MarketingSherpa Blog

Recently, my colleague Brad Bortone forwarded me an inquiry from one of our readers, who asked the following:

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Can you provide any insight into why my newsletter emails would receive a 10% unique CTR and a 3% open rate? Aren’t open rates generally the larger number?

We use XXXXXXXX as our email service provider. Could this be related to how our newsletter renders in the preview pane of email clients?

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In thinking about this, I realized that many email marketers may be asking the same questions, and could benefit from an extensive reply. Besides, I don’t get much mail around here, so I was excited to help out.

Here is what I wrote in my initial reply: Read more…

Social media measurement is in its early phases, and marketers need to decide whether to parse the social media cacophony, much like a radio astronomer, gathering as much data as possible to discern the signs of life or selectively focus on a small, but sufficiently meaningful set of metrics.

The word “sufficient” can span a wide spectrum, and determining what is sufficient is perhaps the question that marketers must answer.

In some sense, you really don’t have a choice. How much data you can afford to collect and analyze is limited by your organization’s budgetary and human resources. If you are not already collecting enough data for “big” analytics (”Approach 1” that I described in my last blog post), it makes sense to get the most out of what you have now relatively quickly, and in the process learn what additional data you need.

I spend a significant amount of time in digital photography, and my friends often ask me for advice on what camera to buy as they are getting more “serious.” My answer is always the same—first, get the most out of the camera you have. Once you start appreciating what your camera lacks, then you can start thinking about investing into those specific features.

In the same sense, getting started is critical. Reading blog posts will not give you a concrete sense of social media (SoMe) measurement until you get your own hands on a monitoring tool—even if you start by  manually listening to conversations using RSS feeds, Twitter, Google Alerts, and the like.

Second, you need to clearly identify your objectives. In our own research project on SoMe measurement with Radian6, I am leaning toward focusing on best practices for specific scenarios—e.g., a Facebook company page—to deal with manageable amounts of data and produce results on a realistic timeline.

So for those not quite ready for “big” analytics, let’s take a look at a quick start approach…

Approach 2: A microscope, not a radio telescope

Commit to a set of metrics you’ll be accountable for, and stick with them. This is a far more pragmatic approach that does not require that every kind of data is available to be measured. If it appears that this approach is not scientific, that is not the case. While focusing on a smaller number of metrics does not paint the whole picture the way that the first approach does, trending data over time can be highly valuable and meaningful in reflecting the effectiveness of marketing efforts.

Taking into account the marginal time, effort, and talent required to process more data, it makes economic sense to focus on a smaller number of data points. With fewer numbers to crunch, marketers armed, for example, only with data available directly from their social media management tools, can calibrate their marketing efforts against this data to build actionable KPIs (key performance indicators).

During Social Media Week, NYC-based Social2b’s Alex Romanovich, CMO, and Ytzik Aranov, COO, presented a straightforward measurement strategy rooted in established, if not venerated, marketing heuristics, such as Michael Porter’s Value Chain Analysis. Their core message is to appreciate that different social media KPIs will be important not only to different companies and industry segments, but “these KPIs also have to align well with more traditional metrics for that business – something that the C-Level and the financial community of this company will clearly understand.”

Alex stresses that “the entire ‘value chain’ of the enterprise can be affected by these metrics and KPIs – hence, if the organization has a sales culture and is highly client-centric, the entire organization may have to adapt the KPIs used by the sales organization, and translated back to the financial indicators and cause factors.

This approach should immediately make sense to marketers, even without any knowledge of statistical analysis.

Social2B focuses not only on the marketing, but also on the customer service component of SoMe ROI, and here is Ytzik’s short list of steps for getting there:

1. Define the social media campaign for customer service resolution
2. Solve for the KPI and projections
3. Apply Enterprise Scorecard parameters, categories
4. Solve for risk, enterprise cost, growth, etc.
5. Map to social media campaign cost
6. Solve for reduction in enterprise costs through social media
7. Justify and allocate budget to social media

An important element here is the Enterprise Scorecard—another established (though loosely defined) management tool that is often overlooked even by large-scale marketing organizations. Given the novelty of SoMe, getting it into the company budget requires not only proving the ROI numerically, but also speaking the right language. Ytzik’s “C-level Suite Roadmap” might appear simple, but it requires that corporate marketers study up on their notes from business school:

• Engage in Compass Management (managing and influencing your organization vertically and horizontally in all directions)
• Define who owns the Web and social media within the company
• Identify the enterprise’s value chain components
• Understand the enterprise’s financial scorecard

Again, no statistics here—it is understood that analysis will be required, but these tools will put you in a good position when the time comes to present your figures.

How to get started

Finally, I wanted to get as pragmatic as possible to help marketers get started and not get stuck in a data deluge. Here are Social2B’s top 10 questions to ask yourself before you scale your SoMe programs:

1. Is my organization and my executive management team ready for social media marketing and branding?
2. Does everyone treat social media as a strategic effort or as an offshoot of marketing or PR/communications?
3. Where in the organization will social media reside?
4. Will I be able to allocate sufficient budget to social media efforts in our company?
5. How will social media discipline be aligned with HR, Technology, Customer Service, Sales, etc.?
6. What tools and technologies will I need to implement social media campaigns?
7. Will ‘social’ also include ‘mobile’?
8. How will we integrated SoMe marketing campaigns with existing, more ‘traditional’ marketing efforts?
9. How much organizational training will we need to implement in integrating ‘social’ within our enterprise?
10. Are we going to use ‘social’ for advertising and PR/Communications? What about ‘disaster recovery’ and ‘reputation management’?

Related Resources

Social Media Measurement: Big data is within reach

2011 Social Marketing Benchmark Report – Save $100 with presale offer (ends tomorrow, April 30)

Always Integrate Social Marketing?

Inbound Marketing newsletter – Free Case Studies and How To Articles from MarketingSherpa’s reporters

Should marketers wait for a grand unified theory of social media ROI measurement, or confidently move forward with what they have available to them now?

This question has been at the forefront of my thinking, as we proceed with MarketingSherpa’s joint research project with Radian6 to discover a set of transferable principles, if not a uniform formula to measure social media (SoMe, pronounced “so me!”) marketing effectiveness.

As I have written previously, some of the popular measurement guidelines provide a degree of comfort that comes from having numbers (as opposed to just words and PowerPoint® slides), but fail to connect the marketing activity to bottom-line outcomes.

To help think through this, I spoke with several practitioners to get some feedback “from the trenches” during SoMe Week here in NYC. With their help, I broadly defined two approaches.

Approach 1: Brave the big data

Take large volumes of diverse data, from both digital and traditional media, and look for correlations using “real” big-data analysis. This analysis is performed on a case-by-case basis, and the overarching principles are the well-established general statistical methods, not necessarily specifically designed for marketers.

Pros

• The methodologies are well established
• There are already tools to help (Radian 6, Alterian, Vocus, etc)

Cons

• Most marketers are not also statisticians or have the requisite tools (e.g., SAS is an excellent software, but it comes with a premium price)
• Comprehensive data must be available across all relevant channels, otherwise the validity of any conclusions from the data rapidly evaporates (Radian6 announcement of integrating third-party data streams like Klout, OpenAmplify and OpenCalais in addition to existing integration with customer relationship management (CRM), Web analytics, and other enterprise systems certainly helps)
• In the end, it’s still conversation and not conversion without attribution of transactional data

If the volume of data becomes overwhelming, analytical consulting companies can help. NYC-based Converseon does precisely that, and I asked Mark Kovscek, their SVP of enterprise analytics, about the biggest challenges to getting large projects like this completed efficiently. Mark provided several concrete considerations to help marketers think through this, based on Converseon’s objectives-based approach that creates meaningful marketing action, measures performance, and optimizes results:

• Marketers must start with a clear articulation of measurable and action-oriented business objectives (at multiple levels, e.g., brand, initiative, campaign), which can be quantified using 3-5 KPIs (e.g., Awareness, Intent, Loyalty)
• Large volumes of data need to be expressed in the form of simple attributes (e.g., metrics, scores, indices), which reflect important dimensions such as delivery and response and can be analyzed through many dimensions such as consumer segments, ad content and time
• The key to delivering actionable insights out of large volumes of data is to connect and reconcile the data with the metrics, with the KPIs, and with the business

How much data is enough? The answer depends on the level of confidence required.  Mark offered several concrete rules of thumb for “best-case scenario” when dealing with large volumes of data:

• Assessing the relationship of data over time (e.g., time series analysis) requires two years of data (three preferred) to accurately understand seasonality and trend

–   You can certainly use much less to understand basic correlations and relationships.  Converseon has created value with 3-6 months of data in assessing basic relationships and making actionable (and valuable) decisions

• Reporting the relationship at a point in time requires 100-300 records within the designated time period (e.g., for monthly listening reporting, Converseon looks for 300 records per month to report on mentions and sentiment)

–   This is reasonably easy when dealing with Facebook data and reporting on Likes or Impressions

–   However, when dealing with data in the open social graph to assess a brand, topic or consumer group, you can literally process and score millions of records (e.g., tweets, blogs, or comments) to identify the analytic sample to match your target customer profile

• Assessing the relationship at a point in time (e.g., predictive models) requires 500-1000 records within the designated time period

Understanding the theoretical aspects of measurement and analysis, of course, is not enough. A culture of measurement-based decision making must exist in the organization, which means designing operations to support this culture. How long does it take to produce a meaningful insight? Several more ideas from Converseon:

• 80% of the work is usually found in data preparation (compiling, aggregating, cleaning, and managing)
• Reports that assess relationships at a single point in time can be developed in 2-3 weeks
• Most predictive models can be developed in 4-6 weeks
• Assessing in-market results and improving solution performance is a function of campaign timing

Finally, I wanted to know what marketers can do to make this more feasible and affordable. Mark recommends:

• Clearly articulate business objectives and KPIs and only measure what matters
• Prioritize data
• Rationalize tools (eliminate redundancy, look for the 80% solution)
• Get buy-in from stakeholders early and often

In my next blog post on this topic, I’ll discuss an approach to SoMe measurement that trades some of the precision and depth for realistic attainability—something that most marketers that can’t afford the expense or the time (both to learn and to do) required to take on “big data.”

Related Resources

Social Media Marketing: Tactics ranked by effectiveness, difficultly and usage

Always Integrate Social Marketing?

Inbound Marketing newsletter – Free Case Studies and How To Articles from MarketingSherpa’s reporters

Social Marketing ROAD Map Handbook

In a recent blog post for our sister company MarketingExperiments, I shared my experiences at the fifth Design for Conversion Conference (DfC) in New York City. Today, I want to focus on a topic from Dr. Dan Goldstein’s presentation, and its relevance to usability and product testing for marketers — how focus group studies can effectively misrepresent true consumer preferences.

Asking you for your input on our Landing Page Optimization survey for the 2011 Benchmark Report has firmly planted the topic of surveys at the forefront of my thinking.

Calibration is not the whole story

The need to calibrate focus group data is well recognized by marketers and social scientists alike. The things marketers want to know the most – such as “intent to purchase” – is more obviously susceptible to misleading results. It’s easy to imagine that when people are asked what they would do with their money in a hypothetical situation (especially when the product itself is not yet available), naturally their answers are not always going to represent actual behavior when they do face the opportunity to buy.

However, mere calibration (which is a difficult task, requiring past studies on similar customer segments, where you can compare survey responses to real behavior) is not enough to consider. How we ask the question can influence not only the answer, but also the subsequent behavior, about which the respondent is surveyed.

Dr. Goldstein pointed me to an article in Psychology Today by Art Markman, about research into how “asking kids whether they plan to use drugs in the near future might make them more likely to use drugs in the near future.” Markman recommends that parents must pay attention to when such surveys are taken, and make sure that they talk to their children both before and after to ensure that the “question-behavior effect” does not make them more likely to engage in the behaviors highlighted in the surveys. The assumption is that if the respondent is aware of the question-behavior effect, the effect is less likely to work.

Question-Behavior Effect: The bad

If your marketing survey is focused on features that your product or service does not have—whether your competitors do or do not—then asking these negative questions may predispose your respondents against your product, without them even being aware of the suggestion. This is especially worrisome when you survey existing or past customers, or your prospects, about product improvements. Since you will be pointing out to them things that are wrong or missing, you run a good chance of decreasing their lifetime value (or lead quality, as the case may be).

Perhaps the survey taker should spend a little extra time explaining the question-behavior effect to the respondent before the interaction ends, also making sure that they discuss the product’s advantages and successes at the end of the survey. In short, end on a positive.

Question-Behavior Effect: The good

However, there is also a unique opportunity offered by the question-behavior effect: by asking the right questions, you can also elicit the behavior you want. This means being able to turn any touch point—especially an interactive one like a customer service call—into an influence opportunity.

I use the word “influence” intentionally. Dr. Goldstein pointed me to examples on commitment and consistency from Robert Cialdini’s book Influence: Science and Practice, such as a 1968 study conducted on people at the racetrack who became more confident about their horses’ chance of winning after placing their bets. Never mind how these researchers measured confidence—there are plenty of examples in the world of sales that support the same behavioral pattern.

“Once we make a choice or take a stand, we will [tend to] behave consistently with that commitment,” Cialdini writes. We want to feel justified in our decision. Back in college, when I studied International Relations, we called it “you stand where you sit”—the notion that an individual will adopt the politics and opinions of the office to which they are appointed.

So how does this apply to marketing? You need to examine all touch points between your company and your customers (or your audience), and make a deliberate effort to inject influence into these interactions. This doesn’t mean you should manipulate your customers—but it does mean that you shouldn’t miss an opportunity to remind them why you are the right choice. And if you’re taking a survey—remember that your questions can reshape the respondents’ behaviors.

P.S. From personal experience, do you think being asked a question has influenced your subsequent behavior? Please leave a comment below to share!

Related Resources

MarketingSherpa Landing Page Optimization Survey

Focus Groups Vs. Reality: Would you buy a product that doesn’t exist with pretend money you don’t have?

Marketing Research: Cold, hard cash versus focus groups

Marketing Research and Surveys: There are no secrets to online marketing success in this blog post

MarketingSherpa Members Library — Are Surveys Misleading? 7 Questions for Better Market Research