How to Create Lead Scores with Real-time Data Using Operational Analytics?

Selling your product or service requires identifying potential prospects, initiating contact and qualifying the leads. This sales cycle begins the moment an opportunity enters your CRM and lasts for days or weeks to come. Take into account the number of days, resources, and efforts it takes your marketing and sales teams to convert an opportunity into a customer, and you realize most of your time is spent on outliers.   

Having a lead score metric makes is easy for organizations to approach their leads. Lead scoring is like identifying high value opportunities, the ones who are more likely to convert. According to CSO Insights, sales teams who prioritize sales efforts and create lead scores achieve revenue plans 18% more often and meet or beat their quotas 22% more often.

The goal of lead scoring is to increase sales productivity, increase the number of qualified leads converted into opportunities, and decrease the sales cycle times for qualified leads. Getting real-time insights into lead scores helps your sales and marketing teams take proactive steps to approach and nourish them. Lead scoring involves collaboration among the sales and marketing team and helps them to prioritize leads so that they can ultimately improve your business bottom line.

With real-time data, a marketing team can create personalized email campaigns, a customer success team can prioritize the customer tickets, and a sales team can create lead scores and decide which prospects they should pursue and engage first.

This blog will help you understand what a lead scoring system is and how to create lead score with real-time data using operational analytics.

Table of Contents

• What is Lead Scoring?
  • Why Should you Create Lead Scores?
  • What Data Should you Consider for Lead Scoring?
  • How to Create Lead Score for Your Organization?
• What is Operational Analytics?
• Supercharge Your lead Scores with Real-time Data Using Operational Analytics
  • How to Implement Lead Scoring Using Operational Analytics?
  • Operational Analytics for Sales Teams
  • Operational Analytics for Marketing Teams
• The Faster and Easier Way to Operational Analytics: Reverse ETL
• How to Create Lead Scores with Real-time Data Using Hevo Activate?
• Final Thoughts

What is Lead Scoring?

Lead scoring is a shared sales and marketing methodology for ranking leads based on their sales-readiness. It helps both sales and marketing teams determine the characteristics and value of each lead and decide which leads should be delivered to sales. Leads are scored based on their interest in your brand, their present position in the purchasing cycle, and their fit in regard to your business.

Companies can create lead scores through the allocation of points and using ranks such as A, B, C, and D, or phrases such as ‘good, worse, lucrative.’ The main point is that having a good clarity of a sales-ready lead increases marketing and sales’ combined efficiency and productivity.

If you create lead score, it informs your sales and marketing teams on whether prospects should be pushed to sales or nurtured through lead nurturing. The best lead scoring systems integrate demographic and firmographic characteristics such as company size, industry, and job title with behavioral variables such as clicks, keywords, and web visits.

According to a study by Aberdeen Research, 80% of the top-performing companies use lead-scoring technology to improve their sales and marketing efforts and effectively drive higher return on investment.

Why Should you Create Lead Scores?

“A solid lead scoring approach not only helps to rank prospects against one another, but can smooth the lead flow and serve as the baseline for building a range of business rules that include ownership, role and activities.”

SiriusDecisions, What’s the Score

Companies require lead scoring and marketing automation to manage their leads more effectively. Lead scoring assists businesses in the following ways:

Lead Segmentation

Lead scoring assists marketing team in segmentation depending on the information provided on the website. They will receive leads from several outlets when you expand your marketing activities. Each lead has varying degrees of interest in your service or product.

Leads come from a variety of purchasing histories, sectors, and regions. Because they are unfamiliar with your products, they will approach them uniquely. Your task will be to understand their purchasing process at all times and give the best message to them. Lead scoring aids in the identification, evaluation, and sampling of leads.

Lower Cost-Per-Lead (CPL)

For most companies, generating a large number of leads after a certain point of time might be challenging. Acquiring leads from trusted providers is a simple approach to receive them quickly. However, while acquiring leads, they have to keep an eye on the Cost-Per-Lead (CPL) to ensure a healthy return on investment (ROI).

Most marketers are worried about the regularity and quality of the leads they receive. The only way to assess the quality of purchased leads is when they visit their brand’s call-to-action pages. Using that assessment, they may evaluate leads based on their current activities and decide where they should be put in the purchase process.

Prioritize Sales Prospecting

The marketing team receives a sizable portion of the company’s budget in most SaaS businesses. The team must make optimum use of its resources to fulfill its objectives.

Teams may use lead scoring systems to classify acquired leads based on their behaviors on their website automatically. This saves the sales team valuable time that can be used for other sales tasks. Teams should revisit their lead scoring model as needed to assess its efficacy or to make changes in response to new events.

What Data Should you Consider for Lead Scoring?

Let’s have a look at the five key sales and marketing data points that can be used to analyze the quality of the lead and create lead scores.

• Individual Data
• Company Data
• Attribution Data
• Behavioral Data
• Sales/CRM Data

Before doing this, make sure your marketing and sales team discuss what characteristics would make an ideal buyer. They should decide on a set target score above which a lead can be passed on to sales.

Individual Data

This information includes information about the individual assessing your product on behalf of their company. Job position and seniority are the most typically used data points for lead qualifying. This aids in identifying decision-makers among leads.

Company Data

This dataset contains firmographic information on your leads, such as their business size, industry, revenue, and so on. Such data allows you to separate tickets based on ticket size and filter out leads matching your target business profile.

Attribution Data

This metric links your lead qualifying approach to your marketing initiatives. 

Let’s look at an example. You own a CRM platform and are launching an ad campaign using the term ‘CRM.’ This campaign generates two leads, one after searching for ‘top CRM software’ and the other after looking for ‘what is a CRM.’ Which of them do you believe has the greater purchasing intent? (Psst, the former is the better qualified).

Similarly, leads who visit your website after seeing your blog post on social media have less intent/urgency than those who enter while actively seeking your product.

Marketing attribution links your leads to campaigns and traffic sources, allowing you to qualify your prospects better. The circle is closed when marketing attribution assists you in identifying efforts that bring in the most consumers, which you can then utilize to prioritize leads and amplify the results of higher-performing campaigns. Leads from historically higher-converting campaigns might thus be assigned higher ratings.

Behavioral Data

This is the data point where gradual scoring kicks in. Leads engage in various actions while connecting with your content and touchpoints. As leads engage in these desired or undesirable actions, their score fluctuates dynamically. Aside from understanding a lead’s purpose for initial qualification, behavioral data also assists in guiding leads through various phases of the purchasing process and presenting customized content.

Sales/CRM Data

This dataset connects sales and marketing data for revenue attribution, lead qualifying, and targeted nurturing. The data from sales interactions and CRM databases may be sent back into marketing for a more coordinated qualifying effort.

How to Create Lead Score Model for Your Organization?

How do you ensure you have created a good lead scoring model in your organization? Here are four steps you can follow to create a lead score model:

• Develop Buyer Persona
• Study Online Behavior
• Find Out Scoring Features
• Assign Score Values

Develop Buyer Persona

To develop a buyer persona, divide your target audience into groups based on comparable identifying qualities and features. These will then be essential data points for producing a lead score model. 

You can begin by evaluating lead demographics to define which traits connect with conversions. By grasping a clear understanding about your target audience and the typical qualities of a lead, you can identify which traits correlate with better conversion rates.

Study Online Behavior

Online behavior relates to how a lead interacts and connects with a company, eventually recording what material or platforms they came into contact with, and what affected their purchasing decisions. Some behavioral touchpoints include what pages they viewed on your website, how they interacted with your company’s social media, what information they downloaded on their system, their email open and click-through rate, etc. 

In addition to determining what material they connected with, determining how long they spent with it will provide extra information into that touchpoint and how it influences conversions. By studying a lead’s online behavior, you may construct a map of which touchpoints correspond to increased conversions.

Find Out Scoring Features

Analyzing which traits and activities correlate with greater conversions provides a clear picture of which aspects should be ranked higher. The greater the point value, the more closely associated a specific person is with a conversion. 

There are several methods for determining and ranking these values. The first is to analyze your marketing and website data. You may also conduct an attribution report to get a more detailed picture of your marketing efforts or content affecting the sales funnel.

Assign Score Values

After correctly analyzing the characteristics, actions, and other aspects, you will need to allocate a point value to all touchpoints so that when a lead’s score is referenced in the future, the established point formula has already computed the score. Because developing the scoring system is a process rather than a one-time review, it will need an ongoing study to verify that your attributes and corresponding point values accurately indicate lead quality.

What is Operational Analytics?

Operational analytics is the process of developing optimal and realistic recommendations in real-time based on trusted, highly-accessible data from your warehouse. It does not focus on assisting with significant or strategic choices that are better aligned with business intelligence (BI). Instead, it helps operational teams make the minor and tactical data-driven decisions that they require every day.

To operationalize your data, modern reverse ETL platforms such as Hevo Activate can be used to replicate data from data warehouse to operational systems that business users use daily such as Marketo, Mailchimp, Salesforce, etc. A marketer does not need to open a Business Intelligence tool to learn about their target audience’s social media behavior. Instead, users may get the customer insights directly in their primary tools.

Operational analytics also ensure real-time data transfer. An e-commerce business, for example, can utilize operational analytics to acquire real-time data on its customers. Whenever consumers begin to exhibit strange behavior, such as abandoning their cart at the last minute after adding many goods, the company can investigate the website’s client-side to determine if there is a defective website component (e.g., a malfunctioning dropdown menu) that is stopping them from purchasing.

Supercharge Your Lead Scores with Real-time Data Using Operational Analytics

Real-time data using operational analytics opens many opportunities for sales and marketing teams. The sales team can create lead scores based on real-time touchpoints of their customers and can prioritize their leads.

You can focus more on touchpoints with real-time data about your customers’ journeys on your website or product. Prospects of all types visit your website. Frequently, they do not all show the same amount of curiosity. Some dig a little deeper and devote more time to it. You can divide these visitors into groups depending on their degrees of interest. Cold prospects are moved outside while hot and warm prospects remain within the rings. You may prioritize and devote more time to those guests who require your attention the most.

How to Implement Lead Scoring Using Operational Analytics?

As of now, you might have understood what operational analytics is. Implementing lead scores using operational analytics is relatively easy and gives you the capability to take real-time actions. This is how you can implement lead scoring using operational analytics:

• Step 1: With the real-time data using operational analytics, you can set the benchmarks for the leads, and whosoever crosses this benchmark can be considered a lead.
• Step 2: Using real-time data, you can check out your leads’ demographics and firmographics information. A list of all these attributes can be created, allowing you to create your scoring models in real-time.
• Step 3: The marketing and sales teams can collaborate to characterize the ideal lead.
• Step 4: They can create lead scores for all your leads, simultaneously.
• Step 5: With a real-time lead score, you can compare the scores with the benchmark you created and use automation to identify leads that cross the benchmark.
• Step 6: Once your lead score is ready, your teams can segment the leads into different categories (Hot, Warm or Cold) based on their scores. Once it is done, send the highly prioritized lead to the sales team for immediate action.

Operational Analytics for Sales Teams

Operational analytics provides sales teams with the data they need to convert clients and create long-term, loyal relationships with them at every stage of the sales process.

How Zeplin Improves Sales Productivity by Prioritizing Leads by Operationalizing Data

Zeplin is a well-organized workspace where the entire team can collaborate to ship attractive products. Zeplin’s sales team wanted to concentrate on users with the most potential for growth inside their organization. The lack of visibility into how leads interacted with the product was a significant hurdle for customer success managers and account executives to prioritize the leads. To have a single view for everyone, they wanted to send the data to Salesforce.

Using reverse ETL, Zeplin’s sales team could prioritize leads based on effective lead scores and close more sales than they could, when they had access to crucial product data within Salesforce. This data-driven sales methodology has helped Zeplin in meeting their sales target and expanding into the enterprise segment.

Operational Analytics for Marketing Teams

Using operational analytics, marketing teams can segment the lead based on users’ touchpoints with real-time data.

How Seesaw Create Lead Score and Prioritize Prospects by Operationalizing Data

Seesaw is a learning platform that connects instructors, students, and families to help kids learn more deeply. Using Seesaw, teachers can create and enable meaningful learning experiences for their students. Students can create, reflect, interact, and make their learning apparent, and families can actively support and celebrate student learning. 

For Seesaw, replicating data into Salesforce to create lead scores was a big challenge. It took them days to create and configure manual APIs. Moreover, the solution wasn’t scalable enough.

With reverse ETL, Seesaw’s nontechnical business users were able to follow changes over time by delivering data from Rockset to Salesforce. With this data in Salesforce, they were able to create lead scores and prioritize the prospects, saving the bandwidth of their marketing and sales teams. Furthermore, they could sync this data to mailing systems such as Mailchimp, Marketo, Hubspot, and others.

The Faster and Easier Way to Operational Analytics: Reverse ETL

Data warehouses can occasionally fail to resolve data silos problems. Your important business KPIs may be segregated in your data warehouse, preventing non-tech teams from fully utilizing your data. These teams rely heavily on your data teams while using traditional ETL. They must request a report from data analysts whenever they want relevant insights. Similarly, when customers incorporate a new SaaS service into their workflow, they rely on your data engineer to create unique API interfaces. 

These difficulties might limit the speed and availability of data for your front-line business users. Reverse ETL tools give real-time data to operational and business platforms (like Salesforce, Intercom, Zendesk, MailChimp, etc.). 

Reverse ETL is the process of converting your data warehouse into a data source and your operational and business platforms into data destinations. Making data easily accessible to these platforms may give your front-line teams a comprehensive picture of client data. Data-driven decision-making may be used for personalized marketing campaigns, intelligent ad targeting, proactive consumer feedback, and other applications.

Let’s look at some of the critical benefits of reverse ETL:

• Democratizes the Data: Data teams may use reverse ETL to deliver data insights to other operational business teams in their usual process. Because data is fed directly from the data warehouse to platforms like CRMs, advertising, marketing automation, and customer support ticketing systems, it becomes accessible and actionable.
• Automates the Data Flow: The cumbersome practice of moving between applications to obtaining information is eliminated via reverse ETL. Reverse ETL sends key KPIs and measurements to operational systems regularly. This allows it to automate a variety of operations.
• Saves Data Engineer’s Time: With the in-built API connectors, reverse ETL saves the time of the data engineers to do manual work. This saves a lot of their bandwidth and allows them to focus on essential tasks.

How to Create Lead Scores with Real-time Data Using Hevo Activate?

Whenever a user visits your website, all their touchpoints are saved in your systems. You can create BI reports and make decisions with all this data in your data warehouse. But these reports do not contain real-time data, making it cumbersome and time-consuming for the marketing and sales teams to create lead scores and prioritize them. 

With the help of reverse ETL platforms like Hevo Activate, you can send real-time data to platforms like Salesforce, HubSpot, Zendesk, Mixpanel to create lead scores and prioritize them. With real-time data into Salesforce, you can take the benefits of Salesforce Einstein Lead Scoring, which lets you examine your prior leads to find which current leads have the most in common with previously converted leads.

Let’s take a look at some of Hevo Activate’s essential features:

• Real-Time Data Replication: Hevo Activate’s outstanding integration with multiple data sources lets you quickly and efficiently transmit data. This ensures that bandwidth is utilized effectively on both ends.
• Secure: The fault-tolerant architecture of Hevo Activate guarantees that data is handled correctly and reliably, with no data loss.
• On-Demand Sync: Hevo Activate allows users to continue or execute sync on demand to complete data sync.
• Intelligent Data Type Conversion: Hevo Activate automatically converts the field types of the synced data during the mapping operation.
• Data Transformation: Hevo Activate offers a simple interface for refining, altering, and enhancing the data you wish to upload.

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Related:

• How to Improve Customer Experience with Operational Analytics?
• What is Data Activation? 4 Steps to Making Better Decisions
• How to Keep Salesforce Customer Data in Sync with Multiple Sources?
• How to Build 360 View of Customer Data for Your Sales Team?
• How Data Teams Can Leverage Operational Analytics for Faster Actions?

Final Thoughts

Knowing how to create lead scores has become the need of the hour for most companies. It helps companies assess a prospect’s likelihood of becoming a paying client. The higher the score, the more likely a prospect will subscribe to their platform. Creating a lead score model in the first attempt is quite tricky, and you need to test your model in real-world settings, tweak it with acquired data, and keep the discussion going between your marketing and sales departments. 

You may not get your lead scoring perfect on the first try, but by taking into account the many aspects and improving through time, you will get an acceptable degree of decency.

A lead scoring system is vital for SaaS organizations to identify trial users who are worth your sales time and existing customers who are ready for an upgrade. This can be achieved more efficiently if you have real-time data about your prospects on the right platform. 

Reverse ETL platforms such as Hevo Activate will help you send data to the application used by your teams daily. To get the best out of your customer’s data and make better predictions, you can leverage Hevo Pipelines, and Hevo Activate to create a bidirectional data pipeline and close the loop of modern data stack. This empowers your sales and marketing teams to make decisions and take action on lead insights in real time, in turn, boosting revenue to your business while reducing wasted effort.