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Why Data Enrichment Is Important

Why Data Enrichment Is Important

Data is a crucial component of any business. It aids in decision making, generating new business ideas, and crafting targeted and relevant sales and marketing strategies. 

But according to Harvard Business Review, low-quality data costs American companies over $3 trillion annually.

Data enrichment allows companies to fill in the blanks and get a clear and accurate picture of their customers. Enriched data is valuable data to the business and shareholders. It helps reduce marketing costs, create personalized campaigns, recover lost contacts and utilize untapped business data.

Data enrichment is the automated process of continuously upgrading and enhancing available first-party internal data by integrating a diverse range of relevant information from disparate internal or external third-party sources. 

There are several forms of data enrichment processes which include,

  • Contact enrichment is the practice of adding relevant, updated client contact information (telephone number, email, social media handles, job title) to an existing contact database.
  • Geographical enrichment refers to the procedure of updating a client’s geographical information(postal address, city, town, zip codes, latitudes, longitudes) to get a more precise geographical positioning of clients. 
  • Behavioral enrichment refers to the practice of studying and mapping customers purchasing patterns. Behavioral enrichment tracks clients purchasing history and highlights areas of interest such as browsing and purchasing frequency. 
  • Demographic enrichment describes the practice of updating an existing client database with relevant information (age, sex, marital status, occupation) that helps a company understand clients’ behavior based on socioeconomic factors.

Here are a few reasons why data enrichment is essential.

Enhance data management with data enrichment tools

A business receives tons of unrelated business data, which by themselves make no sense but, when organized, becomes a treasure trove of relevant information.

Data management is the process of collecting, sorting, organizing, and arranging data to have a coherent and systematic flow. It aims to make information easier to access and use. 

Data enrichment combines unrelated data to form a responsive data set with a logical information flow. It promotes integrating different data subsets to develop a comprehensive logical database that a business can use. 

Enriched data improves predictive models

Data enrichment is common in predictive analytics, business forecasting, and big data management ecosystems.

Predictive models refer to the use of enriched data, machine learning, and algorithms to identify fraud and process inventory. It reduces business risks and optimizes sales and marketing campaigns. 

Enhanced data is the backbone of predictive business models, which help companies accurately forecast outcomes and improve business operations.

Without data enrichment, businesses cannot correlate different pieces of disparate data to form logical data pictures needed to forecast future trends.

Enrichment prevents data decay

All businesses need relevant, up-to-date, and factual data that correctly reflects the current market environment, customer trends, and behavior.

Data decay refers to data quality decline/deterioration due to inaccurate, outdated, unreliable, or unverified data. Data decay misinforms a company's decision-making and marketing campaigns. 

Data enrichment prevents data decay by updating old and inaccurate data with the most current, factual information. It is essential in cleaning, sorting, and updating old information into valuable, real-time data.

Improve data security with enrichment tools

Data security is a big issue in today’s business world. Technological advancement means the threat of cyber attacks, data tampering, falsifying user identities, and password-related threats are on the rise.

Data enrichment collects, collates, organizes, and reports security event data from varied internal and external sources. It creates a comprehensive threat analysis report helpful when taking counteroffensive action. Data enrichment improves a company's threat detection, threat hunting, and incidence response. 

Data enriched security reports pool information from user directories, asset inventory tools, geolocation tools, third-party intelligence databases, and other security sites to quickly identify, neutralize and arrest data breaches.

Data enrichment is a formidable tool that any business can use to populate its database with current, accurate, and valuable information. 

Read along to learn the benefits of data enrichment to a B2B company.

Benefits of Data Enrichment

Data enrichment has numerous and far-reaching advantages to businesses. Here are some benefits of data enrichment.

1. Reduces the cost of inaccurate data

Wrong data is expensive! A report by Gartner concludes that organizations lose up to $15 million annually due to inaccurate data.

Data enrichment streamlines and updates customer databases to get high-value, high-converting leads that translate to increased business. A reduction in database size leads to higher savings from reduced staffing and software costs.

Data enrichment ensures that only top-quality, usable data is always stored in a company's database. 

2. Enriched data fuels focused marketing campaigns

Business marketing relies on current, segmented customer data that provides insights into user behaviors, preferences, and trends.

Data enrichment compiles relevant and current user data to create personalized marketing advertisements and campaigns. Data enrichment allows companies to understand their client’s needs better. It promotes acquisition, retention, and upsell efforts while providing user-specific products and services. 

Personalized marketing helps companies hit their marketing goals faster and cost-effectively than other expensive lead acquisition efforts.

3. Save time and costs on managing faulty CRMs 

Enriched data reduces time spent on managing CRM platforms.

Sales departments annually lose about 550 hours and $32000 per salesperson due to faulty CRMs caused by bad business data. They lose time and money manually updating, searching, and generating leads. 

A study by Salesforce opined that sales teams spend 66% of their time managing weak CRM systems due to inaccurate and incomplete data. 

Updated CRM systems enable sales teams to quickly and efficiently reach new clients, convert and retain existing clients while reducing costs associated with maintaining an obsolete database.

4. Improve customer engagement with enriched data

Enriched customer information allows businesses to craft appropriate and personalized communication strategies. It fosters and nurtures a good rapport between a company and its clients that positively translates to better customer engagement.

Data enrichment reduces customer email bounce rates, unanswered calls, and returned postal letters due to wrong contact information and addresses. 

With enriched client information, companies can initiate specific and relevant emails, prospectus, and calls to their customers, which may spur positive user action.

5. Enhance expansion campaigns with good data

Businesses draw elaborate plans to grow sales, profits, and customer numbers, but that is impossible without solid, verifiable business data.

Data enrichment provides companies with a solid, verifiable database of their business operations, client information, marketing campaigns, and other crucial information during expansion campaigns. By using up-to-date data, companies craft specific campaigns with a high probability of success.

Good data helps companies execute successful expansion campaigns that achieve their target quickly and effectively.

6. Enrichment improves customer segmentation

Customer segmentation analysis is vital in helping marketers craft user-centric marketing plans. The enriched data allows marketers to understand the specific needs of particular segments of customers.

Customer segmentation is the practice of dividing and grouping customers into segments that reflect the overall needs and desires of the target group.

Customer segmentation enables marketers to create action-centric plans that improve the customer lifetime value to the company.

User segmentation improves customer service quality and satisfaction, fostering a conducive and beneficial business environment.

7. Improve Algorithms Accuracy with updated information

Modern business relies heavily on algorithms to solve problems and provide unique business solutions. 

Algorithms depend on enriched data to provide practical business solutions to solve real-time business problems.

Business software equipped with algorithms automate, streamline and execute complex, repetitive, and time-consuming tasks quickly and efficiently.

Business algorithms are complex mathematical formulas used to improve business decision-making, automate process differentiation, and accelerate digital integration, leading to greater productivity. 

Algorithms created from enriched data are more precise, factual and provide more significant benefits to a business.

8. Improve the use of Dark Data

Companies usually collect lots of data that is unused, but it costs them money to store. This unutilized data is referred to as dark data. 

According to a study by Splunk, over 55% of a company’s data is unused and unidentified.

Data enrichment improves the discovery, use, and applications of dark data in a business. The three-step data enrichment process of extraction, transformation, and loading converts dark data into concise, logical, and usable data. AI processes integrated into data enrichment ensure that disparate data sources are tied together to form a more comprehensive and relevant business database.

Data enrichment allows businesses to use previously unused dark data to spur business growth, improve sales and make sound decisions based on verifiable data.

9. Recover lost contacts

Over time a company’s customer database becomes obsolete due to clients changing their email addresses, phone numbers, postal addresses, job titles, marital statuses, or other important contact and personal details. 

When this happens, a business is left with an extensive contact database that is inaccurate and ineffective.

Through their advanced software programs and experience, data enrichment tools and companies can effectively recover these otherwise lost contacts. A business contact database can be updated with the most current client’s contact and personal information through data appending. 

Using a current and accurate contact database will help a company reach and convert more existing customers.


Data enrichment companies like Datagma can quickly and efficiently cleanse, organize, and streamline your company’s CRM platform making it more robust, accurate, and effective.

Using cutting-edge technology and superior AI systems, Datagma helps B2B companies enrich their business data, customers data, financial data, and website data with over 50 exclusive attributes about their prospects and target companies.

Additionally, Datagma also offers an intuitive, customer-centric API that seamlessly integrates into your companies business applications allowing for a smooth, effortless data enrichment process. 

The Datagma API will save you time, reduce your staffing and update all your customer information so that you’re always one step ahead of the pack.

7 Re-engagement Emails to Win Back Inactive Custom..

No matter how hard you try, your email list will always, always, always encounter two things:

A lead scoring model is a system that assigns customers with scores to indicate the potential sales opportunity they present. A lead score is usually a number between 1-100.  A higher point value suggests that the lead is very likely to convert into a paying customer. 

You can determine a lead’s score by evaluating specific signals like how much personal information leads provide, how often they’ve submitted online forms or their social media engagement with the company like shares and retweets.

Lead scoring helps a company develop an effective marketing plan to improve sales and align with the market.

Keep reading to discover the elements of lead scoring models and examples.

What makes a lead scoring model?

What makes a lead scoring model?

A good lead scoring model must accurately classify leads based on set lead actions and behavior continuously. The model uses demographic data, behavioral, user action, and other relevant data to assign relevant scores to potential clients.

Capturing all relevant signals in your lead scoring model helps determine when a lead is ripe for conversion. An effective lead scoring model ensures that the marketing team hands over qualified leads to the sales team. It shortens the sales cycle and helps to utilize business resources productively. 

The following elements are crucial to the success of any lead scoring model.

1. Behavior scoring element

Classifying leads using their behavior helps companies create better marketing and sales plans for their potential clients.

Behavioral or implicit scoring gives a value to an indirect user action that implies their level of interest in a company’s product, like following a company’s social media page.

Implicit scoring differs from explicit lead scoring, which values facts like job title. 

Identifying and valuing both implicit and explicit client information contribute to a lead scoring model’s success. 

2. Effective marketing and sales collaboration

Lead scoring is a collaborative venture between sales and marketing teams. The teams work together to develop a robust lead scoring criteria.

Aligning the sales and marketing teams will improve the scoring quality, enhance lead qualification standards, and streamline lead sorting processes.

When the sales and marketing teams cooperate effectively, prospective leads’ sorting, assigning, and onboarding becomes faster and more efficient.

3. Demographic scoring element

The potential buyer persona determines the type of demographic scoring model a company develops. 

Demographic scoring gives a score to leads from socioeconomic groupings who have the highest conversion potential. Demographic markers include industry, age, nationality, and job titles.

Demographic scoring improves the lead qualification process by allowing businesses to focus on customers with high conversion potential. 

4. Negative score element

Customer information based only on desirable targets and actions may give skewed results over time. A good lead score model should have a corrective feature to identify false high scores. 

Negative scoring excludes leads that don’t show a high potential of converting or leads that don’t qualify to be mapped in the lead scoring model. For example, when a customer unsubscribes from your email list, their score lowers. 

A negative score is a corrective action that aims to correct bias in the lead data collection process. It helps a company enhance its lead database. 

Negative scoring ensures that companies only target client’s with the highest conversion probability or those who genuinely need a company’s product or services. 

5. Accurate scoring weights

Not all user actions carry the same weight. Some actions may be more promising than others, depending on a company lead scoring model.

Assigning different weights to various user actions helps a business calculate a lead score correctly. Lead data with the right scoring weight enables a company to accurately segment its leads and take the appropriate action to convert them.

Accurate lead weighting allows a company only to deploy assets to the most relevant leads. The business uses its resources to convert the most sales-ready lead efficiently.

A user who likes a company’s Facebook post is not as sales-ready as one who fills out a subscription form. Classifying the two types of leads will improve conversion success.

6. Score degradation element

Over time leads may grow cold, and user engagement may drop. The reduced interest may be due to different reasons which the lead scoring model needs to reflect.

Lead degradation refers to lowering the score because of reduced engagement with the company’s product. When a client stops opening promotional emails or engaging on social media, their score should fall.

A score degradation element should signal upward or downward trends to the sales teams for corrective action.

Lead scoring model examples

Lead scoring model examples

A company can use different lead scoring data points based on their target leads, buyer persona, and product.

Here are a few lead scoring models.

1. Email engagement

Emails are easy to track and provide reliable business data used to tailor products for potential clients.

Email lead scoring is an excellent tool for improving a business’s click-through rates and conversions. Emails communicate directly to the lead and create numerous avenues for positive user action. 

Email lead scoring depends on set parameters that accurately describe how leads interact with emails. 

Look at examples of email engagement parameters.

Clicking on a product link

A customer clicking a product link shows a high transactional intent. Such a client is ripe for conversion and warrants a higher lead score than a client who ignores the product link. The model can award 5 points for clicking on links.

When the lead’s total score reaches a set threshold (a score that shows the client is ready to buy), the sales team can take over to convert them.

Email open rate

How often a lead opens company emails is a good indicator of their interest in a product or service.  

The model awards some points when a lead opens a newsletter. A lead with a  high email open rate signals sales teams to ramp up their conversion efforts for that particular lead. 

Multiple visits to an email

Award a point every time a lead visits your email. Numerous visits to an email show clear intent from the lead.

A client who opens the same email several times and their subsequent actions will likely raise their score.

2. Website visits

Companies effectively engage potential clients through their websites. Website activity by site visitors is a good measure of how interested they are in company products.

A company can concentrate on the following metrics to score a customer’s online activity.

High-value page visits

Different pages on a company’s site carry different scoring weights.

High-value pages like pricing pages have a higher lead conversion value than an “about us” page. A lead visiting high-value pages is more sales-ready and likely to convert. You, therefore, award specific points to opening these pages.

Knowing which pages clients visit on a website demonstrates their level of interest in a product or service.

Time spent on company pages

The time spent by a lead visiting a company’s pages is a good measure of their interest.

A lead with a high bounce rate is usually a cold lead with little interest in a company’s products. You can even assign a negative score to a high bounce rate. 

Leads who spend considerably longer periods on a company’s site are hotter and may convert more easily. For these, you award some points.

Scoring leads based on on-site visit duration helps companies segment their leads more accurately. 

The number of pages visited

Leads who visit multiple pages are generally more qualified than a single page visitor. 

If each page visit is assigned a value, then a multiple page visitor will automatically have more scoring weight and more likely convert than a single page visitor.

3. Demographic analysis

Each company has a unique buyer persona that they target depending on their products and services. A high demographic score means that leads fit a company’s ideal buyer persona.

Demographic score aims to showcase the leads who match a company’s unique buyer person. Demographic scoring considers information like age, income, employment, and more.

Here are a few of the unique attributes demographic scoring considers.

Job title

Award points to a client who influences decision-making.

A senior-level employee will have a higher score compared to a junior employee. For example, a company president is more likely to influence a company’s conversion than a junior analyst. 

Company department

The type of goods and services a business provides determines which department they target. A software company will award some points to a client in the IT department.

Enhance lead scoring with data enrichment

Enhance lead scoring with data enrichment

A lead scoring model is a powerful tool that helps companies to optimize the sales funnel. 

A lead scoring model uses behavior scoring, marketing, sales collaboration, demographic data, and negative scores to assign scores to potential customers. 

Different lead models, including email engagement, website visits, or demographic analysis, provide actionable data to the sales team. But a lead scoring model functions if the data is up-to-date and accurate.

Trust Datagma for all your data enrichment needs.

Datagma uses cutting-edge technology and innovative products to enrich your business data. 

Datagma will help you identify what data needs enriching to develop an efficient lead scoring model.

If you’re looking for a partner who will strive to deliver expert data services that are tailored specifically for your company, contact us right now.

How Can Datagma Enrich Data?

Datagma is a revolutionary software that integrates analytics, organizational data, and artificial intelligence to enrich data for your business. 

Data enrichment allows you to monitor the key business performance drivers and make informed decisions affecting them. 

Datagma enriches data using personal data, company data, website data, and financial data. Using innovative products and features, Datagma enables you to measure and monitor campaign effectiveness, optimize customer experiences, increase conversion rates, improve brand loyalty, and generate more revenue. 

Data enrichment can also be accomplished by conducting interviews, surveys, watching videos, reading social media posts, and more. The goal of enriching data is to have correct and up-to-date customer information and customer feedback to identify patterns across multiple information sources.

Data enrichment provides you with accurate and real-time data, which allows you to make faster and wiser business decisions. Additionally, data enrichment eliminates data decay, saves time, reduces costs, and increases customer conversions. 

Organizations that don’t fully utilize their customer data limit their ability to grow and succeed in the marketplace.

Read on for a few use cases on how Datagma will enrich your business data and improve your sales and conversions.

Datagma enriches personal data

Datagma has been helping companies in industries such as finance, healthcare, retail, transportation, and logistics take control of their customer data through superior AI and machine learning technology. Datagma scours the internet and provides you with the most current, up-to-date client confidential information from public sources without breaching any privacy laws. Datagma enriches personal data so you can make better business decisions.

Personal data enrichment involves updating customers’ contact, geographical and professional information. Personal data enrichment empowers organizations to manage their customer relationships more efficiently by delivering targeted offers based on individual client preferences. Enriching personal data further improves data security by ensuring that customer data is well protected. 

Constant data updates can more effectively manage data decay which is a major issue in business. Common forms of personal data enhancement include updating clients’ professional titles, email addresses, telephone numbers, postal addresses, zip codes, social media handles, date of birth, gender, town, city, and country names.

Datagma is a powerful data management software that helps you enrich your clients’ database allowing you to offer customized and relevant products and services to customers. Personal data enrichment uses advanced analytics to identify market opportunities, uncover hidden insights, and reduce the risk of making poor business decisions. 

Datagma collects accurate and important company data

Datagma is perfect for company data enrichment using its intuitive and dynamic features powered by cutting-edge AI and an extensive online library of information. Datagma will update all your target company’s relevant information with no hassle.

If your business deals with companies, then having updated information about your target companies is vital.

Company data enrichment is the process of updating a target company’s data from reputable third-party sources. 

Common forms of company data enrichment include updating the contact person/prospect’s details, job title, phone number, and email address. The target company’s official websites, telephone numbers, email addresses, fax, physical location, industry, tags, and social media handles must also be current and accurate.

Enriching company data allows you to have a personalized, more collaborative interaction with your target company and provide high-value products and services.

Using current data minimizes instances of shipping, delivery, fraud, and billing errors. Accurate company data increases business trust and allows you to provide fast, efficient, and professional services while reducing the costs of maintaining a bulky and erroneous CRM.

Datagma breaks down complex financial data into organized and meaningful information

Datagma excels in cleaning, updating, and sorting financial data through its easy-to-use API and integration-enabled CRM systems. The tool takes unrefined financial data and sorts it to create a clear financial picture.

Financial data enrichment with Datagma includes updating the last funding amount, funding type, revenue collection, funding date, expenses, overdrafts, funding stage, and IPO of a targeted institution. You can then use the data to target qualified financial institutions or NGOs.

Similarly, enriching your company’s financial data improves transparency, accountability and provides better business insights. 

Enriching financial data allows you to monitor business growth, develop better market-oriented business policies, identify and track income sources, make faster business decisions, achieve regulatory compliance, and protect business data.

Grow your website with Datagma

Datagma provides accurate, enriched website information by leveraging advanced technology to search the web in real-time. Datagma enables you to track website traffic by country, advertising platforms, SEMrush traffic insights, Alexa rankings, and more website metrics. 

Enriching business information allows you to improve your business website, provide better customer service, improve business intelligence, and increase revenue. 

You can leverage the enriched data to improve website security, keep your content updated, and improve your marketing efforts. An updated website allows you to quickly learn your customer’s needs and deliver targeted and specific ad campaigns to enhance client conversions.

Datagma’s features include finding the best prospects for marketing campaigns, identifying new leads from competitor websites, finding the most influential people in a given industry or location without leaving the Datagma interface.  

With Datagma, all the important information about your website is available with just a click of a button. You’ll have access to over 1 billion public online records about prospects, emerging technology, and competitor sites.

Enrich your B2B Data with Datagma

Data is the black gold of business, and for your business to flourish, you need accurate, real-time, and actionable business data.

The revolutionary data platform, Datagma, is up to the task and will provide you with the best business data available. Datagma uses advanced AI and innovative products to provide your business with the latest personal and company data about your leads, updated website information, and meaningful financial records.

Datagma provides enriched personal data, including the client’s professional titles, email addresses, telephone numbers, postal address, zip codes, social media handles, date of birth, gender, town, city, and country names.

Datagma also provides company data, including company name, official headquarters address, tags, subsidiary locations, company type, founded date, and exact employee numbers. This information is important in creating an effective marketing strategy and providing unique solutions to your target companies problems and needs.  

Datagma also excels in providing enriched financial data. Your business will improve its financial transparency and accountability by gathering, cleaning, sorting, and categorizing business and client financial data, opening the door for funding and grants from financial institutions. Further, having current financial records will allow your business to comply with government regulations.

Datagma also improves your website information which allows your business website to remain relevant and convert more leads. Through services like website traffic tracking by country, Alexa ranking analysis, SEMrush traffic insights, and advertising platform analysis Datagma allows you to improve and enhance your business website. 

You can revamp your website to provide accurate and innovative solutions to your site visitors in real-time. Datagma will quickly and efficiently collect, cleanse, and organize all your business data and allow you to convert more leads and provide better products and services.

Datagma relies on cutting-edge technology and an intuitive API that can plug into any of your business systems to manage and streamline your business database. A leaner, error-free database will free up more of your time and money, improving your productivity. Datagma is a game-changer that will skyrocket your business and keep you ahead of the pack always.

Enrich your B2B data today by signing up for the free Datagma program and enjoy over 50 exclusive business attributes about your prospective leads and companies.

How To Use Lead Prioritization For Your Business

Businesses use lead prioritization to push a lead down the sales funnel, determine fit leads from interested leads, and determine high-conversion lead sources. With lead prioritization, they leverage artificial intelligence to analyze the potential customers. Prioritizing leads also helps to understand the company’s strategy, maximize their data-driven practices and understand the value of each data source.

Every business requires an effective sales team making calls and setting up meetings with the right prospects at the ideal time. This is after collecting and prioritizing leads through various methods to increase the business’ level of efficiency.

Lead prioritization simply involves the separation of crucial leads from unnecessary ones so that a team can reach out to potential customers first. 

Your company’s video may go viral and cause a 400% increase or more lead volume, but your conversion rate may drop significantly without the best lead prioritization strategies. 

If you have a lead’s email address, you can use Datagma to find out all other information about the lead, like their name, gender, employment status, job title, date of birth, etc. You can also find out more information about the company they work at, like the number of employees and other industry insights. You will then use this information to determine if the lead is worth the sales and marketing time (i.e., lead prioritization). 

The following are some ways you can use lead prioritization to boost your business.

1. Quickly move the lead down the sales funnel

Calling or responding to potential customers while they’re still on your webpage increases the chances of closing the deal. It is easier to connect with them when you call them in the first five minutes, unlike thirty minutes. Lead prioritization allows you to identify potential buyers and contact them promptly.

With Datagma, your company’s sales team can gather crucial information about the lead and communicate rapidly in the proper manner.

The team’s lead response time can either make or break the company’s success. Inbound leads have marketing spend against them, which may be in the form of:

  • Advertisements
  • Events
  • Partnerships
  • SEO content

Therefore, these leads are costly, and the team needs to capture them and make sales to earn a positive return on investment. They also mean that they are looking to buy or are in the midst of a purchase decision meaning they are an ideal customer.

2. Prioritize leads to determine who are the fit leads

Prioritize leads to determine who are the fit leads

You have to consider whether the product or service fits your lead’s needs or merely interests them. Therefore, you will prioritize the lead whose need and profile fit entirely with your solution over a customer who is vaguely interested in your service or product.

When prioritizing a lead, there are four quadrants that you can choose to score your leads against based on the information you find out about them from Datagma. They include:

  • High fit/low interest
  • High fit/high interest
  • Low fit/low interest
  • High fit/low interest

The sales team members should always prioritize those leads with high interest and high fit. These are leads who fit your buyer persona almost perfectly, and they have made some effort to get your product or service, e.g., by responding to your email.

High fit and low interest customers may think that your business offers ideal services or products, but they may not believe that you are the best solution to their problem, e.g., they match your buyer persona but don’t open your email. The marketing team should nurture these leads to convince them, e.g., by providing them with valuable information rather than sales emails.

For the low fit and high interest leads, the marketing team can follow up with freemium products to build a good relationship with them in case they become a good fit in the future. 

You don’t need to waste any time with the low fit and low interest leads. 

3. Leverage artificial intelligence

The current market offers beneficial lead generation tools like HubSpot that consider crucial data points applicable to the sales process. The sales team can integrate Datagma with Hubspot in the company’s CRM to get a complete view of multiple and varied data sources to help the sales team analyze and prioritize leads.

Despite the achievements of artificial intelligence in the business industry, there are limitations as well. Artificial intelligence models will also require business logic to provide essential success to a company.

Most excellent-performing organizations recognize the benefits of artificial intelligence in helping them make prospects intelligently and make more sales.

To keep up with the competition for customers, sales teams must integrate artificial intelligence into their strategy. 

4. Determine high-conversion lead sources

A company’s sales team needs to implement technology like Datagma to ensure that the team members focus their marketing efforts on the highest quality leads.

Datagma scours publicly available resources to tell the sales and marketing teams everything there is to know about the lead. 

For example, if someone signs up to your email list, Datagma helps you know everything about them to determine if they match your buyer persona. Once you figure out the best lead sources, you can double down on those sources.

Did you know that the highest conversion rates are from website inquiries, webinars, customers, and employee referrals?

An effective sales team generally comes across a large amount of data, with one of the crucial pieces of data to consider being lead-to-opportunity conversion by lead source. 

The data shows the lead sources that result in the most important and valuable contacts willing to move down the sales funnel. 

5. Align with your company’s strategy

Align with your company’s strategy

The sales team needs to be aware and updated on the company’s priorities to know which market to focus on. The company’s strategy allows the sales team to analyze the leads collected and ask the right questions when responding to potential customers.

Understanding an organization’s strategy mainly involves its five-year plan to know where it is headed. It includes aggressive international expansion and other things such as:

  • How to find an alternate route to sell products to industries if the intended means fail
  • How to round up use cases while keeping the target customers in mind for effective product pitching
  • How to focus on more critical potential customers who are expected to grow tremendously in the future

6. Maximize data-driven practices

To be highly competitive, the team needs to adopt scientific methods to prioritize leads. These methods mean the business will maximize data-driven business logic, data-driven best practices, artificial intelligence, and data enrichment tools like Datagma.

Whether your business buys leads from lead-generating companies or gets them free from social media and other platforms, the main aim is to qualify them accordingly and prioritize them based on this data to boost sales.

You should not allow your sales team to spend significant time and effort on leads that do not guarantee decent returns. 

7. Prioritize leads to understand the value of your data sources

One of the main problems of having too many data points is that only a few are derived from reliable sources. When the team factors in too much non-credible data, there are high chances of following unqualified leads. Therefore, prioritizing leads that match your buyer persona help to understand which sources provide the best leads.

Some non-authoritative data sources include web traffic, app downloads, and companies’ websites that Alexa does not index. They contribute to the creation of a comprehensive strategy that consists primarily of credible data sources.

An example of a good but not entirely reliable source of data is app downloads. The more an app is downloaded, the more customers generate higher traffic making such data essential in lead prioritization.

8. Prioritize leads based on scores

Prioritize leads base

Assign scores to a lead based on their value to the company. If Datagma determines that your lead is the CEO of your target company, you can give the lead a high score based on the job title attribute. You can use such attributes, including the lead’s contact with your brand, to score leads and ultimately prioritize leads with the highest scores. 

Consider a company that may not have too many app downloads or web forms but is backed by reputable capital firms such as Softbank and Sequoia. Such a lead may have a strong potential and must be handed to the sales team immediately for follow-up.

In such situations, the team assigns weights to every data point and give startups with the highest potential more points. Then the team should re-evaluate every strategy you come up with at least three or four times in a year to adjust the weight on the data sources.

Prioritize your B2B leads with enriched Datagma data

You can use lead prioritization to identify the fittest and interested leads, score them, and push them down the sales funnel. You can then leverage lead generation tools to get a complete view and prioritize high-conversion data sources. Lead prioritization also helps to align your sales efforts to the company’s strategy and maximize data-driven approaches.

But before priorization, you need to ensure that you’re working with the best possible data. Datagma enriches your data to help you find out everything about your lead, from personal data, company data,  and financial data. You can then use the information to determine the value of the lead to your organization and the next step to drive conversion.

Sign up for Datagma to find more information about your leads!