How predictive analytics transforms the way marketers work

One of the best ways to foresee the future is by looking into the past. This is why at the foundation of every marketing strategy lies data – and lots of it. Being able to connect with customers via the right channels and through the right media can make or break a marketing campaign.

However, with user behaviors constantly in flux, looking into the future of campaigns has proven to be a greater challenge for marketers.

Consumer behaviors and market landscapes have changed rapidly over the last year, and will continue evolving. Segmentation and optimization for marketers are no longer what they were before. Marketers today need to understand the consumers’ fluidity in the new normal and focus on dynamic segmentation and optimization processes.

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Tracking the shifts in consumer behavior is now an added responsibility for marketers looking to roll out effective marketing campaigns in a volatile world.

By leaning on data, they can stay agile and cope with fluctuations as data can help them make sense of these changes and distinguish short-term ones from those that are here to stay.

This is where predictive analytics comes in.

Determining business success with predictive analytics

Using data, algorithms, and machine learning capabilities, predictive analytics forecasts the probability of future outcomes. This comes extremely handy in predicting consumer behaviors, personalizing customer lifecycle, and optimizing marketing campaigns.

In a period when markets and consumers can change at any given time, optimization requires faster decision-making from marketers. Predictive analytics helps streamline this process by handing them collected data on user perceptions and behaviors during the pandemic. With this information, marketers can immediately begin the process of deciding on the best strategy for their business.

“Predictive analytics allows marketers to [make] decisions on which campaign or advertising source to pause and which to scale – as well as their possible return on investment – by presenting them with accurate data on possible revenues or user activities,” says Andrey Moseykin, head of customer growth at multi-platform analytics and attribution tool, myTracker.

Predictive analytics is not a new concept in the world of marketing as it is commonly used to predict churn rates. By forecasting the percentage of users who are likely to leave an app within a certain period, businesses are able to devise ways to keep them from making that decision: App features can be improved or new features can be introduced to reduce these numbers.

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The power of predictive analytics is especially apparent when it comes to determining a customer’s lifetime value (LTV), which measures how much a customer will spend with a business in their lifetime.

Being able to accurately gauge a customer’s LTV enables businesses to focus on building relationships with customers and marketing channels that would bring them higher value in the long term and devise strategies to increase revenue from low-value sources. It also helps businesses with market segmentation, which is useful in planning personalized marketing campaigns – something consumers have come to expect.

However, traditional methods used to calculate LTV can be extremely complicated and are often unable to account for any sudden shifts in customer behavior. Additionally, attempts to predict LTV in-house can be challenging, due to the amount of resources required to do so.

This is where myTracker wants to make a difference.

Optimizing for smarter, better marketing

By leveraging machine learning algorithms, myTracker’s platform helps marketers make more accurate predictions around a customer’s LTV. These algorithms are able to process data in real time and allow marketers to forecast specific sets of metrics, values, or future actions by analyzing large volumes of data. The platform then delivers predictions based on a multitude of factors, such as an app’s or a website’s traffic volume, user actions, and a customer’s profile, among others.

The moment a first-time user logs into an app, for example, the algorithm can already make predictions around their customer value and how much they are likely to spend within the app, based on historical data that myTracker has collected.

Businesses then get an idea which customers they should invest in retaining or attracting, as well as which advertising channels to focus on. Marketers can segment consumers based on the lifetime value they’d bring and create personalized experiences for each group.

Photo credit: myTracker

For example, myTracker helped video-editing app Efectum grow its audience significantly by enhancing its user acquisition activities with LTV predictions on general advertising sources like Google Ads and TikTok. This enabled Efectum to double its return on marketing spend by focusing on the channels with customers who have a higher LTV.

The algorithm knows no geographical bounds. It can predict which monetization models will be the most effective in different countries, cities, and regions, allowing marketers to optimize and localize campaigns to best suit individual markets and achieve specific goals.

“Our predictive analytics tools allow us to make very accurate predictions, with almost 80% accuracy,” says Moseykin.

In true machine learning fashion, the more data the system collects, the higher the accuracy of the forecasts.

Additionally, myTracker’s platform is easy to use. Companies just need to implement its software development kit into their respective apps, and myTracker will do the rest.

At the end of the day, to achieve the most successful outcomes from marketing and product strategies, Moseykin advises marketers to collect all the data they can possibly collect, track each campaign and advertising source they can possibly track, and understand and measure all the stats they have on hand.

With this information, marketers would then be able to evaluate the return on investments and swiftly assess and even optimize the success of their monetization model.

Altering the way marketers work

Ultimately, predictive analytics allows marketers to make decisions faster – decisions that result in quicker optimizations, which would then result in more money saved.

“If you have more money at your disposal, you can carry out more campaigns, and more optimized campaigns means more revenue,” Moseykin explains. “So you see, this is an obvious chain of events which may eventually lead to great results.”

Predicting LTV is just the beginning for myTracker. New types of predictions and algorithms are in the works, Moseykin shares, and capabilities will soon be elevated to predict even more user actions and more specific events. For example, the company is working on helping marketers predict the amount of daily and monthly active users on their apps, which could be extremely valuable when evaluating the possible impact of new features or other updates within a product.

Moseykin says, “We believe that predictive analytics is a great way for the whole [marketing] industry to improve, and that more and more services and platforms will try to implement different predictive tools to their set of instruments.”

MyTracker is a multi-platform analytics and attribution tool for mobile apps and websites.

Start for free on myTracker or request for a demo on their website.

This content was produced by Tech in Asia Studios, which connects brands with Asia’s tech community. Learn more about partnering with Tech in Asia Studios.