Customer Relationship Management

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Chapter 11: Author – Bidgoli, H. (2019) Management Information Systems (9th ed.) Cengage. https://www.cengage.com/

Customer relationship management (CRM) consists of the processes a company uses to track and organize its contacts with customers. The main goal of a CRM system is to improve services offered to customers and use customer contact information for targeted marketing. Businesses know that keeping and maintaining current customers is less expensive than attracting new customers, and an effective CRM system is useful in meeting this goal.

Marketing strategies in a CRM system focus on long-term relationships with customers instead of transactions. These strategies include identifying customer segments, improving products and services to meet customers’ needs, improving customer retention, and identifying a company’s most profitable (and loyal) customers. To get the most out of these strategies, a CRM system helps organizations make better use of data, information, and knowledge to understand their customers. A CRM system captures information about customer interactions for sales personnel and customer service representatives so they can perform their jobs more effectively and efficiently. This information can include customers’ preferences, background, income, gender, and education.

Marketing strategies in a CRM system focus on long-term relationships with customers instead of transactions.

CRM is more than just tracking and organizing contacts with customers. It gives organizations more complete pictures of their customers. CRM systems include tools for conducting complex analyses on customer data, such as a data warehouse and data-mining tools, discussed in Chapter 3. With these systems, organizations can integrate demographic and other external data with customers’ transaction data to better understand customer behavior. Based on this analysis, organizations can better target products to customers and manage customer issues, which increases customer satisfaction and retention. In addition, organizations can classify customers based on how valuable they are to the organization and manage them accordingly.

A grocery store offering loyalty cards with discounts to its customers is an example of how transaction data can be used in a CRM system. Knowing that a customer bought 4 gallons of milk the previous week does not give a grocery store much information, but with loyalty cards, the store can track all sorts of information on specific customers. When customers apply for loyalty cards, for example, they can be asked to give demographic information, such as name, age, marital status, and address. So, instead of knowing that “Customer 49 bought 4 gallons of milk last week,” a store can learn that “James Smith, 35 years old, married and residing in zip code 11223, bought 4 gallons of milk last week.” With this information, the store can assume James Smith has young children (or clearly is not lactose intolerant!). In addition, if James Smith purchases no cereal that same week, the store can assume he is buying cereal from another store (because with the purchase of that amount of milk and the assumption that he has young children, it is likely his children are eating cereal). Therefore, the store decides to send coupons for discounts on cereal to James Smith. This is referred to as “cross-selling”—getting the customer to buy additional products. The store might also send James Smith coupons for a more expensive brand of milk, in the hope that his family will decide it prefers that brand. This practice is called “upselling.”

Organizations can also pay external agencies for additional data about their potential customers. This data might be public or semiprivate, such as whether they own their homes, the value of their homes, and their estimated mortgage or rent payments. This gives organizations more information to analyze.

With a CRM system, an organization can do the following:

Provide services and products that meet customers’ needs.

Offer better customer service through multiple channels (traditional as well as the Internet).

Increase cross-selling and upselling of products to increase revenue from existing customers.

Help sales personnel close deals faster by offering data on customers’ backgrounds.

Retain existing customers, and attract new ones.

Several IT tools discussed throughout this book are used to improve customer service. For example, e-mail, the Internet, Web portals, and automated call centers have played a major role in CRM systems. E-commerce sites use e-mail to confirm items purchased, confirm shipping arrangements, and send notifications on new products and services. Web portals and extranets, such as FedEx.com, allow customers to perform tasks, such as checking the status of shipments and arranging a package pickup. Database systems, data warehouses, and data-mining tools are effective in tracking and analyzing customers’ buying patterns, which helps businesses meet customers’ needs. Yet this information could be used to generate predictive analytics that an organization can use for future planning in offering new products and services. The emergence of big data and the Internet of Everything may open up additional channels for reaching customers for increasing revenue and improving customer service. A CRM system includes the following activities:

Sales automation

Order processing

Marketing automation

Customer support

Knowledge management

Personalization technology

These activities, performed by CRM software, are discussed in more detail in the following sections. The information box “CRM at Delta Air Lines” highlights CRM applications at Delta Air Lines.

CRM at Delta Air Lines

Delta Air Lines serves more than 180 million customers each year. It offers service to 323 destinations in 57 countries on six continents.

Using Salesforce, Delta has implemented a CRM to assist and improve communication and collaboration among its sales teams. One of the major tasks for which the company needed this program was to enable global sales representatives to share account information and address customers’ needs.

Delta used Sales Cloud for account, activity, and contact information. The system provides essential information when sales representatives call on existing accounts or offer services to new customers. An app automates corporate and agency programs and assists sales representatives to track any request.

According to Kristen Shovlin, vice president of Sales Operations, the system gives Delta a complete visibility into opportunities and programs across the globe. Sales teams are now able to access the system using their mobile devices and offer customer service anytime and from anywhere. Delta also implemented a Chatter social network for further improving collaboration among the team members. This platform helps employees share files and find the needed information in a timely manner. Chatter has made real-time collaboration a reality at Delta. CRM Applications

Typically, CRM applications are implemented with one of two approaches: on-premises CRM or Web-based CRM. Organizations with an established IT infrastructure often choose an on-premises CRM, which is implemented much like any other IT system. With Web-based CRM, the company accesses the application via a Web interface instead of running the application on its own computers and pays to use CRM software as a service (SaaS), which is similar to Web-hosting services. The SaaS vendor also handles technical issues. (SaaS is covered in more detail in Chapter 14.) Several software packages are available for setting up a CRM system, including Amdocs CRM (www.amdocs.com/About/Pages/default.aspx), Optima Technologies ExSellence (www.optima-tech.com), Infor CRM (www.infor.com/solutions/crm), SAP CRM, (http://help.sap.com/CRM), and Oracle CRM (www.oracle.com/us/solutions/crm/overview/index/html). Although these packages vary in capabilities, they share the following features:

Salesforce automation—Assists with such tasks as controlling inventory, processing orders, tracking customer interactions, and analyzing sales forecasts and performance. It also assists with collecting, storing, and managing sales contacts and leads.

eCRM or Web-based CRM—Allows Web-based customer interaction and is used to automate e-mail, call logs, Web site analytics, and campaign management. Companies use campaign management to customize marketing campaigns, such as designing a marketing campaign tailored to customers in Southern California or customers in the 18–35 age bracket.

Survey management—Automates electronic surveys, polls, and questionnaires, which is useful for gathering information on customers’ preferences.

Automated customer service—Used to manage call centers and help desks and can sometimes answer customers’ queries automatically.

The integration of CRM, IoT, social media, and analytics has created an environment for CRM that is able to do the following in order to attract new customers and better serve the existing customers:

Social CRM—interaction with customers through the Internet and social media.

Individuation of messaging to customers through analytics using customers’ social media information.

Hypertargeting—delivery of highly customized content to highly specific customer subgroups of the total customer population. This creates a personal communication that makes the customer feel special.

Personalization Technology

Personalization is the process of satisfying customers’ needs, building customer relationships, and increasing profits by designing goods and services that meet customers’ preferences better. It involves not only customers’ requests but also the interaction between customers and the company. You are probably familiar with Web sites that tailor content based on interests and preferences. Amazon, for example, suggests products users might enjoy based on past browsing and purchasing habits.

Customization, which is somewhat different from personalization, allows customers to modify the standard offering, such as selecting a different home page to be displayed each time a browser is opened. As another example, after registering with Yahoo!, a user can customize the start page by choosing preferred layout, content, and colors. There are many examples of customization in retail, too, such as Build-A-Bear Workshops, where children can design their own teddy bears, or Nike, which allows customers to create their own shoes by selecting styles and colors.

Customization, which is somewhat different from personalization, allows customers to modify the standard offering, such as selecting a different home page to be displayed each time a browser is opened.

Because personalization and customization help companies meet customers’ preferences and needs, customers often experience a more efficient shopping process and, as a result, are less likely to switch to competitors to get similar products or services. However, using personalization requires gathering a lot of information about customers’ preferences and shopping patterns, and some customers get impatient with answering long surveys about their preferences. In addition, collecting this information might affect customers’ sense of privacy. For example, drugstore customers might be concerned that the drugstore has their prescription histories, that the information might be misused and even affect their insurance coverage. To ease these concerns, companies should include clear privacy policies on their Web sites stating how personal information is collected and used.

Amazon is known for using personalization to recommend products to customers with the message “Customers who bought this item also bought” followed by a list of suggestions. Amazon’s recommendation system is made up of a huge database containing customers’ previous purchases and a recommendation algorithm. When a customer logs on to Amazon, the recommendation system first checks the customer’s purchase history and that of similar customers. Using this information, a list of recommended products is displayed, based on the customer’s shopping history and choices by other customers who have similar purchase histories. In addition, Amazon gives customers an opportunity to rate the recommendations. The more items the customer purchases and the more recommendations the customer rates, the better the recommendations are tailored to the customer.

Many other companies use personalization technology to improve customer service. For example, if you buy a suit from Nordstrom.com, the site might suggest shoes or a tie that goes with the suit or a similar suit in the same category. If you buy a song from Apple iTunes, other songs purchased by listeners like you are suggested. Google also provides personalized services for Google account holders. Users can get personalized search results that are reordered based on their searching histories. Avni Shah, Google’s product manager, has explained that if a user has “fly fishing” in his or her search history and then searches on “bass,” more weight is given to search results that point to Web pages about fish rather than pages about musical instruments. Google also has a bookmark feature so users can save useful search results for later use. Unlike Yahoo!’s MyWeb feature, which saves the text of Web pages, this feature simply saves the link to the page.

To implement a personalization system, several IT tools are needed, including the Internet, databases, data warehouse/data marts, data-mining tools, mobile networks, and collaborative filtering. Collaborative filtering (CF) is a search for specific information or patterns using input from multiple business partners and data sources. It identifies groups of people based on common interests and recommends products or services based on what members of the group purchased or did not purchase. It works well for a single product category, such as books, computers, and so forth. One drawback of CF is that it needs a large sample of users and content to work well. In addition, it is not useful for making recommendations across unrelated categories, such as predicting that customers who liked a certain CD would also like a particular computer.

One application of collaborative filtering is making automatic predictions about customers’ preferences and interests based on similar users. For example, if a user rates several movies and is then added to a database that contains other users’ ratings, a CF system can predict the user’s ratings for movies he or she has not evaluated. You may have seen this feature used on Netflix.com, where lists of other movies you might like are displayed. Recently, Netflix paid $1 million to the team that won a contest to come up with the best algorithm for improving the accuracy of the Netflix recommendation system. Other Web sites that use CF systems to improve customer services are Amazon, Barnes and Noble, and Netflix.

The information box Amazon’s “Personalization Assists Sellers on Its Marketplace” explains how Amazon uses personalization technologies to increase the efficiency and effectiveness of its marketplace.

Amazon’s Personalization Assists Sellers on Its Marketplace

Amazon was one of the first companies to use data mining tools and personalization technologies to tailor goods and services to its customers’ purchasing habits. Since 2009, the company has also been using its personalization technologies to assist the third-party sellers that provide goods and services in Amazon’s various marketplaces around the world. Amazon Marketplace was launched in November 2000. It allows sellers to offer new and used products right next to Amazon’s own offerings using Amazon’s infrastructure. Today, Amazon has over 2 million sellers in Amazon marketplaces based in 10 countries, serving more than 200 million customers worldwide.

Amazon uses its personalization technologies to offer predictive data-driven recommendations to all of its sellers. Personalization technologies help these sellers manage all aspects of their inventories, including how much to carry, what to carry, and how to expand into new markets and geographical locations. Managing inventory during seasonal changes and managing product offerings during the holidays are challenging tasks. Amazon makes these recommendations by using customer data on its site and the data available on social media. Recently, Amazon began recommending products to its sellers that they can sell outside their home countries.

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