Lead scoring helps marketing teams to separate quality leads from the huge bunch of leads and hand them over to inside sales teams so that they are able to reach out to only those prospects who are ready to buy. These methods help in distinguishing potential customers from cold leads. Lead scoring methods attach scores to each of your leads based on various patterns of their on-web activity and indicate whether or not a lead makes sense for the business, thus increasing sales conversion rates.
Traditional lead scoring takes into account two different kinds of information, explicit data and implicit data, to deduce lead scores.
- Explicit data is usually based on the information that prospects tells you straight away such as company email address, business title and area of work.
- Implicit data is implied from the digital behaviour of the prospect such as blog subscriptions, clicking through emails and clicking or viewing certain content on the company’s site.
Marketers monitor various implicit data actions of the prospect to understand his sales readiness. For instance, for a B2B business, a lead whose email ends with @yahoo.com will score less as compared to someone with a business email address who is more likely to close. Another example could be, an organic website visitor who comes to your homepage and clicks on “set-up a demo” will be attributed higher score than someone who simply read a blog.
However, the traditional lead scoring method is prone to human errors and has certain drawbacks-
- It is difficult for marketers to explicate the data in an accurate and consistent manner. It is based on assumption and there is no actual proof if a particular metric is relevant enough to signify that a lead will convert into a buyer.
- It does not always take into consideration the changing market conditions. The criteria used in lead scoring and the weightage assigned to each metric might change from time-to-time. But, organizations do not always update them and sometimes rely on data that no longer relates to buying intent.
- It does not provide sales teams with the opportunity to give feedbacks and incorporate them, hence restricting the company from improving the lead scoring systems to improve sales conversions.
This is where Predictive Lead Scoring comes into rescue. It leverages both historical and predictive analytics to score leads. Predictive lead scoring is a more streamlined process that wipes out the possibility of human error by replacing the need for people to manually weigh the qualifying factors.
Predictive lead scoring analyzes the data around leads that became successful closed won. It harnesses powerful machine learning and adds tons of buying signals to the existing data to predict which leads are most likely to buy from you. This method pulls internal information from the company’s CRM and marketing automation solutions and also considers third-party sources such as the internet to estimate the probability that a lead will buy. This increases the possibility for a better lead management process.
Let’s try to learn how predictive lead scoring actually works:
First, it analyzes all the closed opportunities (both won and lost) and the existing leads in the CRM. It finds out what exactly is known about the leads.
Secondly, it mines the web to add more information to the leads data. The software scans thousands of data signals across the internet such as company websites, third-party websites, social media, press releases and so on to figure out the digital fingerprint of your ideal customer and helps in creating a more comprehensive profile of the leads.
Lastly, the software will calculate the scores of each leads based on the two aspects discussed above and the scoring also evaluates how close these lead profiles are with the existing customers profile.
Hence, this method helps in identifying the right prospects faster and also indicates on which leads the sales team should not waste time.
Now, let’s analyze the benefits of Predictive Lead Scoring over Traditional Lead Scoring Method:
No Need of Guesswork Anymore
Unlike traditional lead scoring methods which heavily rely on assumptions and guesses to evaluate leads, predictive lead scoring system is based on hard analytics and statistical methods and thus evaluates leads quicker and with more effectiveness. In traditional methods, it becomes really difficult to understand which factors influence sales conversion, hence making the system slow and less satisfying. However, predictive lead scoring gives scientific results and helps sales teams in approaching more meaningful leads.
It’s a Data-driven Approach
Predictive lead scoring uses a huge amount of data in contrast to traditional lead scoring which uses a small subset of available data. Predictive lead scoring system digs through the superabundant repository of information available on the web and creates a more comprehensive profile about each lead.
Improve Marketing Effectiveness
Predictive lead scoring gives a better picture of the ideal buyer and helps marketers to create content that suits the interests of the target audience. The website content and marketing pitch could easily be altered in a way that appeals to the readers and addresses the potential buyer’s needs. It helps in improving the overall marketing strategy of the company by diagnosing which campaigns are working and which are not.
Enhance Sales Efficiency
Predictive lead scoring methods sort the prospects on a priority basis and put the hot leads on top. This enables sales teams to contact those leads that are more likely to convert and stop wasting time on scrap contacts. As described before, this method creates a very comprehensive profile of each lead, thereby allowing sales teams to send tailored and more personalized content to them.
Predictive lead scoring method takes into account the “need” of leads while evaluating them, thus assigning high priority to those who require your product or services urgently. This improves sales processes and makes the team more efficient in closing the leads.
Keep your Customers
Predictive lead scoring system not just identifies the most sales-ready leads but also indicates which customers will probably leave. Therefore, it lowers churn rate by specifying which relationships require nurturing.
Ensures Sales and Marketing Alignment
As there is no assumption involved in predictive lead scoring method, there is hardly any possibility of sales and marketing teams pointing fingers towards each other. This method uses data from both marketing and sales efforts and increases coordination between these two teams. It improves productivity and brings in sustainable results as there is a better understanding of high priority and low priority leads. High priority leads can be passed on to sales teams immediately and low priority leads could be nurtured by the marketing team until they become completely ready to buy.
It is not a surprise that technology industry represents the biggest chunk of early adopters of predictive lead scoring method. According to a research, 78% of companies using this method are in the high tech industry. The reason behind this could be that technology companies have a better acquaintance of digital data.
Predictive lead scoring is most applicable for companies with a steady stream of leads coming in. The solution works best for larger companies with huge amount of data to leverage. So organizations with fewer data must work upon building a concrete database first so as to experience the power of predictive lead scoring.