The key to garnering high-quality leads is an accurate database. How do you maintain it well to accomplish the desired sales growth?

Customer data is all about collecting the right information about potential business prospects for application in sales and marketing operations. This includes data like the company’s name, contact information, industry, etc. Such details allow brands like yours to connect with potential prospects. When you can foster strong relationships with your target audience, it becomes easier to generate high-quality leads.

With B2B databases, you can quickly access target leads and procure necessary information about them. Since more and more relevant leads must be generated for sustained business growth, accurate databases offer a speedy and effective way to enhance your outreach strategy. If you opt for a dataset that is inaccurate or out of date, it can create complications. Searching through vast database options could be daunting, so we have compiled a list of the best tools for you to choose from.

Seven Best B2B Databases

In the digital age, customer databases are valuable tools that help brands drive B2B sales and accomplish their business goals. We have prepared a list of the best tools to help you find the right solution.

Saleshandy Lead Finder

It’s a comprehensive email outreach platform that helps you identify prospects and add them to your cold email sequences. With Saleshandy Lead Finder, acquiring contact information, such as email addresses and phone numbers, is simplified to a great extent. You can search leads with their rich database spanning different companies worldwide. It must be noted that this information is updated regularly to provide proactive and verified email addresses. Although they have a large resource, it makes it easier to narrow down targeted leads with advanced search filters. You can utilize this feature to find leads by their names, designation, department, role, etc.

Apollo

It is a cloud-based sales automation software that simplifies lead generation, contact database management, and email outreach. With over 2 million contributing data sources, this platform allows you to collect email addresses through a 7-step email verification process to ensure correct data delivery. Apollo refines your search with relevant filters, such as company name, size, industry, job role, location, etc. When you search data with these, it helps you reach the right prospects and acquire their contact information. Apollo enables targeting the right decision-makers to communicate based on filters, such as industry, designation, company headcount, etc.

Cognism

It is a sales intelligence tool that uses a combination of artificial intelligence (AI) and human verification to provide you with accurate data. When you use Cognism, you can easily access advanced filters such as emails, technographics, and mobile numbers to filter the data and create a targeted lead list. This helps you attract ideal leads without wasting resources on prospects less likely to convert into paying accounts. Cognism is operational in Chrome as an extension and a mobile app. It is very particular about maintaining data compliance and keeps a ‘do not call list’ to avoid disturbing customers who have opted out of unsolicited phone calls. Through such innovative integrations, Cognism ranks among the top CRM and sales engagement platforms helping you build the sales pipeline and generate new leads.

Lusha

Lusha works perfectly for B2B companies of all sizes to supply precise data. It is a go-to-market platform promoting effective sales and marketing. This database offers you an easy-to-use prospecting tool that simplifies lead identification. You can access the latest and high-quality insights and data that promote communication with the right audience at the right time. The highlighting feature of this platform is its smooth operation while setting up, without involving any lengthy onboarding processes. Another distinguishing factor is that Lusha is accredited under ISO 27701- the highest international privacy standard in the world. What’s more— it allows you to be compliant with all GDPR and CCPA privacy regulations.

Lead 411

If you want to focus on targeted communication, this is your go-to database platform. It is powered by intent data, allowing you to find out the contact details of your prospects. Lead 411 supports easy integration with popular ESPs and CRMs, resulting in efficient and automated workflows. Its highlighting feature is unlimited email views in the basic plan while restricting the export to 200 per month. Additionally, it provides you with information on buyer intent.

Clearbit 

It offers data integrated with artificial intelligence, thus ensuring that you have accurate contact information. Clearbit adds to your records and helps you understand your prospects’ buying intent. This valuable information accelerates the lead generation process. With Clearbit, you can easily create, capture, and convert prospects into paying accounts by addressing their demands. You can access a range of business intelligence APIs and integrations that accelerate sales and marketing campaigns.

SalesIntel 

This business intelligence platform provides you with the data you seek. The key difference is—SalesIntel uses patented AI technology and human verification to source accurate contacts. What makes it stand out is the vast database of more than 100M contacts available with emails and mobile phone numbers. It also offers amazing features such as intent data to gain insights into who is researching your solution. SalesIntel also helps you visualize company technographics to understand their tech stack. Another highlighting feature is the company firmographics that promotes the identification of target accounts based on factors such as company size, location, industry, and more.

Final thoughts

Various factors play a role in an efficient database, such as data accuracy, customer intent, data availability, and integration capabilities. Before you make an informed decision, you need to weigh the pros and cons of each platform. The database you select must align with your business goals. The best-fitted tool must allow you to effectively communicate with potential customers, engage with them, and drive the sales funnel.

A good CX can bring you closer to your brand advocates. How can data analytics help you deliver a seamless experience?

Customer experience is what connects your brand to your customers. It is a bridge between brands and their brand advocates that can be defined as the way a consumer perceives your brand. Every interaction your customer has with your brand has the potential to either weaken or strengthen the bond and having an optimized website or a good SDR is just the starting point for providing a positive customer experience.

Good CX involves building relationships by understanding what people want, need, and value. The complete experience includes pre-purchase associations with the brand (via marketing or awareness), the process of researching and making the purchase (either in-store or online), and post-purchase interactions (regarding service, repairs, extras, and more). The goal is to build meaningful connections between the brand and the customer.

Now that we know how customer experience affects our brand, let us understand how data analytics can help us optimize it.

What is data analytics for customer experience?

Analyzing data from customer interactions can give you a lot of valuable insights. You can get a clear idea of customer satisfaction, loyalty, and other metrics that reflect how your customers interact with your product.

You can also utilize data analytics to improve customer experience and overall improve customer satisfaction — thus increasing customer retention in the long term.

Importance of using data analytics for customer experience

Customer experience analytics is obligatory for companies that want to prioritize their customers. It lets companies understand their customers’ journeys, helping them to personalize experiences to meet individual tastes. By interpreting customer behavior, businesses can target their offerings better.

Also, customer experience analytics helps specify pain points in the customer journey. It motivates businesses to proactively resolve issues, resulting in higher customer satisfaction and less customer churn. Predictive analytics also plays a role in strategic planning by foretelling future customer behavior.

Customer experience analytics is a vital factor in driving customer loyalty, growing conversion rates, and enabling business growth.

Steps for analyzing customer data with customer experience analytics

Here’s the 5-step technique you can follow to get the best results of your customer experience analytics:

1. Decide your goal
2. Compile customer data
3. Visualize collected data
4. Select an analytics process
5. Employ the insights

Let’s take a closer look at each of these measures below!

Decide your goal

Before you even begin to collect data or look at customer experience analytics, you must first extrapolate what you’re trying to identify. You must set SMART goals to ensure that you understand the data points that reflect customer needs and business goals.

Collect customer data

When analyzing customer experience data, you will typically consider two main types of feedback: direct and indirect.

Direct customer feedback

Direct customer feedback consists of metrics like:

• Net Promoter Score (NPS)
• Customer Satisfaction Score (CSAT)
• Customer Effort Score (CES)
• Voice of the Customer (VoC)

These are the CX analytics that most product marketers think about as they offer a direct understandings of customer behavior. Direct customer feedback could also comprise responses you receive on social media or comments from feedback surveys.

Indirect customer feedback – Rather than monitoring behavior, indirect customer feedback is influenced by customer behavior. This includes metrics like:

• Average Handle Time (AHT)
• Customer Lifetime Value (LTV)
• Average spend
• Customer churn rate
• Customer renewal rate

Whenever you calculate the LTV, you get an indirect look at how delighted customers are with your product (since they wouldn’t continue paying for a flawed solution, much less upgrade their subscription).
Other ways to accumulate indirect customer feedback include social listening, customer review monitoring, and analyzing voice chat transcripts.

These data points may not be as direct as NPS or CSAT scores, but they’ll help you drill down on the business outcomes that result from the customer experience.

Visualize collected data with different dashboards.

Once you have gathered data on customer satisfaction scores, lifetime value, and churn rates, then it is time to visualize everything using different dashboards.

Choose an analytics method and analyze customer data.

There are various data analytics solutions and procedures that you can use to filter through your customer analytics insights. Each process has pros and cons, so you must be acquainted with the options available to you.
A few different analytics processes to consider include:

Descriptive analytics

Descriptive analytics uses real-time and historical data to spot trends and the relationships between certain metrics.

Diagnostic analytics

Diagnostic analytics uses data to understand why certain events occurred, whether a rise in churn rates, a reduction in lifetime value, or other shifts in the makeup of your SaaS business.

Predictive analytics

Predictive analytics uses models and algorithms to forecast future performance or the probability of certain outcomes.

Prescriptive analytics

Prescriptive analytics uses data to figure out what the best course of action is and make decisions based on multiple factors.

Which one you go with will ultimately depend on the data you collect, which insights you expect to gather, and the business outcomes you are trying to achieve. For instance, predictive analytics is often adequate for businesses attempting to decrease risk or lower costs.

Use the insights to improve customer experience.

Finally, it is time to use your conclusions to improve the customer experience. Remember, collecting and analyzing data is only beneficial if you utilize those insights to make everlasting, favorable changes to your product.

Collecting customer journey analytics but never making changes to the onboarding process or customer engagement strategy would be a total waste of time. As such, you should proactively fix negative patterns you recognize and double down on the features that get new customers in the door.

Conclusion

CX is quintessential to sustaining customers, and various industries are placing importance on data analytics to better comprehend customer behavior, preferences, and needs. You can use this information to create better products and services. Data analytics can help you improve the customer experience by reducing friction, personalizing the journey, and adapting your marketing based on the needs of your users. So, if you thought data analytics was required only for those marketing campaigns, it is time to rethink your strategies!

As companies rush to scale sales personalization, they must understand the importance, strategies, and the ethical conundrum it poses in the process.

Sales personalization has gained a lot of traction during the pandemic. Businesses that understood the term had an easier time surviving and even thrived. A study by McKinsey during the pandemic revealed that 71% of consumers expected personalization, while 76% of consumers got frustrated when they did not receive it.

So, what exactly is sales personalization?

In technical terms, sales personalization aims at providing a personal experience tailored to the user. Imagine walking into a clothing store, where you get greeted with t-shirts of the same color and design. You won’t be ready to even give a chance to the store. A similar experience unfolds when you treat your consumers with a generic sales communication. It is neither special nor personal. Instead, a consumer might find it too commercial for their taste. Sales personalization targets the emotional aspects of the consumer journey and customizes the sales strategy accordingly.

Sales Personalization in 2023

In the 1980s, a professor, Leonard Berry, first coined the word ‘relationship marketing’ now known as sales personalization. From the 1980s right up to 2020, companies used various strategies to leverage sales personalization. These strategies included using sales calls, email marketing, targeted ads, and more. You could have easily used sales personalization to effectively predict your consumers’ journey and gently nudge them towards your products. However, sales personalization underwent a massive change during and after the pandemic. As more consumers opened up to try new products, their loyalties towards brands began to diminish. 

Such a shift in consumer behavior was due to the arrival of the Gen Z consumer segment. Gen Z consumers are open to try new products and crave a personalized experience from the companies. The sales personalization strategies to target Gen Z are inherently different and pose a challenge to companies in retaining their consumers. In 2023, as AI swept through every industry, it forced companies to rethink their strategies for sales personalization and adapt to the changing world. 

Why is sales personalization so vital for companies?

Some firms and businesses have flourished for years. The sole reason is relevancy. Companies adapted to the needs and wants of the times, where sales personalization played a major part in it. So, why is sales personalization so important?

• Customers feel special when recommendations and emails seem personally addressed to them. Another study by McKinsey shows that 76% of consumers tend to purchase from brands that personalize their sales.
• Brand loyalty is a thing of the past. If you can offer a product with better value and personalized sales communication, consumers of competing brands will switch to your product in a heartbeat.
• You can use sales personalization to make your brand stand out. While brand loyalty is in a downward spiral, brand recognition has been at an all-time high. Consumers are more likely to recall your brand and recommend it to their friends and family based on how good your sales personalization techniques are.

Sales personalization strategies in 2023

Sales personalization strategies have always been volatile as times change. You can incorporate certain strategies to help you scale sales personalization.

• Utilize CDM to manage consumer data

Consumer Data is a key metric through which companies design, develop, and launch their products. You can use the same data to target your sales to your consumers. Companies use consumer data management to gather, maintain, and revise consumer data. CDM aims at gathering consumer data and turning it into usable consumer profiles. To simplify the process of managing scores of consumer data, you need the help of a consumer data platform.

The digital expansion in 2023 has made consumers vary in data privacy. As consumer data is the first step towards sales personalization, you must educate consumers and assure them of strictly following data privacy protocols. 

• Analyze consumer behavior with CRM

While CDM is used to manage consumer data, companies depend heavily on CRM tools to analyze consumer data. CRM can analyze consumer profiles to provide actionable, personalized sales communication targeting consumer experiences. Fortune Business Insights has forecasted the CRM market size to grow by a CAGR of 12% by 2030. CRM system can anticipate consumer behavior to help your sales team identify touchpoints of your consumer’s journey. The sales team can easily craft a personalized recommendation or pop-up ad to target consumers based on their purchasing journey.

• Provide real-time product recommendation

The influx of shopping apps has greatly simplified consumer journeys and provided them with complete control of their journey. Companies face a complex situation providing personalized experiences to consumers who have multiple touchpoints in their journey. You can utilize aspects of the consumer journey by offering real-time product recommendations based on metrics like search intent, and previous purchases.

McKinsey conducted a survey involving 60 shoppers. They found that a brand interaction commonly desired by consumers was relevant recommendations based on their buying intent. Consumers in 2023 have a limited attention span, leading to more focused buying decisions. Without relevant and real-time product recommendations, there is a high chance that your sales strategy might fail.

• Focus on Digital Sales Personalization

Smartphones, apps, and digital payment methods are as common as horses were in the past, maybe more. Nearly every consumer and business has a digital footprint in some form. Your consumers might even prefer digital sales communication to dealing with a sales representative in person.

Gartner’s research shows that 33% of all buyers hope for a seller-free sales experience. You can scale your sales personalization by leveraging your consumers’ desire for a more digital sales experience. Provide your consumers with a user-friendly buying experience, shopping cart reminders, email and text communication, a free trial of the product, AR visuals for product verification, and more. Your consumers will feel more comfortable with an increase in digital sales experience.

• Let AI lead

As companies have automated most of their processes through AI, your sales team can also utilize AI for personalized sales communication. 2023 has seen consumers with zero patience. They expect a quicker, seamless, and personalized sales experience. You can use AI to automate experiences that might take time if done manually. 

Suppose a consumer on your app has a doubt, you can use an AI chatbot for immediate redressal, leading to a better experience for the consumer. You can use AI, in almost every stage of the consumer journey. The scalability of your sales personalization becomes boundless with the help of AI. It can provide recommendations, lead your consumers to the correct page or product, and send out personalized sales communication without any constraints of human error. While you shouldn’t hand over the wheel to AI, you certainly can let it take the lead.

• Learn where to draw the line with sales personalization 

Providing personalized sales communication comes at a cost. While consumers want a more personalized sales experience, they are reluctant to share more than the necessary data. Companies must be open with the data they aim to collect and its use. They need to know where to draw a line when collecting user data. Many consumers feel creeped out when they find product recommendations even before searching for them. Sales personalization in such cases can instead have a negative effect on the consumers.  You can focus more on consumer satisfaction during their journey and utilize upstream and downstream engagement metrics to gather data. Your engagement with your consumers will significantly improve without asking for too much personal data. 

Conclusion

Technological advancement has made it easier for companies to provide a personalized sales experience. You must realize that you can effortlessly increase revenue generation and better consumer retention through sales personalization. It is necessary for companies to understand the nuances of the consumer journey. Consumers expect sales communication that they feel is curated for them. Companies can target their consumers with personalized communication at specific touchpoints along the consumer journey. Scaling sales personalization can be easily accomplished by keeping consumer behavior in mind, utilizing the resources at hand, and being adaptable to market changes.