Marketing Analytics Tools

Paid advertising is an important portion of your digital marketing strategy. Here’s how marketing analytics tools can help.

Data Standardization Worksheet

This worksheet begins with the standardization of your performance data. We’ll walk you through how to combine these mismatched data sets in a step-by-step process so that every metric is speaking the same, common language. With all of your data standardized, it’s easier to locate the strengths of each platform and hone in on ways to optimize campaign performance.

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Marketing Analytics Tools

Marketing analytics tools collect and analyze information to provide actionable insights to marketers. But marketing analytics platforms can’t provide the best information just out-of-the-box; they need guidance. There are many marketing analytics tools, ranging from free marketing analytics tools to extremely expensive, proprietary solutions. Some marketing tools are tailored to specific channels while others are tailored to specific demographics. 

As an example, a marketing analytics tool may collect information from a specific social media stream, such as Twitter. This tool might identify tweets that are most popular, tags that perform best, and customers who interact within the platform the most. At the same time, the marketer will need to direct the tool; is the marketer primarily concerned about likes? Follows? Or conversions to links that are posted? 

One of the most famous marketing analytics tools — and one of the most ubiquitous — comes from Google. Google’s AdWords and AdSense products make it possible for third-party ads to be run on a multitude of websites; it is the largest third-party advertising network. At the same time, Google tracks in-depth how these ads are performing, what demographics these ads are performing with, and how likely they are to lead to a conversion.

Many of the more expensive, proprietary suites will combine channels — they will provide a multi-channel overview of an organization’s marketing campaigns. Marketing analytics tools are extraordinarily important because they tell marketers what works and what doesn’t work, what they can improve and what needs to be changed. The more information that can be provided to the platform, the better. Today, many marketing analytics platforms are run by adaptive learning AI solutions; artificial intelligence that can glean a surprising amount of insight from raw data sets.

Marketing Analytics Examples

What are some common marketing analytics examples? 

Consider a simple flyer program, which mails out 10,000 flyers. Each flyer has a unique URL for the purposes of tracking. A marketing analytics solution might track:

  • Out of 10,000 people, 865 responded.
  • Of these 865 individuals, 48 eventually committed.
  • These 48 individuals were 80 percent women and 20 percent men.
  • 85 percent of these individuals lived within a single county.

From examples of marketing analytics, we can also see the value of these marketing analytics techniques. Here, we can see that women are far more likely to respond to these flyers. So, in the future, we might want to concentrate on sending them only to women. We may also want to focus on the county that responded best or “look-a-likes” to that county. We might also want to ask ourselves further: Why did that county respond better? Why did women respond better than men?

Marketing analytics solutions give information. But these are only actionable insights if we can determine what the information means and how to move forward with that information. We might find, for instance, that women aren’t more drawn to our product, but that our advertising was targeted to women; we might need to create advertising that’s more targeted toward men if we want to capture that demographic. 

This underscores the importance of marketing analytics; it gives us information that we can use to improve and fine-tune our advertising campaigns. In this example, we used flyers, but this can apply to everything from email newsletters to banner ads.

Social Media Analytics

Social media is, by far, the fastest-growing venue for the modern marketer. It only stands to reason that social media analytics are also very important. For marketers, different things may mean success when it comes to a social media platform. 

A marketer starting on Twitter may consider in their social media analytics report:

  • New followers.
  • Likes.
  • Link follows.
  • Shares.

Each of these social media analytics examples can mean different things to a campaign. New followers mean that the company’s reach is growing; the more followers they get, the better positioned they are for outreach. Likes, however, show that the content that they are posting is interesting and valuable. Link follows mean that users are willing to follow links that are posted and potentially convert, while shares mean that users are so interested in the content that they want to share it.

Taken individually, these are important factors. But the interaction between these metrics also tells a story. Consider:

  • A company is getting tons of new followers but very few likes. This could mean that they are interested in the company, but find little in value in the marketing; they may soon un-follow if the information being presented doesn’t get better.
  • A company has many followers and likes, but no link follows. Though the individuals following the company are interested in its content, they aren’t interested in its product. The company may have invested in building a demographic that isn’t right for it.

The interaction between metrics on a social media analytics report can be valuable for marketers trying to determine whether their campaigns are successful and where they may be going wrong if they aren’t. By identifying key areas of improvement, marketers are able to continually optimize their marketing strategies. This is especially important in a world as fast-paced as online marketing and social media marketing.

Digital Marketing Analytics

Apart from social media marketing, there are broader digital marketing analytics. This includes social media marketing, but also spreads to email marketing, paid banner advertising, and website marketing. Digital marketing analytics essentially encompasses all types of marketing that occur on the web.

Because digital marketing analytics encompasses so much, analytics has to have a multi-channel approach. A master of marketing analytics will be able to see which customers are coming from email, which from paid ads, and which from website marketing. Further, they should be able to pare down to see which channels have the best ROI, which channels have the best conversion rates, and how these channels are performing both in relation to each other and in relation to their own past performance.

A few marketing analytics tips include:

  • Tracking customers on a granular level. Today, the entire buyer journey has to be mapped out. Buyers may come in through social media but they may finally commit through content marketing. Buyers may come in through content marketing but they may finally commit through email.
  • Committing to regular audits and A/B testing. Demographics and marketing strategies need to change frequently in a world as fast-paced as the internet. It’s important that marketers regularly conduct audits and split-testing to ensure that they are still running the right strategies.
  • Consolidating all information. There are many marketing platforms that are for specific channels, such as email marketing, or social media venues. But it’s best to have a single, consolidated space.

Because digital marketing is so expansive, it’s critical that marketers don’t get “lost.” It’s easy for marketers to end up spending a lot of money and a lot of time on failing strategies if their data isn’t up to par. With the right data, marketers are able to concentrate their efforts on the strategies that work best for them. With the wrong data, marketers can end up chasing down saturated markets or failing markets because it’s what they’ve previously done.

Marketing Analytics Tools Free

When looking at marketing analytics tools, there are many digital marketing analytics tools free or low cost. But marketing analytics tools free usually aren’t going to have the in-depth features that a proprietary or paid-for solution might.

  • Google. Google’s AdWords and Google’s Analytics solutions can be used for free, though if you actually want to start running ads you will need to pay for them. But you can still use their SEO and keyword tools.
  • Hootsuite. Hootsuite provides social media analytics services, as well as features such as social media scheduling. It is free up until a certain number of accounts.
  • Buffer. Buffer is another social media analytics service and social media scheduling solution that is free until you meet a certain activity threshold. It’s one of the most popular options available.
  • Quantcast. Quantcast can provide estimates of traffic and demographics for marketers who are looking for basic demographic and audience information, though it doesn’t have anything detailed enough to really target advertising effectively. But it can be used to see major trends.
  • Twitter Analyzer. Targeted specifically toward Twitter, the Twitter Analyzer solution can analyze marketing sentiment and general sentiment surrounding your organization on Twitter, as well as in-depth metrics around your account.
  • Bit.ly. While it’s primarily a URL shortener, Bit.ly can make it easier for you to track clicks on your social media. This helps with tracking your sales funnel, as you’ll be able to see exactly what brought people in.
  • HubSpot. HubSpot provides a wide array of marketing analytics and social media analytics tools, as well as certifications and information about how to engage in marketing analytics and how to best leverage your data. 

Paying for an analytics tool will often pay for itself for most companies. But companies that are just starting up or that want to remain lean may begin with free analytics tools, instead. Free analytics tools aren’t necessarily less accurate or less insightful — they just tend not to have as robust feature sets.