a road less traveled in customer analytics

8/14/2019 A Road Less Traveled in Customer Analytics

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A Road Less Traveled in

Customer Analytics

Four Differentiating Initiatives

July|2009


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Get a Free List of Your Competitors Best Customers, Today!

Many companies already own the right

data for targeted acquisition from their

competitors, yet most arent aware of it. Is

your company one of them? What you

think you dont know but actually likely do

regarding your competitors customers

represents a huge untapped potential that

could create substantial impact to your

companys bottom-line.

What?

A number of industries, especially telecommunications and finance, are

facilitators of interactions between people be it, for example, a phoneconversation, or, a financial transfer.. These types of interactions allow such

industries to have a unique ability in terms of marketing: direct access to

competitors customers. When a telecom operators customer makes an off-net

call, or when a banks customer makes a money transfer to another bank, they

provide precious bits of information for the company the phone or account

number of a potential customer as well as behavioral information about that

potential customer. Using a blend of traditional and unconventional tools of data

mining and direct marketing, its possible to reach out to these potential

customers and make very specific and targeted offers to them.

But, Why?

The utilization of analytics in designing and conducting marketing activities has

become a de-facto standard among the best of the best, providing significant

benefits to those organizations wise enough to realize its potential. Mainly until

now though, most of the analytics-driven marketing activities have focused on the

existing customer base - for retention, for internal growth, and sometimes, for

win-back. Many of the companies in the aforementioned industries have thus far

wasted the opportunity of using analytics for acquisition. If data mining

techniques have been useful for identifying untapped potential in ones owncustomer base, why not use them to get a better understanding and targeting of

the competitors customers interacting with ones own?

Using already accessible internal data to cherry-pick the competitors customers

provides a highly cost-effective means for acquisition. It also allows companies to

select targets for acquisition that are most related to its own customer base,


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hence increasing the loyalty of its existing customers through the building of a

closer-knit community.

So, How?

Similar to most customer analytics initiatives, competitor customer acquisition

starts with preparing the data required for analysis and targeting. A competitor

customer data mart a data set including one potential customer on each row as

well as summary of his/her interactions with your customers is best-suited for

this job. In this competitor customer data mart, you would have:

Telecommunications (from CDR data)

A unique identifier: Phone Number of the Competitor Customer History: A field regarding the length of time in years the phone number

has been appearing on your network as a called individual.

Value determinants: Fields regarding count, duration and value ofinteractions with this customer from your network (e.g. different number

of your customers calling the number / total MoU for calls to the number)

Behavior determinants: Fields regarding time and type of interactionswith this customer from your network (e.g. SMS interactions mostly /

weekend-heavy users)

Finance (from Transactions data)

A unique identifier: Account Number of the Competitor Customer History: A field regarding the length of time in years the account number

has been involved in financial transactions with your customers.

Value determinants: Fields regarding count and monetary value ofinteractions with this customer from your customer base (e.g. different

number of your customers transferring / total $ of transactions with this

account)

Behavior determinants: Fields regarding nature and type of interactionswith this customer from your customer base (e.g. small and frequent

quantities / currency used in interactions)


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Once such a data mart is ready, the next step involves the use of traditional

analysis and data mining techniques such as value and behavior based

segmentation to identify the best targets for acquisition (in addition to business

case modeling to understand the potential revenues and impact on cost of

acquiring a given customer)Usually, the competitors customers with the highest

amount of interactions with your customer base would turn out to be the most

valuable customers of your competitors, hence the best targets for your

acquisition purposes. Other factors of course need to be examined (i.e. the

benefits of not paying an interconnection fee in telco, for example). Based on the

behavior segments in your target base, you can approach them with value

offerings that are most relevant for their needs (e.g. offering weekend discounts

to potential customers who interact with your base most frequently during

weekends).

Of course, the natural question at this stage would be: Now that we know whom

to target and what to offer, how can we communicate with them? Two

alternative answers exist for this question:

1. In countries where rules and regulations allow such actions and the localculture is such that the potential customers would not be irritated, the

most effective approach would be to reach out directly. In

telecommunications, this means calling them or sending an SMS to their

phone numbers which is already known in CDR data. In finance, this

would mean either making use of contact details provided by your own

customers when performing their transactions, or making dummy

transfers towards your potential customers such as a $0.0001 money

transfer to their account with a personalized message and offer as the

description of the transaction.

2. When existing regulations or local culture does not allow for directcommunications with your potential customers, the next best alternative

is using your own customer base for contact, through the leveraging of

referral programs. Once you know which potential customers you desire,

its easy to identify which of your own customers interact with them the

most. Using highly targeted referral offers such as get the last customer

youve called on to our network and you both get 200 free minutes

your customers would literally work as your intelligent acquisition

channel, grabbing the most valuable customers from your competitors.

What Next?

Using internal data for competitor customer acquisition may seem to be an

unorthodox method for most traditional marketers. Yet, as long as regulations

allow and you avoid invading privacy of customers, it can generate quick profits

and build an avalanche impact, as the more customers you get, the more visibility


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you will have over your competitors base through their interactions. If you are up

for it, we recommend that you start with some quick-wins and test the concept in

your market.


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BI on a $0.99 Budget

Many well-established Business Intelligence

teams run on software budgets of hundreds

of thousands to millions of dollars, which is

considered as a significant barrier for highly

budget conscious and smaller scale

companies, especially in the times of

economic downturn. The good news is; it is

possible to establish a considerably scalable

Business Intelligence practice with a software

budget of just 99 cents.

What?

As the harsh times are calling for desperate measures across the world, not allcompanies are willing to invest considerable amount into software licenses. And,

since commonly perceived as non-operational technology components, business

intelligence software are not the easiest sell to CFOs nowadays. On the other

hand, under current circumstances of the economic downturn, companies need

to be more agile than ever, which means increased need for faster access to the

information. Does it sound like yet another management dilemma? Not

necessarily Dont hold back from business intelligence if only you think it is

costly. You can run your business intelligence on a $0.99 software budget

But, Why?

How many times have you heard about a company that has invested millions into

reporting infrastructure, where almost all reports are still developed manually and

using spreadsheets? What about those invested in hundreds of thousands in data

mining software yet still dont possess a solid customer segmentation model? In

fact, a recent NCC survey in the UK found out that 87% of business intelligence

projects do not live up to expectations when compared to investments. You cant

simply blame it on the technology, since these technologies create wonders

elsewhere. These are cases of overinvestment in technology, where simpler and

cheaper solutions could be sufficient for the needs and capabilities of thecompanies.

The right way of investment into BI software, like any other technology, should

start with a well defined strategy, as well as an implementation roadmap, which

includes the portfolio of reports and data mining models answering key business

needs. Software investment should only then follow, evaluating alternatives

based on the actual complexity of needs. During this evaluation, companies


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should keep an open mind about the free open-source alternatives to maximize

their ROI from BI investments. And dont think that these alternatives are for only

SME-sized companies, as the references of some of these tools include names

such as IBM, Ford, HP, Cisco, Nokia and Miele.

If you have recently established your business intelligence unit or started

structuring one, consider free open-source business intelligence alternatives, test

the concept with them to make sure that it adds value to your business and move

to commercial solutions to scale up later. More importantly, if you think that

business intelligence is expensive, think again

So, How?

Using common office software and free open-source solutions, companies can

build their back-end data systems, process it effectively and present it with a user

friendly front-end. In this section, we provide the list of common functions within

business intelligence scope of operations and some alternative solutions which

would not cost a dime in terms of software licenses. Please note that many otherfree viable alternatives exist and the software listed here are provided as

examples only.

Front End

Reporting: A number of features are critical in development of businessdashboards, scorecards and reports for any reporting front-end:

o Ability to automatically retrieve data from a database servero Ability to work with reporting cubeso Ability to design reports with a developer-friendly interfaceo Ability to customize reports by end-userso Ability to develop graphs in various types and formatso Ability to copy and print reportso Ability to work online and offline

Aside from comprehensive and relatively expensive reporting solutions such

as Business Objects, Cognos or Microstrategy, there is a tool which possesses

all these functions, and has much higher user adoption: Microsoft Excel. It is

possible to link Excel charts and tables to database servers through SQL

queries over ODBC connections or OLAP servers using pivot tables, making it

a user-friendly reporting interface, at no additional cost. Upsides are your

savings from end-user training and adoption programs as well as integration

into all your spreadsheet activities. And if you have a number of power users,

it can give you great flexibility using VBA coding. Many companies already

use Excel for some of their reporting needs. All you need to do is to establish

live connections with your data warehouse and you have your reporting

interface already.


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Processing

Data Mining: When it comes to data mining, many companies evaluatesolutions such as SAS versus SPSS and KXEN, but very few actually ask

whether a viable free alternative exists. The truth is; such free alternatives

exist, like Rapid-I, and are similarly effective when it comes to traditional

data mining algorithms. These algorithms (e.g. K-means clustering, C4.5,

logistic regression) are implemented in quite similar fashion in most

solutions since they are based on publicly available academic publications.

And many companies stick to them even when they have more options

available. The commercial solutions are commonly superior in terms of

performance and scalability, and provide a wider range of algorithms for

advanced users; however, unless your intention is to mine data of millions

of customers with highly sophisticated techniques, you may not be in dire

need of them.

ETL Suite: Extraction, transformation and loading The three letterswhich commonly make up the most time consuming part of any business

intelligence initiative. As business applications move from legacy file

formats to accessible database structures for data management, ETL

process becomes more of a database development capability. Under such

trend, even a simple SQL editor can be used as an ETL environment,

decreasing the need for high-cost ETL studios, although falling short in

terms of working efficiency. Yet, companies looking for free alternatives

to ETL platforms can do even better than SQL editors, as free open-source

options such as Talend Open Studio are available, providing comparable

functionality to commercial applications. Free alternatives are feasible

options especially for companies which do not transform terabytes ofdata every day or extract data from tens of different systems each of

which have different legacy interfaces or file formats that are not

necessarily supported by free solutions. Similar to data mining, scalability,

performance and variety are not among freeware forte, yet not

necessarily all companies have the need for them.

Scheduling: Scheduling is an integral part of business intelligenceautomation, where ETL processes need to be run in certain order and at

specific times of day and data mining models need to follow, scoring

customer segments and risks. So, if you dont own a commercial data

mining server license or a commercial ETL studio with schedulingfunctionality, how do you automate your activities? Luckily, you dont

have to stand all night to run your programs in order, as most operational

systems today have the task scheduling functionality, which would enable

the automation regardless of the software you utilize. Such functionality

can even be configured to e-mail freshly updated reports to selected

recipients after all your month-end business intelligence activities are

executed automatically.


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Back End

Database Server: A free database server is nothing new, with alternativessuch as MySQL being around for many years. However, a new trend has

emerged in recent years, with big commercial players offering free

database servers, such as ORACLE, Microsoft and more recently IBM with

DB2 Express-C. For small to medium-sized databases and data

warehouses these alternatives are workable options, on top of which one

can deploy business intelligence applications. You do not necessarily get

the more advanced functionalities such as performance tuning, load

balancing, etc. but if your data size is not calling for them, you could as

well be better off without them.

OLAP Server: An OLAP server is not an indispensable part of a businessintelligence ecosystem, if your reporting needs are pretty straight

forward, limited and static. However, it is always a good practice to have a

flexible reporting environment, allowing your end-users to filter, slice and

dice their data across various business dimensions, which calls for an

OLAP server. So, if you are up for it, the good news is you can get a fairly

effective one for free. There exist various free and open-source OLAP

server alternatives in the market, such as Palo OLAP server, which can

even integrate with Excel - your free reporting software - through OLE DB.

So how are these solutions available at no cost? Well, there is a catch after all:

these solutions do not necessarily provide warranties or support functions for

free, which means that you are basically dependent on your self-service skills

when it comes to problem resolution. Additionally, as mentioned before,

scalability and performance might be limited when compared to some

commercial solutions. For some companies, these mean that cheap is expensive.

But if your needs are relatively less complicated, less performance dependent and

you are eager to experiment on your own, they could well be worth a shot.

By the way, you might still be wondering what would cost you the 99 cents, when

everything listed is for free Its the cost of coffee you would drink while reading

this article

What Next?

We recommend that companies who are holding back their business intelligence

operations because of the software costs assess the free alternatives for their

needs. Others looking for some cost savings should also evaluate the benefits they

get out of their current software providers and assess viability of the free

solutions in their environment. Some of the large scale organizations which have

already invested in commercial solutions would realize that the cost savings in

migration to free solutions would not necessarily be justified for them considering


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their complexity of needs and attached human resource training and adaptation

costs. Others might realize a sizable opportunity out of this exercise


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Now, Who Can Sell to This Customer?

Most analytical models developed for customer

acquisition, retention or growth do not take into

account that it is the human that does the

marketing, and miss a great opportunity to boost

return on investment. Every call center agent and

sales representative is different, as is every

customer and without a good matchmaking

between them; it is not possible to maximize the

conversion rates.

What?

Today, most business intelligence activities in the direct marketing and sales focus

on building the ideal list of prospects to sell to, identifying the right channel andoffer to use, and in some limited cases, finding the right script to communicate.

Although all of these are almost compulsory for effective operations, they leave

one very decisive element out: THE HUMAN FACTOR. Ideally, in addition to

optimizing all those listed elements, companies should also discover who can sell

best to whom and optimize the matchmaking between their sales representatives

and call center agents with their prospects.

But, Why?

Due to various reasons, such as demographics, personal history, education and

social skills, every salesperson is different from another. Some can better

communicate with youth, others with elderly or women, businessmen,

expatriates, etc. If half of the sales is about the prospect and the offer to make,

the other half is how it is being communicated, which mostly relies on whether

the person communicating is equipped with the best skills. Ability to recognize

which salesperson is best equipped for which type of customer can lead to

substantial improvements in marketing and sales results. If one of the call center

agents can relate to and make wonders with the university student customers,

why continue randomly assigning middle aged businessmen and retired couples

to him or her, when another agent could be performing much better with them?

So, How?

Matchmaking between the marketing, sales teams and the customers follows a

similar approach to most optimization problems, with three main steps:


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1. Preparation of Data: Understanding of the performance of sales personnelwith different customers requires historical data on personnels performance

as well as the profile of prospects each personnel has dealt with. Ideally, this

would mean availability of campaign management data (who offered what to

whom and when) as well as customer segments information based on

various dimensions such as demographics, needs, behavior and value. For

companies lacking such data today, even collecting it for the next couple of

weeks and months with short-term solutions can provide a usable basis for

analysis. Yet, ideally, these companies should revisit their data strategies and

start systematically collecting these key information elements.

2. Identification of Factors: Once data is available, the next step is doing apreliminary analysis to understand what factors (i.e. characteristics of

prospects such as age, marital status, income level, needs) affect sales

personnels performance. Performing simple statistical tests or even charting

personnel performance across different prospect properties can reveal the

most important ones to focus on. For companies with capable resources,

building data mining models to identify the factors and segments most

correlated with personnel performance would generate better results.

Whichever method is used for analysis, a key success factor is the ability to

isolate the effect of offer and in some cases the time of offer. An agent could

be performing best with the high income prospects, but this could be due to

the fact that the agent has been used for communicating an offer only

relevant for these prospects lately. To isolate such cases, preferably, all

marketing and sales personnel should be evaluated based on the same

conditions (e.g. offer, time of day, script).

3.

Optimization of Allocation: The final step is the actual matchmaking, wherebased on the factors identified and the data prepared, optimal allocation of

prospects to representatives or agents is done. Although this is a matter of

allocating the best resource for the selected prospect group, it involves

simulation and operations research techniques to come up with the best

allocation. As the capacity is also a parameter after all, the number of

resources is limited - an agent does not always get the prospects where

he/she would perform the best. It is a matter of maximizing the output from

overall sales team, not each individual separately. As an example; consider

the following scenario, where three agents have different sales conversion

rates for three different segments of customers. Ideally, it would be best if

both Agent A and C sells to the Youth prospects and B sells to the Middle

Aged. However, if each agent can make 100 calls a day and the lists of

prospects to sell to include 100 of each segment, this allocation does not

work out.


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7%

1%

3%

1%

5%

4%

6%

1% 1%

Youth Middle Aged Elderly

Agent A Agent B Agent C

Lets consider three alternatives for this case:

A. Without Any Optimization: In this case, the prospects would be allocated tothe agents randomly, each getting about 33 from all segments. The overall

sales conversion rate in this case would be 9.7%.

B. With Best Performer Approach: Since Agent A is the best performer forYouth, these 100 prospects would be assigned to Agent A. Similarly Agent B

would get the Middle Aged and Agent C would get the remaining, the Elderly

segment. In this case, the overall sales conversion rate would be 13%, a 34%

improvement from the random assignment.

C. With Actual Optimization: The optimization results in allocation of Youth toAgent C, Middle Aged to Agent B and Elderly to Agent A. Even though both

the Youth and Elderly segments are served by non-top performers in these

segments, the overall sales conversion rate in this case would be 14%, a 45%

improvement from the random assignment.

As the example above demonstrates, the return from optimal allocation of

prospects to sales resources can create substantial impact on conversion rates,

which is worth the effort put in for analysis for most companies.

What Next?

We recommend that whatever the size of marketing and sales operations a

company has, it should initially perform a basic assessment of performance of its

frontline staff across different customer segments. In case significant variance

exists across these segments, the next step should be pilot testing the concept tosee how much of an improvement it would bring and do a full fledge optimization

and roll-out afterwards.

Companies should also leverage findings from these analyses in human resources,

recruiting agents and representatives who can sell to the underperforming

customer segments or training those who might have the potential to do so. After

all, the reason that a company can not sell to certain demographics groups might


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simply be the fact that none of its sales personnel can click with those

demographics.


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Your Customers are Changing, are you Following?

The economic downturn is having a

substantial impact on the needs,

preferences and behavior of customers.

Companies need to tap into their customer

intelligence to ensure they adapt as well to

these changing conditions.

What?

An unintentionally overlooked area during this economic downturn by most

companies is customer intelligence, an oversight that can have severe

repercussions. Understanding the impact of the economic downturn on the

overall customer portfolio (such as on product and service usage behavior, brand

loyalty, or payments risk) is mission critical, considering how significant thedownturn has affected their lives. Companies should ramp up their focus on

customer intelligence during these turbulent times to minimize the impact of the

downturn on their customer portfolio while also identifying opportunities to scale

back costs.

Today, most leading companies make use of customer analytics on a regular basis.

When there is little change in the market, the task is relatively easy: by using the

proven tools and techniques, data mining experts produce fairly static segments

of customers and accurate predictions about them on a given basis. But, in times

like these, when the market is significantly volatile, companies need to rely on

more frequent and unique methods of assessing and utilizing customer data.

But, Why?

Here is a list of key reasons why companies should be revisiting their customer

insights and giving them more attention during the economic downturn:

Reordered Priorities: The most effective customer intelligence is the one thatserves business priorities and strategies the best. As the downturn is changing

agendas, it is necessary to see if the new priorities are best served with

existing intelligence. For example, companies that never before invested in

financial risk or churn prediction models should consider doing so in light of

the changing market conditions.

Downscaled Budgets: Inevitably, many companies are trying to find effectiveways to reduce their operational expenses. A detailed understanding and

analysis of customer intelligence can lead to a decrease in the cost of servicing

low value customers by allowing marketers to identify such segments; further,


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it also enables one-to-one targeted campaigning (below the line marketing),

thus leading to a significant reduction in the costs associated with mass

marketing.

Volatile Customers: The economic downturn is causing customers to be morefrugal about their spending, directly affecting their consumption patterns,

and, sometimes, their likelihood of paying. Companies need to more closely

follow the behaviors of their customers so as to identify pattern changes and

allow for pre-emptive intervention (i.e. cancel customer account to prevent

an escalation of debt).

Expired Facts: Most customer analytics models are customized and relevantto given business models or market conditions, meant to serve best under the

conditions they were developed in. During major changes such as economic

downturns, new segments appear in the market and some become

insignificant; under such conditions, the models in place can become invalid.

This applies for models around such topics as churn prediction as well, as the

profiles and reasons of customers churning during a downturn can be

completely different than from the reasons presumed before.

So, How?

In order to make best use of customer intelligence during the downturn,

companies should simply; collect, understand, beware and refocus:

Collect (tactical and critical info): As priorities and business needs change, thecustomer data which should be considered as vital also changes. For example,

during a downturn, contact detail data becomes extremely important, as

churners can then be won-back after the crisis through various methods of

outreach efforts.

Understand (changes in your customer portfolio): During the downturn, thepriorities of customers change, with new needs replacing old ones all of a

sudden, a customers most important need becomes value rather than

quality. These changes in needs prioritization cause some customers to

migrate towards different segments or even require the creation of new ones.

Companies should reassess customer needs and behavior to be able to come

up with the most relevant offers under new circumstances.

Beware (of the increasing risk): Most companies are fighting heavily againstthree types of risks today: defaulting customers, increasing churn rates, and

decreasing value per customer. In order to be successful in this fight,

companies need measures for effectively predicting which customers carry

these risks, so that they can take proactive measures. And, once again,

predictive models built before the downturn can prove to be useless, as the


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factors used to predict such behavior as churn or payment default may no

longer be the right ones.

Refocus (for new conditions): As business priorities change, companies needto revisit which segments and profiles they should be focusing on. For

example, with increasing default risks, companies could consider focusing

more on silver customers with low financial risks rather than gold customers

with high financial risks. Similarly, changing priorities call for changing

reporting requirements. Companies need to follow additional and different

key performance measures these days, which calls for adapting their

dashboards and reports.

What Next?

We recommend that companies take a pragmatic approach in realigning their

customer intelligence practice in this downturn, creating impact from day one.Instead of undertaking traditional large scale customer analytics initiatives which

would deliver results only months later, companies should follow cycles of

analysis and actions with a modular structure.


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About Forte Consultancy Group

Forte Consultancy Group delivers fact-based solutions, balancing short and long term

impact as well as benefits for stakeholders. Forte Consultancy Group provides a variety

of service offerings for numerous sectors, approached in three general phases -

intelligence, design, and implementation.

For more information, please contact

[email protected]

Forte Consultancy Group | Istanbul Officewww.forteconsultancy.com

a road less traveled in customer analytics

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