Defining Good CustomersLead scoring is the process of assigning a number to a customer that represents their estimated potential value to the company. The criteria used differs from company to company, but in general marketers consider a customer’s:
- Interest in the product
- Need for the product and position in the buying cycle
- Ability to approve purchases or agree to contracts
- Maximum potential lifetime value
Traditional Versus Predictive SystemsUsing the traditional method of lead scoring, the CMO or another executive creates a list of traits which are perceived to increase the likelihood of conversion. Each trait is assigned a positive or negative modifier based on how much it could affect the customer’s value. Some examples of positive qualities are working within the company’s primary target industry, holding a high-level position in the C-suite or with decision-making authority, expressing interest in the product by filling out online requests for information, or attending relevant trade shows. Traits that are assumed to lessen a lead’s value include holding a position with no purchase authority, a mismatched income bracket for target market, and living too far from the company’s physical stores to shop regularly. Once all points are assigned the total is calculated, then used to evaluate whether the lead is worth passing to sales team. The score can also indicate a lead’s priority (should it be moved in front of other sales calls or does it need to mature?). Predictive lead scoring (sometimes shortened to PLS) is much less subjective. An algorithm analyzes data on existing customers along with their performance, creates a formula to weigh characteristics that are demonstrated to influence customer behavior, and applies that to incoming leads to predict customer value. The marketer doesn’t create the list of “desirable characteristics.” Instead, the algorithm identifies the most relevant characteristics through techniques like clustering. It estimates not only potential lifetime value but also where a customer is in the buying cycle.
The Benefits of Predictive Lead ScoringThe predictive method is finding favor with marketers, and for good reason. It has a number of advantages over traditional lead scoring.
- Acts on data rather than guesswork: PLS removes the bias factor in deciding what is and isn’t relevant. There is still some potential for programmer bias, but generally enough people work on enterprise software to reduce that risk.
- Considers implicit as well as explicit data: Explicit data such as business titles and whether a lead has a company email are useful, but they only tell part of the picture. Much more can be gleaned from implicit data: posting in trade forums, downloading related publications, interacting with certain content on the company’s website. These are telling signs that a customer is moving forward in the buying cycle.
- Works at scale: Automated lead scoring processes vastly more data than traditional methods. Companies that use it are able to adjust relatively quickly to new or expanded markets, giving them a competitive edge over their rivals.
- Highlights optimal marketing channels: Knowing what actually matters to customers makes campaigns exponentially more effective. Marketers can target their best customers through the channels that matter most to them.
- Suggests tactics to prevent churn: Predictive lead scoring identifies areas where customers fall out of the buying cycle so CMOs can address those weak points.
Laser-focused Lead ScoringAt its core, predictive lead scoring is about prioritizing accuracy and efficiency. PLS zeroes in on the traits that have a measurable impact on lead performance, leading to more revenue and less hours wasted on weak leads.
How can your data help identify your best leads? Contact Concepta to learn more about our business intelligence and data science services!