Marketing Performance

Embracing Cross-Channel Analytics to Create a Competitive Advantage

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There has been more discussion about cross-channel analytics as more organizations leverage both digital and traditional vehicles in their communication mix.

The emergence of Yahoo Web Analytics, Adobe’s acquisition of Omniture (recall that Omniture acquired Visual Sciences, WebSideStory, Offermatica, Instadia, and TouchClarity), and Google’s continued push into the enterprise all signal an increased emphasis on multichannel data analysis.

Before exploring what cross-channel analytics is and how marketers can use it to analyze customer behavior, it makes sense to define analytics and how analytics is being used by marketers.

Basically, analytics is about deriving insights from data. Analysis involves breaking data into smaller parts to increase insight.

For example, chemical analysis entails breaking down chemical processes to understand the chemical reactions between elements of matter. In their books Competing on Analytics and Super Crunchers, Thomas Davenport and Ian Ayres, respectively, make compelling cases for the value of a structured analytics practice in business as a way to create a competitive advantage.

Davenport says that the creation of strategic data assets and business processes is one of the “last remaining points of differentiation” in an increasingly competitive world. More specifically, marketing analytics is a methodical examination of customer and market data to increase understanding of the customer and the market to make and take action to improve your competitive advantage.

What Is Cross-Channel Analytics?

Organizations that pull together data from various marketing programs across different channels use cross-channel analytics to understand the impact of each channel on customer behavior.

For example, in today’s environment, we may need to gather data from an email campaign, a physical event, and an online advertising campaign to better understand purchasing behavior and correlate marketing programs with purchases.

Cross-channel analytics enables you to measure the interaction of various channels, such as websites, customer service, phone support, and print media, to understand how those channels relate to one another and affect customer behavior.

If you believe that customers and prospects are the same regardless of the channel they choose to use, then you must believe that it is time to leverage the data flowing in from every channel to better understand the customer.

For example, if a retail marketer implements a multichannel campaign, the marketer will want to analyze the relationship of the different marketing channels on shopping behavior.

The complexity occurs when a retail marketer implements a multichannel campaign designed to affect both online and offline shopping, and then wants to analyze the relationship between online and shopping behavior across all the channels.

That example can apply to numerous industries, such as financial services, hospitality, communications, etc. The message for every marketer is that such an analysis requires more skills and better tools than the traditional marketing analytics that we’ve previously used to delve into the impact of an integrated campaign on a specific behavior.

What It Takes to Do Cross-Channel Analytics

If you are using multiple channels and you want to both understand what’s really working and enrich your customers’ experiences with your organization, then you’ll have to step into the world of cross-channel analytics.

However, with data spread out across so many applications and locations, doing cross-channel analytics can be tricky. You’ll need skills, tools, and models that go beyond Web analytics. Web analytics focuses on visitors’ behavior, whereas cross-channel analytics tracks individual behavior across channels to understand your customers and their experiences and actions.

Cross-channel analytics requires path-to-conversion analysis and cross-channel synergy analysis. Path-to-conversion analysis requires that you assess all the “events” in the customer pathway that the customer has been exposed to that contributed in some way to the customer’s conversion.

Identifying cross-channel synergies requires looking at how different elements of the communication mix work together to move a customer through the buying process. Assuming that each element contributes something to the conversion path, the questions you want your cross-channel analytics to answer—at a minimum—are these:

  • What percent did each element contribute to the path to conversion?
  • What is the ratio between those elements?
The Value of Cross-Channel Analytics to Current Tools

Cross-channel analytics brings value to many of the business intelligence (BI) tools you’ve already invested in. Here are a few ways that cross-channel analytics adds value to your forecasting, predictive analytics, and modeling capabilities:

  • By creating and using forecasting algorithms from both digital and traditional channels, you will be able to estimate sales based on changing customer behavior. The statistical models you will build will allow you to determine a level of confidence regarding various buying segments’ behaviors.
  • And as your analytical and cross-channel analytics capabilities improve, you will be able to develop models that can predict traffic and revenue impact associated with changes in the communication mix, which can be used, for example, to make real-time adjustments to keywords used in pay-per-click marketing efforts.
  • Cross-channel analytics can make your modeling capabilities and marketing-mix models more effective because you will have a better understanding of the relationship between the different marketing channels and improve your use of decision optimization tools to determine the combination or sequence of messages to maximize engagement and sales.
 The Implications of Cross-Channel Analytics

Gartner reports that the global market for analytics applications, performance management, and BI solutions was $8.7 billion in 2008—roughly 20 times the global investment in Web analytics.

Cross-channel analytics is going to require every company to invest more in acquiring and analyzing data to produce true insights and recommendations that are valuable to the business. As the need for whole-business analysis increases, marketers will need tools and models that bridge online and offline data.

Because customers today can move across several channels in the process of making a single buying decision, marketers are deploying cross-channel marketing and leveraging every communication channel.

Successful cross-channel marketing requires a sophisticated marketing solution that can help marketers analyze customer behavior, make customer-centric decisions, and respond quickly to marketing opportunities.

The ability to overlay many complex layers of data to generate a holistic view of customer behavior and campaign effectiveness has numerous implications for marketing professionals, from improving analytical skills to improving data quality to investing in better tools.

Marketers are going to need to embrace statistics, modeling, and predictive analytics.


Six Best-Practices to Improve Sales and Marketing Alignment

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Your sales and marketing organizations are the most critical links to customers. Having well- oiled sales and marketing machines that work well together can make all the difference in successfully addressing revenue and growth.

The alignment of those two organizations determines how well a company attracts buyers and sells to them. The relationship is more than just a simple handoff at the point a lead is generated; it is the foundation for profitable revenue growth.

Research firm IDC calls marketing and sales alignment one of the greatest opportunities to improve the revenue cycle.

Aligning Sales and Marketing in Four Key Areas

There’s plenty of talk about aligning Sales and Marketing, and most often that conversation is around lead management. But Sales and Marketing need to be aligned in at least these four areas:

1. Market and customer segmentation
2. Go-to-market strategy, process, and planning
3. Sales enablement
4. Opportunity management

That fourth area, opportunity management, is one of the first places any organization can address to see relatively fast improvements and value.

What is opportunity management? It’s the complete process of tracking and managing new revenue opportunities (prospective and current customer business)—from the generation of the opportunity to its conversion into a customer relationship.

When well defined and properly implemented, the opportunity-management process provides insight into both the effectiveness and the efficiency of your marketing and sales efforts.

Six Opportunity-Management Best-Practices

Today’s business environment has brought the topic of opportunity management to the forefront. Organizations cannot afford opportunities to languish on the vine or to expend energy, time, and money pursuing opportunities that will not convert to business.

The following six best-practices can help you increase the effectiveness of your opportunity- management process:

1. Use the customer-buying process as the foundation for aligning both organizations.
2. Track and score leads based on prospect behavior.
3. Collaborate on defining a qualified lead to determine when an opportunity is sales-ready.
4. Measure Marketing’s impact on the sales pipeline and the number of open opportunities that result from marketing programs.
5. Use customer behavior to map the most-appropriate subsequent interactions.
6. Leverage opportunity-nurturing programs.

The first best-practice, using the customer-buying process as the foundation for aligning both organizations, is the very first step any organization can take to improve its marketing and sales alignment.

Doing so has implications for the remaining best-practices as well as for the configuration of your marketing-automation, sales-automation, and campaign-management systems.

A Customer-Oriented Opportunity-Management Pipeline

The notion of a sales pipeline (that is, the flow of business opportunities), is very familiar to most organizations. But developing that pipeline around the customer-buying process may be new territory.

The following two lists hint at the differences between a sales-oriented pipeline and a customer buying-oriented pipeline.

Scenario A: Pipeline Elements

  • Identify the buyer.
  • Send an email.
  • Call to meet.
  • Assess the need.
  • Determine the budget.
  • Submit a quote.
  • Deliver a presentation.
  • Submit a proposal.
Scenario B: Pipeline Elements

  • Visit website.
  • Download a document.
  • Request a call.
  • Describe a project.
  • Attend a webinar.
  • Schedule a meeting.
  • Provide specification and budget.
  • Participate in a demo.
  • Request a proposal

Did you catch the nuances between the two scenarios? If you thought Scenario B described customer behavior and Scenario A described company behavior, you are right on target.

Here are seven steps you can use to create a customer buying pipeline:

1. Define the customer buying process and each incremental behavioral commitment for each buying segment.
2. Group the behaviors into buying stages that map to the buying process.
3. Validate the pipeline with customers, and modify as needed.
4. Determine stage owners.
5. Map marketing and sales tools and processes to each stage.
6. Configure marketing-automation, sales-automation, and campaign-management systems.
7. Monitor, measure, and report results and payback, and modify as needed.

Ideally, Marketing and Sales should work together to engineer a customer buying pipeline. There is also merit—when using someone from the outside—in facilitating the process to ensure it is collaborative, to manage the customer validation, and to support the system-configuration changes.

Using a customer-centric model is a valuable method for improving marketing and sales alignment, effectiveness, and measurement. It enables an organization to shift from a transactional focus to one that is more customer-focused and designed to accelerate and reduce the cost of revenue generation.

Managing Marketing Performance: The Role of Data, Analytics, and Metrics

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Performance management has been applied to various parts of a business for quite a long time, particularly when it comes to manufacturing, logistics, and product development. Applying the concept to marketing is finally coming of age.

Essentially, performance management is the process of measuring progress toward achieving key outcomes and objectives in order to optimize individual, group, or organizational performance. A performance‐driven marketing organization is one that has a set of measurable performance standards, a pointed focus on outcomes, and clear lines of accountability—all of which are important if a marketing organization wants to prove its value.

Three elements play a critical role in managing marketing performance: data, analytics, and metrics. Each of these is actually highly related to the others, with data being the foundation for the other two. You cannot define the data and analytics you need without knowing the metrics, and you cannot leverage the metrics without data and analytics. Each drives the other. The use of data and the ability to draw actionable insights from data—analytics—is no longer the domain of select power users. This ability is now a critical competency for marketing professionals as companies attempt to deal with increasing market pressure and competition. Marketers who embrace data, analytics, and metrics will be in the best position to improve business performance and demonstrate value.

Success depends on two things. The first is understanding the organization’s priorities and business outcomes. One this is understood, you will know what metrics are important and what data you will need in regards to the market, customers, competition and your own company. Second is the ability to connect what the business is trying to achieve with the work that Marketing is performing. Making this connection requires data and analytics.

Today’s challenge isn’t data. Most of us are wallowing in data. The challenge is generating insights and meaning from the data. This is the realm of analytics. By applying analytics to the data, we can glean the necessary insights needed to facilitate better and faster fact‐based decisions. One of the most valuable applications of data and analytics is in leveraging your metrics. The metrics are what enable continuous improvement as you strive to achieve and set new performance standards. Therefore, an initial step is to define an effective set of performance metrics and the associated data and analysis that will be used to determine where the organization should focus to maximize quantifiable results. Too often marketers measure marketing activities and tactics, such as response rates, impressions, and number of participants in an event, and so on, rather than metrics that link marketing to business outcomes such as those related to customer acquisition, retention, and product adoption.

Numerous studies suggest that is one of the biggest disconnects in how marketing performance should be measured. Without closing this gap, marketing may be measuring all sorts sort of things, none of which may be linked to the priorities of the business. It is essential to secure upfront agreement on what outcomes marketing is expected to impact and how this impact will be measured in financial, operational, and comparative terms (how you stack up against the competition, for example).

Beware of falling into the ROI (return on investment) trap where you are trying to determine the ROI for a particular activity or program. Focusing solely on the short‐term ROI of individual activities, tactics, programs, and campaigns may only to lead to more short‐term tactical efforts at the expense of investments for the future, such as new‐product development, geographic expansion, and customer penetration.

For most marketing organizations, there a just a few key metrics required to measure and manage performance. One way to select the best performance metrics is to define how to quantify three things: marketing effectiveness, marketing success, and marketing impact on the business outcomes. Once you have these defined, you will know what you will to measure to determine whether the marketing initiatives are working.

Remember that your metrics need to be directly demonstrate Marketing’s effectiveness, efficiency, and financial value against the business outcomes. It will be important to balance the internal operational efficiencies with external performance goals. While operational efficiencies examine how efficiently people, facilities, and capital are being used, it is the external performance goals that truly matter because these help us measure and assess how effective Marketing is at producing the desired results and impacting the business outcomes.

Each of your core metrics will need a set of underlying measures that can be used to help diagnose how well things are going, identify emerging issues, and enable continuous improvement. This is where data and analytics play an important role in the performance measurement and management process.
While every company is different and therefore the metrics may be different, we can use a typical example to illustrate these concepts. Most companies want to increase their market share—that is, the number of customers they serve compared to the competitive set for a particular market as well as their revenue. Using this business outcome we can create four key metrics: number or percentage of customers acquired, the rate these customers are acquired, the cost to acquire these customers, and
the average order value. Marketing is responsible for identifying prospective customers and then moving these opportunities through the awareness, interest, consideration, preference, and purchase intention stages of the buying process. So an example of a marketing metric directly related to customer acquisition might be the percentage of qualified opportunities that convert to customers with a minimum order value.

Data and analytics will be necessary to determine what this percentage and minimum order value should be. And now that know the outcome and have established the marketing metrics, you can define the data and analytics. Data we will need to track as part of this metric might include (but is not limited to) the following:

* The number of qualified opportunities as a percentage of qualified opportunity that is forecast

* Preference for your company and its offers compared with preference for competitors

The analytics we may need to perform might include (but is not limited to) the typical conversion ratios and leakage between the pipeline stages and the effectiveness and efficiency of various tactics for each pipeline stage. We will also need data and analytics to determine the ROI of the programs that were executed in order to achieve the performance target, the impact of the performance target on the business outcomes (market share and revue), etc. Based on the results and analysis, we can decide what to keep doing, what to change, etc. We are using the metrics, data, and associated analysis to measure and manage marketing performance.

Hopefully, this example illustrates the roles that data, analytics, and metrics  play in managing marketing performance. While this work is necessary, we wouldn’t want to suggest it is an easy undertaking. And it
is not one that many companies can do alone. And even if they can, they must often address technology, processes, and skills to effectively leverage data, analytics, and metrics.

Here are a few things to keep in mind related to these. The technology components you will want to address include dashboards, reports, analytics, data architecture, and data integration. The process aspects should include the procedures around data analysis, measurement, reporting, and performance setting. In regards to people, it may be necessary to explore what skills need to be developed to help your team embrace and use data, analytics, and metrics and what training will be needed to help people adjust to roles and responsibilities based on performance.

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