Staying at least one step ahead of your competitors requires you to stay one step ahead of your customers as well. Instead of following trends and reacting to what your consumers want, a better strategy is to use predictive analysis in marketing.
What is Predictive Analysis in Marketing?
In recent years, more and more companies are seeing the value of predictive analysis modeling. With this tool, you can create a better user experience and arrive at higher conversion rates. All you need are the right data sets and an understanding of how Artificial Intelligence (AI).
This invaluable tool can guide decisions made by your team. Unlike prescriptive analysis, which tells you immediately what you should do in a given situation following an algorithm, predictive analysis provides an additional insight to optimize your creative decisions.
Predictive analysis works on an algorithm, which can run on itself as a form of artificial intelligence. There are different types of models that can run predictive analysis, including response modeling, churn analysis, and affinity analysis.
The goal of this article isn’t to break down the specific algorithms related to predictive analysis. Instead, this article will give you key examples of how predictive analysis improves marketing strategies.
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Predicting Consumer Behavior and Interests
The ability to analyze data in order to predict behavior is important for marketers. In its most basic form, marketers using predictive analysis can interpret the past buying habits of consumers to arrive at underlying patterns. You can then use the analysis to project future buying habits.
Being able to project future habits and behavior can improve your strategy. Marketers can create a priority list of items based on what the predictive algorithms have analyzed. The analysis can also pinpoint which subsets of consumers are also more likely to become repeat customers or even loyalists of the brand.
Predictive analysis can answer important questions. Which customers cost the least to attract? Which of your consumers will give you returns? Which customers buy the highest-margin products? Answering these questions can lead to increased efficiency for the marketing team and higher profitability for the company.
Depending on your company’s strategy, you can use this data to either strengthen your existing base or to focus on creating more innovative campaigns for less successful products.
Choosing the Right Medium for Communication
Predictive analysis can improve marketing in many different ways, depending on how the initial algorithm is designed and depending on the strength of the data.
For example, predictive analysis can help assess whether a particular campaign will have a greater impact on social media versus through a mobile app. The analysis can help you predict whether or not a campaign will fail on your website while succeeding on social media platforms like Facebook or Twitter.
With this analysis, you can better pinpoint existing strengths and weaknesses of your different platforms. Marketers use this analysis to guide the presentation of key brand messages.
Identify Key Factors in High-Quality Brand Experiences
Many predictive analysis models look at connective brand experiences on an individual level, and the many factors that feed into these high-return engagements. There are many different sources of data. Many models use search data – what consumers type on Google – and when certain phrases or engagements peak. Another main source of data is social media engagement.
The analysis can develop a profile of how consumers interact with a specific campaign message, a brand under a company, or a particular communication platform, and this can be used to adjust and optimize campaigns for better engagement.
Predictive analysis empowers marketers to have knowledge of what drives the best outcomes and to know more about behavioral data on an individual level. By knowing a user’s predicted interest in a product or service, marketers can refine and boost their campaigns to become even more effective.