business from a flat single dimension model to a 2D scoring model –

In addition to the Intent Score, you need to find out whether your customers are technically ready to use your solution or perhaps they’re not mature yet to do so.

Finding out the technical readiness level of your leads can be done using Discovery questions (too late, too much trouble, annoying) or automatically and proactively behind the scenes.

Here are a few ideas of data you need to factor in when putting together a Readiness Score:

• Technologies deployed on the prospect’s website
• Specific tech deployed on the prospect’s website such as Marketing Automation (or whatever you decide it’s relevant to your offering)
• Domain Age
• Last Change on their website (date)
• Supported languages
• Firmographics
• Headcount
• Specific headcount (e.g. increase or decline of DevOps engineers)

To be able to extract such data, you can partner with a 3rd party technology that specializes in Lead Enrichment, etc.

Now, sit down and wrap it all in your head – instead of dumping on your reps huge amounts of useless data, simply share two Scores or even better, design your own Qualification Radar as shown in the image below.