Implicit and Explicit Lead Scoring

Both implicit and explicit lead scoring have their benefits and drawbacks. In explicit lead scoring, salespeople use various data to add points to prospects, while implicit lead scoring relies on available data streams. This method helps marketers sell to existing customers and identify false positives. The most effective lead scoring models combine both types of data and use both the implicit and the explicitly collected data to determine the likelihood of purchase. To increase conversion, both implicit and indirect lead scoring should be used.

When comparing explicit and implicit lead scoring, you should first understand the difference between them. Explicit lead scoring is concerned with gleaned information, while implicit lead scoring is concerned with behaviors. Using past behavior and information to identify potential leads is key to successful sales strategies. Fortunately, lead generation software often includes automated lead scoring features. Whether you opt for explicit or indirect lead scoring, you can be sure that your marketing campaigns will yield more qualified results.

In addition to the explicit lead scoring method, implicit lead scoring focuses on behaviors, such as web browsing, email addresses, and website visits. Both types of lead scoring rely on the same set of behaviors to help identify which of them is most likely to convert. For example, a website visitor who has viewed your careers section, but hasn't yet made a purchase, could be an unqualified prospect. For implicit lead scoring, tracking the behaviors of these prospects is crucial. This type of lead scoring relies on behaviors, such as where they've been online, what they've done online, and if they've even liked your Facebook page.

Implicit lead scoring uses behavioral data to assess the interest level of a lead. For example, a lead who has already downloaded your white paper may have a higher score than one who has only attended a webinar. However, a lead who attends a webinar may be more interested than one who hasn't. Furthermore, the number of questions a lead asks during the webinar demonstrates their level of buying influence.

Implicit lead scoring is based on inferred information, while explicit score is based on observed behaviors. Explicit lead scoring uses observed behavior, such as email addresses, IP address, and other direct information. Often, businesses assign positive and negative values to these actions, while implicit lead scores take into account other indirect behaviors, such as website visits, social media interactions, and industry. This way, a company can determine how well a specific campaign is performing.

Explicit lead scoring relies on the information provided by a lead. For example, a prospective customer may read an article, download an eBook, and subscribe to a newsletter, but never make a purchase. While explicit lead scoring is the more common method, implicit lead scoring relies on the prospect's experience with your assets. Unlike explicit and implicit lead scoring, explicit lead score is better suited for companies that focus on sales and marketing.

Explicit lead scoring is a more subjective approach, which should be used only when there is an obvious correlation between the contact's demographics and the product. Explicit lead scoring is the most common type of lead scoring, and is used for both explicit and implicit lead scoring. If a lead's score is higher than a product's score, the salesperson has a better chance of closing a sale.

Explicit lead scoring relies on the information given by the lead online. For example, a download of a white paper could be worth 10 points. But a demo request, a phone call, or a product demonstration could be worth 20 points. Such activity can be an excellent indication of interest. It is vital for sales teams to know what types of people to target. With the help of an accurate score, both marketing and sales departments can better work together.

Implicit and explicit lead scoring are different methods. The first is based on the premise that a lead is considered valuable only when they have shown interest in a product or service. In a reversed scenario, the customer would lose ten points if they do not opt-in to the newsletter. Both scores should be calculated using the same method. An effective system should consider all of these factors. If one of them is not satisfied, it is crucial to rework the model.