I have 73 apps on my iPhone: apps for email, the weather, news, work, and my kids. Like anyone else, I have my favorites. I gravitate toward apps that give me exactly what I need simply and efficiently and allow me to engage to improve my experience. Flipboard and Pandora are in heavy rotation on my phone for those reasons. I receive content and recommendations based on what I have said I wanted and liked, and I get wonderful surprises the more I use them. I'd venture to say that marketers would love an app like that: one that learns what they want (need) based on what they told it they wanted at setup and what they have liked and disliked along the way. I'd venture to say that salespeople would love that same app: one that learns good leads from bad and presents the best leads every time.
Lead scoring has historically been a manual effort based on a best guess with questionable results. Optimally, marketers score leads and give them to salespeople who take the leads and do what they can. The other scenario is that there is no scoring, and marketing dumps all the contacts on sales in no order of priority. Either way, it's a lot of wasted effort that fails to focus on what is really important. Frequently, great leads sit unattended behind poor leads.
Lead management activities are complex. The systems are not always easy to use, reports marketing industry analyst David Raab. In a blog from 2013, he cites third-party research indicating that marketing automation and lead nurturing are the only two inbound marketing tactics where the difficulty score was significantly higher than the effectiveness score. It doesn't have to be that way. We have the technology to deliver simple answers to complex problems. Just as Pandora can learn my listening likes and dislikes with a simple feedback loop and a lot of complex math I never see, marketing technology can learn what a great lead looks like for a particular business and surface those leads at the right time to sales...every time.
A new approach to lead scoring
Determining whether somebody is a hot prospect takes more than just adding up points from a manual scoring system—one point for an email open, three for a Web site visit, and so on. Instead, companies should measure whether the prospect profile matches the profiles of their most profitable customers today. By continuously analyzing prospect and customer engagement data, a system could learn the profiles and paths of every lead. The system could learn which profiles and customer paths are most likely to result in a win and which profiles and journeys will not. Then the system could continuously deliver the leads with the highest potential to sales.
The system could continue to learn based on simple feedback from the salesperson. Imagine buttons saying: "More leads like this" and "Less leads like that." With each win, each loss, each like and dislike, the system learns and rescores and presents the next best lead to sales based on the sum of all that learning.
A system like this would remove a lot of manual effort and friction between the sales and marketing departments and provide real-time feedback to marketing on what's working and what isn't. Research shows that most marketers aren't doing lead scoring at all—not surprising considering the tedium (and poor return) of manual scoring methods. The next generation of marketing automation systems will focus more on these types of predictive analytical activities that connect directly to the bottom line, in addition to making campaign management and lead nurturing more efficient. Automated lead scoring is also helpful because it takes the emotions out of lead qualification. Salespeople get more hot leads and can give feedback (however negative) to the system, not marketers, who in turn can focus on message and strategy instead of lead analysis.
Automated lead scoring gets more accurate over time, and can save companies a lot of wasted effort in meetings and poring over spreadsheets. Lead scoring analysis also helps in nurturing campaigns, by showing which content and interaction paths the most profitable customers took before buying. Such analysis helps marketers avoid mistakes that hurt the business, such as qualifying a lead as "cold" too early, when the data shows that a greater number of customers today took 30 percent longer to make a decision than they did last year.
Rallying the business around automated scoring
A certain breed of sales and marketing employees has a high degree of trust in their own experience and intuition. These employees may not want to give up their decision-making process to a piece of software. But what we've learned in our business is that automated lead scoring is more accurate and up to date than any manual model. The system has no emotion or opinion, only facts, which it augments with input from sales and marketing people to deliver results curated to our business. If your marketing automation system isn't helping you deliver leads that drive bottom-line results, it may be time to find another one.
We live in a data-driven world. It's time for marketing and salespeople to stop guessing, and for the vendor community to deliver systems and services that can effectively analyze rich and large data sets and present simple solutions to complex problems. It's time for lead scoring to work more like Pandora and less like a calculus class.
Christian Nahas is the CEO of Salesfusion.