Fixing your lead quality problem

Fixing your lead quality problem

November 21, 2017

You’ve done everything right for your company’s marketing strategy — there’s a great blog, social media campaigns and even a lead nurturing campaign. There are hundreds of thousands of visitors and you’re making money too, but you wind up finding yourself with thousands of bad leads.

Creating quality leads, rather than just quantity, is an important part of your marketing strategy.

You might have hundreds of thousands of inbound leads but from a distance, they all look the same. Those leads might be decent, but it’s hard to distinguish them without a lot of work.

Geckoboard, a popular SaaS metrics dashboard tool, had this exact problem. It had figured out the perfect strategy for high volume lead acquisition, but as a result there was no way to sift through the noise and provide high quality leads to the sales team.

The answer, as it often is, was data.

Build your ideal customer profile

The first thing to do is focus on the leads you actually want. To do that, you can use audience rules and segment out the most important traits. For you, this might be leads in the software industry that work at companies with more than 100 employees and are in a decision-making position.

When Geckoboard did this, it found that people in this segment converted at twice the rate. The problem? Those leads only account for 0.7% of the conversions, which meant it didn’t scale well with the company.

Years ago you might have dumped all of your leads into a spreadsheet and had someone sort through them to find the ones worth pursuing, then they’d throw them over the fence to sales. In 2017, however, there’s finally an alternative to doing this by hand: machine learning.

You probably associate machine learning with dystopian AI or Google’s bot beating the world’s best Go player, but it’s actually useful to the marketing industry today. You don’t need a mathematics degree to leverage it, either, thanks to tools like MadKudu which help surface insights from that data on their own.

To use those tools you need to remove emotion – and humans – from the equation and switch to a more predictable points-based lead scoring system. For example, you would take attributes such as “number of employees” and give them a ranking: more than 500 employees gets 50 points, 100-500 gets 40, 50-10 gets 10 and so on.

This system allows you to rank every company in a consistent way, without human error or irrational judgment. Once you’ve got a predictable model like this, you can feed it to a computer which will output whether or not the ‘fit score’ falls into a category on its own before having the sales team approach them.

Almost everyone agrees that a great lead scoring system helps increase conversion rates dramatically, and makes your sales team happier: customers convert better, less time is wasted and ultimately your profit goes up – if it’s applied correctly.

Score your leads in real time

Once your customer scoring is watertight you’ll probably start to look toward tooling. One of the biggest shifts in the industry has been the advent of tools that are able to crunch huge amounts of data in real-time which lets you adjust how you handle customers while they’re still getting to know you.

For a long time lead enrichment worked something like this: a lead would come into your CRM, then expensive enrichment software would crunch for hours to gather more information and add it to the lead so you could review it later.

Beginning in 2016, however, tooling got proficient at doing this in near real-time. Tools like Clearbit meant that a visitor could enter their email address and within a few seconds their data was filled into the CRM along with industry data, company size and location.

Geckoboard saw this as an opportunity and wired its site up to Clearbit and MadKudu to get a score in near real-time.

If someone from Slack, for example, typed in their email address, the score would be high enough to request a phone number the moment they signed up and promise a phone call in the near future. If a smaller company did the same, they might get the low-touch email onboarding instead.

The result? The changes meant the sales team could cut 85% of their daily calls and focused on higher quality accounts, ultimately making them more efficient – absolutely worth the investment.

All of these different tactics might sound like they require a lot of work, but it’s easier than you might expect to start today. You don’t need to go all the way and automate your signup form, either. You could instead choose to automatically segment leads in Salesforce, push them into Slack in real-time or just start with your existing data.

The key is starting: the earlier you begin, the more data you’re able to collect and use long-term in both lead generation and understanding what makes users convert.

We’ve got a great guide on four different ways to start this today, or you can read more about fully automating your lead scoring in our free book, Data Driven Sales.

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