If you’re a marketer in today’s business world, you’ve no doubt heard about and likely use some form of marketing analytics. The most common solutions can provide some help when making decisions about your campaigns and see how they’re performing. At its core, marketing analytics should be thought of as the measurement and evaluation of your marketing programs and campaigns, and their performance. This will give you the insights needed in order to maximize the effectiveness of your marketing and see a better return on investment (ROI).
In this modern, high-tech world where the devices connected to the Internet now can be counted in the billions, the sheer volume of data that’s available can seem overwhelming. The important thing to remember is that’s it’s not about the quantity of data, it’s the quality. Having the right solutions to help you filter and find the truly relevant data and metrics can make the difference between getting ahead of the competition or falling behind.
On a basic (and very popular) level, most people think this involves using a program like Google Analytics to track web clicks and site traffic, rank page views, or conduct a search for keyword volumes using Google AdWords to ensure you’re using the correct keywords to improve your search rankings. But in reality, this is actually “web analytics” and not “marketing analytics”. While web analytics can provide insights about consumer engagement and response, they can’t tell you how your marketing activities are performing across different channels. It’s simply a way to track clicks or search rankings without explaining what is driving your financial outcomes.
The customer journey to purchase is now more complex with all the different marketing channels in the connected world. To truly see how effective your marketing was, you need to see where the customer went, how they interacted with both online and offline factors, and what ultimately influenced them to buy.
There are two types of sophisticated marketing analytics programs — Multi-Touch Attribution (MTA) and Marketing Mix Modeling (MMM). Historically, these have been offered as separate analytics solutions. However, Neustar and some other organizations have blended their MTA and MMM into a single solution that provides an incredible powerful analytics tool. When considering a marketing analytics vendor, it’s important to ask if they offer strong stand-alone solutions for each to ensure you are getting the most advanced measurements. If they don’t, it’s not a truly blended MTA/MMM solution. What you’ll be getting is simply an augmented MTA or augmented MMM that includes a one-time model creation but it won’t update with new measurements.
While both track the impact of marketing along the customer journey, they offer different insights. MTA is more people-based and offers a greater degree of detail for the short-term, giving proper credit along the customer journey. MMM provides more of a high-level, broad view with a long-term time horizon.
By using a blended MTA/MMM solution, brands will be able to take advantage of the measurements generated by the holistic, fully integrated analytics. This allows you to strategically plan over both longer time horizons (quarterly, yearly) and for short-term, more tactical reasons. Neustar is a firm believer that the more interconnected your analytics are, the more insights you’ll have to inform your decision-making. The following is an in-depth look at each as a stand-alone solution.
Not to overstate the obvious, but we can probably agree with the premise that the goal of ultimate marketing is to convert consumers into loyal customers. In that effort, you might launch a campaign that includes digital activities such as banner ads, paid search, social media, and an email.
In less sophisticated marketing attribution models, an organization simply gives 100% of the credit to the last “touchpoint” the customer clicked in their journey before making the purchase. This can lead to incorrect measurements about which marketing tactics had an effect on the customer, ROI duplication, or a lack of buy-in from finance on the return of the marketing efforts.
With multi-touch attribution (MTA), brands have the ability to track the full path the customer took on their purchase journey and assign the appropriate level of credit to each tactic along the way. This also provides insights on a customer’s propensity to buy.
With this level of detailed visibility at your disposal, you can truly understand the customer’s path to conversion. You can see which media channels work most effectively and lead to better brand interactions, concluding with the customer turning into a purchaser.
For example, if you’re trying to target millennials, you can employ an MTA model to plan your media spend around the channels that have the best chance of reaching them. Surveys and studies have shown that an increasing portion of this group has been cord-cutting. Based on that assumption, you can use MTA to validate the effectiveness of your campaign and focus more on the media channels that increase the likelihood your target audience will be able to consume the media and make a purchase.
These insights can help ROI and reduce wasteful marketing spend. It’s far more efficient to invest in media that you have reason to believe will be more effective, rather than making a guess based on outdated thinking and no metrics to back it up.
Marketing Mix Modeling
A more strategic marketing analytics program, MMM has been around for decades and used by companies to help in two main ways — understand how their marketing investments and other non-marketing factors will impact sales over mid- and long-term time horizons, and how changes in those investments could impact marketing efficiency and sales growth. Basically, it’s your marketing crystal ball to try and predict the future so you can plan on spending your budget wisely. But it’s done in an educated, nonpsychic kind of way.
MMM works by looking at the relationships between sales, the things that influenced those sales, and the final financial results. When MMM is at its best, it’s able to use those insights to help predict what will happen when you start changing elements of your marketing investments.
As with all analytic approaches that are trained on data, it’s important to test the model performance against a holdout period. If the model is producing “predictions” that are close to the actual results from previous periods, then it’s likely it will be able to predict future periods as well. Along the way, it’s advised to perform “check-ins” to ensure the model can account for any changes in business strategies or external events that will impact sales.
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