From the beginning of the species, when humans had work to do, we usually invented some sort of tool to help us do it. Any time we recognize the need to complete a repetitive task, we almost immediately begin looking for ways to simplify that task by inventing tools, and we tend to refine those tools through use. Tools allow us to complete work faster, achieve greater precision and efficiency, or simplify work so it requires less skill or effort.

Marketing analytics is certainly a discipline that benefits from the presence of tools; beyond that, tools enable the process. Without some basic tools and technology, marketers will find it very difficult to consider anything beyond the simplest metrics in their effort to leverage analytics to improve their outcomes. With tools, marketers can build and manage a sustainable analytics process, one that is sufficiently broad in scope and capable of considering the large volumes of data necessary to provide a true picture of marketing performance. Having the right collection of tools and technology is therefore essential to creating and managing an effective marketing analytics process.

In the context of marketing analytics, there are both “tools” and “technology,” and although these terms are often used interchangeably, it is useful to create a distinction to separate the must-have technology from the tools that provide more specialized functionality:

• Technology: the base platforms, systems, or clouds of technology that enable core marketing processes and results tracking.
• Tools: focused software solutions that enable specific functions, such as the dashboard functionality discussed in Chapter 7.

This division between technology and tools is not sacred; it exists here simply to describe broad differences between these categories.

As the marketing technology landscape continues to expand, with new solutions emerging on a continuous basis, and coalesce, with vendors acquiring each other, the distinction between technology and tools is blurring. Many functions that once were available only in separate, standalone tools are now integrated into the base systems that marketers use. The purpose of drawing the distinction is to clearly identify the technology that is essential infrastructure for marketing, not just analytics, from the nice-to-have tools that simplify specific analytics functions. The balance of this chapter examines the technology and the tools in greater detail, providing examples of how marketers are using them.

Marketing Analytics Technology

As Chapter 5 discussed, there is no “system of record” for marketing. The marketing organization uses an expanding mix of technologies and platforms to fulfill its mission. In this mix are some platforms that are virtually indispensable to the marketing team, and chief among them is marketing automation. Demand Metric defines marketing automation as the strategies, processes, and software technology that enable marketing departments to automate, measure, and improve the performance of strategies, activities, and workflows.²

In the not-too-distant past, marketing automation was considered a luxury and was found primarily in larger organizations. As is often the case when technology evolves, marketing automation has become more capable and affordable, and marketers are better educated about its benefits. As recently as mid-2013, just under 50 percent of marketers reported using marketing automation, and in early 2015, that number has risen to between 61 and 70 percent (depending on the size of the enterprise), with larger enterprises slightly more likely to have marketing automation in place.³ This marketing infrastructure has clearly hit the steep section of the technology adoption curve.

Early in its life cycle, marketing automation was viewed as a productivity aid for marketers. It provided a better way to build campaign landing pages and send out email blasts, and it had some nice reporting and metrics features as well. Through sustained use, marketers began to understand the value of all the metrics data captured in an automation system, and they began to understand how to use it to their advantage. Now, the solution has evolved to the point where “marketing automation” as a solution category no longer does justice to its full range of capabilities. The automation dimension is still there, but it really is better thought of as a “marketing intelligence” platform that serves as an integration hub and data aggregation point for almost every other solution in the marketing technology stack.

There are several implications of this high degree of integration that can exist between marketing automation and other solutions or tools. The implication that matters most to this discussion is this: marketing automation is a valuable aggregator and repository of data for the marketing analytics effort. This data, coupled with the tools for analyzing it that are built into marketing automation platforms, make it a cornerstone technology for marketing analytics under one big assumption: the platform gets fully implemented. The organizations that stop short of a complete implementation will not get the marketing analytics benefits available to them. When marketing automation is fully implemented, it provides as complete a view of a customer’s journey as one can obtain.

To understand the importance of marketing automation in enabling analytics, consider how the buying journey typically progresses. For 81 percent of business-to-business buyers, that journey begins with a web search for a solution.⁴ When that search leads to a vendor that uses marketing automation, data about the visit is captured. The subsequent interactions with that prospect are also captured. Should the prospect share his or her email address to gain access to content or register for events, the system then tracks email opens, clicks, shares, and other metrics. The system will track which web pages the prospect views and how much time was spent on each. Should the prospect register for a webinar or interact with the vendor through social media, all of these things are logged.

By capturing information about every digital encounter a prospect has with a vendor, the marketing automation system accumulates a historical record of the prospect’s journey with the vendor. Companies that use marketing automation can make intelligent, profitable use of the detailed metrics the system solutions collect about the buyer’s journey. This data lets marketers understand with precision how long the journey takes, where it bogs down or fails, learning where to deploy content, offers, or other promotions to speed up the process and convert prospects to customers. Reflecting on this capability, one marketer with a commercial real estate technology company shared that they were integrating their marketing automation system with their customer relationship management (CRM) system to “collect more info about our customers and use this data to dictate the cadence of messaging and the content they receive from us throughout the customer life cycle.”

UrbanBound, a relocation management software and solution provider, uses the HubSpot marketing automation platform. Aria Solar, inbound marketing coordinator at UrbanBound, shares these comments about the solution’s importance:

Solar continues, reflecting on the shift toward analytics, metrics, and data-driven marketing:

Marketing automation solutions have a lot of affinity with CRM solutions, and they are often integrated. When they are, the marketing team has an even broader perspective of the buyer’s journey with the addition of data about sales interactions with prospects and customers. Whether implemented separately or integrated with CRM, marketing automation platforms are the foundation for marketing analytics. Marketers who want to build and sustain a marketing analytics process should prioritize the implementation of a marketing automation solution. Marketing automation is rapidly evolving into the system of record that marketing organizations have lacked.

These solutions are only growing in importance and value. Today their analytics capabilities show what happened in the past, and this solution space is on the cusp of integrating predictive marketing analytics capabilities into their solutions. Predictive analytics, covered in greater detail in Chapter 10, will provide marketers with very accurate models that forecast future purchase decisions based on prospect behavior. The organizations that have mature, complete marketing automation implementations are poised to be the first to exploit this game-changing capability. Marketing automation is an essential platform for those who want to use analytics to become more revenue-driven and results-oriented.

Marketing Analytics Tools

Having marketing automation installed and fully implemented provides a great foundation for marketing analytics. As a marketing team develops proficiency with the analytics process, it begins to exploit the insights the process yields. When this maturing takes place, it often spawns a desire to expand the reach and range of the marketing analytics, which requires additional tools and data. The selection of specific tools to use is a challenge for any organization that wades into the deeper end of the marketing analytics pool. Of course, the type of metrics chosen for its analytics portfolio will drive tool selection, but there are easily hundreds of potential tools available. Where there was once just a handful to choices, marketers now have an embarrassment of riches in the analytics tools category.

This chapter does not attempt to provide a comprehensive catalog of marketing analytics tools, because the offerings change so quickly and some of the information would be obsolete at the time of publication. Instead, the remainder of this chapter will describe two broad classes of marketing analytics and include examples of tools that exist in these categories. The goals here are to make the reader aware that tools exist for a wide variety of purposes and applications and encourage the reader to further research what these are and the latest offerings on the market.

Because there are so many tools available for marketing analytics, it is easier to separate them into two broad categories based on high-level function, and then consider some example tools within each of those categories:

1. Strategic: tools and systems for business intelligence, customer intelligence, understanding buying behavior, advanced attribution, and predictive analytics.
2. Operations and logistics: tools and systems for managing, testing, and optimizing a web presence, mobile, multichannel campaign performance, demand, and geo-modeling.

There is some overlap in the names of these two categories, as all analytics are strategic, and therefore by default so are the tools used to support them. However, even within the realm of strategic analytics, there some that are more operationally oriented than others. Most marketing analytics tools fall neatly into these two categories, which the rest of this chapter explores.

Strategic Marketing Analytics

Strategic marketing analytics are those that help provide direction to the marketing function. The work done under the banner of “strategic” analytics ensures that the marketing team is doing the right things, those that have the greatest impact. These analytics guide marketing to invest its limited resources in the best possible way. To provide the insights necessary to guide the team, strategic marketing analytics uses tools for customer and business intelligence gathering. There is a forward-looking aspect to this type of analytics. Where many operations and logistics analytics are backward-looking—measuring historical results of campaigns and programs—strategic marketing analytics helps peer ahead by providing predictive results.

Most organizations now collect exponentially more data about their business and customers than they once did. As an example of this data explosion, consider how many auto insurance providers now offer apps and devices to help with policy quoting. These devices plug into a vehicle and record a stream of data about how that vehicle is driven. Where there was once a single customer record in the database, there is now substantially more data about a customer’s driving patterns. This example is just one of many that illustrate how much data is available to companies about their customers. The challenge is making good use of that data, which often requires retrieving it from disparate systems and performing some analysis to reveal insights that influence strategy. This is where business intelligence (BI) systems come into play.

BI refers to the software tools that help organizations analyze virtually any source of raw data. The purpose of BI tools is to turn that data into actionable information. They simplify the process of querying data from multiple sources, performing various analyses on it, and then reporting the results, often in the form of a dashboard or other data visualization. BI tools put a lot of power in the hands of the user without requiring technical programming skills to get results. A common challenge with using them is the quality of the source data. Some remarkable and sophisticated analyses are possible with BI tools, but if the data on which the analyses are performed is suspect, the results are unreliable.

Gartner, an information technology research and advisory company, publishes a BI and Analytics Platforms Magic Quadrant,⁶ a visual summary analysis of the direction and maturity of markets and their vendors. The most recent version of the Magic Quadrant categorizes several vendors as “leaders,” including IBM, Information Builders, Microsoft, MicroStrategy, Oracle, QlikTech, SAP, SAS, Tableau, and Tibco Software.

Customer intelligence (CI) is conceptually similar to BI but with a different focus. Where BI has traditionally been oriented around financial reporting, the focus of CI is on understanding customer behavior. Using CI tools, marketing organizations can analyze the customer data they have amassed, usually in CRM systems, using the intelligence locked in this data to improve the customer experience and ultimately generate more revenue from customers. CI helps marketers better understand the customer experience and answer questions like how to increase the share of wallet captured, which segments are most important, or how to improve customer retention. The insights gained from using CI tools provide marketers with a much deeper understanding of who the customers are, how they behave, and why.

Many of the CI tool and solution providers are the same as for BI. Gleanster Research, a company that surveys users and publishes the results in benchmark reports, covers the CI solution space. It has compiled a list of vendors that provide a CI solution, including Infer, Lattice Engines, Lithium, and Marketforce.⁷

CI tools are increasingly providing their users predictive analytics capability. Most marketing organizations are charged with generating leads, so they invest in lead generation activities across multiple channels, using metrics to monitor the lead flow and effectiveness of the various channels and using data from the analytics process to optimize the mix, score the leads, and then pass the qualified leads on to sales. This process is a big part of what many marketing organizations do, and their effectiveness in doing so has a direct effect on revenue. This process has a lot of moving parts, so it takes constant care, attention, and analytics monitoring to ensure that the process is performing at acceptable levels.

The lead generation process is heavily dependent on technology and digital marketing approaches, so this process produces a lot of analytics data. Indeed, managing the lead generation process without analytics data would involve guesswork to determine which channels were performing best. Despite the digital nature of this process, very little of the technology involved in lead ­generation has been about enabling this process in a predictive way. Most of the analytics surrounding the lead generation process provide a historical view of process performance. In other words, it’s possible to know with a great ­precision what has happened through metrics that are largely backward-looking. Now CI and predictive analytics solutions are providing users with the ability to accurately predict the conversion of future of leads in the sales funnel.

In the real world, predictive analytics are allowing marketers to create models that lead to customers most likely to buy, thus accelerating revenue. AgilOne, a provider of a cloud-based predictive analytics solution, documents ten use cases for predictive analytics in its ebook, The Definitive Guide to Predictive Analytics for Retail Marketers.⁸

1. Behavioral clustering: helps better understand how customers behave while purchasing.
2. Product/category-based clustering: segments customers into groups based on which products they purchase. This insight lets marketers make more intelligent choices about which offers to extend to customers.
3. Brand-based clustering: identifies brand affinity groupings that customers have. For example, customers who prefer brand A also prefer brand C, but not brand B.
4. Predictive lifetime value: predicts future lifetime values of customers. Useful for setting spending parameters on costs of new customer acquisition.
5. Propensity to engage: predicts how likely it is for a customer to take certain actions, such as clicking on a link in a promotional email message.
6. Propensity to convert: predicts the likelihood that a customer will respond to call-to-action offers extended to them via email, direct mail, or other means.
7. Propensity to buy: identifies customers who are ready to make a purchase, allowing marketers to trigger those purchases with a special offer, or market more aggressively to those who aren’t ready to buy.
8. Upsell recommendations: helps increase the average size of order by predicting premium products or greater quantities in which a customer might have interest.
9. Cross-sell recommendations: at the time of purchase suggests other products that are frequently purchased together.
10. Next sell recommendations: after a customer has already purchased a product, suggests products that are likely next purchases.

Predictive analytics is a relatively recent phenomenon, but it is gaining rapid acceptance. As is the case with most leading edge technologies, it initially gained a foothold with large enterprises as a specialty solution. It continues to evolve, moving downmarket and becoming more accessible as its functionality is added to marketing automation and CI platforms.

Advanced, data-driven attribution (sometimes referred to as cross-channel attribution) is a method that precisely allocates fractional credit to elements of the full stream of content or media to which a customer is exposed on their buying journey. Chapter 10 provides greater detail on advanced attribution, but it merits a mention here since it falls within the realm of strategic marketing analytics.

Marketers that use advanced attribution can know precisely which offers, elements, and media stream components really produced a conversion. This level of insight into which components of marketing’s multichannel campaigns really deserve credit has long been considered impossible or unknowable, but with advanced attribution, it is a reality.

Advanced attribution is important, because it provides marketers with the data to make intelligent decisions about their media mix. Marketers once relied on intuition and guesswork to determine the right blend of email, online display advertising, social media advertising, paid search, and direct mail. Now through advanced attribution, accurate data is available about each of the channels in use and how they perform together. The advanced attribution solution space experienced significant consolidation in 2014.⁹ Forrester Research has published an evaluation of cross-channel attribution solutions that covers the following vendors: Abakus, AOL/Convertro, eBay Enterprise, Google, Marketing Evolution, MarketShare, Rakuten DC Storm, and Visual IQ.¹⁰

Cohort analysis is the study over time of the behavior of a group of customers that share a common characteristic. These behaviors are any that are of interest to the marketer, and studying them enables the identification of relationships between a population’s (cohort’s) characteristics and its behavior. Cohort analysis provides that often elusive “apples-to-apples” comparison between groups of customers. The literature on cohort analysis frequently uses university graduation year as an example of a cohort. Each graduating class is a cohort, and characteristics are tracked and compared between cohorts to determine, for example, if there is a relationship between a cohort’s graduation year and its average income.

Cohort analysis is important because for key business or marketing metrics, a cohort analysis reveals how those metrics change over the life of a customer relationship. These revelations lead to better customer retention and more precise estimates of customer lifetime value (CLV). Knowing CLV with precision is important, because it guides decisions about how much a firm should invest to acquire new customers. This approach has been embraced by app and game developers, online, e-commerce, and software as a service businesses, where its use is particularly relevant. Besides CLV analysis, some common cohort analyses include daily/weekly/monthly sales, new versus repeat sales, average order value, and time between purchases.

The tools for building cohort analyses include many previously referenced BI and CI solutions, as well as CoolaData, Flurry, Google Analytics, KISSmetrics, Kontagent, Mixpanel, RJMetrics, and others. Cohort.ly and USERcycle both offer a level of free cohort analysis services. When selecting a tool, make sure it provides an Application Program Interface (API) to or otherwise integrates with the environments you want to include in your analysis, and that it is easy to include historical data that is important to your analysis.

Operations and Logistics Marketing Analytics

Analytics tools and solutions that fall within the operations and logistics realm have much to do with an organization’s digital presence. Where once this digital presence was simply the company website, now it encompasses every digital touchpoint: the website, micro-sites, all company-curated social media, search engine optimization (SEO), search engine marketing (SEM), and mobile. Marketers need tools that allow them to understand not just the individual performance of each of these separate digital channels but to have a holistic view of their performance.

The place to begin this discussion is with the website and the analytics tools that help manage and optimize it. What is true with increasing frequency is that buyers investigate potential vendors online, going deep into the purchase consideration process before vendors are even aware of potential buyer interest. For this reason, the website is still ground zero for digital marketing efforts for most organizations. A company whose website does not easily and quickly provide the content potential buyers need will suffer because of it. Tools are needed that give companies real-time insight into how visitors are interacting with their websites, so that companies can quickly pivot to address website and content deficiencies.

The tool that dominates the marketing analytics landscape is Google Analytics. Many marketers got their first exposure to analytics through the use of this tool. Stating that Google Analytics dominates the landscape is not mere hyperbole: it is used by half of all the websites, and as a web analytics tool, it enjoys a market share of 82 percent.¹¹ If a marketing organization had to limit itself to the use of just a single analytics tool, Google Analytics would be that tool. If having a website that performs well is critical to an organization’s success, using Google Analytics to manage that website makes tremendous sense for one reason alone: about two-thirds of all US search traffic goes through Google.¹² If the goal is for visitors to easily find your website through a Google search, why not use the analytics tool the search giant provides?

There are many things about Google Analytics that make its use so compelling. It’s free for anyone who wishes to use it, it’s easy to implement and use, it has great reporting features, and Google is constantly adding features that make it more valuable and extend its capabilities. It’s a must-have tool for any organization that considers its web presence important, because it reveals information about visitors, traffic, and content to which an organization would otherwise be blind. Here are some of the things a user of Google Analytics can learn:

• Visitors: the number of visitors to the website, how many were unique, how much time they spent on the site, how many were first-time and returning visitors, and more. Demographic data about visitors is also available, such as their geographic location (country and city), and which browser and device they were using when they visited your site.
• Traffic: Google Analytics will tell you how much traffic your website is getting, but more important, it can reveal how those who visit your website got there. As the paths to a website get more diverse, it is important for marketers to understand the source of their web traffic, specifically how much came from search engines and other referral sources. Regarding search engine traffic, Google Analytics can also tell you the keywords that are responsible for generating traffic to a website, information that is critical to performing SEO.
• Content: through Google Analytics, marketers can learn how visitors are consuming their web content. The web pages that are entry and exits points are recorded, as well as how many times a visitor viewed a page and how long they stayed there. This information helps marketers optimize and improve the SEO characteristics of their ­content. Google Analytics can even provide statistics about how long it takes pages to load, helping identify and correct slow-loading pages.

The data on website visitors, traffic and content just scratch the surface of Google Analytics’ capabilities. It is a powerful tool that grows stronger with each subsequent release. In 2013, Google offered Universal Analytics, adding features that enable tracking users across multiple devices. With Google Universal Analytics, the ability exists to collect metrics from any Internet-connected device that can send an HTTP data request. The implication of this connectivity is that a very diverse set of devices, such as game consoles, various apps, and even Internet-connected Blu-ray players can share usage data with Google Analytics.

Google Analytics is the gorilla in the digital analytics space, but other vendors exist as well, and their solutions merit consideration. TrustRadius has published analysis in the form of TrustMaps for Digital Analytics software,¹³ in which analytics solutions are ranked on the basis of user ratings and adoption within small, mid-size, and enterprise market segments. Marketers that are considering implementing analytics technology will find this type of analysis very helpful as they attempt to understand the array of offerings in the market.

A company’s digital presence in this modern marketing era is no longer limited to just its website but includes its social media and mobile presence as well. Any approach to analytics needs to span the entire digital waterfront, including social and mobile. For some organizations, social media has eclipsed the website in importance. Google Analytics certainly provides some social reporting capabilities, and a cadre of more specialized solutions also provide insight into brand conversations that occur in the social realm.

Social media tools are necessary for identifying and participating in the conversations about a brand or company that take place in the social realm. These solutions help monitor those conversations, sifting through the seemingly infinite number of social media posts and detecting those you need to know about because they relate to your brand. Simply knowing about these conversations enables participation in them. For example, an individual who rants about a poor service experience with a brand may now see that brand respond as part of an effort to recover from a service failure.

Beyond just providing opportunities to address social media complaints, these tools allow brands to measure sentiment about the brand, its products, or what it represents. They provide users with the ability to do more than simply count likes, follows, or shares and instead measure the true level of engagement a brand has with its followers. Many tools fall into this social media monitoring (also known as social media listening) and analytics category, including HootSuite, Netbase, Radian6 (by Salesforce), SproutSocial, Sysomos, and numerous others. Some solutions are free, so budget should not be a reason that marketers aren’t managing and measuring their social media presence.

The same discussion about social media applies to the mobile channel. If mobile is part of an organization’s media mix, then that organization needs to ensure it is included in the marketing analytics portfolio. Mobile is one of the fastest growing marketing channels, providing the ability to exploit GPS-enabled mobile devices to gather geographic and behavioral data about customers. This data can enable a business to detect the proximity of a customer and push coupons, sales alerts, or other offers to that customer.

Conclusion

This chapter has revealed the tip of the iceberg when it comes to marketing analytics tools. There are many from which to choose, and it’s not uncommon to see firms with a mature marketing analytics process use five or more of them simultaneously. When this is the case, it becomes even more important to have a dashboard (as discussed in Chapter 7) presenting a comprehensive view that makes it easy to access and understand all the important information the marketing analytics process is producing. If BI tools are in use, they will enable the development and publication of a dashboard. There are also vendor solutions specific to this purpose of creating an analytics dashboard, such as Birst, Cyfe, Domo, iDashboards, and QlikView. Free solutions also exist. For the many reasons described in Chapter 7, a dashboard is an important communication and process management tool for the marketing analytics process.

This discussion of tools and solutions that support strategic, operational, and logistical marketing analytics just begins to touch on all the technology available to modern marketers. When it comes to selecting tools, the set of choices is bewildering. How can marketers make the right choice? As this book has consistently advised, start with the objectives. Knowing the objectives narrows your search for tools and technology. Next, go online and research candidate providers, placing emphasis on those that have already created the type of success you want to replicate. As you consider the options, prioritize ease of use. Finally, evaluate the solutions on your short list to make a selection. Most vendors provide free trials so you can see how an analytics tools works in your environment, with your data.

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¹From a speech to announce a grant, February 17, 2005, http://news.microsoft.com/2005/02/17/steve-ballmer-aacis-unlimited-potential-grant-announcement/.

²“Marketing Automation: Insights, Landscape & Vendor Analysis,” Demand Metric, Clare Price & Kristen Maida, April 2014, p. 4; http://www.demandmetric.com/content/marketing-automation-solution-study.

³“Digital Marketing for 2015: Targeting Audiences & Adopting New Strategies,” Demand Metric, November 2014, p. 9.

⁴“Content Preferences Survey Report,” DemandGen, May 2012, p. 5; http://www.slideshare.net/G3Com/content-preferences-surveyfinal.

⁵J.M. Culkin, “A schoolman’s guide to Marshall McLuhan,” Saturday Review, March 18, 1967, pp. 51–53, 71–72.

⁶See https://www.gartner.com/doc/2668318/magic-quadrant-­business-intelligence-analytics.

⁷See http://www.gleanster.com/vendors/customer-intelligence.

⁸See http://www.agilone.com/definitive-guide-to-predictive-analytics-lp.

⁹“Watch This Space: Big Players Gobble Up Attribution Solutions,” Demand Metric, May 16, 2014, http://blog.demandmetric.com/2014/05/16/watch-space-big-players-gobble-attribution-solutions/.

¹⁰See https://www.forrester.com/The+Forrester+Wave+CrossChannel+Attribution+Providers+Q4+2014/fulltext/-/E-res115221.

¹¹“Usage of Traffic Analysis Tools for Websites,” W3Techs, March 1, 2015, http://w3techs.com/technologies/overview/traffic_analysis/all.

¹²“comScore Releases January 2015 U.S. Desktop Search Engine Rankings,” February 19, 2015, http://www.comscore.com/Insights/Market-Rankings/comScore-Releases-January-2015-US-Desktop-Search-Engine-Rankings.

¹³TrustRadius, “Top Rated Digital Analytics Products for Small Businesses, Mid-size Companies and Enterprises,” September 3, 2014, https://www.trustradius.com/articles/top-rated-digital-analytics-products.