Cemre Cemil Ozcan

GDPR in Marketing

What is GDPR?    

      GDPR (General Data Protection Regulatory) is a new digital privacy regulation that came into force on May 25, 2018. It standardizes a wide range of different privacy laws across the European Union, with a single centralized regulation to protect users in all member states. Companies also have to regularly conduct privacy impact assessments, strengthen their consent to use personal data, document how they use personal data, and improve the way they transmit data violations (Ghosh, 2018). Although members of the European Union enforce the law, sanctions include companies outside the European Union.

     Basically GDPR is a regulation to protect personal information such as email, phone number, bank detail, location etc. Companies who do not comply with the regulation on personal data are subject to criminal sanctions of up to 4% of its annual earnings or 20 million euros (whichever is greater) (Bergen, 2018).

What will be the effect of GDPR on Marketing?

GDPR appears to affect marketing in three areas; ``data collection``, ``data storage and processing``, and ``data destruction``.

Data Collection:

``Any organization which attracts people to its website and wants to collect data via a form must communicate clearly to that person what the data is going to be used for`` (What is the GDPR? And What Does it Mean for the Marketing Industry, 2019).

Data Storage and Processing:

Use Limitation: Companies can use the data they collect and store for clear and legitimate purposes. They cannot use this data for purposes other than those they reported while collecting their data.

Security: After the personal data are collected, this data; should be kept in accordance with the security criteria defined in GDPR against theft, improper use, accidental deletion, disclosure, alteration.

Data Destruction:

If an individual requests the company to delete their personal data, the company is obliged to delete this data as soon as possible. If they have shared this data with other institutions, they have to make sure that all data have been deleted from their systems.

Reference:

Bergen, B., 2018. GDPR: What All Marketers Need To Know. [online] Salesforce Blog. Available at: <https://www.salesforce.com/blog/2018/04/gdpr-what-marketers-need-to-know.html> [Accessed 16 June 2020].

Blog.hubspot.com. 2019. What Is The GDPR? And What Does It Mean For The Marketing Industry?. [online] Available at: <https://blog.hubspot.com/marketing/what-is-the-gdpr> [Accessed 16 June 2020].

Ghosh, D., 2018. How GDPR Will Transform Digital Marketing. [online] Harvard Business Review. Available at: <https://hbr.org/2018/05/how-gdpr-will-transform-digital-marketing> [Accessed 16 June 2020].

Whatever is worth doing is worth measuring!

                                   

About Web Analytics

It does include 4 different stages and these are collection, measurement, analysis and reporting of data over the web in order to understand the use of the web and to optimize the web within our conversion goals. Web analytics is just one branch of digital analytics.

Today, when it is difficult to make sense of user behaviour on the web and to attract new visitors/customer to the web page, web analysis and reporting have been gaining a special importance. Nowadays, GoogleAnalytics is free and the most used tool to understand of user behaviour and make web analysis on the web (Thakur, 2017).

Why Web Analytics matters?

The insights and data which are provided by Web analytics help companies to create much more personalized and stronger UX (user experience) for the visitor/user of that specific website.

For key conversion metrics, another crucial element to optimizing the website is understanding the behaviour of visitors.

Also, web analytics show the effectiveness of marketing campaign, so for the future campaigns, companies can create more accurate content for their campaigns (Kejriwal, 2016).

Web Analytics in E-Commerce

It is important to know how online shoppers reach e-commerce site and review what they do while on website, to better understand customers and their needs, and thanks to web analytics, e-commerce companies can analyse several metrics for enhancing the website efficiency and increase the conversion and these are;

• It can be also analyzed users' behavior. Companies can view the every page details visited by customers, for which search queries a user visited the website, and what activities s/he did (sign up, purchase, fill out forms).
• In real time, it can be also viewed which pages users are browsing more and what actions they take instantly. 
• Conversions can be viewed. Conversions are displayed after users who interact with the ad have completed a transaction from a different source and tool. 
• Companies can also analyze data such as e-commerce product performance, sales performance, shopping behavior and average basket amount. 

Reference:

Kejriwal, D., 2016. 6 Benefits Of Web Analytics For Business Growth. [online] Digital Vidya. Available at: <https://www.digitalvidya.com/blog/6-benefits-of-web-analytics-for-business-growth/> [Accessed 9 June 2020].

Thakur, D., 2017. 10 Good Reasons Why You Should Use Google Analytics. [online] Medium. Available at: <https://medium.com/@dineshsem/10-good-reasons-why-you-should-use-google-analytics-699f10194834> [Accessed 9 June 2020].

Click-stream data

Click-streams are routes of the user takes when navigating through a site and it shows the page the user arrived on, the order in which the page was reviewed, how long they stayed and timestamp when they left.

Click streams give companies an understanding of their users` behaviours and habits. When recording click-streams organisations can determine how to arrange/customize their site/page, also it helps to identify weaknesses of website. “Based on such metrics as page requests, visit duration, visit pathway, and visit frequency, users were clustered into browsers, searchers and purchasers” (Hofgesang & Kowalczyk, 2005; Moe, 2003). When we take an example in e-commerce, if it is noticed that user mostly leave the site from the shopping cart page, companies might want to consider improving that page by changing the text colours or arrangement to increase conversions. .

Click-streams are commonly used in e-commerce websites to analyse the pages of shop or commonly visits spends time run and what user puts in their cart among other things; however since its quantitative, it is hard to understand  why users behave in the way they did.

The challenges of click-stream data 

Shortcoming of Log Files: there is a possibility that individual people might not be identified by log file, because computer IP addresses are recorded as a log file, not the user itself. So this can result in wrong assumptions while defining user sessions.

Excessive number of pages: low number of page can help to marketers to forecast or undertand the number of path taken by visitor. But if the number of website pages are increasing like thaudans, it is getting really difficult to measure, in other words it may become infinity.

It should not be forgotten that user privacy is becoming a major concern for internet users. Should it be a necessary to ask for a permission for web providers in order to take or use users` behavioral data?

Reference:

Hofgesang, P. and Kowalczyk, W., 2005. Analysing clickstream data: From anomaly detection to visitor profiling. [online] Available at: <https://www.researchgate.net/publication/228722329_Analysing_clickstream_data_From_anomaly_detection_to_visitor_profiling> [Accessed 4 June 2020].

Moe, W., 2003. Buying, Searching, or Browsing: Differentiating Between Online Shoppers Using In-Store Navigational Clickstream. [online] 13(1-2), pp.29-39. Available at: <https://www.sciencedirect.com/science/article/abs/pii/S1057740803701740> [Accessed 4 June 2020]. 

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].