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].

DATA ANALYTICS & E-COMMERCE

In the new world where technology is developing rapidly, data is gaining importance. Data analysis, besides being a method that makes use of many different disciplines, is mostly associated with the science of statistics. 

What is Data Analysis?

Data Analysis is defined as the modeling process that supports useful data finding, conclusion and decision making processes as a result of collecting, extracting and processing raw data.

Why Data Analysis Matters?

Performing data analysis allows businesses to review and optimize their own performance. If it is applied to the business model after performing data analysis, the cost will be greatly reduced.

Data Analysis Benefit

• Thanks to data analysis, business have the ability to make better decisions, so increase productivity in the company.
• It enables the business to see that it reflects positively to customer with correct analysis.

Why data analysis is important for E-Commerce and how it is used?

Big data analytics has been used by E-Commerce companies mainly for focusing on targeted customer group, measuring campaign`s effectiveness, also providing competitive advantage during high season. 

Thanks to data analysis, E-Commerce companies can deeply analyse their customers and products, like what kind of products do they tend to purchase, which products have been sold the most, or those who have purchased this product have purchased these products. And these approaches lead customers to buy new product and spend much more time in website. For instance, “Amazon’s product recommendation engine drives 35% of cumulative company revenue” (Arnold, 2019).

In e-commerce sites, data analysis is used not only for selling, but also for understanding the customer. It is also very important for analysing metrics such as determining the source of negative comments/review or how long the negative situations are resolved. According to study by Salesforce, "72% of customers will share their good experiences with others" (McGinnis, 2019).

References:

Arnold, A., 2019. Here's How Data And Analytics Can Benefit E-Commerce Business Owners. [online] Forbes. Available at: <https://www.forbes.com/sites/andrewarnold/2019/01/04/heres-how-data-and-analytics-can-benefit-e-commerce-business-owners/#534723b81460> [Accessed 21 May 2020].

McGinnis, D., 2019. 40 Customer Service Statistics To Move Your Business Forward. [online] Salesforce Blog. Available at: <https://www.salesforce.com/blog/2013/08/customer-service-stats.html> [Accessed 21 May 2020].

BIG DATA & E-COMMERCE

What is Big Data?

Even though Big Data, which we have begun to hear frequently today, is seen as a word that has emerged with the advancement of technology and usage areas. We constantly support this phenomenon without realizing it, and we also contribute to ensuring continuous data flow to this environment, which we call “Big Data”.

So what is the source that feeds big data? 

The interactions in the constantly used social media accounts, search engines and the traces left behind when searched, the actions made with the bank accounts, blogs, mails, sensors and all the interactions of the individual users with the internet. 

Why is Big Data Important? 

According to SAS (2020), big data focuses on what to do with it, rather than how much information it has. Data can be taken from any source and analysed to find answers that enable;

• Cost reduction
• Time reduction  
• New product development and optimized offerings
• Smart decision making

The Usage of Big Data on E-Commerce

Asling (2017) stated that ``Big data is proving to be a game-changer when it comes to ecommerce``. Here are some examples how big data can be used and how it can benefit e-commerce companies in order to guide e-commerce companies.

Help to customize easily; it is possible to collect and analyse user information and to offer a special shopping experience to the user with big data analysis.

Provides dynamic and variable pricing; if there are other companies selling the products sold on the website, a dynamic and variable price policy may be needed. Applying such price strategies in situations where competition is high allows businesses to catch the consumers looking for the best price on the internet.

Improves customer service; it is very important that CRM representative can be directly involved in the subject by seeing the previous purchases of a consumer who contacted for any reason. In addition, having the user's personal information may change the support team's approach to this person.

References

Asling, D., 2017. 6 Ways To Use Big Data In Ecommerce - Dataconomy. [online] Dataconomy. Available at: <https://dataconomy.com/2017/07/6-ways-use-big-data-ecommerce/> [Accessed 16 May 2020].

Sas.com. (2020). Big Data: What it is and why it matters. [online] Available at: https://www.sas.com/en_us/insights/big-data/what-is-big-data.html [Accessed 16 May 2020].