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Many businesses with an online presence are using Google Analytics to track their web marketing campaign outcomes, but that's just the tip of the iceberg.

Many businesses with an online presence are using Google Analytics to track their web marketing campaign outcomes. As it provides insights into the performance of assets like websites and e-commerce platforms. But it only tells you about what took place in the past. It's up to you to leverage the information and figure out what it means for the future of your business revenue and marketing performance. Here's where predictive analytics comes in!

Predictions with Google Analytics data

Predictive analytics is all about leveraging statistical algorithms and machine learning (ML) tools to study and analyse historical data to unravel possible future trends and outcomes. To marketers, this means shifting the basis for timely decision-making from what happened in the past to what will likely occur in the future. In other words, predictive Google Analytics taps into raw historical data to develop reliable predictive models for a broad spectrum of digital marketing channels.  Businesses need this ability to foresee the future and formulate appropriate strategies for marketing success.

In marketing, applying ML-powered predictive modeling to Google Analytics can deliver these two primary capabilities:

  • KPI forecasting: Campaign managers can generate projections for KPIs. They may then set up a marketing program informed by high-quality data rather than guesswork.
  • Anomaly detection: ML algorithms can scan tons of website data to reveal abnormal trends or events.

Using Predictive Analytics to Hit Your KPI Targets Every Time

Typically, businesses look at Key Performance Indicators (KPIs) at the end of a given period to measure the effectiveness of various inputs. But as a marketer, you want to know whether you're going to meet your KPI targets ahead of time. You'd most certainly do something differently if you knew your campaign wasn't going to achieve specific preset objectives. With ML-driven predictive analytics, you can tell what's working and what's not, enabling you to take action early enough and avoid missing your target.

Here are critical performance metrics you need to be tracking and forecasting with the help of ML algorithms:

Predicting New Users and Web Visits

One of the most important objectives of a sustained web marketing campaign is to attract new users and increase traffic. Well, are you on track to achieve your targeted number of website users this month? Google Analytics tracks this data, revealing the number of new and returning web visitors for any given period. Now you can leverage ML prediction models to see whether you're on course to meet your target.

You can also drill down these projections into details like sources and destinations of your web visitors. Google Analytics already tracks the number of people that have visited your mobile app, landing page, or e-commerce site in the past, and with ML modeling, there's sufficient intelligence to forecast traffic to each of these online destinations. In the end, you have the necessary information to tweak marketing efforts and address any projected web visits shortfall. 

Traffic Segmentation

It's in the interest of every marketer to know where their web traffic is coming from, and thankfully, Google Analytics tracks this information. The tool generates historical insights into individual sources, channels, or strategies, which ML algorithms may then analyse and project forwards. Based on this marketing intelligence, you can plan and execute remedial action in time, such as cranking up advertising budget for the most promising channels or doubling down on promotions during poorly-performing months.

Comprehensive predictive analytics reveals the future health of:

1. Organic traffic: How many organic impressions for specific keywords do you anticipate for this month? If there aren't a lot of visitors coming to your website via search engines despite your SEO efforts, you may need to revisit your methods. Perhaps your content strategy, mobile friendliness, or web design has something to do with the noted undesirable SERP ranking, which explains the weak growth outlook for organic traffic?  Do you need to change your keyword strategy, such as from long-tail to short-tail?     

2. Referral traffic: You want to establish your website as an authority in your industry. Are you on course to achieve that objective as evidenced by the referral traffic projected to come from other reputable sites? You may need to enhance your link-building efforts to increase the number of visits from other websites.  

3. Social traffic: When do you need to increase your social media participation to remedy projected shortfalls or make the most of high click-through rates? Predictions on Facebook, Twitter, or Instagram engagements, such as views, comments, shares, or retweets can provide the right answer.

4. Paid search: If you're running a Pay-Per-Click (PPC) campaign, you want to work out projections for traffic coming from search engine ads. Predictive analytics is a sure way to find out if your investment in the program is poised to pay off.

5. Direct traffic: Mostly, these are the visitors that come to your website by typing its URL into a web browser. They already know about your business website, perhaps from a TV ad or promotional publication.

6. Email traffic: What's the projected performance of your email marketing campaign? This metric can forecast the effectiveness of your newsletter promotions.


Google Analytics tells you not only who came to your website in the past, but also what they did while there. This metric is an important indicator of how well your site and content are addressing the interests of your visitors. In a typical session, the tool tracks page views, downloads, purchases, and any other action a user performs before leaving your website.

ML algorithms can analyse sessions' data to forecast user activity on your business website. For example, you may be targeting a shopping cart conversion rate of 80 percent depending on industry standards. If predictive analytics work out a smaller percentage, you can go back to the drawing board and find a way to keep prospective customers from abandoning your shopping cart prematurely. You also want to determine if people are staying long enough on your web pages to read content, make a purchase, sign up, or perform any other intended action.

Predictive Analytics for Anomaly Detection

In anomaly detection, ML algorithms scan and spot inconsistencies in time-series data for a specific KPI or metric. The tool uses a period of historical data captured in Google Analytics to train and develop a forecasting model. It can then detect daily, weekly, or monthly anomalies, giving timely notifications to inform swift remedial action.

The ability to forecast anomalies is essential to marketers for several reasons. For starters, it's not always clear when key metrics are on the decline, such as checkout conversions. Technical issues, poor messaging, or wrong targeting can be responsible for a drop in campaign performance, and businesses need to be able to foresee any such abnormal trends and make informed corrective decisions.

Say you've got your predictive analytics forecasting 1,000 sessions for September 12. If the actual figure is significantly lower than that (for example, 800) on that date, you've got an irregularity on your hands. Fortunately, an ML-based anomaly detection system notifies you in time so you can get to the bottom of any underlying web performance, organic search, or PPC campaign issues.

Anomaly detection with ML algorithms has a couple of advantages, including:

Automation Allows Marketers to Focus on Other Equally-Important Campaign Tasks

ML algorithms scour tons of Google Analytics data, autonomously. As such, marketers don't need to monitor trends 24/7 looking for something out of the ordinary. They can focus on other critical aspects of their marketing campaign confident that they'll receive timely alerts whenever a data anomaly is detected.

Elimination of Random Noise

Since not all anomalies in performance metrics constitute an emergency, there's the need to refine data insights based on what matters the most to your business. Some inconsistencies originate from temporary random factors, rather than major technical issues or persistent campaign difficulties. So, ML-driven analytics can filter all the noise, enabling you to prioritise remedial responses to anomalies depending on the severity of their business impact. 

Timely Alerts

You're deploying an ML-driven anomaly detection tool to continuously monitor your Google Analytics data and issue an alert once an irregularity has occurred. The resource generates timely alerts, enabling you to respond quickly to potential problems. How swiftly you move to address an issue, such as declining conversion rates, page views, or PPC click-through rates, impacts campaign outcomes in a big way.

To run a precise and effective anomaly detection program, consider:

  • Identifying important metrics: Choose the KPI you want to monitor for deviations. You can follow several performance parameters simultaneously
  • Segmentation: Select the most critical channels or media to track
  • Duration: You're tracking data changes for a specific time frame, such as a month or week


ML-powered forecasting tools can help marketers get the most out of their Google Analytics data. These resources deliver predictive intelligence based on which campaign managers can plan to streamline budgets and boost performance. They're able to predict KPIs and implement strategies to achieve predetermined targets for any period. The tools can also help detect anomalies in critical metrics and trends to form the basis for timely, informed decision-making. In the world of marketing, the ultimate reward for better forecasting and planning is an increase in return on investment!

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