Analytics in CRM an introduction to analytics

Analytics in CRM an introduction to analytics

In CRM (customer relationship management) the term customer analytics —also called CRM analytics — is used to describe an automated methodology of processing data about a customer in order to make better business decisions.

Pattern-Based Strategy 

Gartner defines Pattern-Based Strategy as the discipline that enables business leaders to seek, amplify, examine and exploit new business patterns. A business pattern is a set of recurring and/or related elements (business activities, events, weak or strong signals) that indicates a business opportunity or threat.

Analytic CRM and Database Marketing

Database marketing is an important part of Analytic CRM. In a plain language, database marketing is a marketing technique that utilizes customer databases. Formally, database marketing is a form of one-to-one direct marketing in which databases of customers (or potential customers) are used to generate personalized communications in promoting products or services. Database marketing may include;

Customer profiling Customer segmentation Customer scoring Customer retention Cross-selling Up-selling

Customer Information used in Analytic CRM and Database Marketing Database marketing techniques described here are well-suited to companies with a large number of direct customers or data providers with a large number of potential customers. Customer information that can be used in analytical database marketing may include the followings; Demographic variables describe characteristics of populations and include age, gender, race, education, occupation, income, religion, marital status, family size, children, home ownership, socioeconomic status, and so on. Geographic variables include various classification of geographic areas, for example, zip code, state, country, region, climate, population, and other geographical census data. Note that this information can come from national census data. Psychographic variables describe life style, personality, values, attitudes, and so on. Behavioral variables include product usage rate and end, brand royalty, benefit sought, decision making units, ready-to-buy stage, and so on. This information can be extremely useful for marketing purposes. Past business history includes various business statistics on customers. This provides essential business indicators and therefore is very important information. In addition, RFM information can be included in the list; Recency: Customers purchased recently tend to buy again. Frequency: Customers purchased frequently tend to buy again.

Analytic Database Marketing Techniques

A prominent feature of database marketing is that there is readily available rich information about customers. Customer databases can become information gold mines. Analyzing them can lead to new marketing opportunities, thereby deepening customer relationships. Common analytic techniques used in database marketing are profiling, segmentation, and scoring. They are described in the subsequent sections.

Customer Profiling

Understating customers is very important in any business. Profiling customers can lead to better understanding about customers. For example, credit/insurance risk levels, marketing responsiveness, churn risk level, and so on. Although customer profiles may be invaluable for conducting businesses, obtaining them may not come easily. The main problem is with the size of customer information. There many dozens or hundreds of customer data fields. Without advanced software tools, analyzing them and extracting most relevant information is a non-trivial task.

Customer Database Segmentation

People with similar attributes tend to exhibit similar tendencies in purchasing patterns. This leads to development of various segmentation techniques. Segmentation is performed in a way that keeps customers of a segment to have similar attributes (or profiles), while customers in different segments have dissimilar attributes.

Database Marketing Methods

Database marketing techniques can be applied to different marketing approaches. The following sections describe various database marketing methods.

Direct Marketing - Mails, Emails, SMS, Telemarketing

Direct marketing is a form of marketing in which marketers promote products directly to customers. Common forms of direct marketing include postal mails, emails and telemarketing. The first two send catalogs to customers.

The problem with direct marketing is with the fact that success rates of direct marketing are very low. For example, some survey suggests that national average of catalog sales success ratio is about 2%! With such success ratio, selling low profit margin products through direct marketing may not be feasible. The following techniques can be used in selecting customers;

• Customer profiling
• Customer segmentation
• Customer scoring

Cross-selling and Basket analysis

Cross-selling is to sell other products to existing customers. To increase the success rate, other products tend to be co-products or related products. For example, a customer who bought a pocket MP3 player will be highly interested in buying rechargeable batteries along with a charger.

Up-selling

Up-selling refers to selling more expensive products or services to existing users of the same type items. The normally involves upgrades or replacements to more expensive products or services. It is noted that up-selling is a bit tricky business. Selling more expensive products to customers who cannot afford is no business.

• Customer profiling
  Profiles of customers who are using higher-cost-version products are developed. The profiles are then used to identify customers who are in the profiles but still using lower-cost-version products. For obvious reasons, they are candidates for up-selling marketing efforts.
• Customer scoring
  Customers are given scores 0 and 1 depending on product versions they are using, i.e., for low-cost-version users are given 0 and the others 1. With the scores, a neural network predictive model is developed. The model is then applied to customer database records to compute predicted scores.
• Customer segmentation
  Segmenting customers may produce segments with many customers using higher-cost-version products. Customers using lower-cost-version in those segments can be good candidates for up-sell marketing.

Customer Retention

In many industries, customer churn is a big problem. For example, telecommunication industry has very high customer churn rate, especially in wireless services. It is noted that acquiring a new customer is several times more expensive than keeping an existing one. Therefore retaining customers is a very important management issue.

The following techniques can be used to identify customer groups of defection risk;

• Defector profiling
  Develop profiles of risky groups based on demographic, geographic and psychographic attributes.
• Defection scoring
  Build neural network predictive models that can predict likelihood of defection.
• Customer segmentation
  Segment customers based on similarity in terms of demographic, geographic, and psychographic attributes.

Customer Intelligence Cycle

Four major components of Customer Intelligence Cycle

In this article, I want to talk about the four major components of Customer Intelligence cycle. When we talk about CRM Intelligence cycle. Customer intelligence cycle has to do with how you get as well as how you use the result of your analysis. So, let us look at the four main components of CRM Intelligence management cycle. 

Collect

 This is the first stage of CRM Intelligence cycle. At this stage , the organization tries to collect as many information as possible about their current and prospective customers, Having. This will also enable the organization to know their campaign strategies that are working and the ones that are not. Another word for this aspect is Data Management Infrastructure,

Analyze
Analysis of captured data is the next step in CRM Intelligence management cycle. At the Analysis stage, the organization tries to look closely at the captured data using some algorithm in order to determine some patterns that are common to their profitable customers.

 This procedure will help the organization to predict the next move of their customers as they move on to retain and raise exit barrier for their customers.

Action
The Action stage of customer intelligence cycle, also known as Communication management Infrastructure is when the organization now use the new knowledge that they have about their customers to shape their interaction with the customers. This will help the organization to interact with customers and know what they are actually looking for in the organization’s product and services. They will also be able to develop new products and services based on customer’s feedback.

Measure
The Measure stage of Customer Intelligence Cycle Management is a continuous process. It is what helps the organization to adjust their customer interaction process based on new realities.

The organization will be able to adjust their process in order to suit what the customers are looking for in the organization.

Measuring profitabity

Customer profitability. Customer profitability (CP) is the profit the firm makes from serving a customer or customer group over a specified period of time, specifically the difference between the revenues earned from and the costs associated with the customer relationship in a specified period.