March 2012

Predictive analytics transforms Dell’s marketing strategy

June 2012

Case study of how a unique marketing strategy based on statistical analysis of

customer relationships delivered significant incremental Enterprise revenue for

DELL Europe

A paper by Elizabeth Press, Sayantika Bhaduri and Sumanth Suresh

Dell‟s Transformation Journey From Computer Hardware to IT Solutions

Dell was founded in 1984, during the height of what IT industry insiders call the “PC/Client Server Era”,

a time when units of hardware sold was the key indicator of success. The 2000‟s and the advent of cloud

computing and

virtualization has

heralded the

“Virtual Era” for

the IT industry.

Application of IT as

an enabler of

business has

become the core

value-add of IT in

the "Virtual Era."

Hardware has

become

commoditized. Thus

demand for IT is

linked heavily to the evolution of customers‟ industries. In order for IT manufacturers to positively

differentiate themselves, they need to be able to best address the infrastructure and application needs

of their customers.

Marketing for IT Solutions Deeper Customer Understanding is the key With the transformation from being a PC-manufacturer to becoming an IT solutions provider, Dell needed

to revise the go-to-market model in order to be successful in the “Virtual Era.” Business Intelligence

needed to create effective targeting methods for deepening relationships with existing customers and

winning new customers. Instead of relying on direct relationships with the customer as a strategic

advantage, Dell needed to gain a deep understanding of demand for the products and proactively

approach customers with solutions. This prompted a new approach to the way marketing campaigns

were carried out.

Analytics Enabled Marketing Campaigns Challenges Customer Insights are incorporated into Campaigns

Leveraging the power of analytics allowed us to take a holistic and solutions-based approach to customer

targeting in our marketing campaigns. Previously, we had used rules-based criteria to identify

prospective customers. We were able to replace the rules-based criteria by solutions-based factors. By

using statistical modeling, we analyzed a large number of explanatory factors and identified most

important indicators of purchase intent.

Customer Targeting for Marketing Campaigns Analytics and EMEA BI teams combine their expertise

Dell Analytics and EMEA BI

Marketing started to work on

the marketing transformation

initiative in November 2010.

Leveraging the team's

combined experience in

strategy consulting, risk

management and predictive

analysis, the Business

Intelligence and Analytics

teams jointly developed this method of targeting. Based on the team‟s collective experience in the

financial services and IT industries, this tailored methodology is comprehensive and deep in its analysis,

yet intuitive and easy to understand for the business.

Analytical Engine

Challenges

Providing Solution is necessary in the virtual era

We are looking through the prism of Client Server

Our direct business model was no longer a strategic business advantage

Outcomes

Dell Positioned itself as a global solutions provider

Expanded our view of the customers with advanced analytics Enhanced targeting in key multifaceted business areas is a strategic advantage

Approach to Customer Selection for Campaigns A deeper look at predictive analytics for customer selection

Our earlier targeting methodology was very successful in the “PC/Client Server Era,” where unit sales

were the main goal. The “Virtual

Era” however demanded a holistic

view of customers, as the goal was

not only unit sales, but deeper

engagement and stronger

relationships. It required us to

proactively approach our Public

and Large Enterprise customers

with solutions to their business

problems. Predictive analysis

enabled us to understand what

solutions customers would need

and when they would need those

solutions.

Predictive Analytics for Customer Selection

“Give me the best customer to call” is the universal ask of all marketers and sales makers

The selection of customers for campaigns in the new solutions-based approach needed to be centered on

addressing the customer‟s needs. Furthermore, the approach needed to clearly differentiate between

customers who might genuinely require a product against those who don‟t in order to enable the sales

teams to better understand the likely customer requirements and contact customers with the maximum

likelihood to purchase.

Among the various analytical approaches to customer selection, a logistic regression was proposed

considering the “Give me the best customer to call” ask of marketers. Logistic regression models the

likelihood of a customer purchasing a certain product. The final outcome was a list of customers with

their individual propensity scores (likelihood or chance of purchasing a specific product). Higher

propensity scores indicated a higher likelihood of response to sales calls.

Implementation of Logistic Regression

Preliminary preparation for Modeling

Once the objectives, outcomes and methods were fixed, we identified the

various data categories (financial, service quality, product related and

market related) for building the model.

Searching for trends and patterns in the data – Exploratory Analysis &

Hypotheses

Exploratory analysis of the data revealed patterns pertaining to purchase

cyclicality and buying trends which were further explored and validated with

the help of business managers and sales teams. We also identified certain

customer sub-segments within the population who were more inclined to buy

the target products. This enabled us to frame specific class and category

variables as inputs to the model.

Model Variables - Creation, Reduction & Selection

We began with a list of 500+ model variables for every product modeled. The variable creation

incorporated various features of the data such as customer RFM characteristics and customer

firmagraphics (such as employee size, industry type). An important feature in our variable creation

process was the involvement of business stakeholders. We had multiple rounds of discussions with the

product managers, solution specialists, sales and BI teams to build variables likely to impact purchase

decision. We were able to identify several significant product affinity variables using insights from

various stakeholders. By identifying product affinities using the inputs of Solution Specialists we were

able to bring a „solution‟ basis to model building. The overall list of 500+ variables was reduced to a

smaller set of 25+ modeling variables by applying multiple statistical, business and sense check filters.

The Logistic Regression Model – Model Selection & Customer Scoring

The 25+ modeling variables were tested in various combinations and different models were iterated.

Statistical tests and out-of-sample validations were used to identify the better performing models. The

iterations were also shared with stakeholders to seek their feedback regarding the business significance

of the statistically significant variables. We also used sign tests and checked individual variable weights

to avoid heavy loading on any single factor. The final model with 5-7 variables was selected based on

fulfillment of all the above criteria.

Customer Scoring

The customers were scored using the selected model and the end result was a purchase likelihood score

for customers to buy a specific product. The final customer selection was made after excluding the

customers with whom we have lost a deal in the recent past (last 6 months) or who are already in a

discussion with the Sales team.

Monitoring Performance and ROI Measurement Intuitive metrics of campaign effectiveness In order to effectively monitor and communicate the performance of predictive marketing methods

across the organization, we created intuitive and simplified metrics to measure the incremental revenue

impact. We tracked the incremental revenue over sales targeted activities by measuring two metrics,

conversion and average order value. Conversion measured the targeting efficiency of the campaigns

while average order value measured the revenue derived from converted customers.

Business Impact of Predictive Analytics ROI, Strategic Customer Insights & Ideation Framework

Usage of predictive analytics for campaign targeting went from an innovative idea to a strategy-changing

practice within a year. The implementation of predictive analytics has impacted the business in three

fundamental ways:

Dell grew incremental revenue and improved sales effectiveness

Sales specialists and management received strategic insights about customers

A continuous ideation framework which promoted a structured approach to incorporation of new

ideas

Delivered significant campaign ROI

Dell increased revenue and improved targeting effectiveness in strategic enterprise products such as

servers and storage. The sales specialist organization used the output of the statistical models for

identifying their targets, enabling an effective and transparent quota setting method for Sales

Specialists.

Research/ Expert Input, Feedback from

last iteration

Hypotheses Formulation,

Exploratory Analysis

Presentation of Hypotheses, Analysis

findings

Open Discussions with larger teams - Product, Sales, BI

Model Building and Extraction of Insights

Strategic Insights to Executive, ROI measurement

Provided Strategic Input for the Business

The insights gained during the modeling process

enabled framing of marketing strategy and

supported the Marketing and Sales teams in

understanding their customers better. One

example of business insight gained through

modeling was that the customer scores revealed a

“Pecking Order” which further validated the

„solution‟ approach used in the model building.

Additional insights that we have presented to

management have included:

Identification of seasonal trends

Firmographic niches for the different products.

Customer trend analysis.

These findings have been inputted into executive level strategic decision making and implemented in

transforming the go-to-market model.

Developed a continuous ideation

framework

We have created and documented a

process in our team to enable

continuous application of the insight

we gained through analysis into

strategy.

The purpose is to enable continuous

structured brainstorming and new ideas

in the organization. IT is a very

dynamic industry and impacting

innovation processes have created a

strategic advantage for Dell. Our

process takes a cross-functional and

thus interdisciplinary approach to

ensure a comprehensive look at the

issue to enable optimal insight and decision making.

High

• Solution Buyers: Customers who are highly likely to buy a specific product as part of an overall Solution

Medium

• Hardware Buyers: Customers who are highly likely to buy a specific product

Low

• Non Buyers: Customers who are unlikely to buy a specific product

“The Pecking Order”

Continuous

Ideation

Framework

And Dell‟s customers are more satisfied…

Our solutions- based approach has enabled us to sharpen our focus on the customer and providing them

with the Power to Do More. This approach can be applied by other companies in almost any industry.

Successful implementation of the principles of comprehensive analytics, collaborative team work with

sales and marketing, straightforward metrics and communication, coupled with a process of continuous

ideation and strategic insight can help companies build better relationships with their customers.

About the Authors:

Elizabeth Press (Elizabeth_press@dell.com) is a Research Sr. Advisor in the EMEA Public and Large Enterprise Business Intelligence Team and based out of Frankfurt, Germany. She has a BA in International Relations from Tufts University and an MSc in International Economics and Business from the Stockholm School of Economics. She has worked in strategy consulting and the finance & technology industries.

Sayantika Bhaduri (Sayantika_bhaduri@dell.com) is an Advisor with the Marketing and Sales analytics team in Dell Global Analytics and based out of Bangalore, India. She holds a Masters in Mathematics from IIT Kanpur and has worked in Marketing Analytics for technology industry.

Sumanth Suresh (Sumanth_suresh@dell.com) is a Sr. Analyst with the Marketing and Sales analytics team in Dell Global Analytics and based out of Bangalore, India. He has a Masters in Engineering from IIT Madras and has worked in consulting and analytics.

About Dell Global Analytics

Dell Global Analytics seeks to improve Dell‟s bottom line through the leveraged use of analytics touching all aspects of Dell‟s business operations. We offer a wide range of analytics services covering management reporting and dash boarding of key business metrics, forecasting and predictive customer response modeling and optimization of key business processes. Value for Dell is unlocked by the application of sophisticated data analysis, statistical and mathematical techniques under a Six-Sigma framework of business process improvement.

The range of supported Dell functions includes Dell Supply Chain, Pricing, E-Commerce, Contact Center Operations, Dell Financial Services, Marketing and Sales.

Office:

Dell International Services India Pvt. Ltd

Divyashree Greens,

Survey No 12/1, 12/2A, 13/1A, Challaghatta, Varthur Hobli,

Bangalore 560071, INDIA

About Customer Insight & Business Intelligence, EMEA, Public and Large Enterprise

Customer Insight & Business Intelligence drives the provision of live, relevant and timely business intelligence information and customer insight to the Public and Large Enterprise sales and marketing leadership teams throughout EMEA. We also provide targeting and strategic insight for EMEA-wide campaigns.

We are located at various locations throughout EMEA.