Adrijana Vujadin, 10533108 – Analysis of the performance of (ADI)GIT_blog

(ADI)GIT_blog has been authored by Adrijana Vujadin, a student at DBS who is writing about very attractive Big data topics in the Digital world. The blog has started to publish articles on January 26 and the last post has been on the Blogger.com platform on February 21 and that is a period that we are going to analyse. 

Overview of 5 blog posts with titles, comments and page views:

The observation period has highlights and summary points include:

Number of Users: 164
Sessions: 472
Pages / Sessions: 1.99
Pageviews: 938
Bounce rate: 59.96%
Mobile users: 73.8%
Desktop users: 26.2%

Our visitors are coming mostly from Ireland (77) and Bosnia and Herzegovina (57), and they are between 18-34 years old. Among them, males are 54.15% and female 45.85%. Visitors have interests in sports, computers, food and drinks, cooking and travelling. 

The relationship between the new visitor (70.39%) and returning visitor (29.61%), but returning visitors are making 90% of conversions.

Top channels help us to know how our visits are acquired:

Source Medium report enables completely ABC analysis to see where are our users acquired, how they behave and do they make conversions:

For the last blog post, Adrijana added a certain goal to check do visitors spend more than 30 seconds on her blog. So, 78.57% of total 42 conversions came from Blogger.com platform and these users have a 50.22% Bounce rate.

To understand better visitors we checked User flow graph to see the various touchpoints of their visits: 

As we can see the landing page is receiving a lower level of traffic than other pages. That is a common case for websites, especially when they have a Blog (Patel, 2019). 

Recommendation for (ADI)GIT_blog:

This Blog has a huge space for improvement in terms of: 

• Decreasing the bounce rate under 55% (now is 59.96% - higher than average ), that means a call to actions, checking technical consideration, content optimisation and engagement with readers, should be included (Yoast, 2017).
• Increasing the number of internal links 
• Optimising better content that will lead to more organic searches (Labunski, 2018). 
• Improving the experience for returning visitors who usually will become our subscribers, readers, followers and potential customers/leads (Seleh, 2016). 

References:

Labunski, B. (2018) 'Google Analytics guide - 10 actionable tips that all boost your traffic and ranking'. Available at: https://www.semrush.com/blog/google-analytics-guide-10-actionable-tips-thatll-boost-your-traffic-and-ranking/

Patel, N. (2019) 'How to Calculate Your Landing Page Conversion Rate (And Increase It)'. Available at: https://www.crazyegg.com/blog/landing-page-conversion-rate/ (Accessed: 26 February 2020).

Saleh, K. (2016) ‘7 essential Google Analytics reports every marketer must know’ - Search Engine Land. Available at: https://searchengineland.com/7-essential-google-analytics-reports-every-marketer-must-know-250412 (Accessed: 26 February 2020).

Yoast (2017) ‘Understanding bounce rate in Google Analytics’. Available at: https://yoast.com/understanding-bounce-rate-google-analytics/ (Accessed: 26 February 2020).

Will AI take over marketing?

According to Adobe’s latest Digital Intelligence Briefing (2018), top leading companies are more than twice as likely to be using AI for marketing (28% vs. 12%). This trend will be followed by medium and small-sized businesses that want to fight for their customers on the market share. Marketers could see the way that technology changed marketing and has designed new challenges, new tasks and new job functions, and that will be the case with new AI revolution on today’s marketing (Schaub, 2019).

Hubspot (2020) predicts trends how AI will reshape marketing in 2020:

1. Content marketers will have to adopt artificial intelligence

Even though we thought that AI cannot write books and journals it can have a significant impact on content. Marketers are already using AI in creating content for Social Media, online ads and email campaigns and they will use it in terms of better ranking and other SEO opportunities. As simple as that, companies that are not using AI in their content will not experience the growth (Roetzer (2019). 

2. Higher personalisation for consumers

We already have examples of personalised emails or recommended content created on user experiences but AI is going to bring hyper-personalised solutions as consumer’s expectations are growing rapidly. Personalised Development Study (2019) reported that 93% of businesses with an advanced personalization strategy experienced revenue growth and we can notice how significant focus will be on this.

3. AI and the ability to automate time-consuming SEO tasks

Marketers will benefit from AI in time-consuming management where an emerging technology will ensure the automated process at self-connecting website pages, self-optimising mobile pages and self-controlling duplicated content.

4. AI will fit into the daily lives of marketers in a more natural way

Roetzer (2019) highlights that the best AI-driven marketing tools will mature by interacting with marketers in a way that feels more human. In most cases, AI is closely described as a challenge that marketers are facing and a new focus has to be moved to positive influence and techniques that simplify marketer’s life.

Source: https://www.singlegrain.com/

To sum up all above, all predictions for AI in marketing look like beneficial for marketers’ busy day, ensuring closer relationships between companies and consumers and there is no doubt that AI continues in reshaping the marketing in 2020.

References:

Conic, H. (2017) 'The Past, Present and Future of AI in Marketing'. Available at: https://www.ama.org/marketing-news/the-past-present-and-future-of-ai-in-marketing/ (Accessed: 20 February 2020).

Adobe (2018) 'Digital Intelligence Briefing 2018 Digital Trends' [Online]. Available at: https://www.adobe.com/content/dam/acom/uk/modal-offers/pdfs/Econsultancy-2018-Digital-Trends.pdf (Accessed: 20 February 2020).

Kaput, M. (2020) '4 Marketing AI Predictions for 2020'. Available at: https://blog.hubspot.com/marketing/ai-predictions (Accessed: 20 February 2020).

Monetate (2019) 'Personalization Development Study' [Online]. Available at: https://info.monetate.com/rs/092-TQN-434/images/2019_Personalization_Development_Study_US.pdf (Accessed: 20 February 2020).

Roetzer, P. (2019) 'How to Create Smarter Content Strategies with AI'. Available at: https://www.marketingaiinstitute.com/blog/vennli-spotlight (Accessed: 20 February 2020).

Schaub, K. (2019) '4 Ways AI Will Transform Marketing'. Available at: https://blogs.idc.com/2019/10/15/4-ways-ai-will-transform-marketing/ (Accessed: 20 February 2020).

Benefits and Challenges of using Customer Data for Marketing

Decker (2020) defines Customer data as the information that customers provide while they are interacting with a company at offline or online avenues. Because of that, every increase in the level of relationship closeness between customers and companies make better results in terms of marketing strategy that helps every company to increase sales and profitability and simplify operations at the whole (Salesforce, 2020).

Martin N. (2018) for Forbes reported significant challenges in data-driven Marketing such as:

Firstly, having the overall strategy is crucial for a marketing department; there is a discovery from a survey that Campaign Monitor conducted where 81 % of marketers consider that having a sustainable data-driven strategy is highly problematic.

Secondly, it can be tough to find and recognise the right data and KPIs. Collecting data without analysing and finding a purpose is just a waste of time and money, and there is a lack of appropriate information as well. Think with Google (2017) highlighted, 26% of marketers said they didn't have the right analytics talent or enough of it where we can see how much data is not utilised conveniently. Besides that, other roadblocks can be connected with the lack of relevant platforms and tools as well as some kind of IT technologies. Lyer (2018) for Martech Advisor called the IT and marketers collaboration as ‘the least appealing option’ because of all challenges that they are facing in the real world.

Source: www.medium.com

By overcoming obstacles, Marketing has various benefits from the wide range of customer data. According to AMA Boston (2019), by creating data-driven strategies that fulfil customer needs and experience customer journeys, the data-driven companies are 23 times more likely to attain customers and 19 times more likely to be profitable. As a result, marketers will have precise customer segmentation based on geographic, demographic, or psychographic characteristics (Deshpande, 2019), as well as targeted marketing campaigns and improved customer relationships that customers are rewarding with their loyalty (Kokemuller, no date).

References:




Campaign Monitor ’Data-Driven Marketing Objectives & Challenges of the B2C Marketer’(no date). Available at: https://www.campaignmonitor.com/resources/infographics/the-eye-opening-truth-about-data-driven-marketing/ (Accessed: 15 February 2020).

Canstello, D. (2019) ’How to Create an Effective Data-Driven Marketing Strategy’. Available at: https://amaboston.org/blog/how-to-create-an-effective-data-driven-marketing-strategy/ (Accessed: 15 February 2020).

Carey, C. (2017) ‘Dealing With Data: Today's Marketing Analytics Challenges and Opportunities’. Available at: https://www.thinkwithgoogle.com/intl/en-apac/tools-resources/data-measurement/marketing-analytics-data-challenges-opportunities/ (Accessed: 15 February 2020).

Decker, A. (2020) ‘What’s a Customer Data Platform? ‘ Available at: https://blog.hubspot.com/service/customer-data-platform-guide (Accessed: 14 February 2020).

Deshpande, I. (2019) ‘What Is Customer Data? Definition, Types, Collection, Validation and Analysis’. Available at: https://www.martechadvisor.com/articles/data-management/customer-data-definition-types-collection-validation-analysis-martech101/#section-v (Accessed: 15 February 2020).

Kokemuller, N. ‘The Advantages & Disadvantages of Database Marketing’ (no date). Available at: https://smallbusiness.chron.com/advantages-disadvantages-database-marketing-22810.html (Accessed: 15 February 2020).

Lyer, C. (2018) ‘3 Challenges with Big Data for Marketers’. Available at: https://www.martechadvisor.com/articles/data-management/3-challenges-with-big-data-for-marketers/ (Accessed: 15 February 2020).

Martin, N. (2018) ‘How To Overcome The Challenges of Data-Driven Digital Marketing’. Available at: https://www.forbes.com/sites/nicolemartin1/2018/12/18/how-to-overcome-the-challenges-of-data-driven-digital-marketing/#397077424767 (Accessed: 15 February 2020).

Salesforce (2020). ‘What is CRM?’ Available at: https://www.salesforce.com/eu/learning-centre/crm/what-is-crm/ (Accessed: 14 February 2020).

Value in Big Data for Marketing

An inevitable partnership as it was called in The Guardian (2013), big data has a tremendous opportunity to change the way of marketing. So, all companies, as well as the marketing department, are facing a large amount of data every day. Moreover, businesses have to considerate only data which has a specific impact on companies processes, products or their customers.

According to Ghosh (2015), marketers are currently afraid of significant growth of data by 40% yearly, which marketers have to analyse due to improving features of the products (services) or customers’ higher satisfaction. All this data makes marketers life and work more challenging than ever. However, they know that without statistics and analytics, their companies are blind deer on the highway (Pearlman, 2019).

As SAS.com discovers, we have three fundamental data types that are beneficial for marketing:

1.  Customer: Web analytic, Social Meda, surveys, previous marketing research can bring a lot of different data, attitudes, desires, needs and customer behaviour that can help in the marketing decision process.

2.  Operational: This type is about the relationship between the marketing budget, management, company's resources and operations on the one side and quality of their measurements on the other side.

3.  Financial: This category is taking into consideration sales, revenue, profits and other objective data types that measure the financial health of the organisation.

Having all this information above help the marketing department in the implementation of customer insights and feedback. We have various examples where gaining data for marketing purpose lead to creating well-targeted marketing campaigns. For instance, Parick Morrissey from DataSift highlights the ability to get 400 pieces of data such as location, gender, sentiment, content from only 140 character tweet (Backaitis, 2013). When we have this kind of information about our customers, we can target them and get more needed data as well as send them product or services that they are looking for. We have witnessed an everyday situation when we are searching for some products, and then these products start to follow us wherever we go, or whichever website we visit they are reminding us of them.

Source:http://jdmeier.com/

How powerful is having valuable information for the corporation we can see from leading companies as Tesco PLC. Tesco PLC has recognised the marketing power of Big Data, where it collected data from its customer loyalty cards with 17 million customers (representing approximately 40% of UK households). Dunnhumbi, a market research firm, has processed all this data and formed the basis for Tesco's rapid rise in the retail sector in Ireland and the UK (DigitalStrategy, 2015).

Considering all this above, we can notice the importance of getting qualitative and valuable data in the marketing process and how it can help us in making beneficial decisions for our business. I would like to end this topic with the saying:

“Quality is never an accident; it is always the result of high intention, sincere effort, intelligent direction and skilful execution; it represents the wise choice of many alternatives.” – William A. Foster

References:

Backaitis, V. (2013) 'Hey CMO! Hey CIO! Work Together or Lose Everything', CMSWire.com. Available at: https://www.cmswire.com/cms/information-management/hey-cmo-hey-cio-work-together-or-lose-everything-023404.php  (Accessed: 10 February 2020).

Digital Strategy (2015) 'Big Data For Marketers – How Your Decision Making Process Is Changing'. Available at: https://digitalstrategy.ie/big-data-for-marketers/?fbclid=IwAR2Ihv3XbASGiSemqz57Vw1lne4UAR32AKRbtiW8C449o3m0FwtWpjlUfj8  (Accessed: 11 February 2020).

Ghosh, M (2015) 'Big data in marketing analytics - Analytics Magazine'. Available at: http://analytics-magazine.org/big-data-in-marketing-analytics/ (Accessed: 9 February 2020).

McKone, D.& Lewis, A. (2016) 'To Get More Value from Your Data, Sell It'. Available at: https://hbr.org/2016/10/to-get-more-value-from-your-data-sell-it  (Accessed: 8 February 2020).

Pearlman, S. (2019) 'Big Data in Marketing 101 & Why it's important'. Available at: https://www.talend.com/resources/big-data-marketing/ (Accessed: 9 February 2020).

SAS.com 'Big Data, Bigger Marketing' (no date). Available at: https://www.sas.com/en_us/insights/big-data/big-data-marketing.html (Accessed: 9 February 2020).

The Guardian (2013) 'Big data and marketing: an inevitable partnership'. Available at: https://www.theguardian.com/technology/2013/oct/16/big-data-and-marketing-an-inevitable-partnership  (Accessed: 11 February 2020).

3Vs - volume, velocity, variety

The big data is a very comprehensive terminology and this blog post will explain the main three concepts which are volume, velocity and variety.

As Forbes (2012) states:

1. Volume indicates the essential characteristic of the big data trend, which is a massive amount of data (Gewirtz, 2018). To have a clearer idea of how massive is volume - we can think of 2 billion users on Facebook, 1 billion on YouTube and 700 million users on Instagram who contribute to billions of images, posts, and videos (Whishworks 2017). Many companies are in possession of a huge amount of data but are struggling to find ways to process it.

2. Velocity indicates the speed at which new data arrives; thanks to social networks and web portals, half-an-hour news is already slightly outdated, and content needs to be continuously updated. Another challenge, besides the speed at which data arrives, is the speed at which decisions are made after the ingress of the data. Some of this data needs to be processed right away and can’t tolerate the time it takes to store it. Industry calls this the “streaming data”. Some of the technologies used to handle this data are IBM’s InfoSphere Streams, Yahoo’s product S4 and Twitter’s product Storm (Dumbill, 2012).

3. By increasing the velocity and volume of big data, variety experiencing significant growth and that means a huge amount of heterogeneous data (Jain, 2016). The power of data is in its variety but processing data from the raw state to the state where data is ready to be consumed by the application often causes some loss. Processing tries to structure all the data but according to Muse (2017), only 20 % of data is structured and everything else has remained semi-structured or unstructured at all.

The real information is worth a fortune, but getting through the data forest has become increasingly difficult as never before. They are larger today than they were yesterday, and tomorrow they will be bigger than they are today.

Source: www.researchgate.net

References:

Dumbill, E. (2012) 'Volume, Velocity, Variety: What You Need to Know About Big Data'. Available at: https://www.forbes.com/sites/oreillymedia/2012/01/19/volume-velocity-variety-what-you-need-to-know-about-big-data/#1aaceaf1b6d2 (Accessed: 1 February 2020).

Jain, A. (2016) The 5 V's of big data - Watson Health Perspectives, Watson Health Perspectives. Available at: https://www.ibm.com/blogs/watson-health/the-5-vs-of-big-data/ (Accessed: 1 February 2020).

Muse, D. (2017) 'What is Structured Data' - Datamation, Datamation.com. Available at: https://www.datamation.com/big-data/structured-data.html (Accessed: 1 February 2020).

Whishworks (2017) 'Understanding the 3 Vs of Big Data - Volume, Velocity and Variety '. Available at: https://www.whishworks.com/blog/big-data/understanding-the-3-vs-of-big-data-volume-velocity-and-variety (Accessed: 1 February 2020).

Gewirtz, D. (2018) 'Volume, velocity, and variety: Understanding the three V's of big data'. Available at: https://www.zdnet.com/article/volume-velocity-and-variety-understanding-the-three-vs-of-big-data/ (Accessed 2 February 2020).

What is Big Data?

Big data was first mentioned in 1997 in a paper by NASA scientists - Michael Cox and David Ellsworth - who state that the visualisation of some scientific problems (specifically fluid and gas dynamics) poses significant challenges to computer systems (Guess, 2012). This primarily means an increased need for storage of vast amounts of input data both in RAM and on hard disks, which is simply characterised as the problem of big data.

The Guardian (2016), highlighted that debate over Big Data has grown quite a bit in the last few years. Everybody is talking about the benefits and drawbacks of the big data revolution. Even the businesses are interested in collecting valuable data, employees are facing the challenges of using them effectively. According to Techrepublic (2020), a study showed that 25% of respondents said they feel free to use collected data, 37% think their decisions are influenced by analysing data, and 74% feel overwhelmed when working with data at all.

But, what is really a Big data?

Oracle defines Big data as ‘data that contains greater variety arriving in increasing volumes and with ever-higher velocity. This is known as the three Vs. In every day's situation, whenever a search engine automatically completes term that user is typing or an online bookstore suggests a title that they think the user might like, it's related to Big Data solutions. New technologies have made it easy to collect data from smartphones and desktops and that data can be collected from different places, by visiting different sites, joining groups, commenting on social networks and websites, downloading and using applications, through different questionnaires, registering and buying products on sites (Segal, 2019).

IBM states that this collected data enables analysts, researchers and business users to make better and faster decisions using data that was previously unknown and unused. Businesses now have the opportunity to take advantage of advanced analytics techniques such as machine learning, predictive analytics, data mining and wide range of statistics to take a bigger market share, get more customers to meet their expectations.

Source:www.crossing-technologies.com

References:

Guess, A. R. (2012) Big Data: A History. Available at: https://www.dataversity.net/big-data-a-history/# (Accessed: 25 January 2020).

IBM 'What is Big Data Analytics'. Available at: https://www.ibm.com/analytics/hadoop/big-data-analytics?mhsrc=ibmsearch_a&mhq=what%20is%20big%20data (Accessed: 24 January 2020).

Oracle (no date). What Is Big Data? Available at: https://www.oracle.com/big-data/guide/what-is-big-data.html#link7 (Accessed: 25 January 2020).

Segal, T. (2019) 'The Deal With Big Data'. Available at: https://www.investopedia.com/terms/b/big-data.asp (Accessed: 24 January 2020).

The Guardian (2016) The big data explosion sets us profound challenges - how can we keep up? Available at: https://www.theguardian.com/science/political-science/2016/jul/02/the-big-data-explosion-sets-us-unprecedented-challenges-how-can-we-keep-up (Accessed: 24 January 2020).

Vigliarolo, B. (2020) Businesses understand the value of big data, but employees aren't being trained to use it - TechRepublic, Available at: https://www.techrepublic.com/google-amp/article/businesses-understand-the-value-of-big-data-but-employees-arent-being-trained-to-use-it/?fbclid=IwAR2BXh62eNj4gAMVQWk82Uv_8GAYzEzag3Kv-N5OKBzqrVlSzUAFchDDa44 (Accessed: 25 January 2020).