Predictive Analytics: What will happen next?

According to IBM (no date), “Predictive analytics is the use of advanced analytics techniques that leverage historical data to reveal real-time insights and predict future events”. In other words, it is a field that tries to make predictions about the future or an unknown/raw data by using the available information together with techniques such as modelling, machine learning and data mining. According to Salesforce research, the importance and usage of predictive analytics has been increasing and the expected global market share is almost triple to about $10.95 billion in 2022, from $3.49 billion by 2016 (Witherspoon,2019).

The Process of Predictive Analytics in 7 Steps:

Let's take a look at how and why Predictive Analysis is used in the e-commerce industry:

      Improving Interaction with the Customer through Personalization:

By collecting data from each touch point, smart algorithms enable organizations to gain customer insight by understanding customer purchasing history, purchase pattern, preferences, page views, interests and other forms of interaction to create a single buyer persona. So, businesses can make suggestions about the products related to your customers by predicting what their next steps are and based on these behaviours. So, according to SAS (n.d.), “Predictive models help businesses attract, retain and grow their most profitable customers”.

Creating Targeted Campaigns:

In addition to customer insights, predictive algorithms collect and analyse data from various sources, such as demographic information, market insights, response rates, and geographic information. Based on these analyses, marketers can determine which campaigns will be more successful and identify the most effective message / product for a single customer. The McKinsey report also shows us that targeted campaigns can offer up to 5 to 8% of their marketing spend (ROI) and increase sales by 10% or more (Dahlström,2013).

Predictive Search:

Predictive in-site search; It enables users to have a user-friendly shopping experience based on customer history, behaviour and preferences. It can effortlessly guess what your customer is looking for. In this context, since customer experience is one of the most important active assets of e-commerce, it is inevitable that they give priority to predictive search to ensure customer satisfaction and loyalty. 

Reference:

Dahlström, P., 2013. The Demands Of On-Demand Marketing. [online] mckinsey. Available at: <https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-demands-of-on-demand-marketing> [Accessed 27 May 2020].

Ibm.com. n.d. Predictive Analytics. [online] Available at: <https://www.ibm.com/analytics/predictive-analytics> [Accessed 27 May 2020].

Sas.com. n.d. What Is Predictive Analytics & Why Is It Important?. [online] Available at: <https://www.sas.com/en_ie/insights/analytics/predictive-analytics.html> [Accessed 27 May 2020].

Witherspoon, A., 2019. What Is Predictive Analytics?. [online] Salesforce Blog. Available at: <https://www.salesforce.com/blog/2019/07/what-is-predictive-analytics.html> [Accessed 27 May 2020].