You probably hear the term marketing insight tossed almost daily. But what does it truly mean? A marketing insight is a useful truth that was previously unknown to the organization—one that when leveraged, results in either increased marketing efficiency or increased marketing effectiveness. If it’s not actionable, or doesn’t drive business goals forward, it’s not an insight.
The chart below correlates TV spend with retail foot traffic. Sometimes they appear to be moving together, sometimes not. At the beginning of the year, we see a big spike in TV spend but only a little spike in retail foot traffic, although toward the end of the year, spikes in retail store traffic seemed to move up with spikes in TV spend. No insight.
But when we add out-of-home spend to the mix, voila, a marketing insight! TV alone is not a predictable driver of retail foot traffic. But TV and OOH used together is. Leveraging this marketing insight, we can now adjust our strategy to ensure that TV and OOH advertising are used in conjunction with each other whenever possible.
A taxonomy is a classification system, the process of adding layers of descriptive data to raw data in the form of tagging, or “metadata”. A familiar example is the general ledger or financial chart of accounts, “tagging” financial information with a consistent account code structure, allowing for instant visibility into detailed financial performance. The same is true for marketing. If we want to know which channel most cost-effectively drives shoe sales among males aged 11-17, campaign spend, activity and revenue data must be precisely tagged by segment, channel and product. A consistent marketing taxonomy allows marketers the flexibility to slice and dice marketing data, or roll it up for a macro view.
When performed consistently, mapping marketing data to a taxonomy that describes not just the channel, but the segment, product, region, business unit, funnel stage the interaction is designed to address, P/O/E, etc., allows us to quickly review performance from all angles. If our objective is real-time, trusted visibility into marketing performance, consistently tagging raw marketing data on the way in according to a marketing-specific taxonomy is essential.
And of course, communication is are key! No matter what you include in your taxonomy, making sure it’s used consistently across the entire org is what will determine long term success. If you’re curious how to roll out a consistent measurement strategy, borrow some pro tips from the global team at Bayer.
You probably already know normalization is one of many critical steps in getting to clean, trusted data. But it’s often lumped in with a number of other functions (ETL, transformation, taxonomy, governance, the list goes on), so exactly what role does normalization play? Normalization ensures consistency in marketing data, making sure we’re making apples-to-apples comparisons. There are two key aspects of data that must be normalized: metrics and units.
Normalizing metrics. Digital metrics often use the same terms to describe two different things. For example, if one dataset calls every video play a “view” metric and another dataset calls every web page a “view” metric, automatically merging the two sets doesn’t can muddy results.
Some industries, such as finance, have a global set of standards they adhere to. Marketing lags behind when it comes to standard definitions, so we must take the initiative to do it within our own organizations—and demand strict adherence to them.
Normalizing units. A very common challenge we’ve heard is datasets with inconsistent units. I.e. one dataset of click metrics includes a week’s worth and another set has a month’s worth, or if sales are reported in different currencies, leading to errors in analysis (and untrustworthy results). Units must be converted to a standard unit in order to create reliable analytics. Here’s an example of normalizing data expressed in both months and weeks so that it is only expressed in weeks. Unlike competitive solutions, Beckon effortlessly solves this exact challenge with a process called frequency resampling.
There are countless ways to use benchmarks to see and optimize performance. Simply put, a benchmark is a standard—either defined by external sources or internal past performance, to add context and perspective to analysis. External benchmarks are measurements of what others like us are able to achieve and are typically used to evaluate our performance relative to our competition, industry as a whole, etc. For example, “the average session duration for a B2B technology website is 3 minutes.”
Internal benchmarks lean on our own performance to set goals and targets, track progress and measure changes that occur after big events, like hiring a new agency, launching a new campaign, or implementing a new strategy. I.e.“email open rates last quarter were 9.5%” or “before we hired the new agency, web visits averaged 150k per month for six months”.
Benchmarking allows us to quantify performance by comparing metrics before and after our efforts are introduced, and puts a “reality meter” up against our goals and targets. For a deeper dive on how to incorporate benchmarks for more insightful marketing reports, check out this quick-hitting presentation.
The customer journey (often referred to as the buyer’s journey, or the purchase funnel) is a simple model that’s been in use for more than 100 years, and is familiar to most marketers. But, while we often consider these stages in our strategy, many fall short in measuring performance along the journey. From the time a customer becomes aware of a product to the time they actually purchase the product, they’re on a journey that can be divided into three stages:
awareness, engagement and outcomes. Of course, more complex funnels can include more stages; (consideration, advocacy, etc) but, these three primary stages are always present.
Understanding the customer journey is imperative for marketers in order to know how best to connect with our audiences. Although most campaigns are designed to exert influence across the entire funnel, many would agree that we can do a much better job moving consumers from one stage to the next if we measure and understand marketing’s effectiveness and efficiency at each stage, and identify the key drivers of performance along the journey.