Using Data Warehouse for CRM

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Usually a data warehouse is used for Business Intelligence. In this article we will describe another usage of a data warehouse: for Customer Relationship Management (CRM).

Customer Relationship Management

is a set of activities performed by a company or organization (business and

non-business) to manage and analyse information about their customers, to keep

in contact and communicate with their customers, to attract and win new

customers, to market products/services and conduct transactions with their

customers and to service and support their customers. For non-business

organization the word “customers” can be substituted with “citizens”, “users”,

“stake holders”, “applicants”, “visitors”, “constituents”, “interested

parties”, “students” or similar, as long as the term describes the people that

the organization serves.

A CRM system is a package

of applications that supports the above activities. Among various

functionalities of a CRM system, below are functionalities that are ideally

supported by a data warehouse or utilising the data from a data warehouse.

Other functionalities may best be served by an Operational Data Store or front

end applications. Please see the table at the end of this article for the

details about which tool is best for which functionalities.

  • Single customer view
  • Permission management
  • Campaign segmentation
  • Manage deliverability
  • Customer services/support
  • Customer analysis
  • Personalisation
  • Customer loyalty scheme

Let’s discuss these functionalities one by one.

Single Customer View

One of the most important

things in CRM data management is the concept of Single Customer View. This

concept was raised because customers could be defined differently depending on

the context and to which department we are talking to. For example, a customer

could be defined as follows:

  • A customer is anybody who has purchased from us.
  • A customer is anybody who has registered with us.
  • A customer is anybody who has paid us.
  • A customer is anybody who is currently subscribed to our newsletters.

And on top of that we have to deal with variations and complications

such as:

  • Order cancellations: what if the customer has

    ordered (purchased) from us but before we deliver the goods he cancelled

    his order? Do we count him as a customer? Hmm, may be not.

  • Contract termination: what if a customer

    signed a contract for a particular service from us for a year then the

    following year she did not renew the contract? Do we still count her as a

    customer? Perhaps not.

  • Unsubscriptions: the customer has subscribed

    to our newsletter and then unsubscribed from that newsletter. Do we count

    him as a customer? May be not.

  • Order life cycle: order fulfillment process

    consists of many stages: quotation produced, contract signed, account opened,

    order placed, order dispatched (for physical goods), order

    fulfilled/delivered, consumed (for services), invoiced, paid, returned,

    credit note raised, refunded, account closed. So, at what stage do we

    recognized them as a customer? Different industries have different order

    stages.

  • Even tough it is not technically a customer

    yet (technically it may be a prospect), some departments such as marketing

    need us to store the prospect data.

  • When does a customer stop becoming a customer?

    When they no longer consume our products or services? In some industries

    such as waste management, the process of ‘discharging’ a customer is done

    in stages, for example: stopping the collections, removal of bins,

    settlement of accounts, deactivate customer status. In some industries

    there is not concept of stop becoming a customer. Once they purchase

    something they become customers forever.

So, practically speaking

potentially we may need to store subscribers, registered users, purchaser and

prospects as customers. What we do in the data warehouse is to build the

customer dimension based on several different sources in the operational

systems: order data, subscription and permission data, registration data and

marketing data. We can use overlay technique I described in this article: Upsert

Dimension Table. Yes it’s true that this will mean that we will have many

attributes on the customer dimensions. And yes we do need to deduplicate the

data, for example based on customer name, date of birth and address, or email

address.

Single customer view

simply means that we need to build a customer dimension that is solid, i.e. no

duplication of data, complete (no missing customers) and correct. Deduplicate

is not always easy, for example name can change when women get married, address

changes when they move houses and email address changes over time too (a hard

bounce could be an indication). Hence we need to use other measures such as

time frame or data age (e.g. we can use the data if it is no more than 1 year

old) or using other criteria such as Social Security Number, date of birth,

etc. MDM and CDI vendors have a lot experience in this area, as well as data

quality and data profiling software such as Trillium.

Permission Management

Regulations differ from

country to country, but the basic principle is we can only send campaign to

customers who have already given us their permissions to send it to them. Based

on the scope, there are 2 kinds of permissions: subscription-based and general

permission.

In a subscription-based

scenario, we receive requests from customers for sending them a particular

communication. For example, say we have 3 communications: weekly newsletter,

low price alert and special offers. Customers can subscribe to these

communications, either only to 1 communication, 2 communications or all 3

communications. In this case, if the customer subscribes to the low price

alert, we can only send them low price alert, we can not send them other

communications. If we have a new communication, we can not send it to

subscribers of other communication. Instead, we need to build a new subscriber

base for the new communication. Subscriber base is created by getting end users

to subscribe to particular communication through website or promotions.

In the general permission

type, customers do not subscribe to a particular communication. Instead, they

give permission for us to send them a particular type of communication.

For example, say we have 2 communication

types: promotional items and subscription items. In this case the subscription

items cover everything that is regular and promotional covers everything that

is ad hoc. Another example: we can have 3 communication types: transactional (such

as order confirmation emails and e-tickets), marketing (such as promotional

campaign) and third party (such as campaign from our sister companies).

Permission is also

categorized based on the communication channel or media, for example: by email,

by SMS/text, by post, by telephone and by RSS feed. There is also a permission

for sending (or not sending) any kinds

of communication to customers. For example, a customer could call or email us

asking to be excluded from any forms of communications.

For a multinational

company, the permission is could be per locale. It is not necessarily country

based, for example: Benelux, Nordic and Scandinavia are often mentioned as one

locale, even though they consist of several countries. In those cases 1 office

serving more than 1 country. It is possible that each locale has more than 1

language. We could also have several brands or names. For example, we could be

operating in a certain country using 3 different company names, each having

their own monthly newsletter or promotional campaign. Permissions can also have

a validity period, e.g. only for 1 year. We need to take locale, language,

brand and validity period into account when constructing the permission fact

table and communication dimension.

Let us discuss the design.

Permission data is ideally stored in a fact table, with all the above items as

the dimension, plus the customer key, date the permission was given, source key

and the permission ID as degenerate dimension if applicable. The measures or

facts are number of subscribers, subscription price, There are 2 possible fact

tables: subscription fact table if you use a subscription based permission, or

general permission fact table if you use the general permission approach

described above. For an example let us discuss the design for subscription

based type.

Fact table name:

fact_subscription

Grain: each time a customer subscribes or unsubscribes to a communication.

Type: snapshot accumulative

Table creation script (ignoring partition for the time being):

create table fact_subscription (

customer_key int not null, -- who the customer is

communication_key int not null, -- what communication the customer is (un) subscribing to

channel_key int not null, -- what media will be used (email, post, text, RSS)

promotion_source_key int not null, -- which promotion is the source or cause of this subscription

brand_key int not null, -- which brand managing this (un) subscription

locale_key int not null, -- which locale managing this (un) subscription

language_key int not null, -- which language this (un) subscription event originated from

expiry_date_key int not null, -- the date when this subscription is valid until

subscription_period_key int not null,-- how long the subscription is valid for

e.g. 3 months, 1 year

permission_id varchar(20) not null, -- degenerate dimension from front end CRM system if applicable

subscription_price money null, -- how much this subscription costs

number_of_subscriptions int null, -- 1 for a normal subscription, 0 for dummy

subscribed_dt datetime null, -- date and time the customer subscribed

unsubscribed_dt datetime null, -- date and time the customer unsubscribed, NULL if still subscribed

is_active_flag tinyint not null, -- 1 if the subscription is active, 0 if it is expired or unsubscribed

created_dt datetime not null, -- system date & time when this fact table record was created

last_updated_dt datetime not null -- system date & time when this record was last updated

constraint pk_fact_subscription primary key clustered (customer_key,

communication_key, channel_key, subscribed_dt))

To get the subscribers’

email address and customer name of Ivory weekly email campaign:

select email_address,

customer_name from fact_subscription sub

join dim_customer cus on cus.customer_key = sub.customer_key

join dim_communication com on com.communication_key = sub.communication_key

join dim_channel ch on ch.channel_key = sub.channel_key

where com.communication_name = “Ivory Weekly”

and ch.channel_name = “Email”

and sub.active_flag = 1

We could store the date

the permission is given as a dimension, but we would recommend storing the

timestamp on the fact table for 2 reasons: a) we don’t loose the time of day

element and b) it is easier to retrieve the timestamp data for campaign

segmentation. It is not advisable to store the permission in the customer

dimension because it would limit the grain to be per customer basis, rather

than per customer, communication and date.

So permission management

is the capability of a CRM data warehouse to store the permission, based on all

of the items described above. And to always keep them up to date. The

permission data needs to be made available to the campaign management system to

support the campaign segmentation process. It is frequently used, i.e. every

time the users create a campaign.

Campaign Segmentation

When creating a campaign,

we need to have a list of customers to whom we are going to send it to. These

end users are known as campaign target audience. Campaign segmentation process

produces this list. Most CRM software has this capability. This is where the

SCV play an important role. The richer the customer dimension, the more

flexible we can create the segmentation. Segmentation criteria that are

commonly used are:

  • Permission
  • Demographics
  • Order data
  • Campaign delivery
  • Campaign response
  • Customer loyalty score
  • Customer profitability

We will give an example

on each of the above items so we are clear about what they are. Permission: all

customers who subscribed to Norwegian weekly newsletter in the last 3 months.

Demographics: all female customers age 20 to 40 who live in Milan. Order data:

top 1000 customers (by order value, excluding VAT) who have purchased

electronic products from us in the last 12 months. Campaign delivery: exclude

customers who had more than 3 hard bounces more in the last 8 weeks. Campaign

response: include all customers who have opened the last campaign. Customer

loyalty score: include customers from the top tier with more than 500 loyalty

points. Customer profitability: include all customers from band A with annual

order value > $30,000.

Campaign Results

What we meant by campaign results are:

  • Campaign delivery data, i.e. whether the

    campaign successfully reaches the target audience. For example, say we have

    an email campaign with 100,000 target recipients. Because of invalid email

    addresses, we only sent 99k and did not send 1k. Out of these 99k that

    went out, 96k were delivered to the target recipients’ mail boxes and 5k

    were bounced. All this information is called campaign delivery data.

  • Campaign response data, i.e. reactions from

    the customers receiving the campaign, perhaps by clicking on a link if it

    is an email campaign, or calling customer service center if it is a postal

    campaign.

  • Orders resulting from the campaign, i.e. out

    of the customers who responded to the campaign, how many actually placed

    their orders, what did they purchase and what are their order values.

Let's go through these 3 points one by one.

Once campaign

segmentation is ready, CRM system executes a campaign and sends it to target

audience. Data about to which customers the campaign were successfully

delivered and to whom it was not delivered, along with the reason why it was

not delivered, should be fed back by the CRM system to the data warehouse. We

are not only talking about email campaign here, but also by post, by telephone,

by text messages and by home page customisation. This campaign delivery data

(i.e. sent, not sent, bounced and delivered) will be useful for future campaigns.

One possible design for storing campaign delivery data in the data warehouse is

a factless fact table, with customers, communication, date, channel, delivery

status and reason as the dimensional keys.

Specific to email

campaigns, when the campaign reaches the target audience email box, end user

may open that campaign email and perhaps click on any particular offer in that

campaign. These open and click through events are fed back to underlying data

warehouse. No mechanism is 100% reliable, but one mechanism for logging open

events is a transparent 1 pixel image inside the body of the email, with a

query string containing a customer identifier on the image tag. The web log of

this image is then processed daily and the hit of this image, along with the customer

identifier and the timestamp, is stored in the campaign response database. A

mechanism for logging click-through events is redirection, i.e. the link on the

campaign email hits a landing page with the destination page URL and a customer

identifier in the query string. A script behind the landing page then records

the time of the event, the URL of requested page and the customer identifier

into the campaign response database before redirect the user to the real page.

The campaign response database is then fed back by the ETL to the data

warehouse to be used for future campaign as additional criteria when doing

segmentation.

Open event could be

stored on the same fact table as the campaign delivery data. An example of

design for a campaign delivery data that contains the open data is below.

Fact table name: campaign delivery

Fact table type: snapshot accumulative

Grain: 1 row for each communication sent to each customer

Creation script:

create table fact_campaign_delivery (

customer_key int not null, -- who the customer is

communication_key int not null, -- what communication is delivered to the customer

channel_key int not null, -- what media is used (email, post, text, RSS)

delivery_status_key tinyint not null, -- 1 if successfully delivered, 0 if failed

reason_key int not null, -- a positive integer containing failure reason, 0 if successful

open_status tinyint not null, -- 1 if opened and 0 if not opened

number_of_opens int not null, -- normally 1 if opened but can be more than 1

sent_dt datetime not null, -- date and time the communication was sent to this customer

delivered_dt datetime null, -- date and time the delivered was logged

bounced_dt datetime null, -- date and time bounce event was logged

first_opened_dt datetime null, -- date and time the message was first opened, NULL if not opened

created_dt datetime not null, -- system date & time when this fact table record was created

last_updated_dt datetime not null, -- system date & time when this record was last updated

constraint pk_fact_campaign_delivery primary key clustered (customer_key,

communication_key, channel_key, sent_dt))

Because number of opens can be more than 1, if you want to record the date and time of the second and

subsequent opens, you will need to put open in its own fact table, separate

from campaign delivery. Normally it does not really matter that we don’t get

the timestamp of the 2nd and subsequent opens.

Click-through can not be stored on the above fact table because the grain is different. The grain of

click-trough is 1 row for each link clicked on a communication. An example of

design is given below.

Fact table name:

clickthrough

Fact table type: snapshot accumulative

Grain: 1 row for each link/URL on a communication, whether clicked or not

clicked.

Creation script:

create table fact_clickthrough (

customer_key int not null, -- who the customer is

communication_key int not null, -- what communication is delivered to the customer

channel_key int not null, -- what media is used (email, post, text, RSS)

URL_key int not null, -- which URL on the communication

click_status tinyint not null, -- 1 if it is clicked, 0 if not.

number_of_clicks -- how many times the URL was clicked by the same customer

first_clicked_dt datetime null, -- date and time the URL was first clicked, NULL if not clicked

created_dt datetime not null, -- system date & time when this fact table record was created

last_updated_dt datetime not null, -- system date & time when this record was last updated

constraint pk_fact_campaign_delivery primary key clustered (customer_key,

communication_key, channel_key, URL_key))

If you want to record the

time the 2nd click happened, put each click on separate row and make

the fact table type transactional. But normally it does not really matter, as

long as we know the number of clicks. It is preferable to put each URL (whether

it was clicked or not) in 1 row like above, with the number of clicks as a

measure.

Some of the customers who

responded to the campaign might place their orders. These orders are tracked

using promotional code if it is a postal campaign, or using a identifier on the

offer link if it is an email campaign, or using a standard software package

such as Omniture SiteCatalyst if the order is placed online. Data that are

normally fed back into the data warehouse to be used in future campaign are who

the customer is, which campaign it is resulting from, and the usual order

attributes such as product type, quantity and values. This way it would enable

us to analyse campaign effectiveness, analyse customer behaviour and to monitor

how much of the company revenue is generated from CRM activities, which could

be used for ROI calculations or backing the proposal for future projects.

Customer Analysis

Various types of customer

analysis could be performed in the data warehouse. To give you some ideas,

below are some examples.

  • Purchase pattern analysis, i.e. what kind of

    products or services does a particular group of customers purchase. The

    groupings could be based on demographics or campaign results. Based on the

    patterns we could try to forecast future purchases and relate it with

    inventory management, in particular the reorder level and purchasing lead

    time.

  • Price sensitivity analysis, i.e. identifying

    changes in shopping and purchasing behaviour if the price changes. In this

    case we also group the customers for analysis, not individual customers.

    We try to identify if there are certain patterns which would be useful for

    setting future marketing strategies and operational directives.

  • Shopping behaviour (especially for online

    businesses), i.e. identifying the factors associated with site traffic to

    measure the effectiveness of site design, checkout process design and help

    increase conversion rates. Shopping behaviour analysis is also conducted

    to gather the customer interests (which pages on the online store the

    individuals are more interested with), to be included as a factor when

    doing personalisation exercise such as site personalisation and

    personalised offers.

  • Customer attrition analysis, or customer churn

    analysis, i.e. to answer the questions such as how many customers defected

    from us each week or month, how many new customers we are getting each

    week or month, what kind of customers we are loosing (in terms of

    profitability and demographics) and what kind of new customers we are

    gaining (in terms of product or service range and demographics). Also

    included in this kind of analysis is changes in the type of service or

    product that the customer is having (this does not apply for supermarket

    but it is applicable for health care and financial services, for example

    the type of account).

  • Customer profitability analysis, i.e. revenue

    that we receive from the customer minus the costs associated to that

    customer, over a certain period (say weekly or annually). We want to know

    which customers we are loosing money from, and which customers are making

    money from. The formula to calculate the revenue side is normally not

    difficult but allocating the cost to each customer activity is technically

    and politically not easy.

  • Fraud detection, i.e. large increase in credit

    card purchases which deviate significantly from the individual or group

    normal pattern (for financial service industry); unusual returns of goods

    by the same customer (identified by name, post code and customer card

    number) within short period of time, compared with the daily and seasonal

    behaviour of the product line of suspected product code (this one is for

    retail industry); spiky account balances and unusual withdrawals/deposits

    (for banks), drops in recent invoice values not accompanied with lower

    usage activity (for telecom industry). Another method is to use 2 (or

    more) groups of samples, one containing the fraudulent transactions and

    the other representing good transactions. These groups are then fed into

    the mining model for training.

Each type of analysis requires

different data model, and different industry requires different data model. For

example, customer profitability fact table in utility sector (gas and

electricity in particular) could be an incremental snapshot type, containing

monthly snapshot of all accounts monthly revenues (calculated based on service

types, rates and usage) and proportionate cost structure for the same period of

time. The revenue may be per kwh but the base cost may be by weight (tons of

coal) which makes the equation non-linear hence for some customers we could be

making a loss and for others we are making handsome profit.

Although dimensional model can do

a lot of analysis, in some cases we have to use multidimensional models, i.e.

cubes. Many types of customer analysis especially those that involve predictive

analytics, behaviour recognition, statistical analysis, non-linear estimation,

cluster analysis and patterns finding, would require data mining running on multidimensional

database, sometimes more than 1 MDB/cube. Some analysis would require building

applications running multidimensional queries on cubes.

Personalisation

What we meant by

personalization is tailoring our web site, products, services, campaigns and

offers for a particular customer or a group of customers. There are large

categories of personalisation: 1) we ask the customer what their preferences

are, or 2) we guess their preferences based on their shopping behaviour,

purchase history and demographic attributes. Once we know (or we think we know)

the customer preferences, we offer them our products and services which we

think would suit their preferences. Examples of personalisation are:

  • Price and product alerts, i.e. we let the customer

    know if what they like appears in our data warehouse. Price alerts are

    notification to the customers when there are special offers (lower price)

    on certain products or services that satisfy their criteria, for example

    if they would like to fly to certain cities or purchase certain type of

    digital camera. Product alerts are notification to the customers when a

    certain product appears in our database. For example: they declare their

    favourite singer or musical preferences, then we notify the customers when

    a certain album or single that suit those preferences appear in our

    database. The basic working principle is matching: on the one hand we have

    many suppliers supplying us with thousands of products and services every

    day and on the other hand we have a lot of customers with certain

    preferences. All we have to do is to match them automatically.

  • Personalised offers, i.e. we offer our

    customers certain products or services that we think match their needs or

    profile. There are 2 broad categories on how to choose the products or

    services: a) based on their past purchases (or shopping/browsing if it is

    an online store), and b) based on the customer attributes. Example of past

    purchases: because a customer purchased Canon S300 ink jet printer 3

    months ago, they may need BCI-24 colour ink cartridge today. Example of

    customer demographic attributes: the customer had a 3 months old baby so

    she may need baby products. For online stores and online services,

    customers could be identified by using cookie or asking them to login and

    once identified we could track their shopping behaviour, i.e. which

    product or service category they are spending a lot of time on, etc.

  • Recommendations, which is basically the same

    as personalised offer. But this term is normally used when the customer is

    still shopping on the web site (for online businesses), unlike the term

    ‘offers’ that normally used when they are not shopping, i.e. via email or

    post. Recommendation tends to be targeted to one customer, where as personalised offers can be targeted to

    a group of customers that

    satisfies certain criteria, for example, those in certain age range or

    live in certain cities.

  • Site personalisation (specific to online

    businesses), i.e. the web site contains different products and services

    (and prices) depending on who the customer is. There are 2 methods which

    are widely used to identify the customer: login and cookie. Login is the

    most (if not the only) certain way of identifying who the customer is,

    i.e. by supplying credentials, such as user ID and password. Serving the

    same purpose as login are: bio metric ID (such as finger print), challenge

    response device (such as a device that displays different response numbers

    every time it is activated, based on certain seed number which has been

    planted into the device) and security token (such as security card). Using

    cookie is probably 50-60% at best, never achieve 80% certainty. Some

    people disabled cookies on their browser, some installed certain plug-in

    on their browser which prevents cookies, some people regularly cleaned

    their temporary Internet files including cookies and of course some people

    don’t use their own, permanent computer, i.e. Internet café, a friend’s

    house, a shared home computer, an office or campus computer, library’s PC,

    etc.

The content of site

personalisation may be generated by a CRM system (as an XML), by setting up a

campaign that is executed once a day. The logic behind this campaign does a

data mining on a multidimensional data warehouse or, if we prefer a simpler

way, by running a rule-based logic stored as metadata against the dimensional

data warehouse. These rules are conditional rules, e.g. similar to IF … THEN …

statement but with a lot of IFs. Price and product alert do not need a

dimensional data warehouse. They can run on a 3rd normal form ODS.

Or even on the front end CRM system.

One of the logic behind

personalised offer (and recommendation) is ‘what similar people are interested

in’. ‘Similar people’ can be quite a challenging term to implement. Some of the

most popular classification techniques are nearest neighbour, neural networks

and classification trees. Nearest neighbour is classification of customers

based on their position in multidimensional space. Imagine that each

dimensional attribute or each distinguishing factor that contributes to the

grouping is a vector or arrow. The direction of the arrow is determined by the

value of the attributes. A customer is defined by joining all the arrows by

putting the beginning of the next arrow at the end of the previous arrow. This

way a customer consists of all their dimensional attributes. Customers that are

close to each other are classified as “similar”. Close or far is defined as

multidimensional distance, i.e. square root of sums of all components’ squares.

The difficult thing to do here is assigning numeric values to the dimensional

attributes. As we all know dimensional attributes are mostly non-numeric. If

the dimension has a hierarchy (such as city or location) then the numeric score

depends on whether they have the same parent or grand parent.

Classification trees method is

using a diagram where a branch has 2 sub branches. At each branch whether we go

to sub branch 1 or sub branch 2 depends on the value of the attributes which is

compared to certain criteria (normally a constant). Starting at the trunk,

after following all the branches and sub branches we will arrive at the leaves.

Now if we bring say 1 million customers to through these paths, some of them

will end up at leaf 1, some at leaf 2, some at leaf 3, etc. The leaves are what

we call classes. A customer is said to be “similar” if they are in the same

class, or a near by class, which is defined by the number of levels.

Customer Insight

Customer information is

not useful without its intelligent analysis. Analysis is always evolving and

finding new ways to increase revenues through customer insight. Customer data

plays vital role to build customer insight. Customer insight is a model to view

available customer data and to analyze customer behaviour over period of time.

Using a data warehouse

one can create rich customer dimension and use it to create customer insight.

Business analyst can analyze complete customer data set in following ways:

Customer shopping

Analysis:

Using historic order data, customer-shopping behaviour is analyzed. For

example, business analyst can find answers to all the following questions by

doing customer data analysis:

  • How many times customer has purchased from us?
  • What is time gap between two consecutive

    purchases?

  • What is the purchase pattern?
  • What product has he purchased most?

By answering above

questions business user can understand customer-shopping behaviour and can

design future marketing strategy to retain that customer.

Customer permissions

analysis:

Permissions play vital part in defining customer data. Every enterprise has

different set of rules to define these permissions. These permissions are

stored in data warehouse for future analysis of customer permissions. For

example, as described above in permission management section

customer/subscriber can subscribe to different communications types over time

period and all these historical subscription events are stored in subscription

fact table. Business user can use CRM tools to do subscription analysis for

different subscribers over period of time.

Customer Loyalty Scheme

Customer loyalty scheme

is the way to reward high valued customers and build loyalty among customer

bases. Many enterprises use customer scoring/point based system to build

loyalty-based program. Customers are scored based on their previous shopping

behaviour and points are calculated accordingly. Customer scores can be stored

in customer dimension. CRM system uses these customer scores to design

campaigns and group customers as per their loyalty points. Different customers

are offered different promotions as per their scores.

Below we illustrate which

tool is best to serve various CRM functionalities: data warehouse (DW), online

transaction processing system (OLTP, i.e. front office application) or Operational

Data Store (back office integrated operational database in 3rd normal form). If

the cell does not have a ‘Yes’ in it, it does not mean that the tool can not do

the functionality. It may be possible, but it’s not the best tool to serve that

purpose.

Customer Support

Customer support is one of the important aspects of

CRM industry. Many companies use various CRM tools to build customer support

systems. Support system helps to solve customer queries, provide them

promotional updates etc. For e.g. Customer call center to update billing

address or phone number etc.

Many companies use ODS to store

latest customer’s data to provide quick and efficient search capability to

fetch up customer’s information. The underlying ODS database can be populated

from OLTP databases or from data warehouse (in rare cases) for latest customer

information. Many CRM vendors provide tools and techniques to transfer data

between ODS and data warehouse. ODS can be populated from data warehouse/ Data

Marts for customer specific data which is not persisted in OLTP databases.

Functionality OLTP DW ODS
Single Customer View

- Subscribers

- Bookers

- Registered users

- Customer matching

  Yes Yes
Permission management

- Subscription based

- Tactical campaigns

- ISP feedback loop

- Communication preferences

Yes Yes Yes
Segmentations

- Order data

- Demographic data

- Campaign delivery

- Campaign response

- Customer loyalty score

- Customer profitability

  Yes Yes
Campaign Content

- Promotional Offers

- Routine Newsletter

- Purchaser Lifecycle

- Subscriber Lifecycle

- Cross-selling

Yes    
Campaign

Results

- Delivery rates

- Open rates

- Click through rates

- Conversion rates

Yes Yes  
Customer Support

- Complaint Handling

- Cross selling

- Pre-consumption support

- Consumption support

- Emergency support

- Post consumption

Yes   Yes
Customer Analysis

- Purchase pattern

- Price sensitivity analysis

- Shopping behaviour

- Customer attrition analysis

- Customer profitability analysis

- Fraud detection

  Yes  
Personalization

- Alerts/Notification

- Special Offers

- Recommendations

  Yes Yes
Customer Loyalty Scheme

- Scheme Administration

- Customer scoring

- Classification

- Satisfaction survey

  Yes Yes
Order processing

- Quotation

- Registration

- Custom pricing

- Placing orders

- Contract management

- Order confirmation

Yes    
Finance

- Invoicing

- Payments

- Refunds

- Arrears

- Account management

Yes    

Vincent Rainardi and Amar Gaddam

28th December 2006

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