Metrics

Analytics: The Essential Ace in Every Hand

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None of us would agree to play a card game with cards missing from the deck; we would know that the odds of winning would be significantly diminished. Yet surprisingly, many marketers are willing to implement marketing programs sans analytics.

In the past few weeks I have attended several marketing conferences. At each event, marketers are talking enthusiastically about how to make Web sites, SEO, social media, email campaigns, and mobile better. However, there is very little conversation about how to be smarter. Analytics is an essential card — actually an ace — in every marketer’s deck for enabling fact-based decisions and improving performance, and most importantly, for being smarter.

While the ace alone has value, when played with other cards its power is truly revealed. And when it comes to analytics, the other card is data. Yes — we have all heard the common complaint about the elusiveness of quality data. Unfortunately, data quality has been an issue in organizations for so long that it has now become the ready excuse for why marketers cannot perform analytics. To harness the power of your analytics card, identify your data issues and create a plan to address them.

Another reason that you may overlook this missing card in your deck is that guessing or gut instinct has been working well enough. Unfortunately, this approach may not suffice in the long-term and your “luck” may run out as organizations push to make “smart” decisions. As marketers, analytics is our opportunity to actively contribute to fact-based decisions. Through analytics, marketers achieve new insights about customers, markets, products, channels, and marketing strategy, programs and mix. It also enables marketing to help improve performance, competitiveness, and market and revenue growth.

As the importance of analytics gains momentum, marketers with analytical acumen will be in great demand. According to some resources, the complexities of data analysis and management are becoming so enormous that there is a shortage of people who are able to conduct analysis and present the results as actionable information. Taking the initiative and honing your analytical capabilities will enable you to make sure you have this ace in the deck — and preferably, in your hand.

Most of us are already working with a time and resource deficit. Try to find a way each quarter to bolster you analytical skills. Attend a conference, read a book, take a class, and bring in experts you can learn from. Here are some key analytical concepts and skills to add:

· Quantitative Decision Analysis
· Data Management
· Data Modeling
· Industry and Competitive Analysis
· Statistical Analysis
· Predictive Analytics and Models
· Marketing Measurement and Dashboard

If you can build your analytics strength, you’ll always have an ace in your pocket.

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Power Up Your Marketing to Prove Business Value

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Numerous studies throughout 2012 reiterated just how challenged marketers are in proving
Marketing’s business value.

The Capsicum Report found that “Marketers lack commercial acumen and don’t speak the language of the business, reporting their contributions in terms of ‘activities’ or ‘outputs’ rather than the business key performance indicators.”

The Economist Intelligence Unit reported that “the CMO’s traditional dilemma of demonstrating effectiveness, return on marketing investment, and relevance to the business still persists.”

The Forrester Evolved CMO study stated that “to prove their value and justify investment, they (CMOs) must tie marketing closer to business results.”

The 11th annual marketing performance management study conducted by VisionEdge Marketing and ITSMA reported a continuing trend of the C-Suite’s perception that only about 25% of marketers are able to demonstrate their impact and contribution to the business.

Some marketers, though, are cracking the code, and we can learn lessons from them as we work to power up our marketing.

One of the key differences about the stellar performers is that these marketers view and present themselves as businesspeople first. This elite group is customer-centric above all else, and it’s driven to transforming or establishing Marketing as a center of excellence within the organization.

These marketers work at ensuring that Marketing focuses on producing results that matter to the business, particularly in customer acquisition, retention, and value, and they are able to communicate those contributions in ways that are relevant to the C-Suite.

These marketers consistently apply five best-practices:

1. Aligning marketing activities and investments with business outcomes

2. Developing outcome-based metrics and reporting capabilities to demonstrate their
accountability

3. Employing and developing analytical skills

4. Investing in the infrastructure, processes, and systems to support their work

5. Building collaborative alliances with Finance, IT, and Sales colleagues.

They also recognize that deploying those best-practices is only part of the equation for boosting their performance and measurement competencies. They realize that playing a more strategic role takes added muscle, which they build by…

  • Embracing strong talent, balancing creativity with science derived from valuable customer and market insights
  • Emphasizing innovation for all aspects of marketing—related to strategy, implementation, processes, and so on.

Every organization can benefit from adding such power and muscle to their marketing team:

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Take a look at  the most recent 2013 Marketing Performance Management Report: Executive Summary (FREE DOWNLOAD) or Purchase the Full Report at the VisionEdge Marketing Online Store!

Need to Engage and Connect With Prospects and Customers? Marketing Automation to the Rescue (Maybe)

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Today, a suitable marketing automation platform is available to meet just about any company’s requirements and budget. These platforms often include systems for managing digital assets, allocating resources and tracking marketing expenditures, automating Imagecampaigns (online and offline), measuring marketing activity and demand generation, and managing Web content and leads.Many companies invest in marketing automation platforms as a way to make their marketing organizations more efficient. Though marketing automation can achieve that objective, two key benefits of these systems is that they help you connect better with prospects and improve the opportunity to engage prospects and customers.

What Marketing Automation Isn’t

Marketing automation isn’t magic. Success requires taking a methodical and disciplined
approach to segmenting, defining the customer-buying process, establishing agreed-upon
definitions of stages, creating personas, establishing common metrics, and committing to
faithfully using the system

.Marketing automation allows you to tailor your content and interactions to enhance how you connect with and engage prospects and customers. As a result, you can positively affect the conversion rate and sales cycle. And, in these tough times, who wouldn’t want to see higher and faster conversions?

Take a Customer-Centric Approach to Configuration

Such benefits alone present a good business case for marketing automation. But for a system to “be all that it can be,” it must be properly configured and deployed. Proper configuration and alignment require and enable stronger alignment between Sales and Marketing.

Many companies configure their systems around how they might sell and evaluate an opportunity (e.g., whether they’ve identified a budget, project, or need). However, before you deploy, take an outside-in view and configure the system around how your customer finds, evaluates, selects, and buys products in your category.

For your investment and that approach to pay off, Sales and Marketing need to agree on how the customer buys, the buying stages, and what constitutes a qualified opportunity, in terms of both fit (segment, budget, size, etc.) and buying behaviors. This approach allows you to use fit and behavior to create a lead-scoring schema.

Create and Measure Four Customer Interactions

Marketing and sales teams are typically proficient in connecting at the beginning and end of the conversation, but the real challenge is managing the middle of the conversation. The middle conversation is when prospects and customers are in the “in-between”—between initial contact and interest, on the one hand, and the short list and final selection, on the other.
A properly configured and deployed marketing automation system enables you to manage the middle. How? It makes it possible to cost-effectively sustain a dialogue with qualified
opportunities until they are ready to buy while enabling you to monitor the interaction between those opportunities and your organization.

You’ll want to set performance targets for these four kinds of interactions, and then use your marketing automation system to create, measure, and monitor them:

• Connections
• Conversations
• Engagement
• Consideration

Think of connections as those contacts with whom you have established communication and rapport and who have agreed to be “touched” by your organization. A connection doesn’t necessarily result in a conversation. Connections are just that: two entities that have a link between them.Think of how many people you may have in your LinkedIn network that you are connected with but don’t necessarily have conversations with. Conversations suggest an exchange—the sharing of ideas, opinions, or observations. Consider how many people you “talk” with on a variety of 3 topics on any given day. Though some of those people might be interesting, they may not necessarily be the right people—or they may not be ready to move the relationship forward.

Ultimately your marketing efforts aim to create engagement, and you want your marketing automation system to support those efforts. Engagement consists of interactions that indicate the strength of the relationship.

Finally, you want to produce and measure consideration because it is the precursor to conversion. Consideration simply refers to those prospects and customers who are actively “shopping” for the products and services you offer and are considering your offer among the options.

If You Build It, They Will Come

The premise of marketing automation is that it will help Marketing increase the number of
business opportunities for your company, deliver sales-worthy and ready leads to Sales, improve your visibility into the pipeline, and enable your marketing organization to focus on efforts that will drive the highest conversion rate and the lowest cost.

The value proposition is that marketing automation will shorten your sales cycle and help
improve your forecast accuracy.And it’s all possible with this one caveat: Marketing automation is only as good as the effort you make in using it. To use it properly and realize the kinds of results you want will likely require changing processes, addressing Marketing and Sales alignment, and improving skills.

Research suggests that when marketing and sales processes, skills, and systems are aligned, an organization can see a five-fold improvement in revenue. If you are willing to make the necessary investments, you can realize the benefits of implementing a marketing automation platform.

Big Data Promises Marketers Big Insights

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By: Laura Patterson, President

The amount of data being generated is expanding at rapid logarithmic rates. Every day, customers and consumers are creating quintillions of bytes of data due to the growing number of customer contact channels. Some sources suggest that 90% of the world’s customer data has been created and stored since 2010. The vast majority of this data is unstructured data.

vem big data 2It is not surprising, then, that study after study shows that the majority of marketers struggle with mining and analyzing this data in order to derive valuable insights and actionable intelligence. A recent report by EMC found that only 38% of business intelligence analysts and data scientists strongly agree that their company uses data to learn more about customers. As marketers we need to learn how to leverage and optimize this flood of data and incorporate it into customer models we can use to predict what customers want.

Big Data

Many marketing questions require being able to perform robust analytics on this data. For example, understanding what mix of channels are driving sales for a particular product or in a particular customer set or what sequence of channels is most effective. These types of questions often require large sets of data, or what is being referred to as Big Data.

Big Data isn’t new; it’s just gone mainstream. A recent study found that almost half (49%) of US data aggregation leaders defined Big Data as an aggregate of all external and internal web-based data, others defined it as the mass amounts of internal information stored and managed by an enterprise (16%) or web-based data and content businesses used for their own operations (7%).

 But 21% of respondents were unsure how to best define Big Data. IDC defines big data as: ‘a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high-velocity capture, discovery, and/or analysis.’

Holistic Approach

Big Data incorporates multiple data sets—customer data, competitive data, online data, offline data, and so forth—enabling a more holistic approach to business intelligence. Big data can include transactional data, warehoused data, metadata, and other data residing in extremely massive files. Mobile devices and social media solutions such as Facebook, Foursquare, and Twitter are the newest data sources. Most companies use Big Data to monitor their own brand and that of their competitors. The use of “Big Data” has become increasingly important, especially for data-conscious marketers. Big Data is a valuable tool for marketing when it comes to strategy, product, and pricing decisions.

Big Data offers big insights and it also poses big challenges. A recent study by Connotate found the top challenge with Big Data was the time and manpower required to collect and analyze it. In addition, 44% found the sheer amount of data too overwhelming for businesses to properly leverage. As a result, many companies aren’t maximizing their use of Big Data.

The effort however associated with managing Big Data is more than worth it. The promise of Big Data is more precise information and insights, improved fidelity of information and the ability to respond more accurately and quickly to dynamic situations.

How to Handle Big Data

So while Big Data might seem a bit daunting, these steps will help you navigate using Big Data:

  1. Clarify the question. Before you start undertaking any data collection, have a clear understanding of the question(s) you are trying to answer. Using Big Data starts with knowing what you want to analyze. By knowing what you want to focus on, you will be better able to better determine what data you need. Some common questions asked are ’which customers are the most loyal’ and/or ‘which customers are most likely to buy X‘? Big Data is about looking beyond transactional information, such as a click-through data or website activity.
  2. Clarify how you want to use the data. Will you be using the data for your dashboard, to define a customer target set for a specific offer or to make program element decisions (creative, channel, frequency, etc.)?
  3. Think beyond the initial question. Invariably the answer to one question leads to more questions. If you’re not sure, hold a brainstorming session to explore all the ways the data could be used and potential questions the answers might prompt. Structure your data in a dynamic way to allow for quick manipulation or sharing. Aggregate data structures and data cubes aid with this step. Construct your data cubes so that
    they contain elements and dimensions relevant to your questions.
  4. Identify data sources that need to be linked. Once you identify the question and how you want to use that data you will have insight into what data you need. To run analysis 3 against data you will need to consolidate and link it. More than likely you will need to collect the data from disparate data sources in order to create a clear, concise, and actionable format. It may be necessary to invest in some new tools so you can pull and analyze data from disparate locations, centers, and channels. These tools include massively parallel processing databases, data mining grids, distributed file systems, distributed databases, and scalable storage systems.
  5. Organize your data. Create a data inventory so you have a good understanding of all your data points.
  6. Create a mock version of your data output. This is a key step to helping you determine the data sets. It will also help you with thinking about how you will convert the results into a business story.

Smart marketers use the data to tell a story that will illuminate trends and issues, forecast potential outcomes, and identify opportunities for improvement or course adjustments. They use the data to gain big insights into customer wants and needs, market and competitive trends. Tackle Big Data and tap into big insights that enable you to take advantage of market opportunities, deliver an exceptional customer experience, and give your customers the right products when, where, and at the price they want.

Measuring Marketing’s Contribution to the Pipeline

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For businesses, a pipeline is a targeted list of potential buyers who might have an interest in your products or services. Many companies face the challenge of capturing the attention of potential buyers and moving as many of these potential buyers as possible through the pipeline stages of contact, connection, conversation, consideration, consumption, and community. More and more companies are relying on Marketing to continuously and effectively grow their organization’s opportunity pipeline. Potential buyers who are not converted into customers are often referred to as leaks or pipeline leakage. Our role, as marketers, is to “plug the leak” and improve conversion rates. If the Marketing and Sales aspects of the pipeline are not connected and aligned properly, the potential pipeline leakage can be very large. So a crucial step is ensuring Marketing is properly aligned with Sales. Marketing and Sales alignment allows for the creation and implementation of strategies, programs, and tactics that will facilitate pipeline opportunity development and movement. Once your company achieves this alignment, the next important step is for Marketing to focus on marketing initiative that will effectively and efficiently contribute to pipeline performance and the generation of customers. We must be able to clearly demonstrate and measure our contribution to the pipeline.

Unfortunately, a Forrester Research study, “Redefining B2B Marketing Measurement,” found that “the metrics that most B2B marketers say they use — like number of leads generated and cost per lead” — rank in the lower half of the effectiveness list.” In fact, number of leads generated and cost per lead may actually work against us if we don’t look further into the buying process. At first blush, one program may produce more “leads” than another at a lower cost and therefore appear more efficient. But what is really important is how many of the opportunities convert (don’t leak) to the next stage in the buying process. If there is a higher conversion rate from the more expensive program, than it is actually more effective. If we only look at a marketing program in terms of qualified leads generated and cost, we could potentially be eliminating programs that actually help build the pipeline.

Therefore, we need to move beyond the lead as the marketing metric and leverage metrics more meaningful to the organization — metrics that are more closely tied to customer deals. Customer deals– that is, sales — is for most organizations one of the most important business outcomes. Every company establishes a revenue goal. This revenue target is generated by some number of deals and dollars from existing customers and some number of deals and dollars from net new customers. This brings up the question of what metrics should CMOs and their teams use to measure Marketing’s contribution to the pipeline? Here are four metrics to consider:

1. Pipeline contribution which measures the number of opportunities generated by Marketing that convert into sales opportunities and ultimately into new deals. This metric helps ascertain to what extent marketing programs and investments are positively effecting the win rate and reducing the number of qualified leads that wither and die or are rejected by Sales.
2. Pipeline movement which measures the rate at which opportunities move through the pipeline and convert to wins. This metric helps assess the degree to which marketing programs and investments accelerate the sales cycle.
3. Pipeline value which measures the aggregate value of all active marketing opportunities at each stage within the pipeline. This helps determine what increase in potential business marketing investments may generate.
4. Pipeline velocity which measures the rate of change within your pipeline-both in speed and direction. This enables you to determine whether your sales are accelerating, decelerating, or remaining constant.

When examining each of these metrics it is important to compare the marketing generated opportunities compared to non-marketing generated opportunities. This means we need to understand what is the difference in the win rate, average order value, conversion rate, and velocity between marketing generated opportunities compared to non-marketing generated opportunities. Ideally, over time, by monitoring results and analyzing the data related to these metrics, Marketing can begin to create more predictable results in terms of contribution, conversion, and value.

Creating a Propensity Model

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Recently there’s been plenty of focus on predictive analytics. We were recently privileged to create a Take 10 webcast on this subject for MarketingProfs . Why all the interest? Companies want to be able to apply a variety of statistical techniques from modeling, machine learning, data mining and game theory. This way they can uncover relationships and patterns in order to predict behavior and events, such as attrition, propensity to purchase, incremental lift to maximize impact and optimize marketing mix and spend.  

These models assign scores or ranks to each customer based on probabilities in order to predict a single behavior, such as which customers are most likely to buy a specific product(propensity to purchase modeling), which customers are most likely to be influenced by a specific promotion (response modeling), or to calculate customer lifetime value.  

As with any model development, you will need to perform the usual data cleansing, transformation, initial and ongoing validation and refinement. These steps will help you begin creating a propensity model. 

First, you need a suitable modeling sample. This requires enough records (thousands) that are recent enough to be relevant so that you can simulate various scenarios and perform the appropriate analyses. Odds are you will be using a variety of internal data sources, such as transaction, contact, weblog, text, and campaign data as well as appending external data to improve the quality of your model. The more instances of what it is you are trying to predict the more robust a model you can create. 

Second, once you have your sample, check it carefully for biases. 

Third, establish your criteria and ranks based on weighted attributes and build the model.

Fourth, similar to testing a new pharmaceutical, test your model with both a treatment and a control group. It will be essential to have clean control groups so that comparisons are truly actionable. This allows you to find the buyers, responders, etc. in both groups and also ascertain what kind of people did not perform the desired behavior in the control group but did so in the treatment group. These are the customers whose behavior was impacted only because of the treatment. You can now build a propensity model. 

In parting, once you create and implement the model it will be important to communicate the results and the value generated as a result of the model.