Lead Scores

Why lead scores speed up the sales process

Sales and marketing teams are always looking for ways to increase their leads and close more sales. One way they are doing this is by using lead scores generated by AI. Lead scoring is the process of assigning a numerical value to a lead, which indicates the leads likelihood of becoming a paying customer. The higher the score, the more likely the lead is to convert. Sales teams are using AI to generate lead scores because AI can take into account a large number of data points that humans would not be able to process on their own. For example, AI can look at a leads past purchase history, the type of content they have been engaging with, and their interactions with the sales team. AIgenerated lead scores can help sales teams prioritize their leads and focus their efforts on the leads that are most likely to convert. In addition, AIgenerated lead scores can help sales teams identify patterns and trends that they can use to improve their sales strategies. If youre looking for a way to increase your sales, consider using lead scores generated by AI.


How lead scores work

Lead scores are a prediction of how likely individuals are to make a purchase on a scale of 1-100, where higher numbers represent a higher likelihood to purchase. 

In today’s hyper-competitive world, most businesses are constantly seeing to enhance the effectiveness of their marketing and sales resources. This means finding customers and prospects that can be moved down the purchase funnel with greater efficiency. But this also requires increasing the marginal utility of every dollar spent on campaigns and sales efforts. Lead scores represent a targeting tool that enables businesses to focus their energy and resources on higher yield opportunities


More from Wrench.ai

Wrench.ai, in collaboration with Rule27 Design, is pleased to introduce their latest innovation for Salesforce users: the AI Virtual Assistant. This new app, available on the Salesforce AppExchange, represents a significant advancement in how marketing and sales teams can utilize CRM data. By leveraging Wrench.ai’s artificial intelligence technology, the app empowers users to automate and enhance their customer engagement strategies.

Key features of the AI Virtual Assistant include:

  • CRM Data Enhancement: It enriches contact details in the CRM, providing deeper insights into customer personas and personality traits. This enhancement aids in crafting more personalized messages for both individual outreach and broader campaigns.
  • Data-Driven Outreach Recommendations: The app guides users on which prospects to target, what offerings to emphasize, and the most effective communication strategies.
  • Simplified Data Research: By eliminating the need for extensive research, the app ensures more informed and relevant customer interactions.
  • Boosted Personalization: Users can create content that resonates more effectively with their audience, potentially increasing engagement and productivity by up to 50%.

Wrench.ai’s CEO Dan Baird: “Our goal is to give customers AI insights that are data-driven and accessible so that they can make better decisions. This is what’s at the heart of the Wrench.ai platform, from which we can spin off tools like the AI Virtual Assistant. This means we’re building an ecosystem that puts the much-needed tools in customers’ hands. With the AI Virtual Assistant, we’re proud to offer a transformative tool for Salesforce users to bridge data gaps and generate actionable insights from routine sales prospecting and outreach. This allows teams to launch more impactful marketing initiatives in less time. As a result, every campaign becomes sharper and every interaction more insightful. Based on the success of our clients, we are confident Salesforce users will see results more quickly.”

The AI Virtual Assistant is a tool that not only bridges data gaps but also generates actionable insights, leading to more impactful marketing initiatives and sharper campaigns.

For those interested in exploring this new dimension of sales and marketing management on Salesforce, the AI Virtual Assistant is now available for download on the AppExchange. To learn more, visit this link. Additionally, the first 100 users to register will have the opportunity to use the AI Virtual Assistant free for an entire month. Don’t miss this chance to revolutionize your approach to customer engagement with AI.

To read the press release in full click here.

At Wrench.ai, we are thrilled to announce the recent collaboration with Refuel Agency, a prominent force in specialized marketing. This partnership marks a significant leap forward in AI-driven marketing solutions, where innovation meets experience to redefine campaign effectiveness.

Refuel Agency, recognized as a marketing powerhouse, has been a trailblazer for over three decades, offering media and marketing services to connect brands with military, teen, college, and multicultural audiences. This strategic collaboration allows Wrench.ai to integrate its AI expertise with Refuel’s deep understanding of diverse audiences, ushering in a new era of highly personalized campaigns.

Derek White, CEO of Refuel Agency, and an early Internet pioneer, emphasized the immediate value advertisers will experience. “AI is the most interesting and important development in marketing of this decade, and we are proud to be an early adopter of the technology,” says White. He highlighted that clients will witness immediate benefits from the AI integrated into their unique approach to identifying, reaching, and engaging niche audiences.

Dan Baird, CEO of Wrench.ai, echoed this sentiment, stating, “We’re creating a new era where AI-driven insights ensure that every campaign is hyper-personalized and maximally impactful. It’s not just about scaling marketing campaigns, it’s about making them smarter, more intuitive, and truly connected to what audiences want today.”

AI-powered marketing campaigns are already available through Refuel Agency, with new solutions continually rolling out. This collaboration brings forth a synergy that leverages client first-party data to apply artificial intelligence for persona development, deeper audience segmentation, creative optimization, media mix performance, lead scoring, and more.

For more information about Wrench.ai, reach out to info[at]wrench.ai.

About Refuel Agency:
Refuel Agency, headquartered in Princeton with offices across the United States, has been a leading provider of media and marketing services for over 35 years. Working with Fortune 500 companies, top agencies, and boutique firms, Refuel’s omnichannel approach embraces digital, mobile, social, video, experiential, out-of-home, and print advertising. Learn more at Refuel Agency.

About Wrench.ai:
Based in Salt Lake City, Wrench.ai harnesses machine and deep learning technology to empower marketing and sales teams. Wrench.ai’s innovative solutions enable businesses to rapidly build personalized and impactful campaigns at scale.

To read the press release in full click here.

Before launching into how to interpret a match score, allow me to address how we define one. At a technical level, match scores measure the affinity or likely similarity in meaning between two objects. In the world of AI, objects are usually some form of text that represents how an entity communicates who or what it is. 

Here’s an example of where you would use one: Company A wants to launch a new product, so it will create a compelling message for a promotional campaign that describes its unique offering – and to maximize resources, budget, and efficiency – will only target customers with a match (or lead) score over a certain number. The higher the number, the higher the indication that a customer is likely to engage at some point during a promotional campaign. Those who engage are also likelier to convert, or purchase.   

How can you match likely customers to the new product? The first step is to identify the two objects – we also refer to them as entities – to measure the affinity between the two. In the example above, the first object would be a description of the product, while the second would be a description of the customer, which could be a social media profile, like a LinkedIn profile. Note that if you have a small customer data set, you could do the matching manually. It might take a lot of time, but it’s possible. Let’s say you have a customer data set of one million customers; there’s no other way to do this than through automation (that’s where we come in, as we specialize in using AI for very large data sets).   

I will spare you the technical details, but once we have two entities we can see how closely the language surrounding them shares similarities or affinities.

The power of the match score lies in its inferential power, or its ability to predict the likelihood of a strong match or a weak match. 

For a sales or marketing team, high match scores between customers and a brand suggest that the brand’s message or description will have a positive resonance with high-scoring customers and a less positive resonance with low-scoring contacts. Notice that I did not use the term “negative resonance”; customers may have lower scores because they are not as familiar with a brand, but with a nurturing campaign they could eventually exhibit a higher match score because they are signaling more familiarity with the brand.

 Conversely, high-scoring contacts could indicate that their public personas are more informed about the brand category and would therefore not require the same degree of education as their low-scoring counterparts.

The question most clients have is: “What constitutes a high or low score?” Generally, scores can range from 0 to 100, with a high score being anything greater than 60. Individuals scoring over 60 usually indicate someone who is an innovator or someone who is publicly expressing a higher degree of familiarity with a brand or a product.

Individuals with scores less than 35 can be considered uninterested or unfamiliar with the content of the comparison entity.

The most important thing to note is that scores need to be viewed in the context, which includes the population sample (are you matching warm leads from your CRM or a cold list?) and industry (is your product super technical, or easy to understand?), and possibly other variables. Match scores can provide statistically significant guidance on who to target based on the goal you are seeking to accomplish. Marketing and sales efforts that incorporate match scores are much more likely to be effective because they take into account more informed targeting in promotional and outreach efforts, rather than the typical casting of a wide net, where everyone is considered to be part of the same playing field. 

When it comes to marketing and sales outreach, the general wisdom is that the closer you are to targeting an audience of ideal customers, the more likely you are to convert them. The more you convert, the more money you make. This seems pretty straightforward – if you can figure out who your ideal customers are.

Segmentation or lead scores?

Segmentation is one way to categorize customers for more focused targeting, but it’s easier said than done because there are many ways to categorize customers – from demographics to psychographics to geography (and so much more).

In our experience, the most efficient and effective way to segment customers in order to maximize profit potential is by lead scores. How do you define a lead score? A very basic definition is this: It’s a number assigned to a customer that reflects the likelihood they will act in response to a specific message or product offering. The higher the number, the higher the likelihood the customer will engage – and buy.

A customer’s lead score can also change, based on the product or message. When we work with clients we ask them to be very specific about the goal they want to achieve with a specific product or campaign. If the goal is to upsell customers on a new product, we will want to identify potential leads more likely to purchase sooner than those who are likely to wait (for a variety of reasons).

Lead scores and the buyer’s journey

The approach to converting early adopters won’t be the same as nurturing late adopters and moving the latter group into a “consideration” stage. Late adopters need more time, more evidence, and likely a drop in price before they’ll act. To no one’s surprise, they won’t be ideal customers if you launch a new product. So how do you identify early adopters?

Predicting behavior to focus efforts

Lead scores can “predict” which customers are likely to take action and those who won’t. Here’s an example: If a sales team can predict who is likely to respond to a cold call or an email, it follows that they can prioritize who to target to optimize their time and increase sales. If you’ve never thought lead scores could make a difference, I’m here to tell you that we’ve seen them work for our clients.

I won’t get into the specifics about how the Wrench lead score algorithm works, but I will say that we’ve made it easy for clients to upload contact and customer lists via a CSV file or a CRM integration. Wrench’s web app provides a straightforward method for uploading product or brand descriptions. From there, the Wrench platform quickly generates lead scores so a marketing or sales team can quickly prioritize who to target.

Lead scores make the most of customer data

Using lead scores, one of our clients found that only 17% of the contacts in their database fit their customer profile. This insight gave the client the information they needed to prioritize high-scoring contacts that were more likely to convert, saving them time and resources. Put another way, if you knew which 20% of your customers were likely to convert, you would know where to spend more of your time and attention and see results faster. Wrench’s Lead Score AI feature can increase a conversion rate up to 5x, and in a highly competitive landscape, this can be a significant advantage.

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