Imagine a robot that sees what type of clothes you search to buy on the internet, the type of food you like, the games you play, among others and then suggests you clothes or food or games that you might like. That’s exactly what predictive analysis does. By analysing historical data and applying advanced statistical algorithms, businesses can forecast future trends, optimise ad placements, and enhance targeting accuracy, leading to more efficient and effective advertising campaigns. The market for predictive analytics software was valued at $ 5.29 billion in 2020 and is forecasted to grow to $ 41.52 billion by 2028, according to Statista.
How can predictive analysis help in creating good marketing campaigns?
With new products and trends coming up everyday, consumer preferences are always changing. In such a scenario, predictive analysis examines data from various sources while also taking into consideration the contextual factors like weather and location in order to predict the consumer sentiments. It also takes data from web pages and social media posts in order to predict emerging trends and give the marketers two step ahead advantages. Furthermore, machine learning can enhance marketers’ ability to make better decisions by discovering subtle patterns in the data from customers. These personalised insights help the marketers in refining the customers into various segments and then target them accordingly which fetches better ROI. This way, the marketers can create personalised campaigns that resonate with the customers and potentially bring in new leads.
Additionally, predictive analytics also helps the marketers in finding new customers while retaining the existing ones. By identifying signs of customer disengagement, predictive analysis allows marketers to address weaknesses like poor customer service or products that are not preferred by many. By analysing data patterns, these tools can also pinpoint which customers are most likely to leave. Once identified, these high-risk customers can be targeted with personalised re-engagement programs designed to improve their experience and reduce the chances of them leaving. For example, if a telecom provider, from data, identifies that its customers are likely to switch to other providers, it will likely provide the customer with fancy offers in an attempt to retain them.
The challenges
Despite its numerous benefits, predictive analysis in advertising does come with challenges. The reliance on vast amounts of data raises concerns about privacy and data security. Ensuring compliance with data protection regulations and maintaining transparency with consumers is crucial for maintaining trust and avoiding potential pitfalls. Another major issue is the quality and availability of data. Predictive models depend on accurate, comprehensive data to produce reliable forecasts. However, if the data is incomplete, outdated, or inaccurate, the predictions can be misleading. Additionally, managing and integrating data from diverse sources can be complex, often requiring substantial effort to ensure data consistency and reliability.