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Five Marketing Measurement Mistakes to Avoid

Volume 6, Issue 2 - February, 2016

Zack Gore, Senior Analyst, Visual IQ

With the introduction of new device types, platforms, and technologies, marketing measurement is more important than ever. But as more customer and business data becomes available, it can lead to challenges in the ability to measure success. Marketing measurement mistakes are common, but can easily be avoided. Here are five common measurement pitfalls, along with practical approaches to avert them:

  1. Failing to Clearly Define Business Objectives, Goals, and KPIs
    How do you know if you are successful if you haven’t delineated what success looks like? Have you identified goals for each business objective? Are your existing business objectives and goals quantifiable, measureable, and realistically attainable? Are you using the right KPIs to understand how you’re pacing towards your objectives? All of these questions need clear answers if you hope to solve your organizational challenges. Having a structured framework sets the tone for your measurement efforts and enables you to hone in on what’s truly important to the business.

  2. Setting Benchmarks Without Accounting for Data Complexities and External Factors
    A key component of setting KPIs derives from benchmarks. Regardless of how an organization collects benchmarks – whether it’s their own data, competitor data, industry analyst data, or vendor data – it is important to contextualize that data in order to produce the most informed benchmarks for KPI measurement. For instance, while analyzing internal historical data for benchmarks is common, organizations are constantly changing and often have cyclical fluctuations that involve short-term promotions or media blitzes. An organization that advertised in last year’s Super Bowl but decided to sit this year out, for example, will need to factor this change into their annual benchmark. Externally, there are other factors, such as the strength of the economy, industry evolution, and technological advancements that require benchmarks to be updated frequently. Without digesting all of the variables at play, you will be making the proverbial apples to oranges comparison mistake.

  3. Expecting Perfection
    Marketers are faced with the challenges of trying to predict human emotional responses to marketing stimuli, and accurately measuring which tactics were most effective in driving each desired outcome. These challenges can be dissected in a variety of ways, but each minute, hour, and day that goes by without optimizing is a lost window of opportunity. Consider the concept of progress vs perfection. Marketers all dream of having perfect data sets, absent of caveats, so that they can accurately quantify marketing investment ROI and quickly optimize from there. But in reality, this perfect state rarely exists – it’s simply not practical to wait for perfect data prior to making optimization decisions. Velocity of measurement and decision-making are critical, and the risk of waiting too long to optimize often outweighs acting on the data at your disposal. While mitigating risk is important, it comes with an understanding that there is no exact predictive science on human behavior, and that you may be forced to optimize quickly, even if the data only directional.

  4. Correlation Does Not Imply Causation
    Most people are familiar with this old adage, but ironically this mistake is made repeatedly. In layman’s terms, correlation is often coincidence; causality is proven cause and effect. For example, a marketer may see an uptick in revenue that is seemingly the result of a recently launched remarketing campaign. While a strong relationship may exist, it doesn’t necessarily mean that another, unrelated variable or tactic didn’t also impact revenue. This point highlights the importance of considering all data touchpoints across all your channels. While even the best marketers can confuse correlation for causation, mistakes can be avoided through critical thinking and robust marketing attribution solutions that account for the subtle, yet important distinctions.

  5. Believing That Attribution is Only a Direct Response (DR) Tool
    More and more brands are leveraging advanced attribution to measure their media performance. Most often, these solutions are used to measure the effectiveness of lower-funnel DR tactics, after prospective customers have already been exposed to media designed for awareness and consideration. Brand marketers are thought to have a more difficult job, as they don’t have a direct line of sight to performance like their DR counterparts. KPIs for brand marketers are typically focused around site metrics or brand surveys, and upper-funnel media are not thought of as revenue drivers. However, measuring media through an attributed lens, which fractionally assigns credit to each marketing touchpoint, tells a different story. Advanced attribution reveals that brand media is often undervalued in driving bottom-line revenue, as it assists lower-funnel tactics. This insight underscores the importance that each media tactic plays in the marketing funnel, and why both brand and DR media should be part of your attribution solution.

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