Marketing analytics enables personalization, but what other benefits does it offer — and how relevant are they to your team?
With digitization now widespread, marketing is poised to become more science and less art. Data-savvy marketers have access to a never-ending stream of valuable insights into their customers, their performance and opportunities for improvement going forward.
Marketing departments that don’t embrace analytics and become data-driven risk getting outpaced by competitors — and by consumer expectations. A recent Salesforce survey of 6,700 people found that 76 percent of consumers expect marketers to anticipate their needs. This is perhaps unsurprising — we live in the age of hyper-personalization, with Big Tech setting the standard for delivering seamless, connected, omnichannel customer journeys.
At its core, marketing analytics is about identifying patterns within data that help you make smarter marketing decisions. You can analyze a single platform or campaign, or look at how multiple campaigns interact with each other and with your customers to drive revenue.
Insights from this kind of analysis will give you a deeper understanding of where your customers are and where the market as a whole is going. You can stop being reactive and start being proactive — and ensure that your marketing activities achieve maximum efficiency and impact.
To make all this more concrete, let’s take a look at seven key benefits of marketing analytics.
Marketing teams tend to run a lot of different efforts across lots of different platforms and teams. Getting the data from disparate efforts into one place requires a high level of coordination.
A solid marketing analytics program will identify all the different tools and platforms being used by your teams and centralize the data they generate for analysis. Getting the data from all of your paid efforts in one place allows you to understand what’s working, where to put more budget, and how you can share learnings across teams and channels.
Imagine that an image is leading to numerous clicks on Facebook but you aren’t getting the same number of clicks on your Twitter ads. Now you can consider bringing that winning image over into Twitter to see if it performs better. Over time, you can add elements to your marketing mix and build out a more complete picture of what’s happening and — importantly — what’s working.
Being able to combine and cross-analyze data from all your efforts is foundational, and it will enable more sophisticated analytics involving things like attribution and ROI. It’s about getting all your data in one place, in the same format, at the same level of freshness.
Check out our guide here to get started on seeing your marketing mix.
On its own, raw data is useless. Marketers only derive value from their data when they use it to assess current performance, identify opportunities for improvement, and use these insights to optimize future campaign performance.
A/B testing — an important element of marketing analytics — allows marketers to instantly compare contrasting approaches, like running two starkly different ads (both in terms of design and copy) for the same campaign. Even better, everything can be A/B tested: messaging, creative elements, retargeting strategies and more.
A/B testing involves getting comfortable with the end-to-end process of devising a hypothesis, setting out the test parameters (channels, KPIs, duration), executing the plan, and relaying these insights into your next effort. You’ll start by making small-scale adjustments to one particular problem, such as tweaking the CTA on a low-converting ad.
Once you have a firm grasp on the process, you’ll likely go bolder. You can A/B test large strategies instead of merely fine-tuning the details. For example, what works better: selling direct to consumers (D2C) or selling via intermediaries (B2B2C)?
Brand awareness is crucial. If consumers have never heard of your brand, how do you expect them to buy from you? And how will they search for your name on Instagram, Google, and so forth?
Measuring brand awareness has traditionally been tricky. Large-scale ad campaigns may make your brand more recognizable, but they don’t always translate into higher sales.With marketing analytics, companies can delve into brand awareness analysis without having to rely simply on recent changes to the bottom line or likes on Facebook. Social listening/tracking, search volume data, content reach, web traffic (especially direct traffic), and share of voice analysis are all great places to start.
You’ll want to look at key metrics across your social channels (followers, engagement rate, etc.) and compare these against your competitors. This will give you an initial, very rough way to estimate your brand awareness.
You can then include more data points in the awareness analysis. For example, you may implement social listening tools to pick up all relevant online chatter, analyze search volume data and direct traffic, conduct surveys with your target customer base, set up Google alerts, and collect data from Google Search Console.
Check out our resource page to learn more about how to run sophisticated top-of-the-funnel analysis.
Generally, prospects are far from homogeneous. They each have their own background, preferences, and demographic profile — meaning you can’t simply adopt a one-size-fits-all strategy when marketing.
Profiling and segmentation allow you to group similar prospects together according to the factors you deem most important. They enable you to devise targeted strategies that will resonate with each individual segment’s preferences. By speaking to customers the way they want to be spoken to, you’ll increase your marketing success and reduce wasted efforts.
For instance, if you’re an outdoor sporting goods store, you may geographically target customers, showing ads for surfboards to customers located on the coast, and ads for skis to customers living in mountainous, high-altitude regions.
You can continue to analyze your campaign data and tweak your customer profiles accordingly. Perhaps your surfboard ads fell flat among consumers based in the Pacific Northwest. When you take a step back, you realize that these consumers have both beaches and mountains on their doorstep — and in the winter, they’re more likely to be looking for skis than surfboards.
Individual customers follow their own paths prior to purchasing your product, and afterward as well. These journeys likely involve multiple touchpoints across a variety of channels. Your marketing analytics may demonstrate that most customers use social media for brand awareness, search engines for consideration, and your website for purchases.
Armed with these insights, you can provide consumers with appropriate information at each stage of their journey. For example, you may consider using less detailed, but more aspirational content on your social media pages. However, knowing that consumers use search engines to help them find out more about your products, you could design your website around their most common FAQs and clearly lay out everything that your product has to offer.
Your effort might begin with a tool like Google Analytics, which allows you to establish targets, i.e., steps that a customer will take before purchasing from you (clicking on your promotional ad, booking a demo, etc.). Once you’ve set up your goals, you can then begin to map out the sources that drove customers to complete each goal, helping you to link your customer journeys together.
Data from Google Analytics will guide improvements to the customer journey, identifying areas where you can remove friction. If you notice that plenty of prospects click “Book a demo” but only a small percentage fill out the corresponding form, then analyze why. Are there too many form fields, for example?
You can get even more sophisticated by continuing the mapping beyond the sale. Look to understand how a customer interacts with content and what it takes to upsell and cross-sell them. Then find moments to automate and bring more of a product-led growth (PLG) motion into your business.
Customer journey analysis reveals the path your consumers took before purchasing — but it doesn’t outline which of these steps was the most important (i.e., resulted in the highest ROI). That’s where attribution modeling comes in. It reveals which touchpoints were present in a customer’s journey, and also assigns a level of credit to each touchpoint depending on how influential it was. Marketers can then direct investment toward efforts that have proven successful.
As you feed the insights from attribution modeling back into your marketing strategy, improving campaign ROI and unlocking more budget as a result, you’ll want to consider refining your attribution model to ensure it’s most appropriate for your business model. Most models start with last-touch, meaning all credit for a purchase goes to the last touchpoint before conversion — but there are many others, including time decay, first touch, u-shaped, and more.
Check out Bizible’s guide to see what might be the best model for your company.
Imagine you have a firm grasp on all your different marketing strategies, have seen precisely how customers move through the buying journey, and worked out which channels and strategies are most effective. Now you need to put all this information together and work out how much it costs your company to acquire each customer. Thankfully, this is as simple as dividing the amount you spent on a campaign by the number of customers that you acquired.
A marketing analytics program will track the performance of all your cross-channel campaigns, helping you easily identify which strategies, products and demographics have the lowest cost per acquisition (CPA). Invest more heavily into these areas going forward to increase your marketing ROI.
You’ll also be able to compare your average CPA to industry standards and benchmark yourself against the competition. Why do different campaigns result in different CPAs? Is it down to a subpar strategy or simply segment-specific variations? You’ll want to identify a target CPA for each of your strategies and segments and A/B test different strategies until you reach your goals.
For more on this, check out our guide on how to tackle cost-of-acquisition analysis.
Your journey toward modern marketing analytics starts when you sit down with your BI team to understand where in this journey your organization currently is.
Next, determine the largest projects you want to solve for. From there, it’s as simple as either plugging into your company’s existing system — like bringing your marketing data into your data warehouse using Fivetran — or working with your BI team to start your free trials on the right data stack for you.
If you need more guidance, head to our Marketing Analytics Resource Center. We’ve created an array of content resources — ebooks, case studies, videos and more — to help marketers become more data-driven.