success guide the future of marketing analytics...

SUCCESS GUIDE

THE FUTURE OF MARKETING ANALYTICS TECHNOLOGYHow new cross-channel analytics platforms are empowering marketers

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Copyright © 2013 StrongView Systems, Inc. All rights reserved.

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Table of Contents

INTRODUCTION ............................................................................................................... 4

Analytics Defined ...................................................................................................................5

LIMITATIONS OF THE CURRENT ANALYTICS DEVELOPMENT MODEL ................ 6

Inflexibility of a Static Data Model .....................................................................................7

Data Access Challenges ........................................................................................................8

Required Understanding of Technical Concepts .............................................................9

Degrading System Performance .........................................................................................9

4 REQUIREMENTS OF A MODERN MARKETING ANALYTICS PLATFORM .......... 10

1. Integrated ..........................................................................................................................10

2. Intuitive ..............................................................................................................................11

3. Elastic ..................................................................................................................................12

4. Performance ......................................................................................................................12

SUMMARY ....................................................................................................................... 13

ABOUT STRONGVIEW ................................................................................................... 14

4 Success Guide: The Future of Marketing Analytics Success Guide Copyright © 2013 StrongView Systems, Inc. All rights reserved. SV-G-11113

“Big data” and analytics have been front and center on the corporate agenda for the past few years, promising to transform the way companies do business and significantly improve their top and bottom lines. Applying analytics to business data in order to describe, understand, analyze and communicate business performance is not new. What is new is the sheer volume of digital information that is now being generated on a daily basis (i.e. the “big” in big data) and the technological advancements that have made aggregating and analyzing this much information a possibility for a wider range of companies.

Marketing organizations in particular have long sought to leverage as much customer data as possible to increase the performance of campaigns through better targeting and personalization. Historically, the ability to achieve one-to-one marketing at scale has been hampered by limited access and visibility to data collected within different organizational departments and the prohibitive cost of building systems capable of analyzing and storing high volumes of data over time. However, this is beginning to change with new marketing analytics solutions that break down historical data silos and make high-volume data storage and analysis affordable.

This Success Guide will delve into the current state of marketing analytics and the opportunities now available to cross-channel marketers. Before we dive in too deep, let’s first clearly define what we mean by the term "analytics."

By 2020, there will be 5,200 GB of data for every person on earth, amounting to 40 Zettabytes (43 trillion gigabytes).

IDC, 2012 Digital Universe Study, December 2012

40Zettabytes


Analytics Defined

Analytics is the practice of discovering, analyzing, discerning, predicting and communicating meaningful patterns. Analytics use statistics, operations research and software tools to summarize and analyze large amounts of data in order to quantify the performance of business processes and functional areas. As the practice of analytics lies at the cross section of data storage, management and analysis, and it has traditionally been the domain of IT personnel trained in database management, data modeling and business intelligence, as well as business analysts and statisticians.

This arrangement has made it difficult for marketers. Analysis of customer data lies at the heart of customer segmentation and the subsequent launch of effective marketing campaigns. Even though marketers thrive on data, they are typically dependent on database administrators and technical resources to gain access to – and extract insight from – the data stored in the company’s data repositories. Thankfully for marketers, that's changing.

More and more marketing organizations are now moving to make analytics a core competency. This is largely being driven by the increasing amount of data that can be tracked and tied back to specific digital marketing programs. Customers leave a digital data trail behind them every time they click through a promotional email, browse for a product on a retailer's website or save an item to their online shopping cart. Thanks to these “digital footprints,” marketers have access to an unprecedented amount of data to not only target customers with relevant campaigns based on their interactions across channels but also analyze the results that were generated from those efforts.

Instead of having IT manage all analytic functions across departments, progressive organizations are allowing their marketing departments to build out their own analytics capabilities. In fact, Gartner predicts that by 2017, CMOs will spend more on IT than CIOs1. The shift makes sense, as marketers are in the best position to capture valuable insights from customer data using analytics – and these insights now have a direct impact on both top- and bottom-line potential.

1 Gartner Webinar "By 2017 the CMO will Spend More on IT Than the CIO," January 2012

Instead of having IT manage all analytic functions across departments, progressive organizations are allowing their marketing departments to build out their own analytics capabilities.

an·a·lyt·ics - noun. The practice of discovering, analyzing, discerning, predicting and communicating meaningful patterns in data.


LIMITATIONS OF THE CURRENT ANALYTICS DEVELOPMENT MODELMarketers’ dependence on IT and technical staff stems from the process that enterprises typically follow to store and analyze corporate data. As you can see in the diagram below, the process starts with identifying the data sources, modeling the data and then normalizing it. At that point, marketers need to focus on setting metrics and developing the analytic reports that leverage the data.

Of course, before you can follow any of the steps above, you first need to have a data repository or warehouse in place that can support your reporting and decision-making needs. A data warehouse allows you to store current and historical data and add to it over time to create trending reports and dashboards for monitoring the overall health of a business and its various functional areas. This process begins with identifying various types of data that need to be brought into the warehouse, which can include financial, inventory, sales and marketing data generated by various transactional or operational systems.

Next, the data that will be brought into the data warehouse needs to be modeled based on informational attributes and relationships that are of interest to the business. These can include products, customers, suppliers and orders. During this step, data architects are typically used to define how various data tables are going to relate to each other within the structure of the data warehouse. The resulting data model is essentially a logical representation of the how the company conducts business.

Because the data coming into the data warehouse will likely come from a variety of disparate sources, it first needs to be normalized. This is done through an "extract, transform and load (ETL)" process that standardizes and loads the data into the right tables and columns.

Once the ETL process is complete, the business then needs to define the metrics or key performance indicators (KPIs) they want to track – as well as the dimensions or attributes that will be used to analyze those metrics.

Once the KPIs are agreed upon, business analysts next proceed to develop reports and dashboards that are used by managers and other business users to track performance of these metrics.

This process has always been a major undertaking, which has only become more difficult with the volume and velocity of big data pouring into businesses today. Not surprisingly, this influx of data has exposed several limitations of the traditional data analytics model.

BUILD ANALYTICAPPLICATIONS

AGREE ONMETRICS/KPIs

BUILD ETLROUTINESMODEL DATAIDENTIFY DATA

SOURCES


Inflexibility of a Static Data Model

The problem with this static analytics development model is that it is extremely inflexible to change. Most businesses operate in an ever-changing environment. As a result, companies are routinely entering new markets, adding and divesting products and changing organizational structures.

Due to today's fluid business environment, business intelligence (BI) and other analytic applications can become outdated, sometimes within weeks of when they are rolled out. The biggest shortcoming of analytic solutions based on a static data model is that they represent the state of the business at a specific point in time.

As illustrated in the following diagram, the static data model is designed to only bring a limited number of data attributes into a data warehouse with finite capacity. This forces marketers to choose ahead of time what data they anticipate will be useful.

When data architects are identifying data sources or designing the model for their data warehouse, they are constructing a view of the business as it is conducted at that point in time. As soon as a new product is added or a sales process is modified, this change has to be reflected in the data model, which results in all of the upstream reports and dashboards becoming outdated.

Making matters worse, incorporating these changes into the data warehouse forces IT personnel and data architects to restart the development process from step one. Not surprisingly, this approach places companies in an endless loop of modifying their analytics infrastructure to represent their latest business reality, costing time and money.

The biggest shortcoming of analytic solutions based on a static data model is that they represent the state of the business at a specific point in time.

The expense and inflexibility of a traditional data warehouse requires marketers to be selective and choose a limited number of attributes to analyze.

Traditional Static Analytics Model

CRM

ERP

SOCIAL DATA

DATA WAREHOUSE

EXTERNAL DATA

E-COMMERCE

NEAR CAPACITY

DATAMODEL

Limitations of the Traditional Static Analytics Model Constrained • Static • Expensive • High Administration


As you can see in the diagram below, building an analytics solution on a static data model introduces significant latency into the process, which means marketers are always a step behind in accessing the latest customer information to drive their campaigns.

The static analytics development model also fails marketers on a number of other fronts, compounding their dependence on specialized technical resources.

Data Access Challenges

Most businesses, regardless of industry, have their customer data spread out across a number of disparate data sources. As mentioned earlier, this data needs to be integrated before a complete picture can be gained from any analysis – and this has important implications for marketers in particular.

Let’s say a marketer is interested in launching a loyalty campaign that is driven off a list of its most valuable customers who have made a specific purchase in the last seven days. In order to generate this list, the marketer would have to connect to the company’s CRM system to identify customers who have a high score based on customer lifetime value. She would then need to access customer data in the company’s financial or transactional system to identify those who have purchased that specific product. These two customer lists then have to be reconciled and integrated before the marketer could conduct such a campaign.

MarketingEngagements

NewAttributesto Analyze

IT DataModeling

Update

New BusinessRealities

Build NewReports &

Dashboards

IT DEPARTMENT

MARKETING DEPARTMENT

1-2 Weeks

3-5 Weeks Daily

Daily

Daily

Average Time Elapsed: 7+ weeks

Caught in the Static Model Update Loop

The traditional analytics development cycle introduces significant delays in leveraging insights to drive engagement.


In this era of big data, it's becoming even more critical for marketers to be able to effectively source and integrate customer profile data, interaction data and other external data.

The good news is that gaining access to the increasing amount of data now available to marketers makes it possible to determine the current state of each individual customer and market to her based on present needs, interests and desires.

The bad news is that while the importance of data’s role as a dynamic indicator of customer motivation and interest is rising, marketers continue to remain dependent on IT personnel to access and integrate these streams of customer data into a comprehensive and actionable profile.

Required Understanding of Technical Concepts

Further complicating matters, the current model of building analytic applications requires business users to be familiar with technical BI concepts such as fact and dimension tables, hierarchies and data granularity. In a worst case scenario, a power user might be required to write SQL queries in order to create ad-hoc reports, which in turn requires the user to know how the data is modeled.

Because an overwhelming majority of marketers are not trained in these technical concepts, it can be particularly challenging to extract insights from marketing data. As a result, marketers are again dependent on technical resources to create and modify analytical applications.

This requirement to understand technical concepts also places a heavy burden on a company’s IT organization, as they are trapped in another endless loop of creating and modifying reports and dashboards. IT departments will try to address this issue by developing pre-built reports and dashboards for business consumption. However, pre-built reports in most cases only represent a starting point for business users, as each analysis leads them to ask unanticipated questions of data, which in turn leads them back to IT.

Degrading System Performance

Another failure of traditional BI and analytics technology has to do with how quickly business users can find answers to their questions. Over time, as data is added to the warehouse, the performance of these systems tends to degrade. Historically, companies have been dealing with rising data volume by adding storage and fine tuning the database, but that only works when data is being generated at a relatively slow rate.

Unfortunately, companies no longer have the luxury of this approach. As constantly connected customers interact with brands across multiple channels, they are generating an unprecedented amount of data that is filling up data warehouses at a faster rate than ever before. As a result, the threshold of system performance is reached much sooner, creating even more burden on IT departments.

Access to the increasing amount of data now available to marketers makes it possible to determine the current state of each individual customer and market to her based on present needs, interests and desires.


4 REQUIREMENTS OF A MODERN MARKETING ANALYTICS PLATFORMGiven the many shortcomings that traditional analytic systems pose for marketers, companies need to rethink how they can best leverage data to power more effective and efficient marketing programs. With the right system in place, marketers can develop automated programs that drive more revenue while consuming less internal resources – both technical and human. With big data, solutions that rely on static data models are doomed to obsolescence. Instead, marketers need to implement a modern marketing analytics platform that meets four key criteria.

1. Integrated

An effective analytics platform for marketers must empower them to source and integrate all data streams that can play a role in improving the effectiveness of their marketing campaigns. At the very minimum, marketers need to be able to access customer profile data from CRM systems and customer databases as well as real-time dynamic customer interaction data which then must be normalized and made available for analysis.

However, to fully understand a customer's state and likelihood to buy at any given time, markers also need access to external data streams that can paint a more complete picture, such as weather or socio-economic data. To stay competitive, marketers now need to be able to respond in real time and access new data streams quickly and effectively. This requires a system that doesn't leave the marketer dependent on technical resources to add and test attributes from any of the data sources that they think would help bring added visibility into the performance of a particular campaign or segment.

The low cost and performance of a cloud-based data warehouse enables marketers to bring in all data, which opens up opportunities for gaining insights from unforeseen analyses.

Dynamic Analytics Model

CRM

ERP

SOCIAL DATA

CLOUD DATAWAREHOUSE

EXTERNAL DATA

E-COMMERCE

DATAMODEL

Benefits of a Dynamic Analytics Model Flexible • Elastic • Cost Effective • Low Administration


2. Intuitive

An analytics system used by marketers need to be intuitive. Most mainstream marketers aren't trained in the technical concepts required to use most traditional BI and analytics platforms available today. The easier the system makes it to analyze large volumes of data with powerful, self-service tools, the more marketers will take full advantage of the data they have to improve campaign performance and track trends over time.

Part of making the system easy to use involves taking reliance on IT out of the equation. That requires a flexible data model that incorporates all the metrics, dimensions and filters that marketers need today – and might need tomorrow. Next, the solution should include pre-built reports and dashboards to provide a starting point, as well as an easy and intuitive way to connect various tables, perform ad-hoc analysis and create new analytical applications.

In the end, the marketer should be able to quickly get a detailed view of the customer based on a number of dimensions – without any help from IT or a team of data scientists.

For example, a marketer should be able to easily bring up a pivot table that enables her to quickly analyze the effectiveness of a segment based on key performance indicators by all relevant attributes. In order to make the analytics relevant for marketers, the solution should also make it easy to define and test customized segments on the fly based on a number of customer attributes through an intuitive drop-down driven interface.

The following graphic details how marketers can more easily and quickly gather new insights by using an agile analytics application development cycle that uses self-servie analytics to take IT out of the equation. As a result, marketers can take advantage of new insights in as little as three days, versus weeks with the a static model that relies on IT.

MarketingEngagements

New BusinessRealities

Self-Serve Analytics

MARKETING DEPARTMENT

1-3 Days

Daily

Daily

Average Time Elapsed: 3 Days

Respond Rapidly to Changing Analytics Needs

An agile analytics development cycle enables marketers to quickly generate new insights based on changing business realities.


3. Elastic

While there has been a lot of hype around big data, one thing is certain: the volume of data will continue to accelerate, in most cases exponentially. This fact presents some significant challenges for storing the data and making it readily available, especially for data warehouses with finite capacity. In order to be effective, a modern analytics platform needs to leverage a data store or data warehouse that is able to seamlessly expand as data volumes increase. Fortunately, this elasticity can be obtained with new cloud-based storage options, which offer many advantages over traditional solutions – including the ability to add new nodes in real time to dynamically expand capacity.

4. Performance

In general, there is an inverse relationship between the volume of data and the performance of the queries that can be run against it. So as the amount of data increases, marketers need a data warehouse that is designed to accommodate all the data rapidly flowing in while still maintaining query performance – regardless of the size of the datasets.

In the typical model, increasing capacity and performance requires IT to add more storage and servers, which is both expensive and time intensive. However, selecting a data warehouse that leverages newer advances in database and cloud-based technologies enables marketers to query ever-increasing data sets without any change in the amount of time it requires to return the results. Without getting too technical, these include columnar databases and cloud-based solutions that leverage a technical process called "parallelizing" that distributes queries across multiple nodes.

Com

plex

ity

Business Value

REPORTINGWhat happened?

ANALYSISWhy did it happen?

PREDICTIONWhat might happen?

MONITORING & AUTOMATIONResponding to what is happening.

The Evolution of Marketing Analytics

Newer advances in database and cloud-based technologies enable marketers to query ever-increasing data sets without any change in the amount of time it requires to return the results.

Automation becomes essential as the complexity of analytics increases.


SUMMARYAn effective analytics platform offers marketers the promise of higher ROI by providing better visibility into campaign performance and unique insights for optimizing performance over time. The rise of big data means that marketers have more data at their disposal than ever before, which in theory should empower marketers to execute more effective campaigns. However, the same challenges that have traditionally frustrated marketers trying to leverage analytics are only made worse with big data. Marketers have remained dependent on database administrators and technical resources to gain access to the data stored in company’s data repositories and extract insights from it.

The current process of developing analytics infrastructure and applications is partly to blame for a marketer’s inability to fully benefit from analytics. Applications built according to this old process represent the state of the business at a specific point in time and can become outdated, sometimes within weeks of when they are deployed. This reality puts companies in an endless loop of modifying their analytics infrastructure to represent the latest business reality.

In addition to the static business model, marketers continue to remain dependent on IT personnel to access and integrate data from a number of sources to construct a comprehensive view of the customer.

The current model of building analytic applications also requires marketers to be well versed in the technical concepts of business intelligence. This further increases marketers’ dependence on specialized IT resources for any subsequent modifications.

Another failure of traditional BI and analytics technology has to do with the speed with which business users can find answers to their questions. Over time, as data is added to the warehouse, the performance of these systems tends to degrade.

The challenges that have traditionally frustrated marketers trying to leverage analytics are only made worse with big data. The current process of developing analytics systems is partly to blame.


In light of these shortcomings, StrongView recommends that any modern analytics platform designed for marketers should have the four attributes covered earlier.

In summary, these include:

• Integrated: Data should be easily sourced and combined from a number of data sources via an open and flexible data model.

• Intuitive: It should be easy to incorporate any desired attribute and aggregate data based on the selection.

- Reports and dashboards should be easily built without relying on IT resources.

• Elastic: The underlying infrastructure (data warehouse/data store) should easily adjust based on performance or storage requirements.

• Performance: Query performance should be maintained as data volume increases.

With the right marketing analytics solution in place to take advantage of big data, marketers can begin drawing insights from all customer profile and interaction data, as well as external data streams to gain a complete view of their customers’ current state and conversion likelihood. StrongView offers this capability with Message Studio's cloud-based InteractionStore, providing marketers with unlimited access to interaction data.

ABOUT STRONGVIEWStrongView's cross-channel marketing solutions provide enterprise marketers with the tools, services and insights required to effectively engage today's constantly connected customers. Combining a powerful cross-channel campaign management solution with market-leading data access and analysis, StrongView's Marketing Cloud enables marketers to understand the current context of each customer and respond in real time with relevant messages across email, mobile, social, display and web.

A champion of "Present Tense Marketing," StrongView is committed to delivering solutions that reflect the new reality of the technology-empowered customer. Based in Redwood City, CA and backed by leading venture capital investors, StrongView has been helping global brands in retail, travel, finance, entertainment and online services overcome the limitations of other marketing platform providers for more than a decade.

For a stronger view of marketing go to www.StrongView.com, and follow us at www.twitter.com/StrongView and www.facebook.com/StrongViewInc.

StrongView Toll free U.S. +1 (800) 971-0380 Toll U.S. +1 (650) 421-4255 Toll U.K. +44 (0) 118 903 6068 [email protected]

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