Your business is ready to acquire new customers. Your lead generation is impeccable. Your website conversions? Well, you’ve got high volume, but your business also has high churn.
When a visitor shares their email address and becomes a lead, you send them through a nurture campaign, either the same one for every single lead, or through whichever path your traditional lead scoring method decides. On the face of it, this works: Your conversion rate is good. But the high churn rate could indicate that your current (or non-existent) lead scoring method does not identify high-value customers with much accuracy.
And if you can’t identify that accurately, you can’t send them through optimized nurture follow-up, which means you’re leaving conversions on the table.
The traditional lead scoring definition is fairly straightforward: It’s the process of assigning points to each lead that comes into your business. The more points a lead has, the more “likely” they are to become customers.
The lead scoring process varies from company to company, and sometimes person to person. Many marketing and sales teams rely on demographic data like age, marital status, geographic location, industry, and role to rank how likely each individual lead is to become a customer. The better the score, the sooner that lead is contacted.
The drawback? Traditional lead scoring relies on individual judgment. Such a subjective process is naturally prone to human error and faulty assumptions. The problem with using a “gut feeling” is that it’s not backed by data and can vary wildly based on day, mood, and other factors.
The other drawback to traditional lead scoring is that it’s difficult to discern whether your efforts are attracting the right kinds of leads in the first place. Are your leads likely to become longtime, profitable customers, or are they likely to purchase once with a discount and never return?
Good news: Lead scoring techniques have modernized.
Predictive lead scoring can take you from relying on explicit data like company name and business title to implicit data like online behaviors. Broadly, predictive lead scoring uses machine learning to analyze data and predict future outcomes. Most predictive lead scoring programs combine past customer data with current prospect data to assess which leads will be ideal customers.
However, using customer data alone is not enough to predict how profitable a customer will be. Ideally, predictive lead scoring would identify implicit data like customer attitudes, behaviors, and personal preferences. To do this accurately, you simply cannot rely solely on customer data.
The more data an algorithm has, the more accurate it will be. And data on 5,000 customers is simply not enough to assure accuracy. For a truly accurate picture of high-value leads, a system must compare that customer data against data for the general population. From there sales and marketing teams can review the profile of customers that provide the most customer lifetime value (LTV). With those insights into preferences and behaviors, they can more accurately score their leads and ensure they pursue the best leads first.
There are some pretty grim statistics about lead readiness and conversion. On average, only 25% of leads turn out to be legitimate; accordingly, 79% of marketing leads never convert to sales.
In e-commerce, this often looks like a lead putting merchandise in their cart and not completing checkout. Predictive lead scoring can look at cart abandoners and prioritize which ones to nudge toward purchase based on their predicted LTV.
Conversions don’t stop after one sale. Predictive lead scoring can identify first-time customers with high LTV potential and direct them to a communication tailored specifically to them. This could look like a personalized shipping page through a provider like Malomo that sets them up for the next step in their re-purchase journey. Or it could be a post-purchase email sequence that neatly targets their attitudes and behaviors.
If marketing teams don’t have a full, accurate picture of their most valuable customers, it’s difficult to discern the messaging that will spur conversion. With a full picture of high-LTV customers, marketing can more easily identify and test the ad and website messaging that resonates and pass higher quality leads to the sales team.
Marketers can also more thoughtfully build out a funnel. If you know that your ideal customer prefers to be contacted via SMS instead of email, you know to focus your efforts. If you know they prefer TikTok over Instagram, that can directly inform your ad strategy.
Consider how you would score customers if you knew more valuable ones trended to come from the east coast, had a specific product in their first purchase, and had certain demographic traits like married and were in their mid 30s.
Now map that alongside your standard web behavior tracking. You know 80% of customers who buy visit a specific page on your site. Because you have a very enriched way to score and rank customers, they go through a journey catered specifically to them.
All this translates to lower marketing spend, lower lead generation costs, and a happier customer relationship.
The beauty of predictive lead scoring is that it also improves your marketing automation efforts. Identifying messaging, behaviors, and preferences mean your brand can create targeted campaigns that perform extremely well.
Let’s say you know that your TikTok-loving, SMS-using ideal customer likes to save money by taking advantage of an auto-replenish discount. On TikTok, they see your product. Eventually, they’re interested enough to click on one of your links, where they can sign up for automated text messages that announce new products, promotions, or suggestions. Finding value in these texts, they eventually click to your site and, seeing the auto-replenish option, make their first of many recurring purchases. Hooray! This automation built specifically for someone like them converts without manual effort.
Contrast this to an automation sequence without access to predictive lead scoring data. Your company spends a lot of money on Instagram ads that direct users to your site and try to get them to sign up with their email.
Now, the leads that you DO get don’t regularly convert to paying customers. And when they do, they often make one purchase and never return. Drats! If only you’d been able to predict what they needed to convert.
