The worst thing that you can do as a call center manager is to rely on guessing games when building leads. While newer call centers on newer projects may have to do a little guesswork when it comes to building customer relationships and leads, you don’t want to rely on randomization to achieve success. For this reason, you must identify who your most reliable buyers will be, and this is why lead scoring is going to be your best strategy.
- What is Lead Scoring and How Does it Work?
- Traditional vs. Predictive Lead Scoring
- What is Predictive Lead Scoring and How Does is Increase Sales?
- Predictive Lead Scoring and Call Center KPIs
- Predictive Algorithms Qualify Leads
- Top Predictive Lead Scoring Tools
- Conclusion: Marketing Automation and Machine Learning Build Leads
What is Lead Scoring and How Does it Work?
Lead scoring is how a call center or sales company ranks the value of any prospective customer or potential lead. Every business has a different set of metrics that let them know how interested a potential customer is in their products or services.
Call centers need CRM software to provide specific analytical insights that increase first call resolution rate and customer satisfaction. Call center software suites that include tools like Salesforce and Hubspot track customer needs and deliver actionable insights, but lead scoring takes this information to the next level. Combined with tools like Phonewagon for call tracking, you’ll make the most of every call.
Lead scoring establishes an ideal buyer profile by using customer data to determine who is more likely to do business with the company. In essence, there’s a gamification aspect to lead scoring since customers with higher scores are intrinsically more valuable. If you combine these tools with predictive behavioral routing, you’ll be sending your calls to the agents best equipped to close a sale.
For example, when a lead enters your sales funnel, specific criteria determine the lead’s value. From which vector is the lead entering the funnel? Is he or she reaching out because they saw something on your site? Has the customer bought other products from your company or participated in other programs?
All of these preexisting criteria will add a numerical value to their lead score that can be used to prioritize reach outs. For example, customers with a higher monthly revenue will also have a higher score so that your agents understand that they have a higher priority.
The right lead scoring solution is ideal for identifying those that have expressed interest in your brand while filtering out those that might end up generating unproductive leads.
This is even advantageous when working with B2B clients – your lead scoring solution can attribute lower scores for some potential leads based on the scale of the company or the geographical region in which a company does business. All it takes is establishing what your company would declare “the ideal buyer,” and the scoring process can begin. We wrote a guide to lead scoring that hits on the specifics, including implicit criteria, negative criteria, and how to judge the viability of your collected data.
Traditional vs Predictive Lead Scoring
While traditional lead scoring is great for those companies that are growing, it has its weaknesses for those that are just starting. Let’s take a look at a few that can affect your bottom line:
- It’s not as useful if there isn’t a high volume of leads.
- It’s useless if your agents aren’t actively scoring leads in real-time.
- It requires that specific data points are established ahead of time. If a business is new, these data points aren’t always known.
- Leads aren’t always scored accurately using this system since this system is based on the judgment of agents and marketers.
For these reasons, a more streamlined system should be used. Artificial intelligence and big data are a large part of modern business, which is why predictive lead scoring that’s powered by machine learning, is being implemented across the enterprise landscape.
What is Predictive Lead Scoring and How Does it Increase Sales?
Traditional lead scoring can falter as a result of human error, which is why predictive lead scoring can be very beneficial. Unlike conventional lead scoring, predictive lead scoring is designed to directly utilize your analytic data to find your ideal customers.
CRM software can be utilized to attribute scoring values for your customers, and predictive lead scoring solutions will perform this scoring automatically. The “predictive” in predictive lead scoring refers to predictive modeling, which is based on a series of algorithms. These algorithms are designed to find your perfect or near-perfect customer so that your agents won’t have to do the guesswork, especially if you’ve been tracking call performance using call recording data.
With the use of historical and demographic data, a much more accurate and reliable data set is constructed. Since this is all machine learning-based, a predictive solution will pick up on criteria that your marketing team will have missed, which can produce a higher level of quality leads. The best part? Since this is done using machine learning and predictive analytics, many processes can be run simultaneously, which frees your team up for other tasks.
This kind of software not only draws from substantive wins, but it also analyzes what didn’t work to score potential leads. It also views information that customers have in common so that demographics are created that can be scored and used by your team.
Predictive lead scoring uses different lead scoring models to create a methodology. “Logistic regression” is being used in many solutions. Logistic regression is a data mining algorithm that will calculate the probability of a customer being created from a lead.
Logistic regression is formula-based, and it can drastically reduce the number of bad leads. Traditionally, marketers created these algorithms using Excel. With a predictive model, this is done quickly without the need for extra work from your team.
Another tool used by a predictive lead scoring system is “random forests”. This type of algorithm creates a forest of “decision trees” that can be used to map the behavior of your customers. For example, using this method will create a virtual forest of decision outcomes, and the tool will use this forest of decisions to determine which leads are more likely to convert.
This methodology uses randomization, which when scaled upwards, can help identify some of the factors that could drive conversion.
Predictive Lead Scoring and Call Center KPIs
Advancements in predictive lead scoring are gradually making traditional methods increasingly less viable. Algorithms that determine scores for leads are continuously being adjusted and evolved so that they provide increasing value.
Lead scoring has always required massive datasets, but predictive lead scoring has continuously degraded this requirement with such nuanced methodologies and algorithms. This is carried out with more ease because these predictive solutions can pull data from third-party sources in a seamless manner to bolster the information being gleaned.
Neural networks are also being used in modern-day solutions, which will allow for decisions to be made about scoring more organically. Neural networks allow for the solution to more intelligently catalog data from various sources at the same time.
This isn’t a technology that a contact center can afford to ignore; there are just too many advancements being made that will be integral for increasing output and reducing average handle times in today’s call center software. Could you get by with traditional lead scoring? Sure, but wouldn’t you want a solution that can evaluate thousands of leads simultaneously while your team goes about other, more productive tasks?
Predictive Algorithms Qualify Leads
In many cases, predictive lead scoring can use algorithms to independently determine scoring factors, but some common criteria may be used to evaluate these leads. These can include:
- Yearly Processing Volume: Some businesses have higher sales revenues processed online than others. Predictive scoring systems can find these and put them higher on the priority list.
- IP Country: If your organization only does business in a specific geographical area, then many predictive lead scoring solutions can filter these out based on the IP address of the lead. This will ensure that your team only reaches out to viable links.
- Firmographic Information: If one of your B2B leads utilizes a similar CRM system or has information that is available through an app that provides insights, then lead scoring can use this information to provide this contact with a score.
- Interactions: Has your contact clicked an email link from your company? This is a crucial indicator that there’s interest, and a predictive system will place this contact higher when scoring.
- Web Analytics: What sites have your contacts visited? If your prospective customer has visited your site or sites in your same vertical, then the software may attribute a higher score to the contact.
Top Predictive Lead Scoring Tools
Currently, there are scores of solutions on the market for predictive lead scoring. In this section, we’re going to provide you with four that have the most robust options so that you can separate the wheat from the chaff with less effort.
One of the best features of HubSpot’s predictive lead scoring solution is the fact that it’s already included in one of the most popular marketing automation platforms currently on the market. Their solution is available out of the box to all enterprise-level customers, which is excellent for those that want a nice one-stop-shop style of experience.
The solution comes with a default model that’s based on patterns used by successful customers, but there’s a significant amount of customizability for those that need it.
This solution is perfect for those that have already been storing engaged and unengaged contacts in HubSpot. The software, which comes in an app, will determine which customers fall into low, medium, or high lead score categories. The software even provides a pie chart based on several analytic criteria.
|It’s already a part of the Hubspot ecosystem.||The deeper functionalities like MQL qualifier lists can be challenging to learn for new users.|
|It comes with pre-installed lead score criteria that have been gathered based on patterns from other successful customers.||Smaller companies with a smaller amount of leads may not need as comprehensive a solution.|
|Managers can configure Hubspot to automatically email the sales team by email when customers with high lead scores come into the funnel.|
Unlike HubSpot, Infer is a dedicated lead scoring platform that is designed to connect to your CRM or marketing automation solution. The software uses a live API connection that allows it to connect seamlessly to just about any CRM solution that’s currently or will be available.
The software also allows managers to seamlessly utilize thousands of data points based on firmographic, technographic, or demographic information. The software even has built-in information on 19 million companies and 42 million prospects. Like the best predictive software, it’ll even use machine learning to identify patterns in both B2B and customer prospects using data extracted from your CRM.
|The software will instantly upload the scores directly into a CRM or marketing automation solution.||This is a solution that could certainly be less expensive.|
|Infer uses fit scoring, which is their version of logistics regression, to quickly determine customer viability.|
|The behavior modeling feature will accurately predict which leads will convert within three weeks.|
While solutions like Infer are excellent for traditional B2B since it utilizes communities of like-minded prospects, solutions like PipeCandy perform just as well in similar spaces as they correspond to D2C and e-commerce. As a result, PipeCandy is an excellent tool for organizations that are looking to partner with or sell to other companies in this particular space.
PipeCandy integrates easily with your CRM to determine wins and losses to create new scoring outcomes for your leads. The analytics and metrics readouts are also very clear and present a concisely organized visual that you can use to adjust your strategy.
PipeCandy works well for companies with smaller data sets by using its “Attribute Importance” functionality. This feature allows managers to decide which factors are most valuable when scoring leads. For example, if you want to add more value to those prospective clients with higher revenues, the software allows you to tweak its methodology with ease.
|The “Attribute Importance” feature allows managers to determine the attributes in which to score a lead.||The software has some noteworthy shortcomings. Since it’s AI-based, the solution may make mistakes, such as categorizing Apple as a food and beverage company.|
|There’s a plan for every organization. PipeCandy has Begin, Experiment, Grow, Leapfrog, and Dominate plans at different price points.||The “download contact” feature has some bugs that can lead to missing information.|
|PipeCandy provides actionable eCommerce insights, and their predictive scoring algorithms are very accurate.|
Maroon.ai is a predictive software that not only scores leads but helps generate new leads as well. The software is designed for what the company calls “deep context discovery,” which is designed to help organizations discover their target buyers. This makes the solution a go-to for anyone that’s just starting because it virtually automates some key processes.
The software is also great for integration into existing CRM solutions like Salesforce and Informatica, and the API is customizable for those looking to integrate the AI-powered system into other products. Maroon has a variable pricing structure that offers a significant number of options – there’s even a free version of Maroon.ai for those smaller organizations.
|This is a very accurate solution since it has 12,000 data signals and attributes for enterprise clients to utilize when scoring leads.||Despite integrating well with solutions like Salesforce and Informatica, the software could use more integrations with other marketing automation solutions.|
|Maroon.ai helps clients level the playing field by using their Predictive 2.0 classification. This provides visibility into some of the products that potential leads are purchasing from competitors and attributes a higher score to those that intersect with your offerings.||The dashboard can appear cluttered and too busy.|
|Maroon provides identifying attributes that include the priority level of the lead, its “Maroon Score,” industry, and model validation.|
Marketing Automation and Machine Learning Build Leads
As little as 27 percent of your leads may be qualified, which means that identifying qualified leads quickly is critical or else it could lead to wastes of resources. Predictive lead scoring eliminates the chance of this waste. These solutions can help organizations identify target markets, prioritize higher-scoring leads, and take some of the strain off of marketing teams and sales reps.
Predictive lead scoring is simply a tool that you must use to make the most of your salespeople’s time. The more you use a solution like this, the more it will increase the ROI of your outreach since the AI learns from both wins and losses alike.
Overall, software like this can help you better manage your sales funnel so that you can boost your likelihood to close based on an almost entirely automated process. Check out our guide to understanding the sales funnel so that you can quickly turn leads into customers.