GOZDE BALIOGLU- 10522500

https://gozdebalioglu.blogspot.com/

https://gozdebalioglu.blogspot.com/2020/01/what-is-big-data.html

https://gozdebalioglu.blogspot.com/2020/02/3vs-of-big-data.html

https://gozdebalioglu.blogspot.com/2020/02/importance-of-value-for-big-data.html

https://gozdebalioglu.blogspot.com/2020/02/benefits-and-challenges-of-using.html

https://gozdebalioglu.blogspot.com/2020/02/artificial-intelligence-usage-in.html

https://gozdebalioglu.blogspot.com/2020/02/analysis-of-google-analytics-for-gozdes.html

Analysis of Google Analytics for Gozde’s Blog

Since the creation of Gozde’s Blog (20th of January,2020), Google Analytics code has been placed. In this report, the statistics are presented over the period from the 20th of January to the 24th of February.

According to Audience Report has shown above, the number of website visitors is 67 while 38.5% of them are returning visitors. It shows that the average session duration is 1 minute and 37 seconds, with a 63% bounce rate.

When the number of visitors analyzed into country-based detail, it is an outstanding fact that 60 visitors out 67 were connected from Ireland. The rest of the visitor’s countries are diversified, as Bosnia & Herzegovina, France, India, Turkey, Austria and the UK.

When Audience Report drilled down in device category, the number of desktops and mobile users are the same by 33 and tablet user is one person.

When it is analyzed the browser detail, the majority came through Chrome by 65.67% while the minority connected via Edge by 1.49%.

When the Acquisition Report has been investigated, 51.5% of the visitors came directly via the URL while 29.7% of the traffic came through social channels, and 20.1% of others came by referrals. It can be stated that Organic Search has not been used since the on-page optimization has not been implemented.

Behaviour Report located above indicates that the number of page view is 390 while 310 of them are unique page views.

According to Page Views, the most visited page is the home page of Gozde’s Blog. Furthermore, the most viewed Blog Post is “What is Big Data”.

Artificial Intelligence Usage in Marketing

Artificial intelligence has been using in marketing by leveraging the customer data and evaluating it to predict the next moves of customers. AI-based solutions help marketers to provide better customer journeys since it enables them to draw a better picture of their target audiences; as such, insights have gotten by AI help marketers to increase their not only campaign performance but also return on investment by predicting customer’s lifetime value. Therefore, it is not a surprising fact that 72 % of the marketers considered AI as a business advantage as a result of the study implemented by PwC.

There are several usages of AI in marketing; in this post, I am going to share three beneficial practices and how it helps businesses in marketing strategies and practices.

Product Recommendations

AI enables businesses to cluster and interpret the customer data and it pairs the data with customers’ likes and dislikes.  Thus, companies are able to recommend not only highly relevant but also personalised products to their customers. As a great example, Spotify is known as a pioneer of product recommendation in the music industry. To secure its position, Spotify declares in its registration statement that they will continue to invest in artificial intelligence and machine learning capabilities to deepen the personalised experience.

Chatbots

Chatbots help both businesses and customers in understanding complex requests, personalised responses and improving the interactions (Hubspot.com, 2020.). AI programs make the simulation of conversations with natural language processing.

Speech Recognition

Nowadays, speech recognition is an accepted phenomenon with the launch of Alexa, Siri and Google Assistant. However, it might be a goldmine for marketers since voice shopping is an increasing trend. As a matter of fact, according to Invesp’s research (2019) Voice Shopping will reach to $40 Billion in the U.S. by 2022.

References

Spotify Technology S.A.(2018)  Form F-1. [online] Available at: https://www.sec.gov/Archives/edgar/data/1639920/000119312518063434/d494294df1.htm#rom494294_4/ [Accessed 20 Feb. 2020].

Pwcartificialintelligence.com. (2017). The Future of AI is Here - PwC. [online] Available at: http://pwcartificialintelligence.com/ [Accessed 20 Feb. 2020].

Shukairy, A. (2019). The State of Voice Shopping – Statistics and Trends [Infographic]. [online] The Invesp Blog: Conversion Rate Optimization Blog. Available at: https://www.invespcro.com/blog/voice-shopping/ [Accessed 21 Feb. 2020].

Hubspot.com. (2020). Why Chatbots Are the Future of Marketing: The Battle of the Bots. [online] Available at: https://www.hubspot.com/stories/chatbot-marketing-future [Accessed 21 Feb. 2020].

Benefits and Challenges of Using Customer Data for Marketing

Since data gain more importance for every field day by day, it brings the debate over database marketing and the benefits & challenges of customer data usage in marketing to create customer profiles.

Benefits

Database marketing is used by gathering and analyzing all the data types mentioned above together, and it helps marketers to perform well-targeted marketing campaigns with a better understanding of who their customers are. According to “The New Marketing Reality” report by Econsultancy & IBM, 33% of marketers think having the right technologies for data collection and analysis is the most useful in understanding customers. By collecting consumer data, businesses not only can find out the traits shared common across people who buy a similar type of products but also investigate their buying journeys. In other words, businesses can create models for the buyer’s journey based on this data for similar prospects (Minkara, 2014). As such, artificial intelligence can be used for matching products and customers to do product recommendations as one of the most significant advantages of data-driven marketing.

Companies can adopt a customer-centric approach to their customers which is significant for a healthy customer relationship by putting them into the centre and treated as individuals. After understanding the motivation of buyers, marketers can be positioned properly in presenting the right content to the right audience at the right time. Furthermore, real-time data and multiple channels can be used to trigger the customer’s impulses. Thus, the potential for sales and personalized buyer conversation can be increased by using the data on customers’ history with the company.

Challenges

When it comes to drawbacks for using customer data, it should be mentioned that both customer data platforms and CRM systems need investment and setting it up can be costly for small-scale businesses. Some of them prefer to work with third-party providers; however, it is also an ongoing cost to willing to pay.

As another challenge, the data’s quality should be distinguished. According to Discovery.org, sales and marketing departments lose 550 hours and as much as $32,000 per sales representative due to bad quality of data. 

Today it is essential to use customer data to perform better; however, it should be appropriately done and coordinated with data protection laws. Businesses operating in Europe should address GDPR regulations by getting consents from consumers to use their data and being transparent on how it is being used.

References

Heller, C. (2019). How to Keep Bad Web Form-Fill Data out of Your CRM. [online] DiscoverOrg. Available at: https://discoverorg.com/blog/bad-crm-data-quality-web-form/ [Accessed 19 Feb. 2020].

Ibm.com. (n.d.). The New Marketing Reality. [online] Available at: https://www.ibm.com/downloads/cas/X9DKEKOD [Accessed 19 Feb. 2020].

Minkara, O. (2014). Using Customer Data for Marketing: The Good, Bad & Ugly. [online] Aberdeen. Available at: https://www.aberdeen.com/cmo-essentials/good-bad-ugly-using-customer-data-for-marketing/ [Accessed 19 Feb. 2020].

Importance of Value for Big Data

As a well-known fact that the bulk of data have no meaning if it is not converted into something useful. Just collecting a large amount of data from various sources with an outstanding speed level is meaningless if we cannot interpret it. Hence, value stands for extracting the vital information from the vast data set. With the help of advanced data analytics, useful insights can be derived from the collected data (Sheriff, 2019). As such, value is the most crucial element of 5V’s of big data in terms of the decision-making process since it gives long term enterprise value.

The value gathered from big data helps companies to improve their marketing strategies. From the data gathered, businesses are able to build different customer profiles for their segmentations. Today, almost everybody is exposed to a significant number of marketing contents even if it is relevant, engaging, personalised or not. Thus, most of these contents are tuned out and deleted. To avoid this situation, businesses should use the data they collected to build different customer profiles for their segmentation with customization of product and market offerings.

According to Washington State University businesses are turning to omnichannel marketing to create seamless and connected customer interactions across all devices and platforms. With the help of advanced analytics, marketers are able to identify who is accessing to the content on which devices, what type of content they are engaging, their state in the buying cycle, the products they are interested, and their location information give marketers the advantage of the ability to create highly personalised contents. Hence, it leads to better outcomes in engagement, conversion and brand loyalty.

Furthermore, companies can benefit from their big data in terms of determination of new opportunities by using their historical data as a predictor of the future development and they can focus on predictive analytics to expand their target markets. Datasets in which each data point may incorporate less information, but when taken in aggregate may provide much more (Martens and Provots, 2014). This concept is essential for predictive analyses since it transforms datasets into future insights. Companies like Netflix and Procter & Gamble are now able to leverage this data to drive product development. They use the data to build predictive models for new products by curating the attributes of past products and the relationship between those attributes and their commercial success (RapidMiner, 2020). As an outstanding example, Amazon designed a shipping system based on the predictive studies which predict what buyers are going to buy before they purchase it and ship the products right into their doors before people click to purchase. Predictive analytics has captured the support of wide range of organisations, with a global market projected to reach approximately $10.95 billion by 2022, growing at a compound annual growth rate (CAGR) of around 21 per cent between 2016 and 2022, according to a 2017 report issued by Zion Market Research (Edwards, 2019).

References

RapidMiner. (2020). Extract Big Value from Big Data | RapidMiner. [online] Available at: https://rapidminer.com/glossary/big-data/ [Accessed 16 Feb. 2020].

Edwards, J. (2019). Predictive analytics: Transforming data into future insights. [online] CIO. Available at: https://www.cio.com/article/3273114/what-is-predictive-analytics-transforming-data-into-future-insights.html [Accessed 16 Feb. 2020].

Martens, D. and Provots, F. (2014). Predictive Modeling With Big Data: Is Bigger Really Better? | Big Data. [online] Mary Ann Liebert, Inc., publishers. Available at: https://www.liebertpub.com/doi/full/10.1089/big.2013.0037 [Accessed 16 Feb. 2020].

WSU Online MBA. (2020). The Value of Data in Marketing and Business. [online] Available at: https://onlinemba.wsu.edu/blog/the-value-of-data-in-marketing-and-business/ [Accessed 16 Feb. 2020].

Sheriff, S. (2019). Understanding the 5Vs of Big Data - Acuvate. [online] Acuvate. Available at: https://acuvate.com/blog/understanding-the-5vs-of-big-data/ [Accessed 16 Feb. 

3V's of Big Data

What are the 3V's of Big Data?

Doug Laney, a Gartner analyst, introduced the 3V’s (volume, variety, and velocity) of Big data under the name of 3D Data Management.

Volume: The size of data has been collected is growing day by day in line with the increased usage rate of social media, e-commerce, IoT, AI and more. Therefore, those drive data complexity through new sources and forms of data. As supporting evidence of how the volume of data has been expanded day by day, Jacobson (2013) states that “90% of the data in the world today has been created in the last two years”. It is an outstanding estimation that, according to Desjardins (2019) “By 2025, it’s estimated that 463 exabytes of data will be created each day globally”. 

Variety: It refers to all structured, semi-structured and unstructured data. Numeric, structured data is not the only reliable type of data source as it was in the past. With the rise of big data, the data generated from emails, photos, videos, the audio started to gain importance as new applications are introduced. According to Forbes, “ There are 2.5 quintillion bytes of data created each day”. Furthermore, PCMag illustrates as an outstanding fact that 90% of the big data collected is coming from unstructured data gathered from various sources like social media, apps, IoT, customer purchase history, even customer service call logs.

Velocity: It is the speed of the data generated. As a consequence of the growth of the big data with large volume and variety in short time periods, it should be generated at a dramatic level of velocity.  The data comes into the server in real-time and it should be in a continuum to prevent the delays especially in case of real-time and near-time processing while real-time processing requires a continual input, constant processing, and steady output of data and near-time processing is when speed is important, but processing time in minutes is acceptable instead of seconds (Wilson, 2015).

 

References

Jacobson, R. (2013). 2.5 quintillion bytes of data created every day. How does CPG & Retail manage it? - IBM Consumer Products Industry Blog. [online] IBM Consumer Products Industry Blog. Available at: https://www.ibm.com/blogs/insights-on-business/consumer-products/2-5-quintillion-bytes-of-data-created-every-day-how-does-cpg-retail-manage-it/ [Accessed 31 Jan. 2020].

Griffith, E. (2018). 90 Percent of the Big Data We Generate Is an Unstructured Mess. [online] PCMag UK. Available at: https://uk.pcmag.com/news-analysis/118459/90-percent-of-the-big-data-we-generate-is-an-unstructured-mess [Accessed 4 Feb. 2020].

Laney, D. (2001). Application Delivery Strategies. [online] Blogs.gartner.com. Available at: https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf [Accessed 23 Jan. 2020].

Marr, B. (2018). How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read. [online] Forbes.com. Available at: https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#74d2d79660ba [Accessed 4 Feb. 2020].

Desjardins, J. (2019). How Much Data is Generated Each Day?. [online] Visual Capitalist. Available at: https://www.visualcapitalist.com/how-much-data-is-generated-each-day/ [Accessed 31 Jan. 2020].

Wilson, C. (2015). The Difference Between Real Time, Near-Real Time, and Batch Processing in Big Data. [online] Syncsort Blog. Available at: https://blog.syncsort.com/2015/11/big-data/the-difference-between-real-time-near-real-time-and-batch-processing-in-big-data/ [Accessed 4 Feb. 2020].