As a mobile app marketer, you must have known about predictive analytics and the role they play in marketing apps effectively. Marketers call this predictive marketing which is basically about making a precise prediction about the audience and how your app can effectively be marketed for them through the use of analytics.
While predictive marketing continues to be popular for its effectiveness, it is important to know to utilise this approach for better marketing results. Before we explain the ways to use predictive marketing let us briefly look at its definition.
What is predictive marketing?
Predictive Marketing is data-centric marketing practice that allows you to extract relevant data about your audience and utilise this data to detect user behaviour patterns and appropriate marketing activities that can be effective. Utilising data-driven insights about the users predictive marketing helps you to target your audience with apt marketing decisions.
Here are the key benefits of predictive marketing at a glance.
- Predictive marketing helps marketers to focus on user demands with optimum precision and accuracy.
- Predictive marketing also helps to leverage machine-learning and artificial intelligence technologies to target your audience with user-centric contents and recommendations.
- With data-driven insights playing the central role, your marketing has a lesser scope to go in the wrong direction.
The principal ways predictive marketing can benefit
There is a multitude of ways predictive marketing based on data-driven analytics can benefit mobile app marketing. But grossly we can take a few major areas where predictive marketing can play a major role.
Customer behaviour modelling
For major mobile commerce or ecommerce marketers predicting customer is of paramount importance to maintain growth. Even smaller companies now can also access intelligent tools to predict customer behaviour. There can be numerous predictive customer behaviour models but we can at least refer to a few simple types of models that are effective for app marketers. Let's have a look at them.
- Customer segments: By using predictive data analytics we can easily segment customers into interest groups or as per demographics or age groups.
- Customer behaviour predictions: The data analytics based algorithms can help us with effective customer behaviour predictions such as the likelihood of subscription, engagement and chances of conversion or purchase.
- Customer-centric recommendations: With predictive marketing tools the marketers can recommend products that are more likely to gain traction or engagement.
- Regression analysis: Predictive tools also helps marketers to conduct regression analysis which is basically about finding the correlation between product purchases with different customer variables.
Lead generation and conversion
Most marketers agree that predictive analytics give them a solid basis on generating marketing leads early that build the foundation for continuous growth. Let us have a look at 3 use cases of predictive marketing.
- Predictive Lead Generation: Based on the likelihood of responses and actions identifying prospects and leads.
- Identifying Appropriate Models: Based on the attributes of similar customers identifying appropriate models.
- Customer Segmentation Based on Leads: Based on specific customer leads segmenting customers into specific groups.
Identifying marketable products and services
By using data visualisation tools customer behaviour can be predicted with more precision and accuracy and appropriate course of actions can be laid out. The detailed visuals of customer data converging demographic categories and buying behaviour, marketable products and services can be identified. In the context of mobile app marketing, apps likely to gain traction and engagement can be identified.
Targeting customers contextually
Targeting customers at the right context or to be precise with the right content at the right time and at the situation ensures optimum result from the marketing perspective. Predictive marketing can play a big role in targeting the right customer at the right time and in the right situation. For app marketers targeting users contextually can boost the prospect of user acquisition and business conversion.
Some effective predictive analytics models that play an effective role in this respect include affinity analysis, response modelling, and churn analysis. These models can help to target users in a timely and effective manner to ensure a better result.
Most important advantages of predictive app marketing
Predictive app marketing fits into the scheme of things for mobile app marketers because of ensuring prescribing more precision-driven steps. There are several advantages of predictive marketing for mobile apps. Let's explain a few of them here.
- Acquire Users
Predictive tools allow you to get hold of the most valuable user data and accordingly target them with appropriate offerings. By knowing where your users are coming from and what are the key channels that are providing you with the most valuable users, you can optimise your marketing for these users and channels.
- User Engagement Timing
After knowing the channels from which you get most of your users, you can identify the time when most users are available for engagement. Predictive app marketing tools can deliver you relevant insights about when the users are likely to remain available based on their availability and past behaviour.
- Identifying Effective Channels
Thanks to predictive tools marketers can also identify and the most effective marketing channels that can work best for the app. This actually boosts up your conversion rate.
- Cross-sell and Upsell
Every marketer wants to know about the next purchase users are going to make and predictive app marketing can really boost a marketers scope through insight on the types of purchases users are likely to make. Such insights help app marketers targeting only users who are looking for similar apps.
Predictive marketing in the context of mobile app marketing offers an array of advantages, most notably when it is important to target users specifically based on various data-driven insights. As the app marketing is increasingly getting competitive, the role of predictive marketing looks more promising than ever before.
Atman Rathod, Co-founder, CMARIX TechnoLabs
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