MJ Digital

Below is the analysis of the website traffic accumulated over 6 weeks for my blog MJ-Digital using Google Analytics. Audience Overview: The above report shows the audience metrics for the blog, in terms of How many unique users came to the website (Users)? How many times they visited the website (Sessions)? How many times the users viewed different pages of the website in total (page views)? How much time they spent on the website (Avg. Session Duration)? How many users exited the site immediately after coming to the site (the Bounce rate) Due to the short term duration the site was live, it will be very hard to judge the quality of site performance. One way to do is to compare with industry benchmarks especially for bounce rate (Neely, 2019) and average session duration. Acquisition Overview: The above report shows which channels brought traffic to the website. Since the blog was promoted through p

While Artificial Intelligence is starting to become commonplace across business functions (Chaffey, 2019) , below are 5 of the AI trends which are believed to impact in the marketing domain in the coming decade. Conversational search: AI technologies will boost Machine Learning (ML) capabilities associated with Voice search (Balkhi, 2018) and Natural Language Processing (NLP) which will eventually lead to the new era of conversational marketing. Brands like Air Canada are already using NLP technologies like Persado to boost customer engagement (Chaffey, 2020) Content Marketing: With advancements in NLP, there is great interest in adopting Natural Language Generation (NLG) as well for the automation of content marketing. NLG solutions like Quill, Wordsmith are already being used by some companies, while others like Amazon are developing their own NLG systems like Polly and Washington Post’s Heliograf which are customized to their specific organizational needs. (Marr, 2019) .

As e-commerce exploded in the last decade with the advent of stable internet and powerful mobile devices  (Fagan, 2020) , so has the amount of customer data that is being generated with all the various customer touchpoints now available online ecosystem. Types of Data collected (Roberts, 2013) Identity data – Contact details, social media ids etc. Quantitative data – Transactional information, online activity etc. Descriptive data – Family, Lifestyle, Career details etc. Qualitative data – Opinion, feedback etc. While there is a lot of value that can be derived from this rich customer data (Stringfellow, 2017) , this also presents a new set of challenges in managing this data as well. Customer Data Platforms To overcome these challenges and to reap the benefits of customer data, there are specialized big data solutions called Customer Data Platforms (CDP), which combine the functionality of Customer Relationship Management (CRM) and Data Management Platforms (DMP) f

In the old days, the challenges for marketers was reaching many people,  convincing them to buy their products and develop positive  brand perceptions, all to be done within the budget allocated. Brands create an ad, broadcast through channels and hope their sales increases. If it didn't, they make a new ad. But times have changed after the digital revolution.  Today's marketing challenges (IBM Analytics, 2013) are way more complicated than that,  Huge volumes of structured data from various touch-points controlled by the brand.  Huge volumes of unstructured data from all external sources like social media not controlled by the brand.  Optimizing the message for the diverse communication channels and variety of devices used by customers to interact with the brand  Changes in lifestyle and demographics of target customers.  This is where Big Data comes in to help marketers tackle these challenges by providing highly valuable customer insights through information an

Big data is not just large very large sets of data. While Big Data has many characteristics (Kitchin and McArdle, 2016), there must be 3 main dimensions for a dataset to get qualified as Big Data often referred as the 3V’s, namely Volume, Velocity and Variety. To simply explain these dimensions, let’s take the example of the online retailer Amazon.com. Volume : Volume refers to the size of data that is being collected, processed, analysed and stored. For example, Amazon.com’s average monthly traffic is more than 2 billion which includes its 150 million App users who browse through the estimated 120 million products sold by around 2.5 million sellers (Oberlo.com, 2019) . This huge traffic base generates a great volume of data points, in terms of orders, transactions, inventory changes, customer communications etc and need to be managed properly. Velocity :  Velocity refers to the speed with which data is collected, processed, analysed and stored. Amazon.com sells around 40