CUSTOMER RELATIONSHIP MANAGEMENT, IV UNIT, JNTU KAKINADA

UNIT..IV

ANALYTICAL - CRM - TERM DEFINITION

Analytical CRM (Customer Relationship Management) denotes the systematic electronic analysis of collated customer data. Customer data is defined as contact data, customer properties and information derived from both online and offline behavior. Market research data is collected and analyzed just as customer behavior is analyzed with respect to the shops they enter and the purchases they make. Contrary to operative CRM, analytical CRM focuses on the exact analysis and enrichment of all data. This means that data is not just collected, but runs through various processes to glean valuable information.

ANALYTICAL CRM

Analytical CRM supports organizational back-office operations and analysis. It deals with all the operations and processes that do not directly deal with customers. Hence, there is a key difference between operational CRM and Analytical CRM. Unlike from operational CRM, where automation of marketing, sales-force and services are done by direct interaction with customers and determining customer’s needs, analytical CRM is designed to analyze deeply the customer’s information and data and unwrap or disclose the essential convention and intension of behavior of customers on which capitalization can be done by the organization.

Primary goal of analytical CRM is to develop, support and enhance the work and decision making capability of an organization by determining strong patterns and predictions in customer data and information which are gathered from different operational CRM systems.

The following are the key features of analytical CRM:

• Seizing all the relevant and essential information of customers from various channels and sources and collaboratively integrating and inheriting all this data into a central repository knowledge base with a overall organization view.
• Determining, developing and analyzing inclusive set of rules and analytical methods to scale and optimize relationship with customers by analyzing and resolving all the questions which are suitable for business.
• Implementing or deploying the results to enhance the efficiency of CRM system and processes, improve relationship and interaction with customers and the actual business planning with customers.
• Combine and integrate the values of customers with strategic business management of organization and value of stakeholders.

Analytical CRM is a solid and consistent platform which provides analytical applications to help predict, scale and optimize customer relations. Advantages of implementing and using an analytical CRM are described below.

1. Leads in making more profitable customer base by providing high value services.
2. Helps in retaining profitable customers through sophisticated analysis and making new customers that are clones of best of the customers.
3. Helps in addressing individual customer’s needs and efficiently improving the relationships with new and existing customers.
4. Improves customer satisfaction and loyalty.

The power of CRM provides a lot of managerial opportunities to the organization. It implements the customer information in an intelligent way and creates views on customer values, spending, affinity and segmentation. Analysis is done in every aspect of business as described below:

1. Customer Analytics- This is the base analytic used to analyze customer knowledge base. It provides a better view of customer behavior and by modeling, assessing customer values and assessing customer’s portfolio or profiles and creates an exact understanding of all the customers.
2. Marketing Analytics- This helps discovering new market opportunities and seeks their potential values. It also helps in managing marketing strategies and scale and plan marketing performance at district, regional and national levels. Marketing analytics also focus on campaign management and planning, product analysis and branding.
3. Sales Analytics- Sales analytic provides essential environment to plan, simulate and predict sales volumes and profits by constantly analyzing organizational sales behavior. It helps in pipelining all the selling opportunities in an efficient way by indulging and improving the sales cycle.
4. Service Analytics- Analytical CRM has major role in enhancing the services which answering all the questions regarding customer satisfaction, quality and cost of products, complaint management etc. It even helps in improving and optimizing the services by sophistically analyzing the service revenue and cost.
5. Channel Analytics- This type of analysis helps to determine the customer behavior on channel preferences, like web channel, personal interaction, telephone channel etc. This information is efficiently integrated in customers’ knowledge base so that they can be contacted accordingly.

The essential results produced by Analytical CRM system could diversely help the organization to tackle customers’ based on values. It also helps in determining which customer is best to invest in, which can be treated at an average level and which should not be invested in.

 

 

WHAT IS ANALYTICAL CRM

               We know that there are three types of CRMapplications – Operational CRM, Analytical CRM and Collaborative CRM. In our previous post we have discussed about Operational CRM. Here we will talk about what is Analytical CRM, its key features and benefits.

               Let’s start our discussion with an example. Imagine a scenario where a company knows what customers want to buy even before launching the product. Though this happens only in ideal scenario but almost similar kind of result can be achieved using Analytical CRM.

 

SO WHAT IS ANALYTICAL CRM?

It is a systematic approach to analyze customer data and interactions to improve various business processes in Sales, Marketing and Service. The main purpose of Analytical CRM is to gather customer information from various channels and gain knowledge about customers’ behaviors and buying pattern as much as possible. It helps an organization to develop new marketing strategy, campaign management, customer acquisition and retention.

 

Difference between Operational CRM and Analytical CRM:

Operational CRM deals with automation of Sales, Marketing and Service processes that involve direct interaction of customers’ requirements. Whereas Analytical CRM handles those operations that do not have direct dealing with customers. It analyzes customer data to enhance decision making capability of an organization.

KEY FEATURES OF ANALYTICAL CRM:

 

1.      Gathers all relevant information about customers from various channels/sources and builds a knowledge base for an organization

2.      Analyzes customer data based on rules and methods set by business and prepares report to improve customer relationship and interaction

3.      Helps business to segment customers and run more customer centric marketing campaign to increase sales

4.      Decides what if scenarios – probability of a customer that purchases one product could buy another product

5.      Monitors events like customer may purchase gifts on his marriage anniversary

6.      Helps business to predict the probability of customer defection and take necessary steps

7.      Assists top management to do better financial forecasting and planning

 

Benefits of using Analytical CRM:

1.      Higher lead conversation rate

2.      Better customer experience by addressing their needs more effectively and efficiently

3.      Better market analysis before running a campaign

4.      Increase customer loyalty and satisfaction

5.      More accurate financial forecasting and planning

Analytical CRM analyzes data coming from every aspect of business and generates reports.

1.      CUSTOMER ANALYSIS REPORT:

This is the basic report based on analysis of customer knowledge base. This gives 360 degree view of a customer that helps a company to gain further insights about customer’s needs and preferences.

2.      SALES ANALYSIS REPORT:

This type of reports shows the organization’s sales trend for a specified period – monthly, quarterly, yearly or any time frame that is significant for business. It provides support to streamline all sales opportunities by improving sales cycle. This helps managers to identify market opportunity, predict sales volumes and profit by analyzing historical sales data.

3.      MARKETING ANALYSIS REPORT:

This kind of reports helps to discover new marketing opportunities and improve marketing performance by maximizing Return On Investment (ROI).  It decides marketing performance based on various parameters like region, channels, political influence. It also focuses on campaign planning and execution, product analysis.

4.      SERVICE ANALYSIS REPORT:

Service Analytics is a major area in Analytical CRM. It provides the insight about customer satisfaction, quality of service and areas of improvement in service. It finds out opportunities to cross sell or up sell products. It helps to track employee performance and productivity, tells management to conduct required training for employees.

5.      CHANNEL ANALYSIS REPORT:

Channel Analysis report helps business to understand customers’ behavior across channels like email, phone call, social media or face to face interaction. This kind of knowledge can be used to interact with customers more effectively and efficiently.

For an organization, collection of customer data and its analysis is a continuous and iterative process. Decisions based on customer data and feedback become better and more accurate over the time.
Analytical CRM uses various data mining techniques like predictive modelling, supervised modelling. We will discuss about data mining in CRM in another article.

RELATIONSHIP DATA MANAGEMENT 

Relationship management is a strategy in which an organization maintains a continuous level of engagement with its audience. This management can happen between a business and its customers or between a business and other businesses.

Customer Data Management (CDM)

Definition

Customer Data Management (CDM) is a solution mechanism in which an organization's customer data is collected, managed and analyzed. CDM is geared toward resolving customer requirements and issues while enhancing customer retention and satisfaction, allowing an organization to convert customer data into Customer Intelligence (CI).

Customer Data Management (CDM)

With CDM, one or more software applications are integrated to facilitate access to reliable and efficient customer data. Attracting and retaining customers requires a clear understanding of customer requirements. CDM streamlines customer relationship management (CRM), marketing and customer feedback management (CFM).

CDM must be tightly integrated across the departments of an organization, including IT, sales and HR. CDM processes include:

• Categorization: Customer data is classified and subclassified.
• Correction: Collected data is verified for accuracy and consistency. When necessary, contact details are updated, and duplicate records are removed.
• Enrichment: Incomplete data is collected and completed.
• Collection: Customer data and insight activity is collected via a customer feedback system or sources, like sales, customer support, surveys, reports, newsletters and other customer interactions.
• Organization: Customer data is organized and shared throughout an organization.

TOP  WAYS TO EXPAND YOUR CUSTOMER BASE

 

How to use your customer database

A customer database is generally used by companies which have repeat buyers, which go through regular updates, give repeated services or for cross selling different products to the same customer. Nowadays, customer databases are being maintained by each and every company so as to remain in touch with their customers and provide them swift and rapid service. There are several ways a customer database can be used by companies.

1) Identifying prospects 

  Identifying the buyers who regularly buy from you and also customers whom you can cross sell your products can be found from your customer database. You can decide the marketing and advertising strategy and message on the basis of features demanded by your current customers. A certain set of features may be common in a group of buyers and you can be on the lookout for other such customers who match the demography / psychography. Finally, your existing customer can become a repeat customer through usage of your customer database. 

2) Which customer should receive promotional offers – 

Recently, we had to carry out a promotional activity for a company by giving promotional gifts to their key customers. Thus the major question was, whom do we categorize as our key customer or our average customers or as someone who just drops by from time to time. At this point of time, if we didn’t have a customer database with us, classifying our customers would have become a major problem. On the other hand, promotional activities can be of various types. On purchasing a product, you can send the customer a thank you note, you can remind him of his overdue service and other such measures which makes the customer happy that he took the decision of buying the product from you.

3) Increase customer loyalty 

  I love Pizza. But i hate going through the hassle of introducing myself every time to a pizza joint so i can order my favorite pizza. That’s why i like Domino’s. Domino’s pizza is known for its home delivery, and it has succeeded in the same through its ability to maintain an impeccable customer database. Whenever a customer calls, Domino’s knows the persons mobile number, home address and his last order. This is how you command customer loyalty. Today, customers want convenience. Make the purchase experience as convenient as possible for the customer and he will remain loyal to you.

4) Reactivating customers 

A customer might not remain in touch with your company. In that case, you need to send such offers to the customer that your company becomes the center of attention again for your product category. Look at the way Intel advertises itself. Through a 4 second jingle in every advertisement, it just reminds customers that Intel is there for them. Similarly, if your brand has moved out of the customers eyes, it will be moved out of the customers minds too. You need to send timely reminders to re activate the customer for your brand. The best example of re activating non users is social media companies. Remain absent from your favorite social media network for a few days and you will find a prompt reminder from your network that they are missing you. That is the way you bring back lost customers.

5) Avoid common database pitfalls – 

A few decades back, where databases meant files upon files of Microsoft excel, there was a big confusion as the same customer was called by different executives and there was no co ordination of how to manage the customer database. This resulted in the customer becoming irritated with the company and the company wasting its time, money and resources on the repeat calls. Today, the situation is different. Companies have large MIS and CRM databases to track their prospective and existing customers from time to time. These systems have helped to minimize errors caused by using an unorganized database.

TOP  WAYS TO EXPAND YOUR CUSTOMER BASE

If you're a small business owner, you're likely on the lookout for new customers on a fairly regular basis. After all, having a steady stream of interested parties who become paying clientele is the benchmark of a successful business. Whether you're in a service industry, retail or otherwise, there are some tried and true methods for expanding and keeping your customer base evolving:

1. KNOW YOUR AUDIENCE. Knowledge is power, and knowing just whom you are targeting for business will put you one step ahead of your competitors. Before you set out to gain new clients ask yourself the following questions: Who is my ideal customer? What makes them tick? What can my business provide them that will make their lives better or more efficient? Knowing the answers to these questions will give you a foothold in acquiring new clientele or customers.

2. NETWORK. Socialize at both industry and non-industry events and don't be shy about pitching your business and engaging new people. Go to business functions, join chambers of commerce, attend trade shows. Always have your business cards handy and your one-minute elevator pitch ready to go. You never know who you might meet and where that meeting may take you.

3. RESEARCH YOUR INDUSTRY. What are the latest trends in your area of business? Who are the industry leaders, movers and shakers? What's coming up on the horizon? What's changed in the past few years? All of these questions and more should be easily answered by you or your employees in order to have your company viewed as a leader in its field.

4. JOIN INDUSTRY GROUPS. Member associations for your particular area of business are key to networking but also showcasing your skills. As well, being part of an industry association often allows you to have access to training, upgrading or accreditation courses in your field of business – which will give you the competitive advantage in the marketplace.

5. STEP OUTSIDE OF YOUR BOX. Spread your net far and wide and step outside your comfort zone in order to differentiate yourself to your potential customers. If your business is primarily retail, consider providing a form of consultative services every so often. If you are in the one-on-one consultative business, mix it up once in a while and perhaps engage another consultant or two. The key is to show your flexibility, openness and innovation so that potential clients and customers will be drawn to your company.

6. TWEET, LIKE AND STUMBLE. Social media is here to stay. This is particularly the case in the business world. If you want to expand your reach, get online and become familiar with the more recent methods of expanding your business' footprint as well as engaging new customers. Facebook, Twitter, StumbleUpon and other social media platforms are easily-accessible and proven ways to profile your company as well as offering channels to new and potential new customers.

7. OFFER INCENTIVES. People love free stuff and discounts, so use this fact to get new customers in the door. If you're a service industry, offer an introductory free class or consultation, allowing people to test-drive your services in advance. A related tactic is to offer discount coupons, product specials, etc. as a method of enticing the curious with the end goal of getting them in the door.

8. LEVERAGE EXISTING PARTNERSHIPS. There's nothing like word-of-mouth marketing and you already have existing ambassadors for your company. Your business partners who may include suppliers, can be some of your greatest allies when it comes to increasing your customer base. In addition to the most obvious tact of asking for referrals, consider providing your clientele a deal that gives them product or services from both you and your business partner. Done effectively, it's a "win-win" situation, with both businesses potentially gaining a new customer or two.

9. SHOWCASE YOUR EXPERTISE. There are a number of ways that you can get in front of potential new clients in order to pique their interest about your business or service. Start a blog that discusses relevant topics in your area of business. Write an e-book that is offered free online via your company website. Participate as a featured speaker at conferences, educational facilities or trades shows that cover your industry and you will get in front of people who are already engaged, interested and potentially ready to sign on to your product or service.

DATA  ANALTSIS

What Is Data Analysis?

A Definition of Data Analysis

Data analysis is a primary component of data mining and Business Intelligence (BI) and is key to gaining the insight that drives business decisions. Organizations and enterprises analyze data from a multitude of sources using Big Data management solutions and customer experience management solutions that utilize data analysis to transform data into actionable insights.

              It can also include the various ways the data is visualized to make the trends and relationships intuitive at a glance.” Data analysis involves asking questions about what happened, what is happening, and what will happen (predictive analytics). As Junk puts it, “analytics is generally the data crunching, question-answering phase leading up to the decision-making phase in the overall Business Intelligence process.”

Data Analysis Model

Gwen Shapira, a solutions architect at Cloudera and an Oracle ACE Director, outlines seven key steps of data analysis for Oracle’s Profit magazine. Shapira explains that while each company has its own data requirements and goals, there are seven steps that remain consistent across organizations and their data analysis processes:

·         Decide on the objectives – Determine objectives for data science teams to develop a quantifiable way to determine whether the business is progressing toward its goals; identify metrics or performance indicators early

·         Identify business levers – Identify goals, metrics, and levers early in data analysis projects to give scope and focus to data analysis; this means the business should be willing to make changes to improve its key metrics and reach its goals as well

·         Data collection – Gather as much data from diverse sources as possible in order to build better models and gain more actionable insights

·         Data cleaning – Improve data quality to generate the right results and avoid making incorrect conclusions; automate the process but involve employees to oversee the data cleaning and ensure accuracy

·         Grow a data science team – Include on your science team individuals with advanced degrees in statistics who will focus on data modeling and predictions, as well as infrastructure engineers, software developers, and ETL experts; then, give the team the large-scale data analysis platforms they need to automate data collection and analysis

·         Optimize and repeat – Perfect your data analysis model so you can repeat the process to generate accurate predictions, reach goals, and monitor and report consistently

Benefits and Challenges of Data Analysis

Data analysis is a proven way for organizations and enterprises to gain the information they need to make better decisions, serve their customers, and increase productivity and revenue. The benefits of data analysis are almost too numerous to count, and some of the most rewarding benefits include getting the right information for your business, getting more value out of IT departments, creating more effective marketing campaigns, gaining a better understanding of customers, and so on.

But, there is so much data available today that data analysis is a challenge. Namely, handling and presenting all of the data are two of the most challenging aspects of data analysis. Traditional architectures and infrastructures are not able to handle the sheer amount of data that is being generated today, and decision makers find it takes longer than anticipated to get actionable insight from the data.

Fortunately, data management solutions and customer experience management solutions give enterprises the ability to listen to customer interactions, learn from behavior and contextual information, create more effective actionable insights, and execute more intelligently on insights in order to optimize and engage targets and improve business practices.

Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.

DATA MINING

Data mining is a process to structure the raw data and formulate or recognise the various patterns in the data through the mathematical and computational algorithms, data mining helps to generate new information and unlock the various insights. The data is first placed into a data warehouse to do the required the required extraction of data to produce meaningful relationships and patterns. There is two type of data mining one is descriptive, which gives information about existing data of the organisation, while the other is predictive: which makes forecasts based on the data.

Data mining is a pattern discovery task against a pool of data; therefore it requires classical and advance components of artificial intelligence, pattern distribution and traditional statistics, the point to be noted that data mining is done without any preconceived hypothesis, hence the information that comes from the data is not to answer specific questions of the organisation.

Data mining also helps in exploring trends from the data.

What Is Data Mining?

Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. Data mining is also known as Knowledge Discovery in Data (KDD).

The key properties of data mining are:

·         Automatic discovery of patterns

·         Prediction of likely outcomes

·         Creation of actionable information

·         Focus on large data sets and databases

Data mining can answer questions that cannot be addressed through simple query and reporting techniques.

Automatic Discovery

Data mining is accomplished by building models. A model uses an algorithm to act on a set of data. The notion of automatic discovery refers to the execution of data mining models.

Data mining models can be used to mine the data on which they are built, but most types of models are generalizable to new data. The process of applying a model to new data is known as scoring.

Prediction

Many forms of data mining are predictive. For example, a model might predict income based on education and other demographic factors. Predictions have an associated probability (How likely is this prediction to be true?). Prediction probabilities are also known as confidence (How confident can I be of this prediction?).

Grouping

Other forms of data mining identify natural groupings in the data. For example, a model might identify the segment of the population that has an income within a specified range, that has a good driving record, and that leases a new car on a yearly basis.

Actionable Information

Data mining can derive actionable information from large volumes of data. For example, a town planner might use a model that predicts income based on demographics to develop a plan for low-income housing. A car leasing agency might a use model that identifies customer segments to design a promotion targeting high-value customers.

                  

Data Mining and Statistics

There is a great deal of overlap between data mining and statistics. In fact most of the techniques used in data mining can be placed in a statistical framework. However, data mining techniques are not the same as traditional statistical techniques.

Traditional statistical methods, in general, require a great deal of user interaction in order to validate the correctness of a model. As a result, statistical methods can be difficult to automate. Moreover, statistical methods typically do not scale well to very large data sets. Statistical methods rely on testing hypotheses or finding correlations based on smaller, representative samples of a larger population.

Data mining methods are suitable for large data sets and can be more readily automated. In fact, data mining algorithms often require large data sets for the creation of quality models.

Data Mining and OLAP

On-Line Analytical Processing (OLAP) can been defined as fast analysis of shared multidimensional data. OLAP and data mining are different but complementary activities.

OLAP supports activities such as data summarization, cost allocation, time series analysis, and what-if analysis. However, most OLAP systems do not have inductive inference capabilities beyond the support for time-series forecast. Inductive inference, the process of reaching a general conclusion from specific examples, is a characteristic of data mining. Inductive inference is also known as computational learning.

OLAP systems provide a multidimensional view of the data, including full support for hierarchies. This view of the data is a natural way to analyze businesses and organizations. Data mining, on the other hand, usually does not have a concept of dimensions and hierarchies.

Data mining and OLAP can be integrated in a number of ways. For example, data mining can be used to select the dimensions for a cube, create new values for a dimension, or create new measures for a cube. OLAP can be used to analyze data mining results at different levels of granularity.

Data Mining can help you construct more interesting and useful cubes. For example, the results of predictive data mining could be added as custom measures to a cube. Such measures might provide information such as "likely to default" or "likely to buy" for each customer. OLAP processing could then aggregate and summarize the probabilities.

Data Mining and Data Warehousing

Data can be mined whether it is stored in flat files, spreadsheets, database tables, or some other storage format. The important criteria for the data is not the storage format, but its applicability to the problem to be solved.

Proper data cleansing and preparation are very important for data mining, and a data warehouse can facilitate these activities. However, a data warehouse will be of no use if it does not contain the data you need to solve your problem.

Oracle Data Mining requires that the data be presented as a case table in single-record case format. All the data for each record (case) must be contained within a row. Most typically, the case table is a view that presents the data in the required format for mining.

THE DATA MINING PROCESS

 illustrates the phases, and the iterative nature, of a data mining project. The process flow shows that a data mining project does not stop when a particular solution is deployed. The results of data mining trigger new business questions, which in turn can be used to develop more focused models.

The Data Mining Process

The Data Mining Process"

Problem Definition

This initial phase of a data mining project focuses on understanding the project objectives and requirements. Once you have specified the project from a business perspective, you can formulate it as a data mining problem and develop a preliminary implementation plan.

For example, your business problem might be: "How can I sell more of my product to customers?" You might translate this into a data mining problem such as: "Which customers are most likely to purchase the product?" A model that predicts who is most likely to purchase the product must be built on data that describes the customers who have purchased the product in the past. Before building the model, you must assemble the data that is likely to contain relationships between customers who have purchased the product and customers who have not purchased the product. Customer attributes might include age, number of children, years of residence, owners/renters, and so on.

Data Gathering and Preparation

The data understanding phase involves data collection and exploration. As you take a closer look at the data, you can determine how well it addresses the business problem. You might decide to remove some of the data or add additional data. This is also the time to identify data quality problems and to scan for patterns in the data.

The data preparation phase covers all the tasks involved in creating the case table you will use to build the model. Data preparation tasks are likely to be performed multiple times, and not in any prescribed order. Tasks include table, case, and attribute selection as well as data cleansing and transformation. For example, you might transform a DATE_OF_BIRTH column to AGE; you might insert the average income in cases where the INCOME column is null.

Model Building and Evaluation

In this phase, you select and apply various modeling techniques and calibrate the parameters to optimal values. If the algorithm requires data transformations, you will need to step back to the previous phase to implement them (unless you are using Oracle Automatic Data Preparation,

In preliminary model building, it often makes sense to work with a reduced set of data (fewer rows in the case table), since the final case table might contain thousands or millions of cases.

At this stage of the project, it is time to evaluate how well the model satisfies the originally-stated business goal (phase 1). If the model is supposed to predict customers who are likely to purchase a product, does it sufficiently differentiate between the two classes? Is there sufficient lift? Are the trade-offs shown in the confusion matrix acceptable? Would the model be improved by adding text data? Should transactional data such as purchases (market-basket data) be included? Should costs associated with false positives or false negatives be incorporated into the model.

Knowledge Deployment

Knowledge deployment is the use of data mining within a target environment. In the deployment phase, insight and actionable information can be derived from data.

Deployment can involve scoring (the application of models to new data), the extraction of model details (for example the rules of a decision tree), or the integration of data mining models within applications, data warehouse infrastructure, or query and reporting tools.

Because Oracle Data Mining builds and applies data mining models inside Oracle Database, the results are immediately available.

WHAT IS CUSTOMER LOYALTY?

Definition: Customer loyalty indicates the extent to which customers are devoted to a company’s products or services and how strong is their tendency to select one brand over the competition

Define Customer Loyalty: Customer loyalty means a consumer’s devotion to a company, product line, or brand.

What Does Customer Loyalty Mean?

What is the definition of customer loyalty? Customer loyalty is positively related to customer satisfaction as happy customers consistently favor the brands that meet their needs. Loyal customers are purchasing a firm’s products or services exclusively, and they are not willing to switch their preferences over a competitive firm.

A customer who consistently purchases the same product, service or brand.

TYPES OF CUSTOMER LOYALTY

                                                     Consistently purchasing the same Customer loyalty is when a customer consistently purchases from a business. This is typically based on the customer's needs, preferences and experiences with the business. The following are common types of customer loyalty.

PRODUCT

product. This can be due to the features or quality of the product. For example, a customer may try several shampoos until they find one that they prefer. Once a preference is established it may continue for years. Calculating customer loyalty for products requires extensive market data and is often based on representative samples. For example, a firm might define a loyal customer as someone who purchases the product at least once a month for six consecutive months.

SERVICES

Subscription based services often benefit from loyal customers that represent monthly recurring revenuestreams. Calculating loyal customers is easy for subscription based services as ample data is typically available. Six consecutive months is a common threshold. Restaurants and other non-subscription services typically base customer loyalty on purchase frequency such as once a month or six times a year.

BRAND

Brand loyalty may result from reputation, customer experience or a customer who identifies with the brand. Customers may be loyal to all the products offered by a brand or some subset.

DISTRIBUTION

A customer may be loyal to a particular location simply because it's convenient. For example, a customer may be loyal to a restaurant chain because it's close to their office. If the convenient location closes, the customer may be unlikely to seek the chain out elsewhere. Likewise, brand or product loyalty may end if products aren't available where the customer shops.

PRICE

A customer may be in the habit of consistently purchasing the same product or service based on price alone. For example, a customer may always purchase the same brand of coffee because its always the cheapest on the shelves. Such a customer will immediately switch if prices go up and may be indifferent to efforts to improve product quality.

RELATIONSHIPS

A customer may be loyal to a person such as a salesperson but not the underlying products, services or brand. Often a top salesperson who leaves a firm is able to attract their former customers to their new firm's products.

CUSTOMER VALUE ASSESSMENT

A customer value proposition can be defined and assessed through the development of the Customer Value Assessment (CVA).  The Customer Value Assessment is a set of criteria across four categories that capture a customer’s wants, needs or expectations. Using these criteria, organizations gain insight into the customer’s perception of their organization. 

 The four CVA categories include Solutions, Responsiveness, Economics, and Relationship.

 

Solutions – Does the organization offer solutions that customers prefer?

Solutions represent the core products and services provided by the organization to fulfill its purpose.  Ideally, solutions would successfully solve the customer’s problem, or fully satisfy the customer’s want or need. Three elements further define this category.

• Portfolio – the offerings of products and services intended to fulfill the customer’s wants or needs.
• Characteristics – the intended function that the provided products and services fulfill.
• Presentation – the format, packaging, supporting information and method of delivery of the products and services provided.

Responsiveness – Does the organization provide what customers want, when they want it?

Responsiveness represents the customer-desired time for all transactions.  This can be expressed in several ways, such as time to respond, availability, scheduled when convenient, regularly scheduled and frequency. Three elements further define this category.

• Delivery – the time to deliver the provided solutions.
• Transactions – the time to respond to each customer interaction.
• Inquiry – the time to respond to market and customer required changes.

Economics – Does the organization provide attractive financial value?

Economics represents the customer’s financial considerations when evaluating the purchase of products and services.   Three elements further define this category.

• Cost – the total cost to the customer of usage, consumption or ownership of the product or service provided.
• Investment – the additional or incremental tangible value provided with or without cost to the customer.
• Finance – the terms, risk, profit contribution and other economic considerations considered by the customer in acquiring the products or services.

Relationship – Is the organization important to the customer?

Relationship defines the customer’s perception of the organization that provides solutions.  Three elements further define this category.

• Service – the assistance provided to the customer in support of the core solution and transactions.  Service is intended to improve the customer’s experience by assisting the customer to meet their needs.
• Trust – the level of confidence that the customer has that the organization can be trusted, requiring knowledge, intention, and capability.
• Involvement -the level of engagement the customer desires from the organization, such as weekly reviews, annual senior leadership meetings, or even no contact at all.  Ideally, an organization would be considered a valuable partner to the customer’s business.

The Customer Value Assessment defines customer relationships.

The CVA can be used to accomplish several key objectives:

• Define customer types
• Determines what each customer type requires
• Assesses if the organization gives customers what they want
• Examines the gaps and their causes
• Determine the changes necessary to close those gaps

A DEFINITION OF CUSTOMER RETENTION

Customer retention is the ability of a business to retain customers. It is both a measure of customer loyalty and the capacity of the business to keep customers satisfied by good service and quality of the product sold.

                   Customer retention refers to the activities and actions companies and organizations take to reduce the number of customer defections. The goal of customer retention programs is to help companies retain as many customers as possible, often through customer loyalty and brand loyalty initiatives. It is important to remember that customer retention begins with the first contact a customer has with a company and continues throughout the entire lifetime of the relationship.

IMPROVING CUSTOMER RETENTION

Research indicates that returning customers tend to spend more. So along with the other benefits it is in a store’s interest to spend time and money on customer retention. There are many ways for the store to achieve this:

·         Stock – by selling quality product the customer will be happy and likely to return for more.

·         Pricing – in pricing a product at a reasonable one for the market the customer will consider returning to purchase more in the future.

·         Customer Service – good customer service from the minute the client enters a store will help sales and the memory will stay with the customer. Good after sales customer service in the case of an issue, helps offset further problems and assures the customer of the store’s good intentions.

·         Appealing Shop Layout – making a store easy to navigate, warm in the cold months, cool in the summer ones and customer friendly decor can help hugely in retaining a customer. Nobody will want to return to a cold store where product is difficult to find and the paint is peeling off the walls.

Other customer retention strategies include:

1. Blogs
2. CRM Systems
3. Loyalty Programs A customer loyalty program is a rewards program offered by a company to customers who frequently make purchases. A loyalty program may give a customer free merchandise, rewards, coupons, or even advance released products
4. Magic Moments
5. Overcome Buyers Remorse
6. Personal Touches
7. Premiums and Gifts
8. Questionnaires and Surveys
9. Regular Reviews
10. Social Media
11. Welcome Book

RETENTION AND CROSS SELL ANALYSIS

Cross selling and Upselling is one of the most widely discussed concept in marketing analytics. Every other day when you visit a supermarket, restaurant to purchase something, this concept comes into live action. This concept is being taught in every marketing class across the world, thereby students are expected to know of it.

Definition – Cross-Sell and Up-Sell

Cross-sell involves the sale of multiple products offered by a single product/service provider to a new or existing customer. Up-sell is selling higher value products/services to an existing customer.

For example:

1. You plan to purchase a mobile phone within a price range of Rs. 30,000(~$500). However, you eventually end up purchasing a mobile phone of Rs.42,000 ($650) because the salesman presented various other phones with fantastic features and you got swayed away with them. (This is Up-Selling).
2. You plan to purchase a mobile phone worth Rs. 30,000(~$500), but the salesman offered you a charming deal of buying mobile phone with exclusive JBL headphones for Rs.40,000 (~$634) only and you again got swayed away. (This is Cross-Selling).

Cross-selling is a core component of a customer centric relationship strategy and requires an integrated view of the customer. The success of a cross-sell program depends on enablers such as organizational commitment; well-defined business strategy; effective execution; regular monitoring; and effective targeting strategy. Cross-selling has proved to be a defining strategy for profitable growth across multiple sectors.

BENEFITS OF CROSS SELLING

Cross Selling offers benefit to both the ends of marketing cycle i.e. customer and firm.

For the Firm

• Builds customer equity
• Differentiates from competition, enhances market position
• Promotes diversification and innovation
• Stimulates universe expansion and entry into new markets
• Balances growth between new and existing customers, low and high margin products and segments
• Enhance customer profitability
• Discourages customer attrition, improves customer loyalty

For the Customer

• Patronizes the brand
• Broadens choices of product and services
• Offers convenience through one-stop shopping, flexibility, consolidated bill and others
• Increases customer satisfaction
• Lowers price
• Encourages better customer service from relationship marketing

Bain, a top global consulting firm was given a project to increase the number of cross-selling relationships among existing customers and to increase spend of each customer targeted for a leading Bank. Bain’s analysis identified profitable customers for cross-selling and determined that, on average, cross-selling the new product would increase individual customer spend and profitability significantly.

EFFECTS OF MARKETING ACTIVITIES

This paper examines whether sales promotions effectiveness depends upon the consumer’s brand loyalty and her buying behaviour and whether consumer’s behavioural characteristics in term of purchase frequency and level affect the response to promotional activities and moderate the effect of brand loyalty during the consumer choice process. Different specifications for the utility function, exploiting information on the selling price, promotional activities such as displays usage, ad features in the store, 3x2 and discount, and differently brand loyalty measures have been estimated into a discrete choice framework, that is into the rational brand choice paradigm, paying attention to their effects on individuals’ probabilities to choose the specific brand during each purchase occasion. 

THE EFFECTS OF ADVERTISING AND PROMOTION

Advertising and promotion are essential components of a successful business. Their effects include brand establishment, growth within your target market segment, the discovery of new secondary markets, the development of customer loyalty and defense against competition. Businesses without marketing strategies may save money, but they operate at a distinct disadvantage.

Brand Establishment

Advertising and promotional efforts help businesses establish themselves in the market as viable brands. Unknown companies can become known quantities through their marketing efforts and can create an image of their liking in the process. For example, when a business launches a marketing campaign that equates its products with Babe Ruth, the Empire State Building and Broadway, the consumer may infer that the brand is touting itself as a New York icon. The brand may be new and the products largely unknown, but the message will draw the attention of consumers, who may give it a try.

Primary Market Growth

Businesses can build an existing market segment and improve market position substantially through the use of advertising and promotion. If your business has been around for some time and has found a dependable primary market segment, you may wonder how to add to the existing base of customers without losing the ones you have. Targeted advertising and promotional campaigns can show your current customers you have even more to offer, thus increasing their spending and your business's revenue.

Customer Loyalty

Promotions can increase customer loyalty by getting the word out about special pricing, rewards programs and other incentives to buy. Existing customers are your most important target market, because you have an established relationship and you typically have access to the data that makes direct marketing possible. When you regularly remind customers about your offerings, they may begin to visit your company first for whatever they need, bypassing other options. Making it clear that you have the best product and the best price through the use of marketing often results in increased loyalty.

Combatting the Competition

Marketing is an effective defense against competition. It keeps your brand name in the public eye and prevents other companies from gaining a foothold among the market segments you have secured. Promotions attract new and old customers and can keep your company relevant when competitors appear. If a competitor runs a sale, you can counteract the attention she gets by running and advertising one of your own. Keep the consumer focused on your brand and don't allow him to be swayed by another.

New Markets

Advertising and promotion can do more than just maintain the strength of your primary market; they can also expose you to new secondary markets that add revenue and possibilities for your development. Marketing attracts the general consumer and can affect industry professionals. If a company sees that your brand delivers what it needs, it might ask you to deliver product on a wholesale basis. New secondary markets can even exceed the value of your primary market over time.

Problems

               The main sticking point for businesses that fail to engage in advertising and promotional campaigns is the cost. Many businesses avoid taking on new expenses and never really consider the return on investment and potential for growth involved. If your marketing strategy is well researched and your message rings true, the investment you make can repay you many times over.

REPORTING RESULT

CRM reports, if you want to use your system well

Customer Relationship Management (CRM) system? It’s nothing more than a database. Sure, there are lots of powerful features. But these features all have the same goal: to collect data that can be used to help improve your sales, service, and marketing departments. My clients who have figured this out are the ones who get the most value from their CRM system.

How? By making sure that their databases are running reports for management to see—because, without reports, the data is useless. Many CRM systems come with scores of reports. But in my experience, these are the five that are the most popular.

Pipeline Report 

This report should tell you the details of all open opportunities such as the customer, opportunity size, expected date of close, and the responsible team. It should also display what actions were most recently done and what actions are scheduled in the future. You should be able to sort your pipeline reports by region, close date, and team. This way no opportunity falls through the cracks. With close management, this report will also help you better estimate future cash flow, based on when opportunities are scheduled to turn into sales. Consider limiting these opportunities to the ones that exceed a certain size, so you make the best use of your time.

Activity Report  

                         As long as they’re meeting their goals and quotas, most of your sales reps and customer service agent shouldn’t need micromanagement. But, if you’re like most of my clients, there will be a few that need a little more attention. That’s where the Activity Report comes in. Here, you’re tracking activities (i.e. calls, appointments, actions) completed in the prior week and activities scheduled for the next week. You should be able to do this on an individual rep/agent level, and see details like customer info, type of activity, notes and due/completion dates. This way you can help reps manage their time.

Open Issues Report

 My smartest clients don’t run from problems – they gravitate toward them. They gravitate towards problems so they can work to get them resolved. That’s why the Open Issues Report is so popular. It’s a simple report, made up of a designated activity (i.e. call ticket, task) that indicates a problem. The problem may be external (customer complaint, a service issue, a return) or internal (key employee out of the office, server down, a shortage of decaf coffee). The report usually shows the nature of the problem, who’s working on it, notes, what’s been done, and what’s scheduled to be done, as well as an estimated resolution date. Problems are closed out when resolved with resolution codes or explanations because – believe it or not – some managers like to go back and look at problems in the past and what was done to fix them.

Leads Report 

Whether you’re sending out emails, tweaking your search engine optimization, running events or sharing content on social media, the end result is always the same: you want new leads… Remember that your CRM system is the repository of all data you’ll be using to drive your marketing campaigns. As new leads come in it will be important to track them so you can see which campaign performed the best. Having a weekly leads report that shows you the prospects contact info, product interest, where the lead was sourced, last action, and next scheduled task is how you’ll keep up on all potential opportunities.

Lost Sales Report 

This, without question, is the most hated reports amongst my clients. But it also tends to be one of the more important ones. As you add opportunities to your Pipeline Report, make sure you have a process for closing them out, regardless of whether the opportunity was successfully or unsuccessfully resolved. Then, once a month, sit down with your Lost Sales Report. This report will list all of the sales that you didn’t get, along with the reasons you noted when you closed out the opportunity. It’s hard, but you’ll learn – and hopefully use the data from the report to make needed changes going forward.

Remember – your CRM system is just a database. A very important database. The more you use that data – through reporting – the greater value you’ll get from your CRM investment.