Month: November 2020

Sales Force Automation (SFA) refers to the use of technology and software applications to streamline, automate, and manage sales activities efficiently. It minimizes manual work by automating repetitive tasks such as lead management, customer follow-ups, order processing, scheduling, reporting, and performance tracking. With SFA, sales representatives can focus more on selling and building customer relationships instead of spending excessive time on administrative duties. The system also provides real-time access to customer data, sales pipelines, and market insights, enabling better decision-making and forecasting. For managers, it ensures transparency, performance monitoring, and quick evaluation of sales progress. Additionally, SFA enhances coordination within sales teams and reduces errors caused by manual record-keeping. Overall, Sales Force Automation improves productivity, customer satisfaction, and profitability by making sales planning and execution more structured, data-driven, and efficient.

Key Features of Sales Force Automation

• Lead and Opportunity Management

SFA systems help capture, track, and manage leads effectively. Sales teams can monitor each prospect’s journey, from initial contact to final conversion. Opportunity management tools prioritize high-potential leads, reducing time wasted on unqualified prospects. Automated reminders ensure timely follow-ups, preventing loss of opportunities. The system also provides insights into customer interactions, helping salespeople understand buyer intent and tailor their approach accordingly. By centralizing lead data, SFA improves visibility, ensures accountability, and increases conversion rates. This feature ultimately strengthens sales planning and ensures resources are used efficiently to generate maximum revenue.

• Contact and Account Management

SFA enables centralized storage of customer details such as contact information, purchase history, preferences, and communication records. This allows salespeople to maintain personalized interactions, building stronger relationships with customers. Account management tools track interactions across multiple channels, ensuring consistency and improving customer satisfaction. Sales teams can segment clients based on their profiles and tailor offerings accordingly. It also helps identify upselling or cross-selling opportunities. With organized customer data readily available, sales representatives save time and reduce errors. Contact and account management, therefore, is a critical SFA feature for ensuring customer loyalty and driving long-term business growth.

• Sales Forecasting and Analytics

SFA tools provide powerful forecasting and analytics features to support data-driven decisions. By analyzing past sales trends, customer behavior, and market conditions, they predict future demand more accurately. Sales dashboards present real-time performance metrics, helping managers track progress toward targets. Analytics also highlight areas of improvement, reveal sales patterns, and identify risks early. With these insights, sales leaders can adjust strategies and allocate resources effectively. Accurate forecasting not only improves planning but also ensures better inventory management and profitability. Thus, forecasting and analytics enhance the precision, reliability, and efficiency of the entire sales process.

• Activity and Task Management

SFA systems simplify scheduling and task management by automating reminders for follow-ups, meetings, calls, and deadlines. Salespeople can organize daily, weekly, and monthly activities in one platform, reducing confusion and missed opportunities. Task tracking ensures accountability and helps managers monitor individual performance. By streamlining workflows, sales representatives spend more time engaging customers and less on administrative work. This feature improves discipline, ensures timely execution of plans, and boosts productivity. With automated task management, the sales team operates in a more structured and efficient manner, directly contributing to higher customer satisfaction and revenue growth.

• Reporting and Performance Monitoring

One of the most valuable features of SFA is automated reporting. The system generates detailed reports on sales activities, pipeline status, and performance metrics. Managers can assess productivity, identify top performers, and detect gaps in execution. Real-time monitoring ensures quick decision-making and corrective actions where necessary. Sales representatives also benefit from self-assessment, as they can track their progress against set targets. Reports can be customized for different levels of management, ensuring clarity and alignment with organizational goals. By improving transparency and accountability, this feature strengthens sales planning, performance evaluation, and long-term strategic development.

• Mobile and Remote Access

Modern SFA platforms provide mobile and cloud-based access, enabling salespeople to work from anywhere. Field representatives can update customer data, record meeting notes, and check sales pipelines instantly after client interactions. This ensures real-time accuracy and faster decision-making. Mobile access also enhances responsiveness, as salespeople can address customer queries and opportunities without delays. For managers, it provides visibility into field activities, improving supervision. Remote access reduces dependency on office systems, offering flexibility and convenience. By keeping the sales force connected and informed at all times, this feature significantly enhances productivity, efficiency, and customer engagement.

• Email and Communication Automation

Modern SFA systems often include automated communication tools that manage emails, reminders, and follow-ups. Sales representatives can schedule personalized emails, send bulk messages to targeted segments, and receive notifications for customer responses. Automated communication ensures timely contact with prospects and customers, maintaining engagement without manual effort. It reduces the chances of missed follow-ups, strengthens relationships, and improves lead conversion. By streamlining messaging, this feature saves time while keeping interactions consistent and professional.

• Territory and Quota Management

SFA platforms provide tools for managing sales territories and quotas efficiently. Managers can assign leads, accounts, and opportunities based on geography, product line, or team capacity. Quotas can be set for individual sales representatives or teams, with real-time tracking of performance against targets. This ensures balanced workload distribution, fair evaluation, and strategic coverage of markets. By monitoring territories and quotas, organizations can optimize resource allocation, identify high-potential areas, and motivate the sales force to achieve goals effectively.

Techniques of Sales Force Automation

• Automated Lead Management

This technique captures leads from multiple sources such as websites, emails, and campaigns, and automatically stores them in the system. It prioritizes leads based on scoring models, ensuring salespeople focus on high-potential prospects. Automated reminders and notifications help with timely follow-ups, reducing the risk of missed opportunities. By streamlining the lead journey, from generation to nurturing and conversion, this technique saves time and increases efficiency. It also ensures no lead gets overlooked, enhancing customer engagement. Automated lead management is one of the most effective ways to maximize sales outcomes with minimal manual intervention.

• Workflow and Task Automation

Sales Force Automation streamlines repetitive tasks such as scheduling meetings, sending follow-up emails, generating invoices, or updating client records. Workflow automation ensures that these tasks are completed consistently and on time, improving efficiency. Salespeople no longer need to waste valuable time on administrative duties and can focus more on selling and building customer relationships. Automated task allocation ensures accountability, while reminders prevent delays. Workflow automation also provides real-time tracking of progress, allowing managers to monitor activities closely. By reducing manual workload, this technique ensures faster execution, higher productivity, and improved customer service in the sales process.

• Customer Relationship Management (CRM) Integration

SFA integrates with CRM systems to centralize customer data, including contact details, purchase history, and communication records. This technique helps sales teams track client interactions across multiple channels, ensuring consistency in engagement. With CRM integration, salespeople can personalize communication, identify upselling opportunities, and strengthen relationships. Automated data synchronization reduces duplication and errors, providing accurate insights into customer behavior. Managers can access detailed reports and forecasts, aligning strategies with customer needs. This integration improves collaboration between sales and marketing teams as well. CRM-linked automation is a powerful tool that enhances efficiency, customer satisfaction, and sales planning effectiveness.

• Sales Forecasting Automation

Automated forecasting uses historical data, market trends, and customer behavior patterns to predict future sales more accurately. Machine learning and AI-enabled forecasting tools enhance precision by identifying patterns that manual methods often overlook. This technique provides real-time insights into demand fluctuations, helping managers adjust targets and allocate resources effectively. Automated forecasting reduces reliance on guesswork, minimizing risks of overstocking or underperformance. Dashboards and visual reports simplify data interpretation, making it easier for managers to plan strategies. By enabling data-driven decision-making, sales forecasting automation strengthens overall sales planning and boosts profitability in dynamic market environments.

• Reporting and Performance Analytics Automation

This technique automates the generation of sales reports and performance analytics. Instead of manually compiling data, SFA systems provide real-time dashboards showing sales achievements, pipeline progress, and team performance. Automated analytics highlight strengths, weaknesses, and areas for improvement. Managers gain visibility into sales trends, helping them make informed decisions quickly. Customizable reports cater to different managerial levels, ensuring clarity and accountability. For salespeople, automated reports provide self-assessment opportunities and motivation to achieve targets. By eliminating manual reporting errors and delays, this technique improves accuracy, transparency, and responsiveness in monitoring overall sales performance.

Disadvantages of Sales Force Automation (SFA)

• High Implementation Costs

Implementing Sales Force Automation involves significant investment in software, hardware, customization, and employee training. For small and medium businesses, these expenses can be burdensome. Maintenance and periodic upgrades further add to the overall cost. Companies may also need expert consultants for integration with existing systems, raising expenses even more. If not properly planned, the return on investment may take longer to achieve. This high financial requirement sometimes discourages organizations from adopting SFA fully. Without sufficient resources, businesses risk incomplete implementation, resulting in wasted investment and limited benefits from the automation system.

• Complexity and Resistance to Use

SFA systems can be complex, requiring employees to learn new processes and adapt to digital tools. Sales teams may resist adoption due to fear of technology replacing their roles or discomfort with new systems. Resistance often leads to underutilization of SFA features, reducing effectiveness. Additionally, if the system is overly complicated, sales representatives may feel burdened rather than supported, slowing down operations. Without proper training and change management, businesses may struggle with employee dissatisfaction and lower productivity. Thus, human resistance and system complexity are significant disadvantages of adopting Sales Force Automation.

• Dependence on Technology

SFA makes sales processes highly dependent on technology, which can pose risks during system failures, technical glitches, or network downtime. If servers or software malfunction, sales operations may come to a halt, causing delays and customer dissatisfaction. This dependence also creates vulnerability to cyber threats such as data breaches and hacking. Furthermore, reliance on digital tools may weaken personal judgment and creativity in decision-making. Overdependence reduces flexibility, as employees may struggle to perform tasks without the system. Hence, while automation enhances efficiency, it also exposes businesses to risks tied to technology reliance.

• Data Security and Privacy Concerns

Since SFA systems store sensitive customer information, they are highly vulnerable to cyberattacks, data breaches, or unauthorized access. Protecting such large volumes of confidential data requires advanced security measures, which are costly and complex to implement. Inadequate safeguards can lead to loss of customer trust and legal complications under data protection regulations. Additionally, integration with third-party applications may increase risks of data leakage. Employees handling the system may also misuse data if proper monitoring is absent. Thus, data security and privacy remain a critical disadvantage, making organizations cautious while adopting Sales Force Automation systems.

• Risk of Reduced Human Interaction

Over-reliance on automation in sales may lead to reduced personal engagement with customers. Automated emails, reminders, and chatbots may streamline communication but often lack the personal touch that builds trust and loyalty. Customers may feel undervalued if interactions become overly mechanized, resulting in weakened relationships. Sales is not just about efficiency but also about emotional connection, persuasion, and relationship-building. By prioritizing automation, organizations risk losing the human element in sales, which could negatively affect long-term customer satisfaction and brand loyalty. Hence, reduced human interaction is a significant drawback of SFA.

A sales territory is the regional, industry, or account type assigned to a specific salesperson or sales team. A sales territory owner is responsible for prospecting into their customer base and meeting their territory quota.

CRM systems, the term territory management designates a process of lead routing and account management based on a prospect or customer location. The typical scenario is this a company has substantial national or international presence and a single point of entry for incoming enquiries (like a website or a toll-free phone number). After identifying a prospect or client location, these inquiries are passed only to responsible employees in local offices.

Territory management in CRM software offers a number of advantages. For example, it allows one to compare sales or marketing statistics between different territories. It can show which territories are showing positive dynamics (sales are growing) and which ones are declining, allowing management to make appropriate decisions before it gets too late. Finally, it can help in building accurate sales forecasts for each territory.

Businesses who cater to a large audience categorize their prospects into territories based on similar characteristics such as geography, business type, business size, referral source, needs, etc. Referred to as territory management, this sales practice is an effective way for sales managers to enable sales reps to focus and prioritize on leads assigned to them. Organizing sales teams by territory also helps to identify profitable territories, sales reps who are meeting targets, and the potential sales areas to improve.

Sales managers divide their sales team into specific groups so they can effectively handle their territories and maximize opportunities; however, the process of assigning territories to sales reps becomes a problematic exercise if you don’t have an automated system in place. A sales management system like CRM software comes with territory management capabilities to define your sales territories.

With a sales CRM like Freshsales, you can create a systematic sales territory management plan to organize your team, auto-assign leads round-robin, assign phone numbers to territories, transfer calls to territories, limit territory access, and more.

Understanding, planning, and managing sales territories can make or break your sales efforts. Your reps need a firm grasp on the specific customer segments they’re accounting for and the general framework of your team’s territories over all.

The way you structure, define, and distribute the territories you work with has massive implications when it comes to your organization’s sales efficiency and bottom line.

A solid sales territory plan and exceptional territory management can be significant assets to a successful sales team. Here’s some perspective on how to do them right.

Sales Territory Planning

1. Define your market.

To effectively set up territories, sales leaders must first understand the environment of their business. There are numerous ways for a business to define a market. Factors could include geography, size, and consumer demographics among others.

Know what is unique to your business and prioritize based on what your climate demands. A solidified market will lead to lowered costs, increased sales, and a foundation for setting up effective sales territories.

2. Assess account quality.

After a target market is determined, sales leaders need to evaluate the value of each account. The measurement could be either quantitative or qualitative depending on the product or service the business offers. For example, a beverage company might rank the value of their accounts by net profitability while a company that relies heavily on customer recommendations could focus on accounts that are more likely to provide a referral for their company.

3. Assess territory quality.

After assessing the quality of each account, it is important to rank territories. As with the accounts’ values, this process is subjective based on different business needs and priorities. If your business sells products across industries, your territories could be divided and quantified by those industries. Determining what sales territory supports which areas of the sales funnel will also help you score territories into high, medium, and low value.

To get a better picture of territory value, include your sales team in these discussions. After all, no one knows the territories better than the reps who work within them each day. That way, you can assign the appropriate reps to maximize the potential of each territory.

4. Assess rep strengths.

The next step towards effective territory management may be the most important of all. After determining the quality of each sales territory, it is crucial that you assign reps with the applicable skills to develop and optimize each set of accounts. For example, a territory that is defined by large enterprise deals needs to be handled by a rep who has experience closing big deals. By strategically assigning qualified reps to accounts, you will empower your reps and ensure the client receives the best possible service.

5. Review and consolidate.

The four steps outlined above prepare a business to put a sales territory plan into action. The last thing a business needs to do is a final diagnosis of costs associated with each territory. Analyzing cost metrics like comparing ideal versus actual number of visits and mileage per rep in each territory will help managers zero in on specific inefficiencies in the system.

After you have reviewed your plan, consolidate it. By following these five steps, your business will be on its way to having a more satisfied workforce, as well as increased customer growth and profits.

Customer Life Cycle value is the prediction of how much revenue a business will achieve from a customer throughout the journey. As companies are not aware of how much business and referrals a customer will bring and how long will he stay, Customer Lifetime Value (CLV) becomes important to determine the monetary worth of the customer.

Developing a successful customer relationship management (CRM) strategy requires a keen understanding of consumers and their purchasing behaviors. These behaviors vary greatly at the different stages within the customer life cycle. It is important to identify these various life cycle stages and to understand the needs of the consumer at each phase. Below is a look at the five main stages with the typical customer life cycle.

1. Reach

This is the initial stage of the customer life cycle. The primary goal at this phase is to bring awareness to your brand and to entice the consumer to want to learn more about your goods or services. Ultimately, you want to generate high-quality leads.

You should have a clearly defined brand messaging strategy and use a variety of marketing techniques, such as social media marketing, banner advertising and content marketing. It is crucial to analyze the effectiveness of each marketing strategy during this stage. This analysis will enable you to adjust your marketing strategies if necessary.

2. Acquisition

At this stage, you are able to obtain prospective customer’s contact information, such as email addresses, phone numbers or social media profiles. This signifies that the consumer is interested in your goods or services but not quite ready to take the leap and make a sale. This is one of the most critical points in the customer life cycle.

You can now start to foster relationships with the customers through strategic engagement. Since you now have their contact information; you can focus on targeted and personalized marketing strategies. Email marketing, sales calls, social media marketing and content marketing all work well at this stage of the life cycle. Don’t solely focus on making a sale. Instead, focus on building trust and fostering relationships.

3. Conversion

This is the phase when you convert a prospective customer into an actual paying customer. You have been able to convince the consumer that they need your goods or services to the point that they make a sale. The most important thing to focus on at this stage is to make sure your customer has a pleasant buyer’s experience.

Having customer-friendly processes in place, such as an easy-to-use website, a secure payment method and an efficient customer service strategy, are vital to enhancing the buyer’s overall experience. This also is the time to analyze the effectiveness of your marketing techniques up to this point. Determine what strategies are working best to make this conversion happen and where adjustment may need to take place.

4. Retention

Don’t make the mistake of thinking that the customer life cycle stops once the sale is made. The truth is that you are only halfway to your ultimate goal. It is now time to continue building on the customer relationships developed during the acquisition stage. Regular engagement with the consumer will help to keep your brand fresh in their mind and to encourage repeat purchases. Sales techniques like cross selling, up selling and loyalty programs are very effective at retaining customers.

5. Advocacy

Creating advocates for your company should always be your ultimate goal. These are loyal customers who not only make regular purchases but also are willing to promote your goods or services to others. They will refer their friends and family members to your business and post positive reviews online. This type of customer loyalty doesn’t happen overnight. You have to develop strong relationships throughout the entire customer life cycle.

The CRM cycle basically consists of four stages: Marketing, Sales, Product, and Support.

• Marketing Stage: In this stage of CRM cycle, the basic focus is to identify customers by running various marketing campaigns (such as emails, blogs, advertisements, and more), create the database for Account (pertaining to Organization) and Contacts (pertaining to individuals), and finally generate leads by analyzing the gathered customer data.
• Sales Stage: In the Sales stage, basic focus remains on leads. They are the individuals who have expressed some kind of interest in your product offering. ‘Leads’ are further categorized into Open, Contacted, Qualified and Un-qualified. Deskera CRM offers a functionality to convert ‘leads’ into ‘opportunity’ for carrying out further sales activities.
• Product Stage: In this stage of CRM cycle, the basic focus is on delivery of product. Deskera CRM offers Product Management functionality that captures details about the product price, vendor, and description, among others.
• Support Stage: During Support Stage, the primary focus remains on resolving customer issues and providing customer support. In CRM terminology, this function is known as Case Management.

Reference Data

Reference data is data used to classify or categorize other data. Typically, they are static or slowly changing over time.

Examples of reference data include:

• Units of measurement
• Country codes
• Corporate codes
• Fixed conversion rates e.g., weight, temperature, and length
• Calendar structure and constraints

Reference data sets are sometimes alternatively referred to as a “controlled vocabulary” or “lookup” data.

Reference data should be distinguished from master data. While both provide context for business transactions, reference data is concerned with classification and categorisation, while master data is concerned with business entities. A further difference between reference data and master data is that a change to the reference data values may require an associated change in business process to support the change, while a change in master data will always be managed as part of existing business processes. For example, adding a new customer or sales product is part of the standard business process. However, adding a new product classification (e.g. “restricted sales item”) or a new customer type (e.g. “gold level customer”) will result in a modification to the business processes to manage those items.

Reference data management

Curating and managing reference data is key to ensuring its quality and thus fitness for purpose. All aspects of an organisation, operational and analytical, are greatly dependent on the quality of an organization’s reference data. Without consistency across business process or applications, for example, similar things may be described in quite different ways. Reference data gain in value when they are widely re-used and widely referenced.

Examples of good practice in reference data management include:

• Formalize the reference data management
• Use external reference data as much as possible
• Govern the reference data specific to your enterprise
• Manage reference data at enterprise level
• Version control your reference data

Transactional Data

Transactional data are information directly derived as a result of transactions.

Unlike other sorts of data, transactional data contains a time dimension which means that there is timeliness to it and over time, it becomes less relevant.

Rather than being the object of transactions like the product being purchased or the identity of the customer, it is more of a reference data describing the time, place, prices, payment methods, discount values, and quantities related to that particular transaction, usually at the point of sale.

Transactional data describes an internal or external event which takes place as the organization conducts business and can be financial, logistical or any business-related process involving activities such as purchases, requests, insurance claims, deposits, withdraws, etc.

Transactional data support ongoing business operations and are included in the information and application systems that are used to automate an organization’s key business processes such as online transaction processing (OLTP) systems.

It is grouped with its associated and references master data such as product information and billing sources.

Transaction data is data describing an event (the change as a result of a transaction) and is usually described with verbs. Transaction data always has a time dimension, a numerical value and refers to one or more objects (i.e. the reference data).

Typical transactions are:

• Financial: orders, invoices, payments
• Work: plans, activity records
• Logistics: deliveries, storage records, travel records, etc.

Typical transaction processing systems (systems generating transactions) are SAP and Oracle Financials.

Warehouse Data

In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise.

The data stored in the warehouse is uploaded from the operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the DW for reporting.

Extract, transform, load (ETL) and extract, load, transform (ELT) are the two main approaches used to build a data warehouse system.

Using Data Warehouse Information

There are decision support technologies that help utilize the data available in a data warehouse. These technologies help executives to use the warehouse quickly and effectively. They can gather data, analyze it, and take decisions based on the information present in the warehouse. The information gathered in a warehouse can be used in any of the following domains:

• Tuning Production Strategies: The product strategies can be well tuned by repositioning the products and managing the product portfolios by comparing the sales quarterly or yearly.
• Customer Analysis: Customer analysis is done by analyzing the customer’s buying preferences, buying time, budget cycles, etc.
• Operations Analysis: Data warehousing also helps in customer relationship management, and making environmental corrections. The information also allows us to analyze business operations.

Functions of Data Warehouse Tools and Utilities

• Data Extraction: Involves gathering data from multiple heterogeneous sources.
• Data Cleaning: Involves finding and correcting the errors in data.
• Data Transformation: Involves converting the data from legacy format to warehouse format.
• Data Loading: Involves sorting, summarizing, consolidating, checking integrity, and building indices and partitions.
• Refreshing: Involves updating from data sources to warehouse.

Business View Data

Business intelligence (BI) comprises the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current, and predictive views of business operations. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics. BI technologies can handle large amounts of structured and sometimes unstructured data to help identify, develop, and otherwise create new strategic business opportunities. They aim to allow for the easy interpretation of these big data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.

Business intelligence can be used by enterprises to support a wide range of business decisions ranging from operational to strategic. Basic operating decisions include product positioning or pricing. Strategic business decisions involve priorities, goals, and directions at the broadest level. In all cases, BI is most effective when it combines data derived from the market in which a company operates (external data) with data from company sources internal to the business such as financial and operations data (internal data). When combined, external and internal data can provide a complete picture which, in effect, creates an “intelligence” that cannot be derived from any singular set of data. Among myriad uses, business intelligence tools empower organizations to gain insight into new markets, to assess demand and suitability of products and services for different market segments, and to gauge the impact of marketing efforts.

BI applications use data gathered from a data warehouse (DW) or from a data mart, and the concepts of BI and DW combine as “BI/DW” or as “BIDW”. A data warehouse contains a copy of analytical data that facilitate decision support.

Applications

Business intelligence can be applied to the following business purposes:

• Performance metrics and benchmarking inform business leaders of progress towards business goals (business process management).
• Analytics quantify processes for a business to arrive at optimal decisions, and to perform business knowledge discovery. Analytics may variously involve data mining, process mining, statistical analysis, predictive analytics, predictive modeling, business process modeling, data lineage, complex event processing, and prescriptive analytics.
• Business reporting can use BI data to inform strategy. Business reporting may involve dashboards, data visualization, executive information system, and/or OLAP
• BI can facilitate collaboration both inside and outside the business by enabling data sharing and electronic data interchange
• Knowledge management is concerned with the creation, distribution, use, and management of business intelligence, and of business knowledge in general. Knowledge management leads to learning management and regulatory compliance.

Common Causes of Data Quality Issues

Data Conversion

Most often your data came from somewhere else before it was in your database. During transfers, data can get lost or modified in ways that make it unusable. If databases are mapped using old data structures or conversions, the data in the new database will be incorrect. Often users do not see what is what is actually being stored, so the data structure and mapping between the old and new databases are often the culprit for errors in the data.

Merging Databases and Other System Consolidations

When you phase out or combine an old system, a poorly planned merge can leave you with little time to plan or prevent errors. As the data is moving into a non-empty database, there is little flexibility for changes in the data structure. Often data will not fit in with the new structure, and duplicates and conflicts will result.

Manual Data Entry

Much data is typed into databases by people, and mistakes are inevitable. People misspell entries, use the wrong format for a field, enter data into the wrong box, or input the wrong value. Because these errors are not systematic, they can be difficult to trace or correct.

Batch Feeds

A batch feed carries large volumes of data, and any problem in it can cause issues magnified by future feeds. The problems may accumulate and become difficult to track down and fix. If an error finds its way into the source system, it can flow through the batch feeds and go unnoticed.

Real Time Interfaces

The problem with data exchanged through real-time interfaces is that there is little time to verify that the data is accurate. There are typically multiple points in the capture process where real time data can be corrupted or lost. The data comes in small packets, and can be incorrect, leaving you with unreliable data.

Disconnect Between Data Priorities with Business Priorities

Data priorities and business priorities should be in alignment. Avoid collecting and managing data that is not important to your business and which can corrupt other data elements. Prioritize data that is important to the business, and keeping the quality of that data high.

Before You Start a Data Cleaning Process, Make a Plan

Audit and Organize the Data

Understanding your data before cleaning improves the efficiency of your project and reduces the time and cost of data cleaning. Understand the purpose, location, flow, and workflows of your data before you start.

Document Data Quality Requirements and Define Rules for Measuring Quality

Create a reference for success, and targets to keep the project in check along the way. Set statistical checks on the data, and set a standard of quality control and completeness.

Create a Strategy

Outline a plan for your data quality that supports ongoing operations and data management. Identify the data sets that meet your quality standard, and the data sets that need to be cleaned. Identify possible solutions with a plan for implementation. Your general plan should be to define, identify, correct, and document data errors, and modify procedures to avoid errors in the future.

Identify and Correct Errors in Data The method to error detection will depend on the database and the dataset you are using. Depending on your team and the issues you have encountered, there are a variety of free or open source tools or services and enterprise solutions for cleaning your data. The pandas python library is an open-source software library for data manipulation and analysis. You can use CSVKit for converting and working with CSV files. R is a popular system for data cleaning, and features such as R plyr, Reshape2 or ggplot2 can be used for cleaning data. Trifacta Data Wrangler, Informatica and Trillium Software are among a few of the companies that offer data cleaning services.

Data is one of the most important part of any marketing platform. Without accurate, complete, consistent and current data marketers will have a hard time emailing different segments, improving marketing performance, optimizing campaigns and analysing past performances. But how do you identify data quality issues inside your own Marketing Automation Tool?

The first step is to talk to your sales and marketing teams. Chances are they are already struggling with data problems. The second step is to run analysis on your data to identify if you are due for a cleaning. And the third stay is to stop bad data from happening in the first place.

Here are some data quality issues that you should look out for and some ways of fixing them:

1. Inability to segment. Think of all the different creative and messages that you want to tailor to your prospects and clients. For example, do you want to deploy an email to all Vis of Marketing only to find out that titles are not standardized and include thousands of variations.

   Solution: Standardize your data into different buckets. In the example of VP of Marketing, any titles that are VP of Digital Marketing, VP of Product Development or VP of Marketing would all be classified into VP of Marketing.

2. Bounce Rate is on the rise. You should monitor your email marketing bounce rate as it is a key indicator of the quality of your data. Analysing the source of high bounce rates will help you eliminate bad data providers while, looking at the date create will help identifying outdated leads and the need to refresh your leads.

   Solution: Either perform email validation prior to deploying a new list or clean out historical emails by nurturing them.

3. While most marketing automation tools will not allow to duplicate email address, you may uncover different emails for the same person. It is not easy to find out if you have duplicate issues. One way is to perform a simple test and check what percentage of your leads have personal domains such as Gmail, Yahoo and so on. If the percentage is greater than 5%, you should look at de duping especially if you are sending different offers to different segments.

   Solution:De dupe your data using automatic de duplication tools or hiring a data cleaning company to do it for you.
    

4. Incomplete Data: You may be sitting on MQLs that you cannot pass to sales due to a few missing fields that are required to reach the desired score.

   Solution: Data appending using third party data providers can help fill in missing data such as industry, address, phone number, revenue information and number of Employees. Alternatively, you can use dynamic forms to fill in your missing data.

5. Missing Deadlines. The final indicator that there are data quality issues are missed deadlines. It could be that your team is so busy dealing with issues with campaign naming that they cannot find the right campaign to attach to the latest emails. There could also be too many custom lists to identify a key segment or they are too busy trying to figure out why the latest email did not perform well. Their daily tasks take longer and longer time to complete.
   Solution: Audit your data and processes inside your marketing automation tool to identify ways to streamline activities and make work more efficient for your marketing team.

But data is only useful if it’s high-quality. Bad data is at best inconsequential. In the worst case scenario, it can lead companies to make costly mistakes. IBM estimates that bad data costs the U.S. economy $3.1 trillion per year. Those costs come from the time employees must spend correcting bad data and errors that cause mistakes with customers.

Several factors contribute to the quality of data, including:

1. Accuracy

Among marketers who purchase demographic data, 84 percent say that accuracy is very important to their purchasing decisions. Accuracy refers to how well the data describes the real-world conditions it aims to describe. Inaccurate data creates clear problems, as it can cause you to come to incorrect conclusions. The actions you take based on those conclusions might not have the effects you expect because they’re based on inaccurate data. For example, data might lead a marketer to believe that their customers are mostly females in their 20s. If that data is inaccurate and their customers are actually primarily men in their 40s, then they will end up targeting the wrong group with their advertisements.

2. Completeness

Data Completeness

If data is complete, there are no gaps in it. Everything that was supposed to be collected was successfully collected. If a customer skipped several questions on a survey, for example, the data they submitted would not be complete. If your data is incomplete, you might have trouble gathering accurate insights from it. If someone skips some of the questions on a survey, it may make the rest of the information they provide less useful. For instance, if a respondent doesn’t include their age, it will be harder to target content to people based on their age.

3. Relevancy

The data you collect should also be useful for the campaigns and initiatives you plan to use it for. Even if the information you collect has all the other characteristics of quality data, if it’s not relevant to your goals, it’s not useful to you. It’s important to set goals for your data collection so that you know what kind of data to collect.

4. Validity

Data Validity

Validity refers to how the data is collected rather than the data itself. Data is valid if it is in the right format, of the correct type and falls within the right range. If data does not meet these criteria, you might run into trouble organizing and analyzing it. Some software can help you convert data to the correct format. For example, if you are collecting data about the time of day users visit your site, you must decide on the format you will use. You might choose to use 24-hour time and use two digits for minutes and two for hours. Examples of this data format would include 14:34, 17:05 and 08:42. Data that doesn’t follow this format would be invalid.

5. Timeliness

Timeliness refers to how recently the event the data represents occurred. Generally, data should be recorded as soon after the real world event as possible. Data typically becomes less useful and less accurate as time goes on. Data that reflects events that happened more recently are more likely to reflect the current reality. Using outdated data can lead to inaccurate results and taking actions that don’t reflect the current reality.

6. Consistency

When comparing a data item or its counterpart across multiple data sets or databases, it should be the same. This lack of difference between multiple versions of a single data item is referred to as consistency. A data item should be consistent both in its content and its format. If your data isn’t consistent, different groups may be operating under different assumptions about what is true. This can mean that the different departments within your company will not be well coordinated and may even unknowingly be working against one another.

An Example of Good Data Quality

Let’s say, for example, that you’re a marketer, and you’re crafting a campaign to promote a brand of organic dog food. You want to determine the best times of day to run online ads to promote the brand’s web store. To figure this out, you can collect data from the brand’s website about when people typically purchase dog food on it. Here’s how you can ensure your data is high-quality:

• Accuracy: Since you’re collecting the data directly right from your client’s website, you can be confident in its accuracy.
• Completeness: To make sure your data is complete, collect the same information about every customer. For example, you may want to know what items they purchased, their order total, how they paid and the times they started and completed the transactions.
• Relevancy: Only the data related to dog food purchases will be relevant to your campaign.
• Validity: Ensure that you collect all of your time data in the same format.
• Timeliness: Import your data as soon as you can and only use data from within a pre-determined timeframe.
• Consistency: If you store your data in multiple places, make sure it is consistent across all of them.

If your data meets all of these criteria, you can be confident that your data is high-quality.

Data Quality

As data management techniques and technologies improve, data continues to become increasingly important for businesses. Growing numbers of companies are using data to make decisions about marketing, product development, finance and more. As more firms reap the benefits of data, using it is increasingly becoming a matter of keeping up with the competition. Companies that don’t take advantage of data and related technologies risk falling behind.

For data to be beneficial though, it needs to be of high quality. The better your data’s quality, the more you can get out of it. If your information is low-quality, it can even be harmful. If you base a decision on bad data, you’re likely to make the wrong choice.

New technologies are also increasing the importance of data and its quality. Technologies such as artificial intelligence and automation have enormous potential, but success with these technologies depends heavily on data quality. Machine learning, for example, requires large volumes of accurate data. The more good data a machine learning algorithm has, the faster it can produce results, and the better those results will be. In a recent survey of senior executives by New Vantage Partners, more than three-fourths of respondents said that the increase in data volumes and sources is driving increased investments in AI and cognitive learning.

Data is becoming increasingly integral to business’ operations. Rather than treating data as separate from their other functions, some of today’s most successful companies integrate it into everything they do. This increased integration means that data quality can impact many aspects of a business from marketing to sales to content creation.

Data quality is also critical because of compliance-related issues. As regulations regarding data continue to evolve, it’s become increasingly important that companies manage their data properly. It’s harder to demonstrate compliance if your data is disorganized or poorly maintained. This is especially vital for financial data and sensitive personal data but can apply to other types of information as well.

Benefits of Good Data Quality

Ensure You’re Using Quality Data

Good data management is crucial for keeping up with the competition and taking advantage of opportunities. High-quality data can also provide various concrete benefits for businesses. Some of the potential benefits of good data quality include:

1. More Informed Decision-Making

Improved data quality leads to better decision-making across an organization. The more high-quality data you have, the more confidence you can have in your decisions. Good data decreases risk and can result in consistent improvements in results.

2. Better Audience Targeting

Data quality also leads to improved audience targeting. Without high-quality data, marketers are forced to try to apply to a broad audience, which is not efficient. Worse, they may have to guess at who their target audience should be. When you have high-quality data, you can more accurately determine who your target audience should be. You can do so by collecting data about your current audience and then finding potential new customers with similar attributes. You can use this knowledge to more accurately target advertising campaigns and develop products or content that appeal to the right people.

3. More Effective Content and Marketing Campaigns

In addition to improving targeting, data quality can also help to improve your content and marketing campaigns themselves. The more you know about your audience, the more reliably you can create content or ads that appeal to them. For instance, if you’re a publisher of a sports website, you can gather data that tells you which sports your website users are most interested in. If you discover that golf is one of your most popular categories, you can direct your content team to create more golf-related articles and videos. If you find that golf is especially popular among visitors to your site who are men between the ages of 45 and 64, you can show golf content to users in this age range when they visit your site. A similar technique can be applied to content used as part of a marketing campaign.

4. Improved Relationships With Customers

High-quality data can also help you improve your relationships with customers, which is crucial for success in any industry. Gathering data about your customers helps you get to know them better. You can use information about your customers’ preferences, interests and needs to provide them with content that appeals to them and even anticipates their needs. This can help you build strong relationships with them. Proper data management also helps prevent you from delivering duplicate content to customers, which can become annoying to your audience and damage your reputation.

5. Easier Implementation of Data

High-quality data is also much easier to use than poor-quality data. Having quality data at your fingertips increases your company’s efficiency as well. If your information is not complete or consistent, you have to spend significant amounts of time fixing that data to make it useable. This takes time away from other activities and means it takes longer for you to implement the insights your data uncovered. Quality data also helps to keep your company’s various departments on the same page so that they can work together more effectively.

6. Competitive Advantage

If you have better quality data than your competitors or use your data more effectively than they do, you gain a competitive advantage. Data is one of the most valuable resources that today’s companies have, as long as it’s high-quality. Better data quality means that you can discover opportunities before your competitors do. You can better anticipate prospects’ needs and, therefore, beat competitors to sales. A lack of good data means missed opportunities and falling behind the competition.

7. Increased Profitability

Ultimately, high-quality data can lead to increased profitability. It can help you to craft more effective marketing campaigns and increase sales numbers. It also decreases ad waste, making your marketing campaigns more cost-effective. Similarly, if you’re a publisher, data can show which types of content are the most popular on your site and which bring in the most revenue. Having this information enables you to focus more of your time and resources on these kinds of content.

How to Collect High-Quality Data

Collecting high-quality data can be challenging. Problems with data quality may occur when a company is attempting to integrate data systems across different departments or applications, implementing new software or manually entering data. They may also occur because a company does not have the proper tools or processes in place. There are things that companies can do, however, to help improve data quality. Taking the following steps can help ensure the collection of quality data.

• Implement a data collection plan: To ensure that the data you’re collecting is high-quality, you need to have a data collection plan in place. Determine the kind of data you need to meet your goals and the methods you’ll use to collect and manage it. You plan should also define the roles of all personnel involved in collecting the data and establish processes for how you’ll communicate between departments on matters related to data. Be specific in your plan to avoid confusion and ensure that you can measure your progress.
• Set data quality standards: Create data quality standards that you’ll use to determine which data to keep, which to get rid of and which to correct. Everyone involved in managing your data should agree on and understand these standards. This ensures consistency across your organization.
• Create a plan for data correction: You’ll need to create rules for correcting data. These rules should define who is responsible for correcting data and the methods they should use to fix it. This is also important for ensuring consistency in your data.
• Plan for data integration and distribution across departments: Additionally, you should create a plan for integrating and distributing your data across the various departments within your organization. Data quality issues often occur at this step, because copying data, manually editing it or sending it to different software platforms creates opportunities to alter it. Creating concrete plans for this step can help avoid these issues.
• Set goals for ongoing data collection: It’s important to remember that improving data quality isn’t a one-time task. Ensuring data quality requires ongoing effort. Documenting data quality issues can help with this ongoing effort by ensuring that mistakes aren’t repeated. The focus should be on continuous improvement of your data collection plan.

Review of the Most Popular Data Analysis Tools for Your Business:

Data analysis is the process of working on data with the purpose of arranging it correctly, explaining it, making it presentable, and finding a conclusion from that data.

It is done for finding useful information from data to make rational decisions.

As it is done for decision making, it is important to understand the sole purpose of data analysis. The main purpose of data analysis is interpretation, evaluation & organization of data and to make the data presentable.

Data Analysis Methods

There are two methods of data analysis:

• Qualitative Analysis
• Quantitative Analysis

Qualitative Analysis: Qualitative Analysis is done through interviews and observations.

Quantitative Analysis: Quantitative Analysis is done through surveys and experiments.

Data Analytics Process

Data Analytics Process includes:

1. Data Collection
2. Working on data quality
3. Building the model
4. Training model
5. Running the model with full data.

Some tips to analyze the data are:

• Remove unnecessary data before the analysis.
• You should not perform the analysis on a master copy of data.

Data analysis is done with the purpose of finding answers to specific questions. Data analytics techniques are similar to business analytics and business intelligence.

Data Mining is about finding the different patterns in data. For this, various mathematical and computational algorithms are applied to data and new data will get generated.

Data Modeling is about how companies organize or manage the data. Here, various methodologies and techniques are applied to data. Data analysis is required for data modeling.