data
Four Models Every Marketer Should Master
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:
- 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.
- 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.
- Opportunity Scoring Model: Enables marketing and sales to agree on when opportunities are sales worthy and sales ready.
- 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!
Weaving Contextual Data into Models
Many companies are developing opportunity scoring models which essentially assign a predetermined numerical score to specific behaviors or statuses within a database. The purpose of opportunity scoring is help sales people know which opportunities are sales ready and worthy, and therefore take priority. Often variables such as title, company, and industry, serve as the basis for the scoring model. However, behaviors can be used too, such as the completion of a contact form, visiting a particular page on the website, participating or viewing a demo, etc. Contextual data adds another dimension to the model by weaving in predisposition information that reflects content, timing and frequency-for example what products they currently use, the last time they purchased, their complete buying history, the types of keywords they used in their search, etc.
Keep in mind, timing is everything. To be effective, contextual data must be delivered to the right person, at the right time, within an actionable context. For example, the date of a key customer’s contract renewal is posted in your CRM system all year long, but that doesn’t mean you’ll remember or even see it. Think how much more useful that data becomes when your system automatically alerts you to the fact that it’s the customer’s renewal date. Sending email messages about renewals too early just creates noise at best and at worst suggests you don’t know their renewal date. Customers are more likely to respond to call to action when it is in context of their workflow. Communication that is contextual is more personal and as a result feels more authentic, shows value, and leads customers want to act. As a result, you can reduce the cost of customer acquisition and the cost of sales.
The end goal of contextual data is to connect with the buyer when they are most predisposed to buy. As a result, you can use contextual data to help build propensity to purchase models, for prioritizing opportunities to support opportunity scoring, to develop more personalized messages, and select the best mix of channels.
This same concept of contextual data can be used to build propensity to purchase models. By identifying the winning experiences associated with a particular segment, you can use this information to craft more relevant messages to similar targets to increase uptake.
Personalization is a compelling and challenging proposition. It’s a moving target and therefore requires a test and learn approach. By adding contextual data into the process you can make your personalization efforts more effective and more relevant.
More Data Does NOT Equal Better Insights
We’re drowning in data. We generate it from our own activity or research; we collect and capture tons more from external sources. And, by now, all of us have been exposed to the conversation about Big Data—the voluminous unstructured data that is collected from nontraditional sources such as blogs, social media, email, sensors, photographs, video footage, and so on.
As the number of channels and customer touches expand, so does the amount of data coming from them. Every day, there are more than a billion posts and 3.2 billion likes and comments on Facebook, and 175 million tweets on Twitter. According to Stephanie Miller, VP of member relations at the Direct Marketing Association, “data is big, getting bigger, and more complex (and expensive) to manage.”
In today’s data-rich and data-driven environment, we are predisposed to gain our insights from data. But action doesn’t always follow collection. A survey of 600 executives by the Economist Intelligence Unit found that 85% of the participants thought the biggest hurdle to unlocking value from data was not grappling with the sheer volume, but analyzing and acting on it. And gleaning the insights from the data is what makes the data valuable.
Merriam-Webster defines insight as the power or act of seeing. Keyword: Seeing. We must use the data to identify and see—to see patterns, trends, and anomalies. And once we gain this insight, its value is proven by the actions we take as result. Data that doesn’t help you see isn’t useful. So, in this instance, more does not always translate into better insights. In fact, according to the recently released 5th annual Digital IQ Survey, consulting firm Pricewaterhouse Coopers (PwC) found that 58% of respondents agree that moving from data to insight is a major challenge.
In 1990, Stephen Tuthill at 3M helped make the connection between data and wisdom. His The Data Hierarchy outlines four important concepts: data, information, knowledge, and wisdom, with data being the raw items or events. Once we have the data, we can sort and organize it into information. Knowledge is then derived from the patterns that result from understanding the relationships between the data and other factors. Wisdom comes when we understand what to pay attention to—what has meaning for us.
So, rather than focusing on more data, we need to focus on capturing the right data and then analyzing it in a way that gives us the power to see (knowledge) and act (wisdom). Bernard Marr from UK-based Advanced Performance Institute reminds us that to get the most out our data “you need to know what you want to know.” Once you know what you want to know, collect and organize the data.
So, now what?
Getting From Data to Insight:
1. Having the data is one thing, analyzing and synthesizing it is another. Synthesis is
where we begin to see the patterns. Once the synthesis is completed, you will need a way
to bring the data to life. Data visualization greatly aids in this part of the process. Data
visualization presents analytical results visually so we can more easily see what’s relevant
among all the variables, capture and communicate important patterns, and even support
predictive models. Visualization is an important step for exposing trends and patterns that you might not have otherwise noticed.
2. Not all patterns are germane. Take the time to review and discuss each pattern and its
potential implications. Talk about why you think each pattern is important and what it
means. This is an essential step for going from information to knowledge.
3. In one simple statement, articulate the insight that emerged out of each pattern or
point of synthesis. We find it is helpful to capture insight on a Post-it Note and place it on
a wall or flip chart to easily track each insight and see the “big picture” that may be
emerging as we go.
4. Incubate the insights. Give yourself and your team at least a day away from the “board.” When you and the team return you can take a fresh look and decide whether to make any changes.
5. Do the insights resonate? Once you are comfortable with the conclusions/insights
you’ve captured, involve other people who were part of the initial steps to gain their
reactions. Be sure to give them the context. The point of this step is to decide if the
insights resonate and are compelling enough to make or affect key decisions. That is, to
determine whether you have acquired the wisdom you need to act.
The success of this approach is contingent on the quality (not necessarily the quantity) of the data set, then following a process proven to identify core insights to support strategic decisions.
4 Steps to Guarantee Your Strategy’s Success
The importance of good planning and execution seems obvious to most of us, and as we continue creating and finalizing our plans for 2014, that importance is even more evident. A good strategy that is well executed has the ability to impact a market, competitive position or business model. Yet, there are many companies at all stages that invest time in planning, but lack the processes and leadership needed to ensure a strategy achieves the desired results. Failure is expensive and wastes precious resources.
What causes execution to go wrong? Two common causes: lack of communication and shifting focus of the strategy. Forbes Insights has conducted studies on why strategic plans fail and has found that an almost all CEOs say “communications is critical to the success of their strategic initiatives and that strategic initiatives often fail due to a lack of understanding, commitment and follow-through by key stakeholders.”
It’s not uncommon for companies to change strategies, the challenge is to be sure the organization can support the shift. For example, if your initial strategy is to compete on price, then your processes and focus need to support the execution of this strategy. If you then try to change your strategy to be one of service, the organization may not have the infrastructure for this strategic shift.
Perhaps a simple way to start addressing execution is to think about how your organization is going to synchronize itself to ensure the right products get to the right customers at the right time. Synchronization is not easy. It can involve reconfiguring supply chains, knowledge of numerous foreign markets, addressing your cost structure and more. Communicating the plan to all the people involved and securing their support are critical to success. It’s common for organizations to resist change.
Managing change and addressing the resistance, which may be well-founded, are important steps. Once the plan is decided upon, the infrastructure and processes put in place to support it, and the organization is on-board, it all comes down to follow-through. Studies continue to indicate that less than 15% of companies routinely track how well they performed in comparison to their targeted performance. The first year’s goals may be measured because they are often tied to bonus thresholds, it’s the follow through that tends to slip. Companies who invest in putting the right people in place to get the right things done and emphasizing process deliver the best results. Companies that combine attention to process with executive development tend to deliver the best return to shareholders.
We like the four steps outlined by experts at Wharton that any company can take to improve the odds of execution success:
1. Develop a model for execution. You can adopt Michael Porter’s theory of comparative advantage, William Sharpe’s capital asset pricing model, or some other model. The important point is to have a model that defines the critical variables to support successful implementation of the plan.
2. Choose the right metrics. Without a doubt sales and market share are going to continue to be the dominant metrics of business, but there are additional metrics that are critical to monitoring performance and success. Keep in mind that marketing is given money to invest on behalf of the organization. The leadership team wants to know how this money is helping the organization achieve more of something, faster, and less expensively then if that money were directed somewhere else. Use these mandates to guide your metrics selection. For example, speed to penetrating new markets as a way of understanding marketing’s impact on achieving something faster.
3. Keep the plan center stage. Once you’ve earned internal and stakeholder buy-in, initiate a formal process that will keep the team engaged and focused. Meetings between the executive team and unit managers should be regular and ongoing and they should foster collaboration, dialogue, and problem resolution in order to maintain momentum.
4. Assess performance frequently. Annual reviews don’t allow the organization to make necessary adjustments. Assess the performance frequently to secure real-time feedback on the quality of execution down the line.
Customer Conversations: The Value of Adding Data & Analytical Skills
Jamie, the VP of Marketing at one of our manufacturing companies, in a recent conversation expressed excitement about securing someone from the finance group to support marketing data and analytics. “It took 2 years of lobbying but now we’ll be able to make better and more informed decisions,” said Jamie. To which I replied, “Awesome!” 
Then, in my usual fashion, I asked a series of rapid-fire questions:
- What decisions are you hoping to make and in what priority order?
- What and where is the data that they will be accessing?
- What is the data capture and management plan?
- Is he just going to start delving into the data( A.K.A. boiling the ocean to see what treasures await) or are there specific insights about customers or the market that you want to gain?
- How will his contribution be measured?
- Is his role specifically digging into and analyzing data- and if so for what?
- Will he serve your team in a broader capacity a.e marketing ops, performance management and reporting?
Well, you can see the line of questioning.
Jamie said, “Whoa, I didn’t really think about what he was going to do or how, I just knew we needed someone who was comfortable with data and analytics because this isn’t my strong suit.” I said, “Adding this capability to your team is a great win, and demonstrating how it will prove and improve the value of marketing will create an even more important win. Now that you have this person, it might be a good idea to take some time to think about and decide function’s scope, role, purpose, etc.”
Jamie said, “Yeah, these are good questions and getting off on the right foot and in the right direction is really important for the team and for him. It almost took a miracle to get this person; we won’t get a second chance at it.”
Jamie asked if we could schedule a meeting next week to discuss things further. I said, “Of course, it would be our pleasure. In the meantime, your person may find our Marketing Operations: Enabling Marketing Centers of Excellence and from Intuition to Wisdom: Mastering Data, Analytics and Models white papers helpful .” As we set a date for our next call, Jamie said in closing, “ Downloading these as we speak.”
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