Every day hundreds of millions of people take photos, make videos and send texts. An analytics review article published by Northwestern University revealed how this content saturation may accumulate in the coming years. In 2013 the total amount of data in the world equaled 4.4 zettabytes, and we presently produce an average of over 2.5 Exabytes of data every single day (Khoso, 2016). The report also predicts that by the year 2020 the world’s data will have increased to over 44 Zetabytes.
Just to put that into perspective, a single Zetabyte is the equivalent of 44 Trillion Gigabytes. Another important statistic to consider is that 93% of all online experiences begin with a search engine, 68% of which use Google to do so. Combine that with the fact that the first 5 results in Google get 67% of all clicks, and you get an idea of why search engine optimization is so important (Patel., 2016). Businesses will surely face a growing number of challenges related to gaining a competitive share in a digital market that is projected to increase exponentially over the next five to ten years.
Intel co-founder Gordon Moore observed that the computing power of integrated circuits through which our computers processed data literally doubled every 18 months all the way back in 1965, and that model has held astonishingly true to this day. It represents yet another example of how a demand for the development of new and creative SEO solutions will correspondingly increase along with the capacity and complexity of computing technology. Artificial Intelligence allows businesses to nurture relationships with leads that aren’t ready to buy. On average, only 20% of leads are sales-ready when they first come in. This means you need a disciplined process, known as lead nurturing, to develop qualified leads until they are sales-ready. Done well, nurturing can result in 50% more sales leads at 33% lower cost per lead (Marketo. 2013).
A great example of Artificial Intelligence’s application for Search Engine Optimization is the famously referenced “Traveling Salesman” statistical analogy, which represents exponentially difficult challenges for even the most advanced classical bit-vector computation systems. Tackling this problem from a Meta-heuristics context would essentially calculate the largest distance of a salesman from his or her destination. If the salesman could only carry one parcel, a possible heuristic function is the sum of the distances that the parcels must be carried.
If the salesman could carry multiple parcels at once, this may not be an underestimate of the actual value outcome of a given task. Meta-heuristics are designed to make intuitive decisions and solve problems that cannot be quantified by classical computational models (Xin-She Yang., 2011). For stochastic problems, the random variables appear in the formulation of the optimization problem itself, which involves random objective functions or random constraints. In our system, a predicate bit vector algorithm is used to convey information on whether a predicate is satisfied or not. (Ashayer et al., 2006).
In this day and age, it is important for business owners to adopt a unique approach that is aimed towards providing simplicity in an overly-complex and data-oriented world. With the aid of Cloud-Based Machine-Learning systems and Artificial Intelligence technology, your website platform can quickly learn and consistently make accurate and intuitive evaluations that expose ideal prospects to the best possible content, when and where they want to see and share it. We believe in providing new & innovative Business Solutions that tackle Search Engine Optimization (SEO), Customer Relationship Management (CRM), Social Media Management (SMM), and many other Digital Marketing Services with much greater efficiency and integrated utility. Earlier days of the World Wide Web, initial search engines were only possessing 1000 Web page indices (Ashayer et al., 2006).
Today however, the methods of acquiring digitally indexed information and sheer complexity of ranking mechanisms has created a significant change in the demand for services that provide more satisfactory digital management services for business owners. The SEO industry, to one extent or another, has undoubtedly been around for as long as the internet has been around to create a demand for it. However, AI augmentation is something that very few established agencies in this field have been able to successfully adopt into their business, which has put their sustainability in the crosshairs of what may very well be the next technological revolution.
In light of this, we are likely to see many marketing firms attempting to implement a neural network interface into their operating processes. The most advanced AI algorithms were open-sourced back in late 2015 by Google Tensorflow & IBM Watson. The neural-networks work by matching an array of unsigned sessions to represent new users and existing users. Computational cross-entropy models will then be parsed with an automated set of predicate response variants that are quickly applied as users navigate a given website domain. Many predicate types used in practice are of a fixed enumerable size.
These processes are nothing new, but the computational power to support neural-networks has only recently become available to the public. As has been shown by (Fabret et al., 2001), counting is not the most efficient algorithm, however, it does not require any additional information, such as statistics on predicates to determine individual user traits and selectivity measurements. All predicates stored in BOVI’s secure server database are associated with a unique keyword container ID or Meta Badge for current and potential users. Each user category is indexed into a predicate library of Identity variables which are confounded by Boolean Algebraic principles: Disjunction, Equivalence, Exclusion, or Negation. Many of BOVI’s statistical frameworks are conversely derived from Bayesian modelling methods as well (Cowles et al., 2009).
These will simplify the dissemination process while providing natural ways for people in many disciplines to structure their data and knowledge. Convolutional functions will add sparse-recursive response capabilities (Khoso., 2016). With this, the attributes belonging to user predicates are calculated using a neural-network that can be best described as a large group of analogous machines in a virtual warehouse all working different iterations of the same problem simultaneously, and learning through process of trial and error, thus optimizing the accuracy of each result.
BOVI Business Solutions offers a real-time platform that puts digital marketing power back in the hands of business owners. It allows brands to understand and orient their presence around the interests and preferences of their consumers, then it provides the infrastructure to put meaningful offers in front of them. Our design architecture will be optimized for ease of access and simplicity by presenting concise analyses from vast amounts of data in a way that is easy to understand and strategize around. Our application interface will allow users to carefully monitor performance metrics, and make informed decisions about how to improve and reach their goals.
Whether you’re a small-town commercial vendor or a large corporate media company, BOVI provides its customers with everything they need to optimize their online presence and engage their ideal prospects.
BOVI also believes in putting the power of this software in the hands of its clients, by allowing them to play an active role in the entire optimization process. Business owners can get back to running their business, rather than spending all of their time mining through data and figuring out how to get the marketing automation protocols set into place. BOVI-Base is a responsive web application where users can review and manage every aspect of their marketing, every step of the way, from a personalized online dashboard. This dashboard’s server is held under Transfer Layer Security encryption and can also be used as an intranet system by which information can be freely accessed and securely distributed throughout an organization.
We will also provide certain insights that will help businesses identify the most effective communication channels by which loyalty can be established or maintained among existing customers, as well as where underacknowledged prospects can be reached, verified, and then converted into sales. Machine-learning algorithms will provide direct and intuitive answers to the practitioner's questions, which will then be cross-referenced with affinity ranking indices such as; SEM RUSH, RAVENTOOLS, and Google’s Analytics platform. Finally, this information will be processed into the BOVI Digital Artificial Intelligence protocol interface using IBM’s Watson Analytics & Google’s Cross-Entropy models provided by TensorFlow.
The sparse-recursive cross-entropy features of Google's Tensorflow libraries are very detailed, and to explain the various features with any discernable detail, I would have to use terminology that only a software developer would likely understand, but I hope this overview perseverates the value of integrating Artificial Intelligence to ensure operations are systematically optimized and the most effective iterations are re-cycled for automation purposes.
Reference:
• Ashayer, Ghazaleh., Ka Yau Leung., Jacobse, H.-Arno. Predicate Matching and Subscription Matching in Publish/Subscribe Systems. Department of Electrical and Computer Engineering and Department of Computer Science University of Toronto. 2006.
• Cowles, Kate., Kass, Rob., O'Hagan, Tony. Bayesian Explained. International Society for Bayesian Analysis. 2009. https://bayesian.org/Bayes-Explained
• F. Fabret, H.-A. Jacobesen, F. Llirbat, J. Pereira, K. Ross, and D. Shasha. Filtering algorithms and implementation for very fast publish / subscribe systems. In SIGMOD Conference, 2001.
• Xin-She Yang. Metaheuristic Optimization. Scholarpedia, 6(8):11472. 2011.
http://www.scholarpedia.org/article/Metaheuristic_Optimization
• Khoso, Mikal. How Much Data is Produced Every Day? Northwestern University. May 13, 2016
http://www.northeastern.edu/levelblog/2016/05/13/how-much-data-produced-every-day/
• Marketo. THE DEFINITIVE GUIDE TO MARKETING AUTOMATION. Marketo Inc. 2013.
file:///C:/Users/munro/Downloads/Definitive-Guide-to-Marketing-Automation-Marketo.pdf
• Patel, Neil. SEO Made Simple: A Step-By-Step Guide. 2016.
http://neilpatel.com/what-is-seo/
