Drivers for adoption of Business Analytics

 

As the time advances and complexity in the business processes increase there in an increasing need of automation for the business management and there is an increase in the need for better governance systems, intelligent decision support systems

First Generation Drivers

• Transaction Automation Processes
• Billing and Accounting Systems
• Sales Automation Systems
• Management dashboards       

These first generation drivers are purely focused on task and function based automation and had a bit of passive involvement

Second Generation Drivers

• Enterprise Resource Planning (ERP) systems,
• Customer Relationship management (CRM) systems 
• Supply Chain Management (SCM) systems

These second generation drivers were focused on business automation, aggregation of multiple functions and business efficacy improvement along with providing a consolidated one stop solution to the business needs

Third Generation Drivers

• Data Warehousing
• Business Intelligence
• Business Process Re-engineering

These third generation drivers took a quantum leap and took the area of analytical thinking to a totally new realm. Focus on cross-functional integration, vision based pro-active improvement steps and a capability to quantify and measure business performance and growth were the key factors

According to Dan Vesset, IDC vice president of business analytics research and IDC analysts in “Worldwide Business Analytics Software 2010-2014 Forecast and 2009 Vendor Shares,” there is a predicted expansion in business intelligence and analytics markets by about 3 percent from a similar review from 2008. However, mainstream acceptance of analytics as a business tool, particularly by top-level executives, has kept the industry in the black during the recession.

Recently, with emerging shifts in business toward cloud computing and SaaS – industries which are expected to register about 20 percent in annual growth through 2014 – related analytics needs should put the BI market in good shape for the next decade, Vesset says. “The fundamentals of demand continue to be the same [for analytics] and, in fact, it is increasing because of awareness of these technologies, especially at the top level of organizations where top executives are more aware. They’re asking people who work for them to look into [BI solutions] and for expansions” of spending and research, Vesset says. Advances in data warehousing and high profile use of customer analytics by companies like Amazon and Google are recent examples that businesses are pointing to as connections between initial investments in analytics and better BI to financial reward

A few of the drivers for adoption of business analytics are given below

Financial institutions and financial focus driving BA adoption

Large enterprises across Banking and financial institutions witnessed a hit and high decline in both the trust and the financial market investments during the recession. The revenues and the turn over of world’s largest financial institutions got hit. Revenues were at rock bottom. But what helped some of them survive through the crucial and difficult timings was a careful and in depth investment on the Business Analytical capability of these institutions.

Revenues of banks, investment corporations and financial institutions have been on a slow and steady rise due to usage of business intelligence and a careful approach in customer retention, customer value management, customer life cycle management and selective investment approach. Analytical applications for Banking solutions can provide crucial process support on integrated tools for financial accounting, cost controlling, risk management, asset-liability management, and profitability analysis. These applications can also help banks address legal compliance challenges. Investment portfolio management, market analysis, competitor analysis, reporting and compliances with national specific and international accounting standards

One of the companies Ramco has shown a great diagram on variety of solutions using BA for financial institutions

 

Manufacturing sector driving BA Adoption

Manufacturing has been one of the core sectors in the development of BRIC nations and other nations that have been listed as one of the top developed nations and now rule predominantly as service driven economies. Even though the services and other skills may be on a rampant growth and IT reining the current job scenario still the core manufacturing sector is what will always govern the existence of these other sectors. Manufacturing sector can have a stronger and multifold involvement in the business analytics since there Analytics is spread across areas like

• Process modeling
• Production planning and performance analytics
• Production scheduling
• Plant operations
• Procurement and ordering management
• Supply chain management
• Vendor management
• Inventory management
• Work force management
• Customer relationship management
• Product development
• Quality control
• Predictive maintenance
• Financial management

After the recession leaner processes and smarter production has been the mantra for all leading to cost reduction in the manufacturing processes and savings for the manufacturing unit

Aggressive Marketing by Major Vendors for SMB driving the BA adoption

All the major BI and Business analytics software vendors are aggressively targeting the large and medium enterprises in all countries. Since the larger conglomerates are already on the saturation and maturity of their IT and strategy operations breaking a piece of pie from that market segment would be difficult or rather not possible. The BI/BA vendors understand that the small and medium enterprise market holding immense potential for enterprise applications, BI/BA software and enterprise portals. The large and medium enterprises in India also invest significantly in budgetary allocations for technological adoption since they are more concerned in a strategic focus of their efforts to gain better and faster returns. Many of the vendors conduct seminars, shows and marketing campaigns to increase the awareness level and attract new small and medium enterprise customers showcasing benefits to their business across multiple domains. These aggressive promotional activities by the vendors are expanding the BI/BA software market across the world

Need for a better corporate governance model and compliance management system driving BA Adoption          

Industry compliance initiatives, ISO, CMMI, Sarbanes-Oxley and Basel II, have placed need a systematic, self evolving and continuous learning centralized governance and management systems. This creates a demand with the business executives to have a efficient and centralized BI/BA system to facilitate reporting. The need for a cleaner and more systematically organized data which can be governed using role based dashboard not only helps in better governance but also is helpful for matrix based organizations. The integrity and accuracy of data set for reporting and analysis is validated at multiple levels. Also rather than allowing each department to have ownership of separate, unreliable data BI/BA systems sets a centralized mechanism that helps in uniform end to end flow of data. Although a standard package of solution for a single BI/BA platform may not solve this problem and the organization will need integration of more than one systems across various departments to solve this problem. Thus companies needing a strategy for addressing compliance and governance issues find a savior in the form of a BI/BA solution.

There may be many drivers for Business Analytics being adopted but one also needs to understand that there are also many constraints and limitations for adoption and implementation of Business Analytics. Based on the cost and benefit analysis a smart business corporation thus will be able to Redefine their Strategy on the adoption of Business Analytics

Customer Life-Time Value (CLV)

Value of a Customer

The 80-20 Rule

• 20% customers in any market yield 80% of the profits
• These are the customers that need to be retained by organizations
• On the other hand, the remaining 80% customers are relatively under-served and therefore represent a huge business opportunity
• Telco companies should therefore build a business strategy around either of these segments, or sometimes both

Also the value of a customer lost is not just a value lost at one time it is

• Customer value lost over the life time of a customer (Quantitative)
• Customer value lost in terms of customer loyalty (Qualitative)
• Future customer value lost due to referral’s and additional products cross sale lost (future predictive)

The worth of a customer is often realized when you lose the customer. But how does the telecom service provider measure the value of a customer and how much the potential benefit they have lost. Is the customer just a one time revenue provider? Is the customer a short term business association or is the customer a long term revenue generation opportunity?

The definition of Customer Life-Time Value (CLV)

“The net value of all revenue generated from a customer from the moment the customer is in an opportunity state including the expenses done towards acquiring that potential customer and until the customer churn occurs and the revenue generation from the association with that customer”

Theoretically the calculation of customer lifetime value depends on

• The cost of acquiring the customer
• Stream of revenues from the customer
• Computations of the recurring costs of delivering service to that customer
• Profitability of the customer even if the revenue is low generate
• Referrals from the customer

CLV thus can be calculated for potential customers, existing customers and past customers that have been lost due to churn. Lifetime value is a key method of determining the value of a telecom subscriber, and of evaluating the strategies used to market to these subscribers. To understand and better position the marketing strategies of the telecom service providers the empirical formula has been devised which is based on various factors and assumptions that include

The CLV formulation is generally based on 5 components

1. Customer value over time (average revenue generated monthly and annually)
2. Customer time of association TOA (number of months with the service provider)
3. The amount spent in the acquisition of the customer
4. The maintenance and operational costs associated with the customer
5. The monthly discount factor

One needs to understand that the CLV is a generic formula and does not catch exactly the consumer behavior but is an indicative of the customer segment and a projection of the revenue that could be generated from the customer over a projected over an assumer period of time of association.

1. A customer’s past and present value( monthly and annually) is known and is already present as meta-data in the telecom service provider’s database but the future value of the customer is predicted based on the current data, usage pattern and business knowledge using predictive analytical tools.
2. The time of association (TOA) is the customer probability to churn (1- the probability of customer to be retained)
3. The amount spend in acquisition of the customer like (campaign cost, marketing cost, administrative expenses all these cumulatively found per customer by amount spend in acquiring the segment of customers in total)
4. The monthly cost is the cost associated in fixing any tickets or complains raised by the customer and any additional network and service related monthly costs associated with the customer
5. The monthly discount given to the customer as part of association with the particular scheme or segment of customers

Importance of understanding of CLV

• Targeting of a correct customer segment that is more profitable and provides sustainable relationship in a long run
• Acquisition of new customers in a particular segment and increase the number of relationships
• Calculation of ROI for a particular segment vs the effort spend in campaign and marketing
• Ability to calculate and increase the profitability of customer relationship
• Increase the duration of profitable relationships
• Decrease the one time cost to acquire customers
• Decrease the operational and maintenance cost of the customers

Thus one can easily say that CLV is a decision making tool for the marketing gurus of telecom service providers that helps them to Redefine their focus and revise their Strategy based on the value that gets quantified and associated with a customer or a customer segment

Customer Usage Pattern Analysis for a fixed line/ cellular customer

In the competitive telecom industry where the billing of a customer has gone to the level of 1/2 paise per minute the most important part is the selling of right plans and right packages to the customers. The APRU (Average revenue per user) can be increased only with the knowledge and in depth understanding of the user pattern. An added advantage that works for the telecom industry is that every time a call is placed on the network, detailed and descriptive information about the call is saved as a call detail record. Thus the need for a micro market research is not needed for the understanding of the customer usage pattern. The call detail records act as the primary source of data mining. Considering that even if call detail records that are generated and stored to a last timeline of 12 months it means millions of call detail data will need to be stored at any time. Call detail records include sufficient information to describe the important characteristics of each call.

At a minimum, each call detail record will include

• The originating and terminating phone numbers
• The network to which the call is dialed
• Date and the time of the call
• Duration of the call.

With the advent of real time recording for prepaid the day the calls were made, and the details of the call will be available almost immediately. For post paid customers though the data registry might take some time till the batch run for the end of day for data mining.

The unbilled amount though is readily available on the IVR and the customer self help- websites, the billing data is typically made available only once per month in total. Call detail records can not be used directly for conclusive results by data mining, since the goal of data applications is to extract knowledge for a customer level but the variation in the usage pattern can predict the volume and trends at phone call level. With telecom companies giving a discount which varies as per time and location of the call made this will add to another few columns in the data mining to determine the profitability of the customers and discount schemes and also help in creating new products and service opportunities. Thus call detail records for a customer can be summarized into a single record that can depict the customer’s calling behavior.

To understand the usage pattern the customer segmentation and customer profiling must be done correctly.

Customer Segmentation

Segmentation is a way to have more targeted communication with the customers. The process of segmentation describes the characteristics of the customer groups (called segments or clusters) within the data. Segmenting means classifying the population in to segments according to their affinity or similar characteristics. Customer segmentation is a preparation step for classifying each customer according to the customer groups that have been defined. Customer segmentation and customer profiling are of prime importance in order to do a usage pattern analysis

Customer Profiling

Customer profiling provides a basis for marketers to ’communicate’ with existing customers in order to offer them better services and retaining them. This is done by assembling collected information on the customer such as demographic and personal data. Customer profiling is also used to prospect new customers using external sources, such as demographic data purchased from various sources. This data is used to find a relation with the customer segmentation that were constructed before. This makes it possible to estimate for each profile (the combination of demographic and personal information) the related segment and visa versa. More directly, for each profile, an estimation of the usage behavior can be obtained.

Parameters for Customer Profiling

• Geographic Location
• Cultural and ethnic
• Company size
• Economic conditions (Spend Analysis)
• Age and gender
• Values, attitudes and beliefs (Customer Loyalty)
• Life cycle (Period of association)
• Knowledge and awareness of other products (Inquiries, Oppty, Surveys etc)
• Lifestyle (Products and services apart from the current product)
• Acquisition Method (How was the customer acquired)
• Credit and payment history (Historical data and credit rating)

After the segmentation and profiling  of a customer has been done the usage pattern analysis of a customer can be proceeded to have a focused approach

Primary Parameters for Customer Usage Pattern Analysis

• How (Prepaid, Postpaid)
• Who (Calls to Fixed line, Cellular phones etc)
• What (Location of customer call origination and recipient call)
• Which Area (Area and Tower wise analysis)
• When (Day/ Time of the call)
• Where (Local, Intra state, Inter state, International)
• How long (Duration of the call)
• How often (How often does the customer make/ receive call)
• Who is the service provider (Same service provider/ different provider)

Secondary/ Drill Down Parameters for Customer Usage Pattern Analysis

• Average call duration
• Average no of calls received per day
• Average no of calls originated per day
• Daytime calls (9am - 6pm)
• No of weekday calls (Monday - Friday)
• No of calls to mobile phones
• No of calls to landline
• No of inter state calls per day
• No of international calls
• No of outgoing calls within the same operator
• No of outgoing calls to other operators
• Usage of Data services (text messages, internet over phone, mcommerce)
• Usage of Value added services (caller tunes, ringtones, reality show polls, video clips, social networking sites, mobile apps, info services, wallpapers, mobile tv etc )

Thus a dashboard can be created for the sales analysis team which has
Histograms for

• Visualization of phone calls per hour ( X Axis:- Hours, Y Axis:- Calls No )
• Region wise Call histograms with Y Axis:- Percentage of customers and

                      X Axis:- Average Call duration
                      X Axis:- Average No of calls per day
                      X Axis:- Percentage of Day time calls
                      X Axis:- Percentage of Weekday calls
                      X Axis:- Average number of calls outgoing per day
                      X Axis:- Billing Range of the customers
                      X Axis:- Number of sms send by the customer

Also due to the advent of Smartphones, 3G and VAS there needs to be a dashboard for the product development which can depict

• Pie chart for Product wise volume of usage
• Pie chart for product wise share of billing to the customer
• Mixed bar graphs for depicting the billing per customer in the same segmentation and profile
• Comparative graphs for comparing the ARPU per user across segments thus providing a campaign decision support

Thus mapping the consumer behavior and defining the usage pattern is the key to  Redefine the Strategy to position products, services in the market and gain a competitive edge in sales using the sales analytics vs. the competitors.

*** Due to confidential of data graphs, detailed report not available

Customer Churn Management in Telecom

In the competitive business world of today retention of existing customers is the best marketing strategy to survive with a competitive edge. Churn management is the term that has been widely used to define change of customer loyalty and for customer turnover. In more detail, using analytics to predict churn management is the concept of statistically detecting and forecasting customers who are intending to move to a competitor service provider. When such customers are identified, they are segmented based on the reasoning for their intent of changing service provider and thus a focused effort with proactive marketing campaigns for retention. Customer retention has become a very important decision in business strategic decisions. Similar to a product lifecycle there is also a customer acquisition lifecycle. After the initial aggressive targeting and acquisition of customers the business comes at a point when the number of customers that can be acquired for a particular business reaches its peak. Thus finding and retaining new customers becomes increasingly difficult and costly. At this stage of the lifecycle it becomes imperative to secure and retain the most valuable existing customers or customers with the highest CLV (Customer lifetime value) than trying to win new ones. In addition, it has been a common practice that finding new customers can prove to be a more expensive business strategy than retaining existing ones.

Telecom companies can increase profitability by creating a predictive modeling for identifying potential churn candidates and non-revenue earning customers; and can increase revenue and profitability by targeted campaigning and promotional offers which will not only retain these customers but also convert the non-revenue earning customers to profitable revenue earning customers. With competition being on the rampant rise and new player emerging in the market like never before the ARPU (Average revenue per user) is reducing. Every player is concentrating thus on what % of the market share they can capture. A single mistake of not retaining and avoiding customer churn can cost the telecom company dearly.

Analytics can thus be used as a very important tool to identify and predict

• Which customers are most likely to churn?
• How can we motivate and persuade them to stay?
• What is the reason for their churn?
• Which customers will stay (churn retention)?
• How much cost will be involved in retaining the customer?
• What is the profitability of the customer?
• What is the ROI (Returns on Investment) for spending done on the customer retention?

Predictive analytics for measuring churn is developed focusing on predicting the probability of the customers to churn out in future. This takes into consideration different aspects of consumer behavior, market conditions, reasons to churn, including past historical data of people those who have changed their service providers in past. Thus a model that is built on past experience and present inputs on an ever evolving market segmentation model will generate the probability factors and rate the customers according to the marketing strategy of the telecom service provider. Then based on the customer segmentation done and the value associated with the customer they are then lured with incentives to change their decision.

For example: If a customer has a history of high billing for a long distance calling and further investigation reveals that he is calling a number from the same service provider in another location then he can be offered a same service provider calling pack or his tariff plan can be changed accordingly to suit his needs.

For example: -If many customers have churned from a particular area due to problems in reception quality and frequently not reachable errors based on the service assurance and ticketing data a comparative analysis can be used to develop an algorithm of these customer tickets vs the customers churned from that area. The focus that thus be directed to the technical department in that area and thus issues can be fixed on time to avoid further churn

The customer churn can be categorized broadly under the below areas

• Churn due to usage tariff/ billing rate plans, value for money
• Churn due to network/call service quality
• Churn due to change in preferences of services/ products not available with current service provider
• Churn due to dissatisfaction on other issues (related to billing, customer service, compatibility with current instruments used iphone, cell phone or handset)
• Ordering, provisioning and fulfillment issues (Turnaround time for conversion)

Customer churn is just like an epidemic of swine flu. The Telecom companies can only do a proactive prevention to defend against the same. But once it strikes it keeps on taking a toll and then it takes a large amount of time, effort and cost to regain what is lost in the epidemic and a huge effort goes into the reactive response to the churn. 1 customer dissatisfied affects 4 others and thus the multiplicity spreads on, thus no need to do the math. Thus ‘Prevention is better than cure’ not just applies on Human Health but also in telecom industry for a long run.       

There can be two ways to manage customer churn

• Proactive Analysis and Customer Management
• Reactive Analysis and Customer retention     

 

Proactive Analysis and Customer Management

It is a very well known fact that retaining a profitable customer over a long period of time is more profitable than acquiring a new customer with higher returns for the short run

The areas in which a proactive analysis can help are

• Analysis of customer segmentation and the segment usage patterns      
• Analysis of competitor offerings for the same customer segmentation
• Analysis of network and call quality monitoring systems           
• Primary research of the customer feedback on the current products/ services
• Analysis of the customer complains and tickets in the CSR systems/ Call centers
• Billing and default analysis
• Enabling a better focused and segmented campaign management system
• Cross selling and strengthening customer’s belief in value for money from the product/ services

 Reactive Analysis and Customer retention

The reactive analysis and churn prevention plan needs to be in place in order to prevent any further spread of the churn due to a seeding customer

• Identify the churn pattern based on the customer segmentation done according to region, profiling and customer lifetime value
• Identify the reason for the churn and trace it back to the customer usage pattern, pattern of tickets and complains by the customer throughout the lifecycle and do a root cause analysis
• Based on analogy algorithms predict the customers with highest risk due to the recent churn
• Device a response plan for the spread of churn and retention of the existing customers
• Create a rapid action force and alert mechanism in place to look into identifying any anomalies in the consumer behavior.

 

Industry research shows that a customer who cancels his account with one telecom service provider has only 20% probability of returning to the same service provider in case the churn is due to poor services. In case the customer churn is due to hopping nature and volatility of the customer choices and mindsets that is around 38% probability of return in case he has had a good experience earlier. In case of a bad experience the probability dips to only 24% of the return.

A customer once lost not only takes with it the customer lifetime value and revenue it can provide to the telecom service provider but also takes along a portion of trust and customer loyalty to the service provider. This is coupled with seeding and spreading of dissatisfaction to its contacts having on an average a 25% chance to be influenced. The only one conclusion that the telecom companies can make out of it is that Churn is Good only while making Butter and not for customers. If they wish to retain their customers they need to Redefine their Strategy towards customer churn management.

 

*** More detailed report is present separately

Customer Segmentation in Telecom

In the past 2 decades there has been a humongous increase in the accumulation of customer data increasing the need for data mining aimed at customer relationship management (CRM) and understanding the customer. It is a very well known fact that the Telecom industry holds the most structured amount of Customer data due to the nature of revenue generation from calls. With the rapid increase in competition customers pose different risks, making it imperative to implement different treatment strategies to maximize shareholder profit and improve revenue and maintain the customers in order to prevent the churn.

Segmentation is the process of developing meaningful customer groups that are similar based on certain characteristics and the customer behaviors. The goal of segmentation is to know your customer better and group those customers to apply that knowledge to increase profitability, reduce operational cost and also customer service. Segmentation can provide a multidimensional view of the customer for better engagement. An improved understanding of customer risk and behaviors enables more effective portfolio management and the proactive application of targeted solutions to increase profitability

To compete with other providers of mobile telecommunications it is important to know enough about your customers and to know the wants and needs of your customers. To realize this, it is needed to divide customers in segments and to profile the customers. Another key benefit of utilizing the customer profile is making effective marketing strategies. Customer profiling is done by building a customer’s behavior model and estimating its parameters. Customer profiling is a way of applying external data to a population of possible customers. Depending on data available, it can be used to prospect new customers or to recognize existing bad customers. The goal is to predict behavior based on the information we have on each customer. Profiling is performed after doing customer segmentation.

Segmentation is a way to have more targeted communication with the customers. The process of segmentation describes the characteristics of the customer groups (called segments or clusters) within the data. Customer segmentation is a method to segregate customers with common characteristics across the entire current, prospective, target and lost customer segments. These segments can then be treated as distinct groups and analysis can be conducted based on the characteristics showcased by the customer. Customer segmentation analysis can save significant marketing effort since the company can then focus only on the required high profitability customers. Multiple tools and dashboard reports can be used for customer segmentation. Using clustering algorithms one can define segments from the entire customer base into groups identified on the basis of various parameters like

• Location/ Demographics (GDP and economy)
• Product/ Services
• Line of business (B2B/ B2C)
• Volume of business/ Company size (Corporate customers)
• Third party distributors/ Vendors/ Alliances
• Customer spending capability
• Customer billing trends
• Age and profiles
• Gender
• Socioeconomic (Culture, ethnicity, education, income)
• Economic conditions (Spend Analysis, Price sensitivity)
• Values, attitudes and beliefs (Customer loyalty)
• Life cycle (Period of association)
• Knowledge and awareness of other products (Inquiries, Campaigns, Surveys etc )
• Lifestyle/ Personality (Interest, Tastes, Preferences)
• Acquisition Method (How was the customer acquired)
• Credit and payment history (Historical data and credit rating)
• CLV (Customer Lifetime Value)

Accurate market segmentation is essential in order to use analytics to successfully acquire customers. Using analytics in making the right marketing and operational decisions can result in getting a greater market share of customers and marketing products with higher returns. Focusing on the right segment predicted using BI tools will not only result in higher customer lifetime value but also increase customer satisfaction via customer loyalty. Customer Relationship Management (CRM) and Business Analytics and Optimization are inseparable.

Customer Profiling

Customer profiling provides a basis for marketers to ’communicate’ with existing customers in order to offer them better services and retaining them. This is done by assembling collected information on the customer such as demographic and personal data. Customer profiling is also used to prospect new customers using external sources, such as demographic data purchased from various sources. This data is used to find a relation with the customer segmentation that were constructed before. This makes it possible to estimate for each profile (the combination of demographic and personal information) the related segment and visa versa. More directly, for each profile, an estimation of the usage behavior can be obtained.

Predictive Segmentation and Analytics

Executives, senior management and sales operations teams in telecom companies generally follow traditional segmentation methods that generally do not give them a deep understanding of which parameters in the telecom product or service offer actually drive customer behavior and define their segmentation. Traditional approaches to segmentation are strictly experiential, looking at past customer choices and behavior. Predictive segmentation, on the contrary looks futuristic, examining how customers falling in the same customer segment may respond to changes in the duration, offering, channels, pricing and competition. The predictive approach uses the market’s response to these future incidents, in combination with past behaviors, as the basis for segmenting. It also gives the sales team a high degree of confidence in their decisions about what to do differently for each segment, because potential moves have been “simulated.” Analytical tools based on predictive segmentation help in the telecom companies living the future with a near real realization of the results and outcomes.

This forward-looking perspective comes from putting representative customers from the  target markets through a simulated buying exercise, presenting them with fully described offerings from all the relevant suppliers in the marketplace. Elements of the offerings are varied using a specific sampling approach called experimental design, which allows a marketer to project the impact of many stimuli by testing just a few of them. Using mathematical formulas to select and test a subset of combination of variables that represent the complexity of all the original variables, marketers can model hundreds or even thousands of stimuli accurately and efficiently.

Predictive segmentation provides a tried and tested formulation for designing, testing, measuring returns and rolling out sales and marketing operations. However the segmentation can not be very rigid and exhaustive since it is human behavior and thus in total the consumer behavior can not be woven into the BA tool as artificial intelligence. Understanding of all expected customer behaviors can not and should not be built into the segmentation solution as it might be resulting into biased decision making. A most common mistake generally done by market operations team is to use segmentation and marketing analytics with the usage of a single format segmentation or a single predictive model. 

SPSS defines the five predictive imperatives to maximize customer value with predictive analytics.

• Base your customer strategy on predictive profiles
• Predict the best way to win the right customers
• Predict the best way to grow customer relationships
• Predict the best way to keep the right customers longer
• Use predictive intelligence at every customer touch-point

Benefits of using predictive customer segmentation

The predictive analytical approach not only produces forecast for predicted segments; it also gives users a high degree of confidence to the executive management on their decisions about what to do differently for each segment. Any potential market scenarios and moves have been “pre-tested” and were embedded while building the segment definitions. These departures from past approaches make predictive segmentation ideal for the highly competitive and fast changing telecommunication sector. Historic data even though it is good in format and extensively detailed can not necessarily be a good predictor of future behavior. This approach of predictive customer segmentation thus typically tests and predicts different permutations and combination of forward-looking moves and thus showcases see the potential impact in a variety of simulated moved. This insight allows executives to choose which business plans to proceed ahead with and which plan to discard, by segment. Thus predictive customer segmentation can help the CEOs and COOs of Telecom companies to Redefine Strategy for their Future

 



*** More detailed report is present separately