From Manual to Prediction: The Importance of AI In Lead Scoring

lead scoring

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One of the most important and critical things for any business is to assign a priority to their leads. Unfortunately (but it’s also a piece of luck), no one has enough time to follow up every single lead personally. Not efficiently.
Here comes in help Lead Scoring.

There are two main ways today of doing Lead Scoring: traditionally or, even better, leveraging Artificial Intelligence technologies.
What’s the difference between those two methods?

How Lead Scoring can be an essential marketing level for your business?

Lead Scoring: How Prioritizing Is Useful For Your Business

According to Hubspot, “Lead scoring is the process of assigning values, often in the form of numerical “points,” to each lead you generate for the business.”
For example, you can score your leads based on multiple attributes. These may include professional information, different touchpoints during their customer journey, actions and behaviours.

Lead Scoring is a way to help marketers and sales.

Marketers can figure out “which qualified leads they should send to their sales team”, by identifying some factors and characteristics.
You can understand if you are going to transform a lead into a prospect.

Lead Scoring consists of assigning a value to each lead, depending on which kind of action it’s done. Many systems assign point values to different actions a lead or customer can take in the funnel.

When a lead reaches a fixed level or, better to say, a value, it’s considered as a hot prospect. This value is often a number.

When people approach inbound marketing, they’re worried they won’t collect enough leads. The second problem is if and how many of those leads are valuable. However, the real problem is: if I’m full of leads and I’ve not time enough to manage them, which of them can bring me more value?

Thanks to lead scoring, marketing teams can make a wiser prediction on how close a prospect or customer is to making a purchase.
After that, it’ necessary to active some alerts that tell you when a prospect has reached the correct score. Then sales are going to contact him.
First of all, there are plenty of ways to do lead scoring and, usually, every business has its own. There is, anyway, a standard procedure.

Before starting a Leading Score strategy, it’s essential to understand if it really fits your company. Mostly, it’s crucial to understand if you have enough leads to justify this kind of actions, and enough data on each lead to make them useful. Once you determined that the method is good for you you need to focus on the real value for the company of those leads.

Next, you have to pick the most important pieces of information and attributes that represent the value for you and your business. For example, some of them might be:

  1. Demographics: knowing demographics data of your customers is useful to understand who are your most valuable target and prospects. For example, you could understand that mums in their thirties with a toddler are your most important segment. Vice-versa, childless men in their early forty, aren’t useful for your business.
  2. Professional pieces of information You might also need this kind of details about your customers. Maybe one of your buyer personas has a specific role on a determined field.
  3. Navigation history What do your prospects and client do more often on your website? Do they stay a few seconds on the e-commerce page? Do they skim the About page? Do you collect many abandoned shopping carts? These are incredibly important data to understand what your prospects are doing on your website, what is working and what could be fixed.
  4. Email and social media data Did your prospect visit your social media accounts? Did they download some of your contents – and if so, which ones-? All those data could be fundamental.
predictive lead scoring with Artificial Intelligence

Traditional Or AI-Predictive? Make Your Choice!

Sometimes, the lead problem is that the follow up from the brand is too slow and not completely accurate: “The problem with this ‘sales-ready’ approach is that it can be challenging for marketers to interpret the data in a way that is both consistent and accurate in a reasonable amount of time.”
That’s why a lot of different ways of doing lead scoring have followed one another, year after year.

Predictive Lead Scoring is based on machine learning. It’s possible to train algorithms to make accurate predictions about new customers and existing ones, based on data we have about them. More specifically, it helps by identifying all the people that are qualified and reach the value we fixed for every feature. Predictive lead scoring has been designed to determine which criteria define a strong lead, create lead scoring models explicitly based on your company’s particular needs, and to be adaptable enough to adjust to a changing market.”

Everything starts with CRM, a Customer Relationship Manager system. Thanks to it, companies are capable to “gather and input data about each lead, which will then be scored by the software based on the criteria selected”. It contains all the sales data, from the first touchpoint to the historical management of contact.
Marketers still need to do some research, anyway, even if it’s much more comfortable than the manual method.

After this one, it comes Marketing Automation, that “made it possible to track leads’ online behaviour and gather certain types of implicit information.” This exponentially increased the amount of data that the system could analyse.
Last but not least, here it comes predictive lead scoring.

Predictive lead scoring allows you not to worry about what properties should be included or how much to value each one of them. After checking which data in common to all customers, the algorithm “comes up with a formula that will automatically bucket your leads for you so you can easily identify the most qualified ones”.

So instead of scoring a lead on a manually defined set of rules, Artificial Intelligence algorithm finds the match. It scores the attributes of a “good” lead and compares the quality of all other leads with similar behaviour. It then predicts and ranks their likely to close the deal.

Predictive Scoring is a method aimed to extract a model from customer behaviour.

However, what AI predictions can do better than humans? Let’s have a look:

  • Collect and analyse a lot more data in a snap. The system can analyse by itself any customer data, form social media to email, in a shorter time. It “can take over the task of shifting through huge quantities of incoming and historical sales data to determine the strongest leads”.
  • Increase accuracy. Thanks to Machine Learning and AI, Lead Scoring software get trained with usage. Algorithms analysing data and pair them to new insights, improving efficacy and predictive capabilities.

Takeaways

  • Lead Scoring is a useful marketing tool because he allows to assigning values, often in the form of numerical “points,” to each lead you generate for the business.
  • During the years, marketers developed two kinds of Lead scoring: traditional and predictive. It starts with CRM. Then it comes to Marketing automation and AI.
  • Predictive lead scoring allows you not to worry about what properties should be included or how much to value each one of them. Artificial Intelligence algorithm scores the attributes of a good lead. Then, they compare the quality of similar ones and predicts and rank they are likely to close the deal.

Sources

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