Predictive Analytics
for Intelligent
Campaign Execution
Organizations in today’s world always try to bring in efficiency
and rigor in every aspect of their functioning. Levers used could
be adoption of best practices, implementing recommendations
of external consultants, harnessing in-house ideas, Information
Technology to aid in faster decision making etc. There is a
perception, (which might be true to an extent) that
implementing one or all of these levers will provide a
competitive advantage in the industry. In this digital world,
where the barriers to the flow of information are limited,
Organizations have realized that anything that is a differentiator
today will become an industry practice within a short span of
time. This is especially true for the companies in the High Tech
space As a result of this; Organizations are in the constant search
of newer levers which will provide them with a competitive
advantage (albeit for a short span).
Predictive Analytics is a concept based on using analytical
techniques to study the current/historic data and applying the
learning to make decisions about the future. It finds applications
in all areas of an organization and is only limited by the
knowledge/needs of the various stakeholders.
The objective of this white paper is to provide insights on how
Predictive Analytics can be utilized as another lever by marketing
departments to build in Intelligence into the execution of
campaigns, specifically for companies in the High Tech industry.
Our experience shows that Intelligent Campaign Execution has
About the Author
Arvind Mahishi R PGDM (IIM Lucknow),
BE (National Institute of Engineering, Mysore)
Arvind Mahishi is currently working in the area of marketing analytics for a leading enterprise software maker. He holds an MBA in Marketing and Finance and has close to 8 years of experience in Marketing Analytics, Business Development and Project Management. He has worked on several statistical tools and techniques that are used to support marketing campaigns and improve database marketing effectiveness.
Table of Contents
1. Introduction 4
2. Predictive Analytics across the Organization 5
3. Intelligent Campaign Execution 6
Propensity Models 7
Market Basket Analysis 10
4. TCS Solution 11
Case Study 13
5. The Road Ahead 14
6. Appendix 14
A. Tools 14
Introduction
Analytics is the buzz word today. “Business Analytics” and “Enterprise Analytics” are some of the key words that are seen in the media. Analytics focuses on gathering the data available in the organization, refining, structuring and presenting it in a manner that will enable better decision making. The focus of analytics is now shifting beyond the realm of providing intelligent views of the data in an organization. It is being seen as a mechanism that will utilize this data and provide recommendations for solving business problems.
This transition from a “Decision Support” to a “Decision Recommendation” mechanism has been brought by the advancements in tools and increased awareness among individuals. The widespread adoption of Information Technology across all functions of the organization have provided a significant boost to this transition by providing data in a more organized way, rather than in silos as earlier.
The combination of tool advancement and awareness has resulted in organizations investing in strengthening capabilities in this area with a view to utilize data available to increase their functional efficiency.
The white paper provides an overview of Predictive Analytics and its application in different areas of the organization. The application of Predictive Analytics to enable Intelligent Marketing Campaign execution, with insights on how this was leveraged by a leading business software maker is also provided. The paper ends with a discussion on how information beyond traditional data sources will be leveraged by
Predictive Analytics across the Organization
The application of Predictive Analytics in different departments of the organization is gaining traction, with organizations feeling the need to utilize all the available data effectively and success stories of Predictive Analytics becoming more widespread.
Application of Predictive Analytics across different organizational functions is presented below:
Sales & Marketing – Territory Management, Warranty Analysis, Identification of Up-Sell and Cross Sell Opportunities, Identification of New Customers, Market Mix Modeling
Operations – Inventory Optimization, Supply Chain Analytics, Production Planning Finance – Risk Management, Credit Monitoring, Financial Modeling
Human Resources – Attrition Modeling, Performance Modeling
While the scope of adopting Predictive Analytics in an organization is present across all departments, the degree of adoption of Predictive Analytics to an extent is determined by the nature of the business the organization is in. For e.g. Logistics companies Leverage analytics around supply chain to a significant extent compared to banks where the focus is more on Risk Management Analytics.
Since companies in the same line of business will be more or less matched in terms of adoption of Predictive Analytics for core areas, there is a concerted effort to find newer venues for adoption, to gain a competitive advantage. While departments of companies in the High Tech space face different challenges which require the application of predictive analytics, this paper focuses on the marketing departments' unique challenges and their solutions through predictive analytics.
High Tech Marketing Campaign Execution and Challenges
Marketing Campaigns in the High Tech industry show certain unique characteristics
n Shorter Life Cycle corresponding to shorter product shelf life
n Frequent execution
n Predominantly online
These pose significant challenges for campaign managers on two fronts – executing campaigns and ensuring maximum return on marketing investment. The latter translates to the following business needs:
n How do we improve our campaign response rates?
n How do we improve our lead generation capabilities?
n How do we increase marketing generated opportunities?
The next section provides an overview of application of Predictive Analytics in various phases of a Marketing Campaign Life Cycle specifically in the High Tech Context and how the business needs highlighted above are addressed.
n
n
n
Intelligent Campaign Execution
Companies in the High Tech space (Computer Software/Hardware, Software Applications), execute
marketing campaigns with objectives varying from demand generation to improving awareness to pipeline acceleration. The tactics employed for executing these campaigns include email, online, Search Engine Optimization (SEO), webcasts, display ads, search ads etc. All these tactics are integrated into the campaign life cycle and are deployed separately or together based on the requirement.
The different phases of the campaign life cycle along with key activities are shown below.
Figure 1: Campaign Life Cycle
ŸIndustry/Segment Identification ŸKPI Formulation ŸResponse Capture ŸLead Nurture Effectiveness Reporting ŸLead Aging/ Management Reporting ŸPost Campaign Analysis-Lead Generation Metrics, Campaign Effectiveness Reporting ŸResponse Analysis ŸCompany Identification ŸContact Strategy Definition ŸChannel Readiness Assessment ŸContent Creation ŸSystem preparation ŸVendor Briefing ŸList Pull ŸData Uploads ŸData Audits ŸReporting ŸData Cleansing ŸContact Quality Ÿ Assessment Planning Budgeting &
Calendar Lead Mgmt Reporting Targeting Campaign Setup Campaign Execution Response Capture
A campaign’s success is driven by two factors –
n Ensuring that the right individual is targeted
n Ensuring that the product/service value proposition is communicated effectively
As is evident from the figure above, the first four phases – Planning, Targeting, Setup and Execution, form the foundation which determines the success/failure of the campaign while the subsequent phases focus more on reporting and managing the responses. Organizations strive towards executing the first four phases as efficiently as possible in order to address the above two factors.
Organizations depend on external vendors like Dun & Bradstreet, MarketFirst, etc. to provide them information on companies and contacts to be included in the campaign. The companies/contacts are provided based on specific criteria and are not differentiated among themselves. Thus there is a need to bring in some intelligence, which will enable these companies/contacts to be differentiated.
Predictive Analytics can be utilized to bring in this differentiation, by using it to select the right companies to focus on and the right contacts to target.
Different mechanisms are available within Predictive Analytics which find application based on the business requirement that the campaign is supporting. Companies usually run campaigns which focus on attracting new customers or selling more (cross sell/up sell) to existing customers. The mechanisms are popularly referred to as ‘Models’. The most popular models are Propensity models and Market Basket Analysis models. The following section provides an insight into how Propensity Models and Market Basket Analysis models are leveraged to run Intelligent Campaigns.
Propensity Models
Propensity implies inclination to something or someone. A propensity model will enable identification of entities (companies/contacts) which are more inclined than the others, to an object of interest
(product/service). The output from a propensity model will be a recommendation on whether the entity will respond favorably or not. This recommendation in turn is driven by a score (usually between 0 and 1) and entities with higher scores show more propensity than entities with lower scores. The objective of the propensity model is to use existing/historic purchase/response data along with the demographic/marketing engagement/contact spread data, to come up with a mechanism which will result in the propensity being expressed as a function of demographic/marketing engagement data. Marketing engagement data includes information like number of marketing touches, channels (online, email, phone, direct mail, event etc)
through which the touches have occurred. Contact spread data includes information like number of contacts at different levels, number of contacts by department etc.
The mechanism is usually Regression1 and will result in an equation with the Left Hand Side being the propensity and the Right Hand Side being the function of one or more
demographic/marketingengagement/contact spread indicators.
Since the score/propensity is limited to a value between 0 and 1, Logistic Regression2 is applied in this scenario.
Propensity = â0 + â1X1 + â2X2 + …..+
å
âi = Weight associated with each indicator Xi = Indicator (Sales, Employees, Industry etc)
[1] 1A statistical measure that attempts to determine the strength of the relationship between one dependent variable (usually denoted by Y) and a series of other changing variables (known as independent variables). http://www.investopedia.com/terms/r/regression.asp
The diagram below shows the application of propensity models enabling the segregation of entities (companies/contacts) into groups with high/medium/low propensities.
Figure 2: Propensity Model Application
LO
W
MED
HIGH
Target Accounts
Before Scoring After Scoring
Propensity
Model
Issuer Transaction Data 200+ Propensitary Behavioural
Profile Variables per Account 4000+ Derived Variables
Per Account
The first part of the diagram shows the distribution of all the entities without any guideline to differentiate between them. The second part of the diagram involves application of the propensity model to create the differentiation and the third part of the diagram shows the entities differentiated into groups with varying propensities.
The Propensity model is built on a small sample of the population and is validated before being applied to the larger population. The inherent assumption being that the sample is representative of the population. The effectiveness of a model is measured in terms of 'lift'. The lift basically is a measure of how effective the model is in terms of identifying the respondents/purchasers, compared to not applying the model. The model’s effectiveness is summarized in the form of a lift chart as depicted below
Figure 3: Gains Chart
Cumulative Gains Chart
% P ositiv e Responses % Sample Selected 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 Lift Curve Baseline
The baseline trend shows the response that will be obtained as a result of not using the model. It is linear and shows that targeting X% of the population will result in identifying X% of responders/purchasers. The Lift Curve shows the improvement in identifying responders/purchasers as a result of applying the propensity model. In the chart, the model enables identifying 85% of the responders/purchasers by targeting only 50% of the population compared to identifying only 50% of the responders/purchasers without using the model. This implies that applying the propensity model will enable realizing substantial savings in terms of being able to target lesser number of entities while obtaining same, if not better response rates compared to not applying the model. In essence, Propensity model enables injection of intelligence in the Campaign Life Cycle.
The Propensity model developed for identifying new customers/respondents is termed as Net New model and models developed for selling more to existing customers (cross sell/up sell) are called Cross Sell/Up Sell models.
From a campaign life cycle perspective, the propensity model can be applied during the Targeting phase (identifying companies) and the Campaign Execution phase (identifying contacts), which will result in ensuring that the right entities are targeted.
Market Basket Analysis
Market basket analysis identifies customers purchasing habits. It provides insight into the combination of products within a customer’s ‘basket’ and is predominantly applied in the retail industry. Ultimately, the purchasing insights provide the potential to create cross sell propositions:
n Which product combinations are bought
n When they are purchased; and in
n What sequence Cannedveg Softdrink Fruitveg Wine Freshmeat Beer Cannedmeat Fish Dairy Confectionery Frozenmeat Cannedveg Confectionery Dairy Fish Freshmeat Frozenmeat Fruitveg Softdrink Wine Beer Cannedmeat
Figure 4: Market Basket Analysis
The above diagram indicates a typical basket and the associations among different products within a basket. The thickness of the line linking the products depicts the degree of association, i.e., thicker is the line
stronger is the association.
The market basket analysis technique can be applied to execute cross sell marketing campaigns. It is even more effective when more than one campaign targeting different products is being executed
simultaneously. The application of this technique will result in a customer wise product recommendation (propensity to purchase) list. This information can be used to prioritize customers, so that customers showing strong propensity towards a specific product will be the focus when campaigns addressing that specific product are executed.
TCS Solution
TCS partners with marketing teams of organizations to support their predictive analytics needs by leveraging its technology expertise (tools – SAS, SPSS etc, frameworks, Centers of Excellence, partnerships with product vendors), process rigor (Six Sigma, Lean Sigma, ISO27001) and Outsourcing experience.
TCS follows a pilot based approach to streamline and stabilize the partnership. The diagram below summarizes the steps followed:
Figure 5: Solution Roadmap
Post Pilot review and Analysis Identify/Kick off a PoC or Pilot Joint Value Discovery Create and Phased Approach Incremental Project Kick-off
Key Operating metrics are developed in the Joint Value Discovery phase. Metrics can be defined at different levels. Illustrative example of metrics for analytics at different levels is summarized below:
Strategic
Improving Response Rate Tactical Sales Accepted Leads
Operational Turn Around Time, Quality Model ROI, Model Performance –
Level Metrics
Different operating models are deployed based on the organization of the customers' team and its geographical spread. Some of the operating models followed are:
Peer to Peer Model
Figure 6: Peer to Peer Model
TCS Client Offshore Analyst Analyst Operations Manager Analyst Analyst
Highlights:
n TCS analyst mapping done with Client Analysts
n TCS Level of ownership is limited to a section of the work product
n Suitable for services where
l Work products can be broken down into logical components
l External Interaction is required for enabling completion of work product (Interaction requiring
physical presence at client location) Virtual Extension Model
Figure 7: Virtual Extension Model
TCS Client Operations Manager Senior Analyst Senior Analyst Junior Analyst Junior Analyst Lead Analyst Analyst Analyst Highlights:
n TCS analysts work as virtual members of the Client Analyst team
n TCS Level of ownership covers the entire work product
n Suitable for services where
l External Interaction is minimal and can be handled from offshore when required
l Data available over the internet or can be provided by the customer
Case Study
Client Profile: The client is a leading provider of enterprise management or business application software and has presence across the globe. The marketing team of the client runs campaigns across regions – EMEA, North America and APJ focused on one/more products/solutions/industries.
Business Need: The marketing team is looking at improving their campaign execution effectiveness by making it more intelligent and thus improving RoMI (Return on Marketing Investment)
Problem Statement: How can we obtain more responses/leads with the same budget?
Solution: TCS partnered with the client marketing team and has been involved in providing Predictive Analytics services both during the Campaign Targeting and Campaign Execution phases.
n A peer to peer model was established with TCS analysts working with regional client contacts
n As part of the solution various Propensity and Market Basket models where built based on the campaign
objectives (Propensity models for Acquisition/Cross Sell campaigns and Market basket models for Cross Sell campaigns)
n Information related to product purchases, marketing engagements (how many touches, tactics used,
channels), firmorgraphic (industry, sales, employees), contact spread (contacts by level, department), Lead/Opportunity history were used as predictors to determine response/purchase propensities Business benefits realized as a result of intelligent campaign execution are detailed below
n Lead/Opportunity generation benefits
l Propensity model built for the North America region resulted in 57% increase in average opportunities
per week / rep, 2x increase in pipeline value, 1.6x increase in talk-time
l Propensity model built for a business intelligence product campaign executed for the North America
region resulted in $300K worth of opportunities being generated on the first day of campaign itself
l 3X conversion rates on demand gen campaigns in the EMEA region
l Market Basket Model built for EMEA region resulted in 2.9X increase in lead generation from a
Telecampaign
n Registration/Response rate benefits
l North America regions saw a 50% increase in events registrations and 2X increase in click rate on
The Road Ahead
Appendix
The focus of predictive analytics has been the usage of static data. With the real time data being made available as a result of advancements in technology, mechanisms to ensure that the models built are able to leverage this data will make them even more effective.
With the advent of social media like Facebook, Twitter etc, the availability of real time information on the internet is growing manifold. A lot of these social media are also used to express opinions about products and services. The focus of analysts involved in predictive analytics is to utilize this source of information to take Intelligent Campaign Execution to the next level – 'Intelligent and Social'.
A. Tools
There are a host of tools available in the market which is used to carry out Predictive Analytics. Some of the popular ones are listed below:
n SPSS n SAS n R n MINITAB n KXEN B. Pre-Requisites
As is evident from the discussion in the paper, the power of predictive analytics can be harnessed only in the presence of a sound data management strategy. The models built as part of the Predictive Analytics are as good as the underlying data. Hence the adoption of appropriate governance practices around how data will be managed is a pre-requisite before any plan of harnessing the power of predictive analytics can be put in place.
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About TCS HiTech Industry Solution Unit
TCS' HiTech industry Solutions Unit provides optimal, customized, and comprehensive solutions across varied High Tech industry segments: Computer Platform and Services Companies, Software Firms, Electronics and Semiconductor Companies, and Professional Services Firms (Legal, HR, Tax & Accounting and Consulting & Advisory/Analyst firms). Building on its vast experience in engineering, business process transformation, innovation and IT solutions, TCS offers a comprehensive portfolio of services that maximize growth, manage risk, and reduce costs. The TCS HiTech Industry Solution Unit partners with High Tech enterprises to provide end-to-end solutions which help realize operational excellence, innovation and greater profitability.
For more information, visit us at http://www.tcs.com/industries/high_tech
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About Tata Consultancy Services (TCS)
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Tata Consultancy Services is an IT services, consulting and business solutions organization that delivers real results to global business, ensuring a level of certainty no other firm can match. TCS offers a consulting-led, integrated portfolio of IT and IT-enabled infrastructure, engineering
TM
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