Sales Lead Scoring Is a Winning Formula
HOW TO OPTIMIZE LEAD SCORING
When lead generation and qualification processes break down, the common cause is often a lack of coordination between marketing and sales. This is why organizations should first look for the flaws in their existing business processes and workflows before they go shopping for a lead scoring system.
Secondly, companies should determine whether they need a lead scoring system at all. If the number of leads they get is relatively low, lead scoring automation is probably not necessary.
The next step is developing a profile of what your best customers look like.
“It is important to develop this profile,” Rothstein says, because it can be plugged into the sales lead scoring system and then used to rank leads based on how closely they align with established criteria. “In this way, you can be assured that your best leads rise to the top and are forwarded expeditiously to your sales force,” he adds
It’s also important to review customer profile information regularly; your best customers can change over time, just as your business and product mixes do.
Another key element is being able to manage the large amount of customer-related data that pours in every day.
“A sales lead scoring system can evaluate many more data points than a human analyst can, so if your company starts moving into advanced technologies, like machine learning, artificial intelligence, and analytics, all of these technologies require much more data than you’ve used historically,” Zinsmeister points out. “When you are getting hundreds of thousands of inquiries for your products each month and you recognize that 90 percent of these inquiries might just be tire-kickers, you need a way to automate lead qualification so you can filter the non-serious buyers out.”
On the IT end, automating lead scoring also dramatically reduces storage costs since companies can eliminate a large chunk of their unproductive data or records about prospects who are not going to buy, Zinsmeister adds.
TRENDS IN LEAD SCORING
Lead scoring is an analytics system, and as such, analysts expect it to undergo many of the same kinds of embellishments that other business analytics systems are getting. Among them are predictive capabilities, machine learning, and artificial intelligence, all of which will further sharpen analytical acuity.
“In the future, as more predictive analytics, machine-based learning, and artificial intelligence are added to sales lead scoring systems, these capabilities will be able to more deeply explore and refine sales leads and will be able to offer even more sales lead qualification tools to marketing and sales professionals,” Rothstein predicts.
And just recently, a new type of lead scoring emerged to help companies predict lead relevancy by analyzing prospects’ presence and activities on social networks.
Zinsmeister reminds companies that they also have to contribute to the evolution of lead scoring systems by revising the business rules that they furnish these systems. This could mean ensuring that systems are continuously calibrated to customer behavioral changes that occur over time.
“If your system only uses static data, over time the quality of the leads that you get from that data will decline,” he states. “But if your system continuously adjusts to new behavior patterns that it detects in the data, it will be more effective and responsive.”
Future generations of sales lead scoring technology will likely be able to highly personalize sales pitches to individual prospects based on what companies learn about them from the data they have and the data they’ll get further along in the customer journey.
And that data is the key to everything. Companies today must be data-driven to compete, and any company that is interested in being data-driven should also be looking at ways to drive value out of their raw sales leads. In the end, every sales lead could potentially be a gold mine that yields significant revenue if it is properly nurtured.
Mary Shacklett is a freelance writer and president of Transworld Data, a technology analytics, market research, and consulting firm. She can be reached at firstname.lastname@example.org.