Marketing Analytics

We are living in an age of accessible data.

At home, you can use your smartphone to access data about your exercise habits, your sleep patterns, and even your medical records. At work, you can use text files known as cookies—and other similar tools—to gather information about your customers.

You can learn almost anything you need to know, from what kinds of products your customers buy to what age groups tend to visit your site most frequently. And you can break this data down, all the way to the individual level if that's what you need.

In the end, it's not the data that matters, but what you do with it. The power is in data aggregation and interpretation, the framework of a process known as marketing analytics.

What is marketing analytics?

The Mailchimp Marketing Glossary offers this definition of marketing analytics:

“A math-based discipline that seeks to find patterns in data to increase actionable knowledge. Analytics employs statistics, predictive modeling, and machine learning to reveal insights and answer questions. Weather predictions, batting averages, and life insurance policies are all the result of analytics. In the world of digital marketing, analytics is critical to understanding and predicting user behavior and optimizing the user experience (UX) to drive sales.”

Here you have the 2 main purposes of marketing analytics:

1. To gauge how well your marketing efforts are performing, measuring the effectiveness of your marketing activity.
2. To determine what you can do differently to get better results across your marketing channels.

Together, these processes let you turn raw marketing data into an action plan and make the most of your marketing dollars.

Why marketing analytics matters

Analytics is more than just a nice extra. It’s one of the best ways to understand your customer journey and find out what’s working in your campaigns and what isn’t. And having that information is crucial for your future online marketing efforts.

Here are a few of the things you can do with marketing analytics:

1. Quantify your claims

Numbers are persuasive. You could tell your CFO that content brings in customers, or you could tell them that 72 percent of marketers believe that content increases customer engagement.

The second one is more likely to get you funding. People are more likely to care about your claims when you include relevant statistics.

Without specific marketing data points, such as your ROI before and after a campaign, you can only think in general terms. Either your income went up while a certain ad was running or it didn't. Either you got more email list sign-ups after you started pay-per-click (PPC) advertising or you didn't.

Analytics lets you take the data from that time period and determine how much a particular campaign actually brought in - its marketing impact. If you got 100 email opt-ins on the first day you started your PPC campaign, how many of those came from the ad itself?

If you determine if the marketing initiative itself worked, getting funding is much easier. And if it didn’t work, you can save money spent on continuing the initiative.

With marketing analytics, you can clearly demonstrate not just that something is or isn't working, but also why. And with that "why," you can convince people to make a change.

2. Turn data into information

Most businesses today have access to customer data and web analytics tools. The difference is in whether your company makes use of that data. Too often, according to the Harvard Business Review, it just ends up sitting in a server without doing anything particularly useful. In the worst-case scenario, it can be misinterpreted and misused, leading your marketing team astray.

For your data to become useful information, you need to subject it to relevant data analysis.

For example: at the beginning of your PPC campaign, your revenues hover around $10,000 per month. After your first campaign, your receipts are up to $15,000 per month. Should you invest in that same ad again?

It depends. Was there an industry-wide uptick in sales that month? Maybe your products started trending for unrelated reasons. And did you have any other ads running at the time? How many of your customers actually came from that PPC ad?

Data analytics gives you the answers to these questions. With those answers, you can make decisions in your marketing program that are based on facts instead of hunches.

3. Compare and contrast your marketing data

Analytics lets you go a step further and compare your data sets to each other. For instance:

• How did your your paid search, social media marketing or organic search income compare to your estimates?
• Were there any differences in revenue generated across demographics?
• How did the ROI on your PPC campaign compare to the ROI on your Facebook ad campaign?
• How much did your PPC campaign generate in first sale revenue versus lifetime revenue? Your ad campaigns, content initiatives, and customer groups are all interrelated. If you understand the intersections, you can weed out irrelevant information and make the best decisions based on your business's unique goals.

4. Stay goal-oriented

Every one of your marketing pieces has a goal, whether it's increasing sales or simply driving more traffic to your company website. The more you analyze and use the data available to you, the more you know about your progress towards your goals.

Marketing analytics lets you measure that progress, and it helps you figure out where the problem might be if progress isn't coming as fast as you’d like.

Say you ran a Facebook ad campaign and your ROI was a little under 3:1.

Your team tells you to try something else, but you look at the analytics. You found that your ad had a great click-through rate, but your homepage bounce rate was high.

The PPC campaign wasn't the problem. But without good analytics, you never would have known.

Using marketing analytics to improve business growth

Data alone is just numbers. You get the benefit when you use that data to direct your marketing efforts towards what works and away from what doesn't.

Segment your customer data

If you segment your customer data based on particular qualities or actions, you can get more specific (and more useful) data. You can segment based on any customer demographic point that impacts your results. These include:

• Age group
• Gender
• Education level
• Annual income
• Family status
• Geographic location

You can also segment data based on consumer behaviors, sorting by customers who:

• Abandon their shopping carts
• Browse your product pages without buying
• Buy from you regularly
• Haven't been back to your website in a while

This segmentation lets you filter your data by relevance, taking what you need and leaving what you don't.

Imagine you have a large cohort of customers who abandon their shopping carts. You want to know whether Facebook or email would be best to bring them back to their cart, so you run a series of tests.

You find out that email gets a better ROI, but only if you include language that creates a sense of urgency. That information saves you from pursuing cart abandoners in places that they’re not looking.

Make sure you have high-quality data

You can only have great analytics with high-quality data. Data from 5 years ago won’t be relevant to your marketing campaigns this year—and if the data has holes in it, it might not be relevant to anything at all.

For data to be high-quality, it has to be:

• Current
• Comprehensive, with no gaps
• Error-free
• As precise as the data analysis requires
• Relevant to the analysis

Marketing analytics should always be goal-oriented, so the last point is the most important. It’s possible to make use of old or incomplete data, as long as you treat it cautiously—but if the data isn’t relevant to the needs of your campaign, leave it out.

Look to the future

It's no longer enough to know what has happened in the past, or even what's happening in the moment (although real-time analytics are important). For the most effective marketing, it’s also important to predict what will happen in the future.

Luckily, you don't need a crystal ball for this—predictive analytics can help you look to the future. These tools use specific data and past trends to determine what results you can expect to see under different conditions. You can use predictive analytics to answer questions like:

• Would a search engine campaign get better results with more money invested?
• Would a search marketing campaign on 1 platform work well with another? Would an email campaign translate to Facebook ads, for instance? Would your Facebook ad work on Instagram?
• What kind of revenue might you get with your social media marketing campaign in a new market? You don't have to get too complex with your predictive analytics, but dedicating some time to them can help you determine whether what you’re doing is working or not.

Look at what doesn't work, not just at what does

It feels good to focus on your strengths, but don't stop there. You can learn just as much by paying attention to where your marketing efforts are falling short. If you experience a sudden drop in sales, can anything from your analytics explain why?

Look at where your content is underperforming, but remember to stay positive and goal-oriented. Treat your shortfalls as opportunities, and use your analytics tools to learn how you can close the gap.

And when you make changes, remember to keep collecting and analyzing data so that you notice when things improve.

The takeaways

Any marketing project is a process. Marketing analytics helps you to determine where your attention should go during that process and the right marketing mixfor your business. Remember to follow analytics best practices:

• Specify the questions that need answers
• Gather high-quality data
• Pick out the information that matters to you
• Consider the past, the present, and the future

Sometimes, the answers you receive spark even more questions. When that happens, you can repeat the analysis process and learn even more about your marketing efforts.