5 Methods To Analyze Your Marketing Efforts.

5 Methods To Analyze Your Marketing Efforts.

A strategic marketing analytics program means knowing which data points to monitor and which detract from your ultimate goal of demonstrating ROI. While this will take more work upfront, it will make analysis a much simpler process in the long run.?

What is the definition of marketing analytics?

Marketing analytics seeks to find patterns in marketing data and create actions. Marketing analytics can cover anything from statistics to modeling and machine learning. It helps businesses understand their customers, products, sales points, and more.

What can we learn from marketing analytics?

Marketing analytics can demonstrate return on investment, plan more campaigns, and measure performance. Using benchmarks, tracking, and predictions, it looks to the past, present, and future to help marketers make data-driven decisions.?

What are examples of marketing analytics?

Marketing analysis can maximize ROI, plan campaigns, and keep up with the competition. Some examples of marketing analytics might include:

  • Customer segmentation
  • Campaign planning and scheduling
  • Competitor analysis
  • UX performance
  • Workflow efficiency
  • Customer lifetime value

Method 1: Single attribution — first-touch and last-touch.

Single attribution is one of the most common marketing analytics strategies. This strategy allocates all the value to the first or last interaction with the prospect before buying.?

  • First-touch attribution credits the lead generation strategy with the eventual sale, regardless of when it happens. For instance, if an SEO-optimized landing page gains a new lead from a web search, who later consumes branded content, engages on social media, and attends a trade show before buying, first-touch assigns the value of that sale to the SEO optimization.
  • Last-touch attribution credits the final communication with the close of new business. In the example above, the trade show would be credited with the sale since it was the lead's last interaction before purchasing.?

Method 2: Single attribution with revenue cycle projections.

Single-attribution strategies are simple, but this simplicity has its disadvantages. Brands with longer buying cycles need to account for that time. They also need to see all the lead nurturing between them to create an accurate picture of the quality of current marketing efforts.

Adding revenue cycle projections to a first-touch/single-attribution analytics strategy can solve this problem. Revenue cycle projects use complete data from previous campaigns to project the eventual outcome of recent and similar marketing efforts.?

Method 3: Attribution across multiple programs and people.

Attribution across multiple programs and people views credit more holistically. You recognize that no single marketing effort is responsible for a sale, and you try to determine the value of each touch by starting with the action that created a sale and working backward.

Once every touch has been identified, you determine how to weigh each one, so their values can be properly assessed. Of course, some assumptions are necessary for this method, and that's OK. Just ensure you are prepared to defend them to the C-suite, or you may risk invalidating the process.?

Method 4: Test and control groups.

Test and control groups are a great way to measure the actual, rather than the projected or assumed, the impact of a marketing campaign on your target audience. In theory, it's as easy as your middle school science fair experiment.

Using test and control groups requires a little extra strategy from the start since you have to plan a program you can test. The goal is to apply the factor you want to measure to one part of your target market. Make sure you divide your audience into two groups matching other essential metrics.?

Method 5: Full marketing mix modeling (MMM).

Marketing mix modeling demonstrates how each unique marketing touch and non-marketing variable impacts sales volume. Statistical techniques create complex equations that can consider an infinite number of factors, including:

  • Advertising
  • Distribution
  • Economic conditions
  • Pricing
  • Product

To be effective, this model requires a lot of data. So much so that most find MMM consumes too much time and energy. Even larger companies can only afford to conduct MMM once every few years.


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