4 Methods To Manage Remote Sales Teams

With the world increasingly going online, it’s no secret that sales activities are now shifting to the digital world as well. Many companies no longer limit their sales teams to geographic boundaries, opening doors to a new breed of salespeople who work remotely.

As with all good things, remote workers also come with their own set of problems. When not managed well, remote teams can be messy, disorganized, and even disengaged. Thankfully, there is a range of methods that sales leaders can do to keep their teams happy and productive.

Expectation Management

All salespeople are familiar with targets. They’re the goals that keep either brings teams together or pulls them apart. Employees are often more engaged when they know what they’re exactly supposed to do and how to do it.

When it comes to incentives, sales managers should also make sure that their teams know the specific standard that they expect and what defines good performance. When expectations are clear, accountability becomes easy.

Create Trust & Transparency

Remote workers are a special breed of salespeople. They need to have a natural degree of independence to succeed with minimal supervision. Trust naturally builds when competent people work together in proximity. However, in remote settings, it takes a little more work.

Trust grows when employees feel part of the company’s mission and can be encouraged by creating a shared sense of ownership through including them in the strategic planning process and providing a safe space to share both failures and successes.

Establish Communication Channels

In remote settings, you can’t just drop by the next cubicle to talk about targets. Sales teams need a designated channel for communication for every level of engagement. Sales managers must also walk a fine line between providing guidance and micro-managing.

Remote teams that come from different countries also have to manage boundaries when I come to work hours, weekends, or public holidays. There should also be dedicated channels for feedback, collaboration, and casual interactions that can help build relationships within a team.

Give Salespeople the Right Tools

Teams also need the right tools to succeed. They need to arm themselves with knowledge on how to utilize company resources such as equipment or software that are necessary to fulfill their roles the best way possible.

Hey DAN helps resolve a myriad of issues that are typically encountered by salespeople. From making sure that they never miss out on meeting notes to help them file for their expenses, companies can outsource the time-consuming operational actions that are not essential to their role.

With Hey Dan, managing remote sales teams become a lot easier with a single call. Through voice-to-CRM technology, you can be sure that your sales teams are taken care of at every stage – from meeting prospective clients to closing a deal.

When these things are out of the way, you can focus on things that technology can’t do – mentoring salespeople to their full potential, managing relationships, and closing sales.

Dangers of Data Mining

Data mining is a powerful tool that can help companies in a variety of ways. From cutting costs, optimizing processes to improving sales, data mining is a game-changing tool that every company should have on their arsenal. There’s no shortage of benefits that data mining brings to organizations everywhere. But what are the dangers?

While data mining can put anyone at the forefront of every industry, it can quickly go badly for companies that don’t do it properly. From the collection process to the actual implementation, there are various entry points for the dangers of data mining. Let’s dive into each one.

Data Privacy

While data mining on its own doesn’t pose any ethical concerns, leaked data and unprotected data can cause data privacy concerns. Through the years, there have countless campaigns on stolen data that have caused an uproar in various parts of the world.

Very personal information like intimate photos, credit scores, or bank account log-in details have been leaked and caused real-life distress to users. People can lose reputations, their life savings, and maybe even their peace of mind in the process.

Ethical Dilemmas

As big data creates a better view of who people are and what they want, it begs the bigger question of whether or not monetizing sensitive data is ethical.

By accessing personal records to exploit people in the name of profit, we can blur the boundaries between what is acceptable or not.

Information such as medical records, location tracking, or even search history, used to manipulate users into buying things that are not clinically proven, they don’t need or cannot afford raises a lot of pertinent questions.

Inaccurate Data

At any given time, there are two main kinds of data available to data miners– bad data and good data. Unfortunately, the internet is rife with the former more than the latter. When companies don’t sift through data properly, they’re prone to using incomplete, duplicated, or outdated data.

Companies can be stuck with a half-baked analysis that won’t add value to their businesses and unnecessarily waste a ton of money in the process.

Overvaluing the Output

While data can help make decisions, it’s also not everything. When it comes to things like management or leadership, the most useful data can sometimes be the unstructured ones.

Not every great decision can be attributed to data, especially when the algorithms behind the process are not yet as refined. As time passes and algorithms get smarter, it may become more reliable.

To get the most of data mining, data miners need to know the difference between the actual business and data. Data gives a picture of a business, but it’s never the whole picture. To get the most out of data mining, it needs to be in collaboration with business units that can serve as a check and balance.

Data miners also have to remember that behind the 1’s and 0’s, there are actual people who will be affected by their recommendations.

Data Warehousing Fundamentals

Data Warehouses have long been a part of company standard operating procedures. However, as time passes, companies need more complex systems that can handle increasing amounts of data.

The main differentiator between the various stages varies in terms of data accuracy, triggers, and interactions between the operational systems and storage mechanisms. Between industries, there are different levels of sophistication required. They each have varying fundamental warehousing needs that affect workflow and customer experience.

Depending on the size, age, and industry of the company, Data Warehouses will be within one of the four stages or in a point of transition. So, where does your company stand?

4 Stages of Data Warehouses

Stage 1: Offline Database

In their most early stages, many companies have Data Bases. The data is forwarded from the day-to-day operational systems to an external server for storage. Unless extrapolated and manually analyzed, this data sits where it is and does not impact ongoing business functions. Transactions such as loading or processing of data have no effects on an operational standpoint.

Stage 2: Offline Data Warehouse

While not entirely up-to-date, offline Data Warehouses regularly update their content from existing operational systems. By emphasizing reporting-oriented data structures, the organized data meets the particular objectives of the Data Warehouse.

Stage 3: Real-time Data Warehouse

Real-Time Data Warehouses gathers information through operational system events-based triggers. Often, these come in the form of transactions such as airline bookings or bank balances.

Stage 4: Integrated Data Warehouse

Daily activities to be passed back to the operating system continuously in the Integrated Data Warehouse. Integrated Data Warehouses are the ideal Data Warehouse stage with the data not just readily available but also updated and accurate.

It’s pertinent to understand what stage your company’s Data Warehousing facilities are before you can improve them. The journey to creating the best version of your Data Warehouse takes time to set up and even more to integrate into your company’s workflow.

Companies need to invest time to transition their Data Warehouses to more efficient stages as their organizations become larger and more complex. Often, there needs to be regular training towards different business units to maintain the accuracy of the data.

Data Warehousing is not an overnight process and requires a long term strategy for both integration, adoption, and maintenance. There must be procedures in place to avoid fraudulent, obsolete, and incorrect data.

Companies must also be on high alert for possible security breaches that may affect the trust that customers place on the brand concerning their personal data. As the data compiled becomes increasingly granular, it becomes important to protect it from malicious intent.

Despite its many challenges, developing a good Data Warehouse is still a necessary investment that should be in every company’s roadmap. Having a working Data Warehouse is the first step to creating efficient Data Mining workflows. There’s no doubt that having a good Data Warehouse will be the standard for companies that want to get ahead and stay ahead of the competition.

What is a Data Warehouse?

 

A combination of different types of strategic data aids, Data Warehousing (DWH) is a process that collects and manages data within a system. More often than not, Data Warehouses gather data from multiple sources while acting as a core designed for analysis and generating reports. Data Warehousing helps business leaders make better decisions, faster.

Data Warehouses are not particular products but an entire ecosystem. They allow users to find past and present information in a more organized manner compared to traditional operational databases. A well-designed data warehouse can access information faster and create better reporting processes.

Data Warehouse Data Structures

Data Warehouses take information from various sources and acts as a repository. There are three types of data in Data Warehouses: structured, semi-structured, and unstructured.

Before users can make use of the data through Business Intelligence tools, it first has to be processed through Data Mining. Data Mining looks for meaningful patterns from the Data Warehouse to create a holistic view of the business and give better recommendations to relevant business units.

Types of Data Warehouses

There are three main types of Data Warehouses: Enterprise Data Warehouse, Operational Data Store, and Data Mart.

The Enterprise Data Warehouse (EDW) is a business’ central data warehouse that classifies data while giving access to the right users. While Operational Data Stores (ODS) are real-time data often used for routine activities, and Data Mart is Data Warehouses made for specific business units.

What are Data Warehouses used for?

Data Warehouses are used by businesses to sort through data from various sources and organize them by context to become usable. Data Miners use them as a preliminary step to find a meaningful pattern.

Many industries regularly use Data Warehousing to improve their operations and data mining practices. From retail chains looking to track items and manage inventory to airlines that need to keep track of repeat customers and route profits, there are infinite ways that Data Warehousing aids businesses from all over the world.

The Struggles of Implementing Data Warehouses

Data Warehousing is not without issues. Data Mining becomes increasingly common, so are the restrictions that come with the amount of data that companies are allowed to store.

The beginning stages of building a Data Warehouse can also be an overwhelming process for companies that did have a good data management foundation. It can be time-consuming and require additional training to adjust people to their usage.

Many large companies also struggle with organizing a large amount of complex data that may not find in the existing cloud storage solutions and may need physical storage that entails additional maintenance, hiring of personnel, and use of space.

Conclusion

While Data Warehousing may seem like a resource-intensive process, it’s an investment that will be useful for years to come. It can help users efficiently access critical data, and integrate systems that would otherwise be a pain to consolidate.

Overall, Data Warehousing should be standard practice and creates a good foundation for Data Mining.

6 Best Free Data Mining Tools (in 2020)

Data Mining is an incredible tool that can be used by businesses to turn large amounts of data into actionable information. Through different data mining techniques, data miners put a structure that enables companies to make better decisions every time.

Through the years, many data mining tools have come and gone. With many companies adapting to the data-driven world, there has been a steady rise of tools built to help them. While some need a subscription or a one-time payment, there are still some available tools that are free online.

In fact, among free alternatives to data mining tools, they have unique offerings that also include data warehouses and knowledge discovery mechanisms. So if you’re looking for free alternatives to expensive mining plans, here are few to consider:

Weka

Developed by the University of Waikato, this machine learning software is best for data analysis and predictive modeling. From data mining to regression, it works best when data is clean and readily available. Through various visualization tools, it can also be helpful with machine learning.

Revolution

R is a commonly used data mining tool by academic researches, engineers, and industrial companies. It is an interactive platform that can perform intricate statistical calculations despite being very user-friendly. It’s known to have great visualization tools and is very comprehensive.

Qlik

An easy to use data mining tool, Qlik allows users to analyze data from multiple sources all at once. With its unique drag-and-drop features, it can easily visualize data and make instant changes. Data security is a highlight feature of this tool. Qlik has sharing options that can let users access reports easily.

Uses of Data Mining

Rapid Miner

Using JAVA, this tool creates insightful predictive analysis and flow-based programming. It’s often used for training and business applications because it doesn’t require extensive coding experience to use.

Orange

A component-based machine learning software, Orange works with both scripts and ETL workflows. It has an exhaustive list of algorithms and various widgets that can use do data analysis and visualization. While reporting can be limited, it is one of the most simple tools to operate using Python.

TeraData

Best used by organizations that have most of their data already migrated to the cloud, TeraData has a range of data mining optimizing tools for every stage of the business. Supported by SQL, TeraData lets users distribute data without too much intervention.

Through the years, data mining has evolved. In fact, it will continuously grow and improve in the years to come. It will only get more granular, efficient, and effective with its analysis. To get ahead of the curve, one must be able to select the best tool and customize it to your company’s particular needs.

With the right tools, data miners can help businesses make better decisions in every aspect of the business. From lessening costs, improving products to increasing profit, there is a room of data science to make the kind of changes that will help companies flourish.

Qualified vs unqualified leads

From prospecting clients, managing relationships to closing sales, they are a million things on salespeople’s minds. For them to do what they do best, they need to know the right direction. They need to know the best relationships to nourish at the right time to convert the right people.

Digital and outbound marketing teams help the sales segment leads into either qualified or unqualified leads.

Qualified versus Unqualified Leads

Unqualified leads are not necessarily bad leads. It just means that they are not yet at the point of their buyer’s journey when they are ready to purchase. Sometimes, it just means that they currently don’t know about your product’s benefits and how you’re different from your competitors. Other times, it means that you are offering a solution that they don’t need yet or that they currently can’t afford.

With enough time, effort, and coordination, unqualified leads can be converted into paying customers. But even after they are acquired, they are less likely to repeat their purchase or remain loyal to your brand. On the other hand, qualified leads are like low hanging fruit. They are often in the process of being nurtured by your campaigns or have already completed it.

Qualified leads are those who can afford your offerings and can make the final decision to purchase. They are likely to have already researched on you or your competitor’s products with an intent to buy.   They have a problem in mind with your product or service as a possible solution; They are more likely to repeat their purchase and be loyal customers.

How to have more qualified leads

Each company will have a different way to segment and define their version of qualified leads. A combination of historical data, industry knowledge, and internal marketing studies can help companies identify their ideal customers. The more defined your company’s ideal customer is, the easier it will be for sales and marketing teams to find them.

Getting qualified leads require seamless work between sales, marketing teams and even customer services teams. For every stage of the sales cycle, there needs to be enough information to help them make a decision. Even qualified leads need education on why they need your offer and why they should purchase from you and not your competitors. It’s also important to keep track of why customers buy from your competitors, or just don’t buy from you again.

Handle your leads more effectively with the use of crm enablement platforms like Hey DAN. With their data entry, management, note-taking, and profile management, you can make sure that you’ll never lose sight of a qualified lead even after they convert.

By utilizing Hey DAN’s Voice to CRM technology, your sales teams can work on the things that matter. Instead of wasting their valuable energy on time-consuming administrative work, they can focus on what they do best. They can make sure that each nurtured lead into not just purchase but lifelong loyalty.

4 Key Stages of Lead Generation

Leads are the potential customers that each business wants to start a relationship with. From online to offline campaigns, lead generation campaigns find prospective customers, segment them, and then nurture them into a purchase.

So, how does a customer become a stranger into an advocate for your brand? There are four keys stages that they have to go through:

Attract

Customers don’t just show up in your store. They need to know who you are and how you can help them with their problems. The first step is building the right channels that represent your products and services. It should show information that any customer will need at every stage of the buyer’s journey.

The next challenge is to make sure that the right people know about you. Using a mix of online and offline campaigns, marketing teams start the process of separating strangers from potential customers. One of the main goals is to build trust with your brand through engaging and relevant content. Customers who take the time to view your channels want to know more before making any commitments.

Convert

Once companies have their potential customer’s attention, it’s time to engage them. To continue the relationship after you first meet, companies need to get their contact details. In this lead generation stage, a call-to-action asks for their personal information in exchange for access to a benefit such as freebies, special promo prices, or exclusive sales.

Often going from the conversion to the closing stage is not instantaneous, and many customers need time before they move into the next level of their buyer’s journey. This time should is filled with educating the customers on just what the products and services are, but how they are relevant to their individual needs.

Close

Once a customer believes that you might be the right answer to their problems and give you their personal information, then companies have now captured them as leads. Unfortunately, not all leads are the same. Some leads are better than others, but great leads are nothing without proper relationship management.

A great CRM system will tell you exactly which leads to prioritize and where they are in their journey. CRM systems help companies keep track of their movement along the sales pipeline.

Delight 

Once a lead has converted into a sale, the relationship is far from over. Companies that pay attention to their customer’s after-sales journey make them more likely to be repeat customers.

Simple actions such as engaging them again through smart content, asking them for feedback on how to improve your offers, or even cross-selling other products can improve the customer’s over-all lifecycle value.

Leads don’t just happen; they come into fruition through the various joint efforts of marketing and sales. They’re also continuously moving across the sales pipeline and always on the lookout for better offers.

Luckily, companies don’t have to worry about the lead generation process by themselves. Sales performance management can be improved partnered with companies like Hey DAN that does CRM Data Entry with their voice to crm solution. Your sales team may now focus more on nurturing their leads, and closing sales.

5 Qualities Of A Great Sales Lead

Marketing teams do their part to make sure the world knows about your company’s offerings. Ideally, they gather leads from a sea of prospective customers from various lead-generating campaigns.

From digital ads to on the ground activations, the marketing department brings in leads for the sales teams to analyze and prioritize. Afterward, sales teams determine which sales lead to pursue.

Up-to-date, relevant data

No matter how great a lead, you can’t build a relationship with them if you can’t keep in touch with them. Some of the information that each prospective sales lead worth pursuing include names, contact details, demographics, and purchasing behavior. Without accurate data, a sales lead can turn cold in days if sales teams don’t follow up on time.

Expressed interest in your product or service

Customers who are interested or in need of your product will likely have a moment they’ll express interest in it. From clicking on an ad to signing up for your company’s mailing list, there are plenty of ways to identify people who have already seen the value of your offer.

Already did their research on your company’s offerings

Sales leads that are serious about the purchase will make sure that they know everything they need to know about your product or service. A good sales lead will search for themselves what your product is, what makes it different, and how to purchase it.

History of replying to your sales teams

Customers need time to decide on whether or not a product or service is the right fit for their needs. However, too much time might also signal a dwindling lack of interest. A way to test how serious they are with their purchase is if they actively reply to your salespeople who reach out to them.

Capacity & authority to pay for your services

It’s not enough to want to avail of a product or service. The customer you are engaging with must be able to make the final decision. Great sales leads have the financial capacity to pay and the authority to make the purchase. It’s great to be an aspirational product, but at the end of the day, businesses need paying customers.

Great sales leads don’t just happen. They’re a combination of marketing efforts, sales strategies, and an effective customer relationship management system. Staying ahead of the competition means making sure that your data is correct. It’s then followed by extensively profiling your leads and identifying which relationships are worth pursuing. Despite being the gold standard, great sales leads are not a guarantee for conversion into paying customers.

Building an effective CRM system is a daunting task for any company. It requires teamwork, consistency, and effort. Luckily, companies don’t need to go through the journey of nurturing your sales leads alone. Sales enablement platforms like Hey DAN can help you on sales performance management. From the data entry, profile updating to even consulting, and identifying new opportunities, Hey DAN can help you nurture your prospective leads into paying customers improving sales.

5 Data Mining Steps

Data Mining is a process that requires many steps. While many companies have data, not all of them are usable or necessary for the particular project objective involved. Here’s are the 5 Steps to Data Mining that you should know about:

Project Goal Setting

For anything to succeed, it has to have a plan. Goal setting is the foundation of every successful data mining project. Through aligning on their project objectives and timelines, business and data mining teams can have a smoother working relationship throughout the experience.

Goal setting allows teams to assign roles and make a clear plan to move forward. Expectation management is key to avoiding issues throughout the data mining process.

Data Gathering & Preparation

For every good kind of data, there is a mountain of bad data. From incomplete, fraudulent to out of date, bad data is everywhere. When not cleaned, it can ruin any campaign. The data gathering and preparation stage is all about making sure that the data is usable.

For larger, more established clients, there must be mitigation of security risk. Trust is a necessary element when dealing with sensitive information. Data processing often uses modern database management systems (DBMS) to improve data mining speed. It is also a primary precaution when dealing with data that is confidential to an organization.

Read about our Bad Data Series, what to avoid and how to provide clean data for your CRM

Data Modeling

With the use of mathematical models and various data visualization tools, there are meaningful patterns discovered in the data. Through conceptual representations of how data objects and rules go hand in hand, they form a Database.

A Database can be conceptual, physical, or logical, depending on the Data Model applied. With the right structure, it can help define relational tables, keys, and procedures. For Data Modeling to work, it needs to have quality data, security procedures, consistent semantics, default values, and naming conventions. There are two types of Data Modeling Techniques: Entity-Relationship (E-R) Model & Unified Modeling Language (UML).

 

Data Analysis

After the modeled data is analyzed, it is then extracted, transformed, and visualized. Data analysis helps bring together useful information to give insights or test hypotheses.

With a combination of business intelligence and analytics models, Data Analysis orders raw data in a way that is relevant to the project goals. Armed with visual representations and insight on previously unrefined data, it is then ready for deployment towards relevant business units.

 

Deployment

In the last stage of Data Mining, relevant partners test the hypothesis. There are four different types of model deployment: data science tools, programming language, database, and SQL script or predictive model markup language.

 

Mined data provides a single source of truth that can guide business decisions moving forward.With coordination between data scientists, IT teams, software developments, and business professionals work together to integrate the new models with the existing production system of an organization. Companies such as Hey DAN are experience and well organized in handling professional data mining.

3 Main Types of CRM Leads

Leads are the lifeblood of a great CRM system. For most CRM systems, a CRM lead is a kind of customer who could already be in your sales pipeline but has not completed their lifecycle yet.

Unfortunately, not all leads are made equal. Focusing on the wrong ones can be detrimental to both your company’s budget and your teams’ time. So what are the types of leads in a CRM system?


3 Main Types of Leads

Information Qualified Lead (IQL) – Cold Lead

In the early stages of interacting with your customers, companies are often given contact information in exchange for freebies, promotional offers, or relevant information. With a stream of nurturing activities from marketing and sales teams, IQLs need to learn more about your company, your offerings, and how it answers to their particular needs.

IQLs can be considered cold leads. More often than not, it’s good to keep tabs and regularly maintain your relationship with them. However, they shouldn’t be a priority for your team’s follow-up strategies.

Marketing Qualified Lead (MQL) – Warm Lead

Marketing Qualified Leads are a kind of lead that is likely to convert into sales. While they’re not likely to buy right now, they are likely to respond better to being nurtured. MQLs are also called warm leads. After a combination of time and effort, they can become paying customers.

Different companies will have different ways of qualifying an MQL. It is often a combination of the prospective customers’ positive interactions with your marketing campaigns, their existing history, or how well they fit into your ideal customer persona.

Sales Qualified Lead (SQL) – Hot Lead

A Sales Qualified Leads is also what you call a hot lead. The main difference between MQL and SQL is their readiness to commit to purchasing your product or service. After being qualified by marketing, prospective customers are nurtured by sales teams to avail of your product or service.

Through careful vetting, sales qualified leads to feel that their needs are understood. The marketing department’s budgets are also better spent, and the sales teams maximize the effort they are putting in.

Nurturing Every Kind of Lead

The key to a great CRM system is knowing exactly where each lead is in their buyer’s journey. As a rule of thumb, it’s always best to prioritize the hottest leads first before going after the colder ones.

It’s not enough to have a lot of leads. You also have to find good quality leads. With limited time but a mountain of opportunity, sales teams need help to find the best prospective customers at the right stage and the right time.

One way to make sure that each lead is taken care of until they are ready to be closed by sales is by working with Voice to CRM enablement companies such as Hey DAN.

With their data management system, consulting services, and opportunity spotting technology, they can help you nurture each kind of lead at every stage of the sales pipeline.