Marketing has a very long story. From shouting on marketplaces and first ads in local newspapers, through TV commercials and outdoor banners, up to modern performance marketing campaigns that happen exclusively online. Today, data analytics in marketing plays a crucial role. In fact, without data analytics, there would be no effective online ads, and companies couldn’t measurably promote their businesses. What do you need to know about data analytics in marketing?
When it comes to promoting your business, the internet and social media offer tremendous possibilities. You can reach potential customers in almost every country on Earth within minutes! But there’s more. These two channels also allow you to measure your efforts. In short, that’s what data analytics in marketing is all about.
However, in order to fully understand the significance of data analytics in marketing, we have to show you the full potential of performance marketing first.
Data analytics in marketing: Performance marketing
Performance marketing is a strictly online strategy. As its name suggests, it focuses solely on driving results. And, naturally, these results have to be measurable. This is why the entire performance marketing field is based on data analytics. Let’s consider two of the most popular performance marketing tools: Google Ads and Facebook Ads.
Google Ads allow you to promote your business in the largest search engine ever. Google Ads are frequently described as CPC. This abbreviation stands for Cost Per Click, and this is the way your payment is assessed. You pay every time someone clicks your ad. It’s the first form of promotion that Google introduced back in October 2000 (yes, 20 years ago!). This is an advertisement form you see every time you type your query in Google. At the top of the SERP page, you see sponsored links that are relevant to your query.
Google uses its other tool (Google Analytics), to help you measure the efficiency of your ads. This tool also calculates the traffic on your website. Thanks to that data, you know:
- Who visited your website?
- For how long?
- Where did they come from?
- Which section on your website was particularly interesting?
And more. In the next section of this article, we will discuss all the benefits that such a solution offers.
Why is that technique effective? Mostly because 46% of clicks go to the top three paid ads in search results. And given that 63,000 searches get processed by Google each second… Well, you get the picture.
While GA is a perfect tool to target people who look for specific products or services, Facebook is unmatched when it comes to targeting people with particular characteristics (interests, professions, education, location, activities, income, etc.).
In fact, there are 1,300+ targeting options, 15 objectives to choose from, and 6 main ad formats available. As a result, you can target your ads specifically to people who meet your requirements. Do you want to advertise your offer to newlyweds? Or maybe you are targeting people who visit North America frequently? With Facebook ads, it’s a breeze.
Data analytics in marketing plays a major role in Menlo Park! Mark Zuckerberg has taken care of an extensive data analytics algorithm that enables them to analyze human behavior, habits, expenditures, and interests. As a result, they possess an unimaginable amount of data about modern customers. And they are willingly selling that knowledge. Among other ways, via Facebook Ads.
Benefits of data analytics in marketing
Generally speaking, data analytics in marketing has two primary purposes:
- To assess how your marketing efforts are performing. In other words, you can fully measure the effectiveness of your marketing campaigns.
- To determine what you can do differently to get better results. You should continuously optimize your campaigns in order to squeeze everything out of them.
What does it look like in the real world?
You may find it interesting – Machine Learning in Loyalty & Marketing – Case Study
TURN DATA INTO INSIGHT
Utilizing data analytics in marketing not only will it help you sell more. Most likely, you will be able to save a lot of money too! Data analytics in marketing is also frequently called data-driven marketing. And today, 40% of organizations aim to increase their marketing budgets exactly on data-driven marketing. Moreover, 64% of marketing executives “strongly agree” that data-driven marketing is crucial in the economy. And the fact is that marketers utilize data analytics in marketing to improve their conversion rates (sell more, attract more customers, and gain more visibility on the internet).
Now, what are the crucial data-driven marketing strategies? According to “The Importance of Data-Driven Marketing” report:
- Customer journey analysis: 63% of surveyed companies utilize this strategy
- A/B testing: 60%
- Website personalization: 46%
- Customer segmentation: 55%
- Usability testing: 55%
- Cart abandonment analysis: 47%
- Copy optimization: 44%
All of these aspects of modern marketing have a significant influence on your campaigns’ and business.
CUSTOMER PERSONALIZATION AND SEGMENTATION
These are two modern buzz words that shape the current marketing industry. Why? Simply because they work. Various marketing researches and reports clearly say that people don’t want to feel like numbers. They want a personalized customer experience. According to Gladly’s 2020 Customer Expectations Report, 79% of surveyed customers said that personalized customer service is even more critical than personalized marketing.
The properly prepared personalization strategy allows you to:
- Send personalized messages to customers
- Speak to them directly through the website/email (e.g., a personalized email will be built based on the given customer’s interests and previous purchases and will be addressed by name)
- Communicate in the less official language
The main goal of personalization is to build lasting relationships with customers.
What about segmentation? It’s a related strategy. Segmentation is simply based on dividing customers into different groups. It enables you to adjust your promotional activities to a specific group of customers. For instance, when selling furniture, you should use different argumentation when communicating with a single man and a different one when you are talking to a homemaker with three children. Segmentation helps you define the most effective marketing message, communication channels, and way of argumentation. It increases the effectiveness of the activities carried out, thus minimizing their cost.
Data analytics in marketing: Artificial Intelligence and Machine Learning
The data analytics in marketing tools and apps are often based on various AI/ML algorithms that improve their work and produce results. Even today, according to Think with Google, marketing leaders are 1.7x more likely to agree that adoption of machine learning improves targeting, spend optimization, and personalization.
(GOOGLE) ANALYTICS INTELLIGENCE
Earlier in this text, we mentioned the Google Analytics platform. What we did not say is that it’s teamed up with a machine learning algorithm called Analytics Intelligence. According to Google, this ML algorithm outstandingly improves the results GA presents.
Analytics Intelligence functionality includes:
- Answers to your questions. For instance, you can ask questions like, “Which channel had the highest goal conversion rate”, and Analytics Intelligence will show you a ranked list of goal conversion rates by channel.
- Insights. This feature will analyze your data and surface insights on major changes or opportunities you should be aware of. For example, it can point out that a particular landing page is more clickable than others.
- User and conversion modeling. This feature utilizes an ML algorithm to model conversions and build target audiences.
Soon, it will be a global standard in every large corporation and marketing agency. In short, predictive analytics is the process based on utilizing AI and machine learning to measure marketing activities in order to spot future trends and opportunities.
The machine learning algorithms can constantly learn from data and, therefore, they improve themselves in time. As a result, they get better and better, and their recommendations and predictions are more and more accurate. What can these predictions be used for?
- To analyze customers’ behavior. With ML algorithms, you can spot correlations and patterns in the customers’ behavior to predict their future tendencies in purchasing.
- To prioritize leads. Not every one of your potential customers is at the same stage in the customer journey. ML algorithms help you spot the “hottest leads” and focus on them.
- Product design. Predictive analytics helps you make more informed decisions about what product or service should be introduced to the market.
- Targeting. Similarly, as in leads prioritization, the predictive analytics apps can show you the most prospective target groups and customers so that you can concentrate your efforts on them.
Naturally, there are no ready-made predictive analytics apps. First of all, it’s still an emerging technology that is going to reach its peak capabilities within several years from now. And second of all, predictive analytics has to be tailor-made to suit your company’s profile, needs, and goals. There’s no place for copy-paste here.
Last but not least, data analytics in marketing can help you optimize the content your company produces. Don’t underestimate that field! Today, content optimization is one of the fundamental aspects of SEO. According to SmartInsights.com, the highest SERP (Search Engine Result Page) results have the highest CTR (Click Through Rate). The #1 result gets 34.2%, the #2 17.1%, and the #3 11.4%. that’s why you should make sure your website is as high in the SERP as possible.
Data analytics in marketing and machine learning algorithms can help you estimate which types of content, questions, and headlines are most likely to become popular among your target audience. As a result, they can reach high positions in Google.
To sum up, data analytics in marketing is a new milestone in the history of advertising. It helps you sell more, advertise more efficiently, and save money. If you are interested in utilizing data analytics in your marketing campaigns–drop us a line! We are always eager to help you get the most of your campaigns, thanks to artificial intelligence.
 Sarah Berry. 57+ Google Ads Statistics to Know in 2019 and Beyond. Nov 27, 2019. URL: https://www.webfx.com/blog/marketing/google-ads-statistics/. Accessed Oct 27, 2020.
 BlueCorona. Your Ultimate Guide To Types Of Facebook Ads. Mar 28, 2017. URL: https://www.bluecorona.com/blog/types-of-facebook-ads/. Accessed Oct 27, 2020.
 This is where Facebook is headquartered.
 Ayat Shukairy. The Importance of Data Driven Marketing – Statistics and Trends. URL: https://www.invespcro.com/blog/data-driven-marketing/. Accessed Oct 27, 2020.
 Google. Automation machine learning measurement statistics. URL: https://www.thinkwithgoogle.com/marketing-strategies/data-and-measurement/automation-machine-learning-measurement-statistics/. Accessed Oct 27, 2020.
 Google. About Analytics Intelligence. URL: https://support.google.com/analytics/answer/7411707?hl=en. Accessed Oct 27, 2020.