Data analysis forms the backbone of modern marketing ecosystems. Every marketing campaign that you run can be analyzed vis-a-vis user behavior, messaging, demographics, and so on. While the jury is still out on if data is the new oil, organizations fiercely compete on data analytics to gain a competitive edge.
Predictive models of data analysis take the game one step further. Instead of analyzing a campaign after it has ended and gleaning insights from it, predictive models help marketers design campaigns that are likely to perform well. In other words, predictive analytics help you decipher how a user is likely to respond to stimuli, based on historical data. For instance, predictive models can tell you when a user is likely to quit, so you can intervene and, thus, reduce your churn rate.
Predictive Analytics In The Web Domain
Within the wide spectrum of predictive data analytics lies predictive web analytics — practices that are geared toward optimizing the internet experience. Brands such as Netflix and Amazon use predictive analytics to suggest titles and products that you might be interested in. These suggestions are based on your online history and browsing patterns. Dating apps try to set you up with your special someone based on the same principles.
Predictive Web Analytics For Marketing
One of the most obvious uses of predictive models in online marketing is personalization of messages. Editialis, a French publisher, used predictive insights to do exactly that. The company analyzed user behavior and fed the data into a predictive model to maximize engagement rates for its content. By using data to personalize messaging, the publisher was able to increase the ROI for its content marketing efforts.
The same principle can be applied to run tighter email campaigns. You can let machine-learning algorithms personalize subject lines according to demographics, time of the day, and other factors.
One of the areas where the capability of predictive analytics really shines is, perhaps, predictive advertising. Simply put, with the help of advanced statistical models and numerous data points that are available, you can optimize your media buying and ad targeting strategies. Smart bidding in AdWords is a case in point.
Predictive algorithms also open up new possibilities for upselling and cross-selling. For instance, someone who has bought running shoes might also be interested in a pair of earphones. Instead of manually making such connections and populating your ad groups, you let the algorithm do it for you. Since machines are much faster than humans at crunching all the data from various sources, you are more likely to deliver relevant ads to your target audience at just the right time.
By doing so, you not only can improve clickthrough rates but also unlock the potential of micro-moments. These rely on real-time relevance. By adjusting ad targeting and placement in real-time, predictive advertising can help companies make the most of micro-moments, such as life events.
How To Use Predictive Web Analytics For Marketing
From optimizing landing pages to running better ad campaigns, predictive algorithms can be useful to the modern marketer in a lot of ways. But how exactly do you make the most of the advancements in the field of data analysis?
It starts with defining the problem. The reason for deploying a data analytics solution for marketing should be closely tied to the overall goal of your organization. For example, a SaaS company looking to consolidate its market position might focus more on reducing churn rate and increasing customer lifetime value. Similarly, a travel startup that’s focusing on quick growth will do well to run awareness campaigns and optimizing conversion rates.
Your short-term and long-term goals will dictate the kind of data you collect and the analytics solution that you deploy. For instance, in the case of reducing churn rate and increasing customer lifetime value, cluster models of predictive analysis are the best fit.
It Is Starting To Become More Cost-Effective
Machine learning and predictive modeling have been around for some time now. However, with the steady rise in structured data, it is now easier and, hence, cheaper to analyze, store and manage. This has led to a slew of companies offering predictive web analytics solutions right out of the bag.
As the importance of big data continues to grow, more companies are likely to emerge in the field, which means more competition; and competition always drives costs down, which is good news for small and medium-sized businesses.
Advancements in natural language processing should also be pointed out here. With chatbots and personal assistants such as Alexa getting smarter, it might be possible in the future for companies to have “predictive assistants.” Instead of complex dashboards and the learning curve, you could simply throw intricate questions at these assistants to get predictive insights.
In other words, as predictive analytics solutions become simpler, they are bound to become more accessible, which can be a godsend for small players and independent marketers.