This group bet on the increasing reliance on behavior scores in the overall lead scoring business. Such a type of recording involves the use of two sets of data, each offering its own insights.
The demographic count, a child of the more traditional method of lead scoring, measures how well a prospect fits your target audience.
Behavior score, on the other hand, indicates sales-readiness based on a combination of activities undertaken by the prospect, beginning from his actions on your website, his/her reaction to your newsletters or emails, and other such measurable activities.
For years, companies depended largely on demographic-based scoring to pass on leads to their sales teams.
But in the past two years, that seems to be changing, with an increasing reliance on behavioral patterns of a prospect.
Companies, especially those in the B2B sphere, can no longer just look at lead scoring as a “strait-jacketed” activity, or worse still, depend on wild guesses made by someone in top management.
Here’s another reason why this year may eventually turn out to be a watershed year for lead scoring – the use of third-party data.
All this while, Enterprises have only been using information gleaned from within the organization. But before going any further, here’s a red flag.
Third-party data here must not be confused with the generally prevailing practice of “purchasing” unverified leads from online generators or affiliates.
What we are talking of is the tracking of data generated by a prospect in his offsite activities. Such lesser biased inputs help marketers in their pursuit to understand who is really a buyer and who qualifies as a “junk lead”.
A combination of all three above-mentioned factors is set to make lead scoring an even more accurate science in the new year.
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What is the Purpose of Lead Scoring?
According to Gleanster Research, no matter how many leads you generate, only 25% of those leads are genuine and even have a possibility to convert. Of those genuine leads, approximately 79% will not convert into sales.
This translates to only 5% of all possible leads actually becoming customers. That’s an annoyingly low number, and every time a lead turns out to not be in that 5% feels like a waste of your valuable resources.
So how do you improve the process so that you are chasing fewer low-quality leads and converting more high-quality leads?
Lead scoring in marketing is crucial in deciding whether the lead is to be given to sales (Sales Qualified Leads, or SQLs) or continued to be cultivated (Marketing Qualified Leads, or MQLs).
Lead Scoring and its’ Importance
As in any other industry, sales is the lifeblood that facilitates your B-to-B company’s growth.
However, your sales team’s success is inherently limited by time; after all, there are only so many hours in the day where prospects are going to be available to engage with your sales team and start the process to become a customer.
Your website might serve as a 24/7 way to inform people of who your organization is and what services it provides, but that only places that prospect at the top of the sales funnel; how do you get them to begin flowing down the funnel and into that prized position of ‘customer’?
Lead Scoring Models
After all, it stands to reason that if Company A has certain qualities and is currently your customer, then other companies that have those qualities have a better chance to become customers than those that don’t.
Similarly, you may find that the majority of prospects who end up converting are customers who have been in the market for more than a year, so leads that have been looking for a solution like yours can be ranked based on how long their search has been in comparison.
A key consideration when looking at lead scoring as a whole, however, is what algorithm you should use to score the leads comparatively.
Listed below are a few instances of creating scoring models to rank possible customers.
Buying intent model
One way to score leads is by analyzing their purchasing intent.
Intent data can be gathered from different digital sources to get a full picture of the prospect’s purchasing journey.
Lead scoring models according to intent data allow you to stand in front of possible clients early.
Express Analytics’ intent data allows you to minimize budget resources and manual needs.
Demographic or firmographic model
A second kind of model for lead scoring is a demographic or firmographic model, where you group and rank prospects based on how similar they are to one another.
This usually manifests as a clustering algorithm such as a K-means or K-neighbors cluster, where all the prospects and customers are clustered, and then prospects are graded based on how closely they are clustered around existing customers.
In this way, B2B lead scoring can act similarly to how B2C companies use demographic information in the customer profiling process.
Online behavioral model
Another kind of model is an action-based ranking model, where you can allocate scores based on activities and moves that leads perform online or on your website.
Attributes with negative scoring
You can also use lead scoring to identify leads that have gone cold; after all, in the interest of keeping your efforts focused and efficient, your sales team should avoid engaging with customers who have behavior that indicates a reduced intent to buy.
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What to Inspect before Implementing Lead Scoring Models?
For instance, your eligibility criteria must match the fields on lead capture forms. Another thing to consider is whether you have sufficient leads to rank.
As the purpose of lead scoring is to evaluate leads in the context of the other leads and customers you have, the fewer leads there are to compare, the higher chance that a low quality lead sneaks through.
Therefore, if leads are scarce, it’s good to invest extra time in lead generation.
Similarly, if sales development executives are busy with lead follow-ups and conversion rates are high, then lead scoring might not be relevant for you.
In case you have several leads coming in and the sales team complains regarding their bad quality, then the solution is to better align your marketing strategy with having more stringent requirements for MQLs before they get turned into SQLs.
What is a Predictive Lead Scoring System?
Predictive lead scoring is a data-focused model depending on machine learning algorithms to locate behavioral and demographic patterns in your best previous customers.
Using a predictive lead scoring system, you can improve lead scoring competently and rapidly depending on current data regarding your leads in different marketing analytics tools and your CRM.
Additionally, the model works actually, and its forecasting becomes wiser as it processes enough data. Hence, you can produce a fruitful marketing strategy or follow up according to the present status of your leads.
AI in lead scoring
Predictive lead scoring inspects the behaviors of customers and forecasts sales by implementing big data and AI into the present lead scoring model.
Whereas, AI inspects audience data from a quantitative point of view and figures out which leads require more nurturing and which are qualified for sales.
Furthermore, predictive lead scoring boosts ROI by upgrading the workflow between sales and acquisition.
ML in lead scoring
Predictive lead scoring produces metrics for current customers’ anticipated value compared to the demographics and behaviors of prospective customers.
Based on this comparison, ML-focused algorithms develop a larger image of members within your target audience who most likely need additional nurturing.
It is crucial to note that predictive lead scoring depends on the data of present customers; improving your profiles of current customers in CRM will update your predictive lead scoring model.
Behavioral Scoring and Lead Scoring Intelligence
Lead intelligence is a pipeline that tries to give a 180-degree view of every prospect on his journey to buy, and marketers are the ones looking at this process through the eyeglass.
The addition of the behavioral data component in lead scoring today is also part of the new thinking among marketers that the name of the game is no longer more leads but “better” or qualified leads.
The effort is to hand over Marketing Qualified leads (MQLs) as “sales-ready” to the colleagues from Sales, thus saving the latter time and resources, and overall helping organizations increase their lead conversion rates.
And in this, behavioral data is being seen as one that is coming to play a major role.
Keeping Behavioral Score
Here’s how lead scoring has evolved through the ages, today, coming to rest on the doorstep of a prospect’s behavior.
In the past, marketers would assign a high score to a person who held a certain rank within an organization, simply because they thought he held the power of decision for purchase.
A marketer based this on what’s generally defined as explicit data. That silo-type of scoring is history.
Marketers then started grading prospects based on information gathered from implicit data, or the actions of a person.
Clicking on a link from within an email, for example, qualifies as one.
But even such implicit-based data scoring is now being honed into a science in the drive to identify quality leads.
For example, a short while ago, anyone who simply clicked on a web page says 10 times got a high score attributed against his/her name and was said to be “sales-ready”.
Today, however, a prospect who has done something more concrete than mere clicking, like filling out a registration form for a webinar on your website, gets more marks than the guy who simply clicked a dozen times.
Thus, a potential buyer’s intent is often revealed through their behavior.
Behavior score, which essentially comes out of behavioral qualification, a set of triggers around the buying process, has a two-fold purpose – it helps measure a person’s sales readiness and provides a clue to which channels provide the best prospects.
Clearly, converting visitors into leads is a complex process, and will remain so as long as your visitors are real people with real emotions and not bots.
This entire process is based on the foundation of lead scoring. Needless to state, the more accurate marketers score leads, the faster it is for the Sales team to close a deal.
A recent survey showed that 62% of sales teams did not trust the lead scoring metrics used by the marketing teams.
According to an Aberdeen Group study, a sales team that did not have some kind of sales intelligence to fall back on spent an average of 200 hours per year on non-selling related activities – such as tracking down data, finding phone numbers, and planning to make sales pitches.
Lead Scoring Best Practices in Sales
Listed below are crucial lead scoring practices to find hot leads:
Specify the criteria for sales-qualified leads
You have to decide what elements add value to lead conversion or what attributes recommend it’s good to disqualify leads.
In lead scoring, your B2B firm should give priority to budget and business size, whereas local businesses will concentrate on geographical data.
Don’t ignore the conversion process
You can determine how near a lead is to conversion when you examine their behavior.
Allocate points to each attribute and action
Allocate higher value points to actions that are near conversion.
Actions carried out by new leads, such as subscribing to a newsletter or visiting a homepage, shouldn’t weigh as much as approaching the sales team regarding pricing.
Leads can collect points for various actions, so consider what a minimum eligible score is before approaching your contacts.
Measure and adjust scores
You shouldn’t simply set and forget the lead scoring process as the customer journey changes over a while, so you have to adapt your lead scoring models to produce targeted ads regularly.
Express Analytics offers Catalog Modelling and Catalog Scoring techniques based on sophisticated statistical algorithms to help businesses not only identify likely customers but also to tell with a high degree of accuracy which leads are most likely to convert.