Developing a Winning Marketing Analytics Strategy — Part One
A good marketing analytics strategy is essential to your marketing department’s ability to make a measurable contribution to the overall performance and growth of your company. Of course, the key word in the previous sentence is “measurable.” The primary purpose of analytics is to measure, monitor and make decisions based on what your data is telling you. As you monitor various metrics, you should be able to “see” patterns, anomalies and results that lead to insights that inform your actions.
Three Benefits of Marketing Analytics
A few of the big benefits a solid marketing analytics strategy provides include being able to understand customer behavior, optimize marketing efforts, and make better plans for the future. Examples of the types of insights and advantages gained per benefit are featured below:
1. Understand Customer Behavior
- Know how customers are searching for your products/services
- Map customer journeys and plan accordingly
- Reduce your cost per lead
2. Optimize Marketing Efforts
- Adjust ad campaigns to produce greater results
- Know which channels and devices are bringing in the most leads
- Enhance the content of blogs, landing and product pages, etc.
3. Plan for the Future
- Understand the ROI of various marketing efforts
- Enable sales to close more business
- Invest your budget where the ROI is greatest
Pre-Analytics Prep
Before you invest time developing and pursuing a marketing analytics strategy, you need to prepare both your data and the various tools, apps, systems and platforms within your marketing technology stack. After all, you can’t make data-driven decisions based on disparate data and disconnected tools. To ensure your decisions are based on the best-possible data we recommend taking three critical steps:
Step One: Locate and Integrate All Data Sources Throughout Your Company
It’s vitally important to your ability to make informed decisions to integrate both major and minor databases, from marketing automation platforms (MAPs) and customer relationship management systems (CRMs), to content management systems (CMSs), analytics tools and more. If you’ve got data that’s buried inside any of the tools in your martech stack, consider adding a data management tool that centralizes your view and access to all of the data contained within your stack.
Step Two: Get Your Data Results-Driving Ready
According to Experian, the average business loses 15 to 25 percent of revenue thanks to inaccurate, incomplete or inconsistent data (aka dirty data). These days, there’s no excuse for having dirty data when you can keep your data perpetually clean, complete, standardized and enriched by adding a data dashboard that monitors and maintains data quality. Beyond driving better results, using clean data will also save your budget and enhance your customers’ experience, loyalty and lifetime value.
Step Three: Connect Your Content Sources and Channels to Data Sources
The best way to track the performance of your marketing efforts is to measure campaign performance across all channels. When you connect your data and content to the various channels you use, it empowers you to scale personalized campaigns. which perform much better than non-personalized campaigns. In fact, 80 percent of customers are more likely to make a purchase when companies provide personalized experiences.
Understanding the Different Types of Marketing Analytics Strategies
Most marketers use a combination of marketing analytics strategies or approaches, and we will discuss the differences and advantages of the two most common below—Marketing-Mix Modeling (MMM) and Digital Attribution.
Marketing-Mix Modeling or Media Mix Modeling: MMM measures the performance of your campaigns and other marketing efforts by determining how different metrics or variables contribute to a specific goal, like signing up for a blog or making a purchase. Campaigns are then adjusted according to how various metrics impact performance. Because MMM uses aggregate data and multiple variables are involved in determining a campaign’s success, this approach is the most popular analytical approach.
How MMM Works: MMM uses multi-linear regression (MLR) to determine how a dependent variable (i.e. a purchase) is affected by multiple independent variables (i.e. media channel, ad spend, etc.). According to Investopedia, MLR is “a statistical technique that considers the effect of more than one explanatory variable on some outcome of interest. It evaluates the relative effect of these explanatory, or independent variables on the dependent variable when holding all other variables in the model constant.”
MMM Pros and Cons: Although MMM is a quick and easy way to uncover high-level insights on trends that occur over a span of time, it cannot be used to gain in-depth understanding of your customer experience or the relationship between the various channels you are using.
Digital Attribution Modeling (DAM): This approach leverages data from multiple touch-points or channels to determine how customers make a purchase. Within DAM, there are several model variations including:
First-Touch Attribution – This model assigns credit for a lead to the first channel that engaged them. It’s easy to
track, but it doesn’t account for the impact other touchpoints have on a customer’s decision to purchase.
Last-Touch Attribution – This model assigns credit for a lead to the last channel they engaged with prior to
purchase. Like its predecessor, it’s easy to track, but it doesn’t account for the impact of other touchpoints.
Multi-Source Attribution – This model gives credit to every channel that contributes to a customer’s decision to
purchase, however, it does not account for the specific level of contribution each channel makes.
Weighted Multi-Source Attribution – This model accounts for all touchpoints in a sales cycle with the added benefit of weighing the contribution of each touchpoint.
According to Scott Rheinlander, a Revenue Operations Professional, “Attribution [modeling] is crucial to centering your entire marketing organization around a common goal of revenue generation.”
Tune in Next Week
Don’t miss Part Two of: Developing a Winning Marketing Analytics Strategy, where we will share the different metrics you should be leveraging to maximize the results of your marketing analytics strategy.