Keeping Your 2013 Marketing Measurement Resolution

According to Statistic Brain, nearly half of us make New Year’s resolutions. Most of these resolutions fall into ten categories: Lose Weight, Get Organized, Spend Less/Save More, Enjoy Life, Stay Fit/Healthy, Learn Something, Quit Smoking, Help Others, Fall in Love, and Spend More Time with Family.  And did you know that less than ten percent of us actually achieve our resolutions? Experts say that being realistic, outlining a plan, and tracking our progress help us keep our resolutions.  These same tips apply when it comes to proving and improving our marketing (quantifying our value and achieving better marketing ROI).  So, whether it is your personal or business resolution, beginning with the end in mind and knowing where to begin is the best route to your desired destination.

One of the key steps for success is to set the proper metrics for determining marketing’s contribution to customer acquisition, customer retention, and customer loyalty initiatives. Just as your personal goals are unique to you, there’s no one-size-fits-all guide to determining which metrics are right for your business.  Each company must establish their own measurable objectives before they can begin to measure initiative effectiveness.   Ideally the marketing objectives would reflect a customer focused approach.  The right metrics provide insight into performance, and  help focus your efforts and refine your strategies. Establishing the right metrics combined with tracking progress will help you assess where improvements and adjustments are needed.  As you embark on your 2013 efforts, take a moment to review your marketing plan to ensure you will be able to say the following:

1. We have established solid measurable customer-centric marketing objectives


2. These objectives are well aligned to specific business outcomes


3. The metrics we selected will demonstrate our marketing is impacting the business and serve as good leading indicators

By putting customers at the center of your marketing objectives, strategies, and metrics, you focus on initiatives designed to engage customers and impact revenue.  And properly aligned objectives and metrics drive results.  We wish you success with all of your New Year’s Resolutions!

The Blurring Line between Marketing Analyst and Marketing Strategist

Marketers everywhere know they need to increase their analytical and accountability prowess. However, this effort is only worth the investment of time, people and money if you can use these capabilities to drive strategic decisions, actionable recommendations, and improve and prove marketing effectiveness. In fact, we believe the line between marketing analyst and marketing strategist will increasingly blur. Strategists need the analytics to stay ahead of emerging opportunities, respond quickly to unexpected threats, and make timely decisions. Analysts need to think about how they build their models and leverage their analysis for the same purposes.

The challenge according to recent research from Econsultancy and Lynchpin, is a majority of marketers worldwide say that less than half of all the analytics data they collect is actually useful for decision-making. Their studies found that just one in 10 companies thought a strong majority of analytics data was helpful, and less than a third said somewhere between half and three-quarters of all data was useful. Over a third of study participants said analytics were not integrated at all with their business plans. The results of our recent  metrics, data, and analytics study are similar.  Best-in-Class Marketers embrace analytics and leverage the insights they derive to both improve and prove the value of marketing.  Access the executive summary the MPM Path to Better Marketing Results for free with registration.

 

Creating a Propensity Model

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.

Measuring Marketing’s Contribution to the Pipeline

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.

Measuring and Linking Relevancy to Buyer Behavior

Various studies over the years have examined the relationship between content relevancy and behavior. Almost everyone would agree with the statement that “content must be relevant.” But what is relevance? According to Wikipedia: “Relevance describes how pertinent, connected, or applicable something is to a given matter. A thing is relevant if it serves as a means to a given purpose. In the context of this discussion, the purpose of content is to positively impact customer or employee behavior, such as increasing purchase frequency, purchase velocity (time to purchase), likelihood to recommend, productivity, etc.

When we ask marketers and others how they measure content relevancy, we often hear, “we base it on response rate.” If the response rate meets the target, then we assume the content is relevant or vice versa. Clearly there is a relationship between relevancy and response. Intuitively we believe the more relevant the content the higher the response will be. But measuring response rate is not the best measure of relevancy. There are many factors that can affect response rate, such as time of year, personalization and incentives. Also, in today’s multi-channel environment we want to account for responses or interactions beyond what we might typically measure such as click thrus or downloads.

So, what is the best way to measure relevancy? There are a number of best-practice approaches to measuring relevancy, many of them are complex and require modeling. For example, information diagrams can bean excellent tool. But for marketers who are spread a bit thin and therefore need a simpler measure, the three step approach below ties interaction (behavior) with content:
Count every single piece of content you created this week (new web content, emails, articles, tweets, etc). We’ll call this C.
Count the collective number of interactions (opens, click thrus, downloads, likes, mentions, etc.) for all of your content this week from the intended target (you’ll need a way to only include intended targets in your count). We’ll call this I.
Divide total interactions by total content created – R = I/C
To illustrate the concept, let’s say you are interested in increasing conversations with a particular set of buyers and as a result this week you:
Posted a new white paper on a key issue in your industry to your website and your Facebook page.
Tweeted 3x about the new white papers
Distributed an email with a link to the new white paper to the appropriate audience
Published a summary of the white paper to 3 LinkedIn Groups
Held a webinar on the same key issue in your industry
Posted a recording of the webinar on your website, Slideshare and Facebook page
Held a tweet chat during the webinar
Tweeted the webinar recording 3x
Posted a blog on the topic to your blog

We’ll count this as 17 content activities.

For this very same content during the same week you had:
15 downloads of the white paper from your site
15 retweets of the white paper
15 Likes from your LinkedIn Groups and blog page
25 people who attended the webinar and participated in the tweet chat
15 retweets of the webinar
15 views of the recording on Slideshare

This counts as 100 total interactions. It’s both possible and likely that some of these interactions are from the same people engaging multiple times, and you may eventually want to account for this in your equation. But for starters, we can now create a content relevancy measure.

R= 100/17 = 5.88.

If we had only measured the response rate, we might have only counted the downloads and attendees, 40, so we might have had the following calculation

R = 40/17 = 2.35

The difference is significant. Over time, we can understand the relationship between the relevancy and the intended behavior, which in this example is increasing “conversations”. Tracking relevancy will enable you to :
Establish a benchmark
Set content relevancy performance targets
Model content relevancy for intended behavior

Focusing on Social Media Metrics Across the Stages of Engagement

CMOs and their teams are trying to determine the best way to create and measure customer engagement. As more customers connect via mobile and social channels this only adds to the complexity. Just as it took organizations time to learn how to leverage and manage websites, we are still learning how to leverage and manage these new channels. There’s no denying that social and mobile channels have become mainstream engagement vehicles that impact customer acquisition and retention. More of the marketing budgets are now being allocated to digital channels, taking these dollars from more traditional vehicles such as print advertising.

The IBM Global CMO Study of 1700 CMOs  revealed that while top marketing executives recognize social media as an important channel for engaging customers, 80 percent or more of the CMOs surveyed said they still focus primarily on traditional sources of information like market research to help shape customer engagement and marketing strategies. Only 26 percent of CMOs track blogs, 42 percent monitor third-party reviews, and 48 percent reading consumer reviews to help shape their marketing strategies. A more recent study by PulsePoint earlier this year found that companies with an established extensive social media presence reported a return on investment that was more than four times that of companies with little or no social network engagement activity. Almost half of the 329 executives participating in the study said that the major impediment to social media campaigns was the lack of a standardized metric that can measure a return on investment. These studies serve to remind us that examining customer interactions across the channels in order to have a holistic picture is important. They also highlight that measuring isn’t the challenge; it is measuring the business value of social media that remains difficult.

In fact, there is no lack of data or metrics to track. As usual, the key challenge is selecting the right metrics and ones that are actionable. A key place to start is to determine what information actually indicates these stages of engagement that you can use to inform decision making. For example, how might you measure the quality and quantity of conversations started via social media channels and the conversion rate of these conversations to consideration behaviors, such as a particular type of inquiry. For example “liking” and “following” metrics may only reflect very early stages of engagement, such as contact or connection. Page views and click throughs provide insight into what captures interest and may also reflect early stage engagement behaviors. Ultimately you want to focus on social media metrics that help you understand how to impact the entire engagement cycle – from contact to conversation to consideration to consumption and eventually consumption and community. By adding these types of metrics, you can learn content and customer communication and interaction create both the most and best interaction and engagement.

Big Data Promises Marketers Big Insights

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.

It 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.