What is Predictive Lead Scoring?

-Evita Godinho

Predictive lead scoring is a data-driven lead scoring methodology that uses historical and activity data and predictive modeling to identify the sales leads that are most likely to convert.

What is Traditional Lead Scoring?

Lead scoring is the method of awarding points based on specific data to prospects and future customers. Demographic statistics might include relevant details. The field of employment, position title, etc., for instance. Other details can include online engagement level or visiting unique website pages that show an interest in buying. Marketers rate the importance of such acts in conventional lead scoring to gauge client intent and qualify leads. For instance, a visitor who discovers the homepage from an organic search and fills out a form or subscribes for more details will most likely earn a higher score before bouncing than someone who opened an email or read a single blog post.

Marketers and salespeople rate prospective buyers on a scale that indicates how likely and perceived value the lead is to turn. The corresponding score determines which industry leader will give the highest priority to a sales agent to be approached. Leads are correctly graded in a perfect environment; and advertisers can pass on the most precious new leads to the sales team along with some handy knowledge about them.

Sadly, we don’t live in a perfect universe; and leads are not necessarily correctly scored. In order to assess and weigh actions that they consider important to making a profit; marketers often draw on their own individual judgement or historical data trends. Based on unreliable ratings, prospects can fall between the cracks or sales teams can waste too much time following under-qualified or ill-suited leads.

What about Predictive Lead Scoring?

Here is where the rescue will come to statistical lead scoring. Predictive lead scoring takes the human error factor out or decreases it and improves the precision of the detection of quality leads. Predictive lead scoring utilises predictive modelling; a popular mathematical approach used on the basis of past behaviour to forecast future behaviour. In order to produce a prediction forecasting future performance, sophisticated predictive modelling algorithms integrate historical and current data. Internal data for these algorithms is provided by related CRM and marketing automation solutions.

All this information is taken in by statistical processing algorithms. In order to identify trends in the results, they analyse good and ineffective leads. These trends classify variables that are most important and helpful in sales prediction. Based on this mix of historical demographic and activity data; predictive lead scoring could be able to come up with an optimal client profile that is most likely to buy and thus be able to classify the warmest leads. It will also help to recognise trends in the data that were previously missing or relationships. Predictive lead scoring lets the marketing and sales staff comply with data-driven lead scoring credentials; beyond only growing the margin of human error in lead scoring.

How it Increases Sales?

Predictive lead scoring is designed to use the predictive data specifically to identify the ideal clients. As a result of human error, conventional lead scoring can falter, but automatic lead scoring avoids most mistakes.

CRM tools will be used to attribute scoring values to the clients; and this scoring will be carried out automatically by predictive lead scoring solutions. In predictive lead scoring, “predictive” applies to predictive modelling, which is based on a set of algorithms. These algorithms are programmed to find your ideal or near-perfect customer so that your agents do not have to guess; especially if you have been using call recording data to monitor call results.

A much more precise and accurate data collection is built with the use of historical and demographic data. A predictive approach can pick up on parameters that the marketing staff will have overlooked; and will deliver a higher degree of quality leads, since this is all machine learning-based. The finest part? Because this is achieved using deep learning and predictive analytics; it is possible to run several processes at the same time which frees up the team for other activities.

Not only does this form of programme draw from substantive wins; but it also analyses what has not performed to score future leads. It also views details familiar to clients in order to build demographics that can be rated and used by the staff.

In order to create a methodology, predictive lead scoring incorporates numerous lead scoring models. In certain solutions, “logistic regression” is used. Logistic regression is an algorithm for data mining that determines the likelihood that a consumer will be generated from a lead.

Conclusion

As few as 27 percent of the leads can be qualified; which means it is important to rapidly find qualified leads or else it may lead to resource loss. The risk of this waste is minimised by predictive lead scoring. Such approaches would help businesses identify potential audiences, prioritise higher-scoring leads, and take some of the pressure off management staff and sales representatives.

Predictive lead scoring is actually a technique you have to use to make the most of the time for the salespeople. The more you use a solution like this, the more the ROI of your outreach will improve; as the Artificial Intelligence learns from both successes and defeats.

Overall, tools like this can help you control the sales funnel better so that, even on an almost fully automatic procedure; you can maximize the chance of closing.