marketing models

Four Models Every Marketer Should Master

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We know–models can be intimidating. But as the need to add analytics and science to our work continues to increase, models have become one of the primary vehicles every marketer needs to know how to develop and leverage. If you’ve already dived into the deep end on models, congratulations. On the other hand, if you’re just dipping your toe into the water, have no fear, because while there may be a bit of a current, it is time to venture forth.

Mathematical models help us describe and explain a “system,” such as a market segment or ecosystem. These models enable us to study the effects of different actions, so we can begin to make predictions about behavior, such as purchasing behavior. There are all kinds of mathematical models-statistical models, differential equations, and game theory.

Regardless of the type, all use data to transform an abstract structure into something we can more concretely manage, test, and manipulate. As the mounds of data pile up, it’s easy to lose sight of data application. Because data has become so prolific, you must first be clear about the scope of the model and the associated data sources before constructing any model.

So you’re ready to take the plunge–good for you! So, what models should be part of every marketer’s plan? Whether a novice or a master, we believe that every marketer must be able to build and employ at least four models:

  1. Customer Buying Model: Illustrates the purchasing decision journey for various customers (segments or persona based) to support pipeline engineering, content, touch point and channel decisions.
  2. Market Segmentation or Market Model: Provides the vehicle to evaluate the attractiveness of segments, market, or targets.  More about this in today’s KeyPoint MPM section.
  3. Opportunity Scoring Model: Enables marketing and sales to agree on when opportunities are sales worthy and sales ready.
  4. Campaign Lift Model: Estimates the impact of a particular campaign on the buying behavior.

These four models are an excellent starting point for those of you who are just beginning to incorporate models into your marketing initiatives. For those who have already developed models within your marketing organization, we would love to know whether you have conquered these four, or even whether you agree these four should be at the top of the list. As always, we want to know what you think, so comment or tweet us with your response!

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Tackling the “Too Hard To” Pile of Marketing Accountability

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If you’re like us, you probably have one of those piles on your desk that keeps being moved from one corner to another. You know that pile you need to get to but avoid because it will take some real effort to tackle. For many marketing professionals, marketing accountability, analytics and ROI are in this pile. Not too long ago at a marketing conference where Laura was speaking, the organizers had set up round tables with specific topics for discussion over breakfast. Laura was sitting at the measuring marketing ROI (return on investment) table (of course, where else would I be sitting?) which was strategically located right next to the buffet line.vem cluttered desk While she was sitting there waiting for people to join her, she kept hearing people say, “Oh measuring marketing,that’s just too hard.” There were hundreds of marketers attending this conference, and about 2 dozen tables of 10 were set to accommodate the early risers. Yet only four other brave souls joined her.

We must stop avoiding this topic and tackle the pile. As Sylvia Reynolds the CMO of Wells Fargo says, “Marketing must be a driver of tangible business results…we must start with the goal in mind and a clear way to measure that goal.” ROI is important for accountability–besides being able to justify spending and enable us to run the marketing organization more effectively and efficiently, knowing what is and isn’t working helps marketing achieve greater influence and serve in a more strategic role. Various surveys suggest that over a third and as much as 42% of marketing budgets are not adequate enough to achieve the outcomes and impact expected.

Perhaps your organization like many others is in the thick of budget planning. A key part of budget planning is to establish and validate the money you plan to spend. The more aligned marketing is with the outcomes of the organization and the more the plan includes performance targets and metrics, the more likely you will be allocated the budget you need to achieve the expected results.

So what does it take to tackle this Marketing Accountability pile? Here are six affordable steps any marketing organization can take to start whittling away at the marketing accountability and measurement pile.

1. Focus. Nothing of importance miraculously gets done on its own. vem focusTo effectively tackle the marketing measurement pile will take all of Covey’s seven habits: from taking a proactive approach and beginning with the end in mind, that is the outcomes you are expected to impact, to keeping the effort a priority when other things present themselves as urgencies to making marketing measurement a win/win for you, your team, and the rest of the organization. More than likely, you are going to need a cross-functional team to tackle this pile – people from finance, sales, IT, operations, etc. working collaboratively together to define the metrics and hunt down and organize the data.

2. Plan an attack. You know that age old question, “How do you eat an elephant?” The answer being, “One bite at a time.” This is true for the marketing accountability and ROI question. If this is a new effort for you, you need to break it into manageable pieces. Quantify your objectives, decide how you will measure them, collect the data that you need to meet the objectives, establish a baseline, gain commitment to the measurement plan, and finally, measure.

3. Get data: “Data is the new creative,” declares Stephan Chase of Marriott Rewards. Establishing metrics, determining effectiveness, understanding efficiencies, all take data. Without data you cannot monitor and measure results. And don’t assume that you have the data that you need to measure your objectives. For example, if you want to measure how many new customers you interest in a new product, you may find that you need first to determine what a “new” customer is. This may require different views of your existing customer records or new strategies for evaluating.

4. Analyze: Once you have the data, the challenge is to generate insights that facilitate fact based decision making. 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. Just looking at numbers doesn’t tell you as much as evaluating trends or creating statistical models that help you identify an optimized approach to your marketing efforts. Consider looking at your measurements for what isn’t immediately obvious such as what might have happened if that campaign had gone to the three bottom deciles of customers?

5. Use a systematized process: You may need to set up systems and processes that enable you to capture and track results on an ongoing basis. Many organizations put a substantial amount of energy into initiating these programs and then let them fizzle as other priorities surface. It takes both process and discipline to sustain a measurement effort. Systems help you automate a process so that the process can become a manageable part of your day-to-day operations. Today every marketing organization is moving at a breathless pace. Implementing test and control environment can keep you from having a fatal, head on collision

6. Train. Many marketers are unaccustomed to living in a metrics-based environment. You may need to invest in measurement, analytics, as well as data training and skills development. Start by taking a skills inventory. Find out who in the organization has data management, analytics and measurement skills.vem train Decide what skills they need to perform at your expected levels. Develop training that fills the skill gaps. Doing this in-house allows you to tailor to your needs, but consider courses from universities, associations and external consultants to fill out your requirements.

Moving marketing performance metrics from the “too hard to” pile to the “we can do it” pile can reap rewards for the entire organization.

For more information on Marketing Alignment and Accountability, download our Free White Paper: Charting a Course for Marketing Effectiveness: Alignment & Accountability

Measuring and Linking Relevancy to Buyer Behavior

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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