Article | August 25, 2021
If you ask how Jeff Bezos is one of the most successful entrepreneurs today, you receive a good deal of answers. nd one of those answers is that he initiated innovation in marketing. He implemented marketing and strategized it in a way that they have become the new rules for successful marketing. He utilized marketing techniques from word of mouth and advertisements to a technology like AI. If Alexa knows that you ordered 10kg of flour last month and have a family of four, the device will remind you to reorder it before you are out of flour.
Thanks to AI, marketing has become simpler with predictive analysis. And, with the merging of IoT, automation, AI, and machine learning, marketing has become more personalized.
The traditional ways of marketing are still in practice, but AI is sure to revolutionize the marketing industry, shorten the sales cycle, and broaden the pipeline. With these new trends in marketing automation, let us see how you can implement AI marketing to boost your sales.
Understanding AI Marketing Methods
Before implementing AI marketing, you need to understand your target audience and the market. Once you have a thorough knowledge of what you offer and what your customers need, your marketing solutions can be designed accordingly.
As a seller, you may think of AI as abstract. But the amount of data that you have is the best source of solution for your marketing. Once you have digitalized your business, implementing automation and AI becomes an effortless task. All you have to do is apply the right software, advertise on the proper mediums and keep your business active on the customer feed.
Those above may sound complicated, but you do not have to worry about the technical part. You need to provide the correct data, and the machines are at your disposal.
Here are a few components of AI marketing platforms that have impacted and will impact marketing for years to come. Once you have an idea of them, you will know how your sales graph will rise with data-driven technologies combined with automation.
Components of AI in Marketing
Deep learning allows you to understand how a customer thinks, purchases, decides and reacts to a shopping process. Deep learning lets a machine simplify data complexity and make the best use of the available data.
Deep learning not only predicts the customer’s behavior but also predicts product marketing. It helps you analyze your product selling options based on seasons, holidays, consumer behaviors, sales, competition, and a lot more. It also advertises your businesses on consumer behavior patterns. Deep learning helps you target the market on a trial basis with minimum investment. This assists you in taking risks and calculating them.
Automation marketing has taken customer experience to another level. You can use any suitable software for e-mail marketing, social media marketing, and ad campaigns.
Marketing automation helps to personalize the customer experience. For example, you can target your customers by sending them birthday, anniversary coupons, creative reminder mails, or simple content about your new services or products. These small gestures guarantee 50% more sales at almost 33% lower costs.
AI with automation marketing includes lead generation, marketing campaigns, data analytics, data reporting, predictive analysis, etc. Thus, incorporating marketing automation in any size of business promises a host of benefits.
Machine learning helps you to design suitable content for the target audience. Implementation of machine learning provides 57% more enhanced customer experience than any other marketing technique.
The best example of machine learning is AWS (Amazon Web Services) which is available for everyone to customize the services according to the customer’s demands. The combination of AI and machine learning is 100% successful in optimizing data and content. As a result, it assures 44% customer retention.
Thus implement AI marketing tools to have a loyal customer base. To start with, incorporate chatbots into your websites and revolutionize your customer experience.
The Game Has Changed.
Businesses have already started including marketing automation techniques. As a result, most of them have seen their sales graph soar with the correct practices.
Some of the benefits of marketing automation are:
Enhanced customer experience
Profitable marketing efforts
The bottom line is that the future of artificial intelligence in marketing is a promising one. It has already transformed the marketing world, and the results are inevitably positive. Thus, implementing the above AI marketing techniques is essential for the sustainable future of every business.
Frequently Asked Questions
How can AI be used in marketing?
AI is used in marketing by incorporating automation software. This software interacts with customers in real-time and can communicate seamlessly. The best example is chatbots. AI studies data, processes it and uses it to provide a personalized experience to the customers.
How is AI reshaping marketing?
AI studies each customer individually. Then, according to their behavior and shopping patterns, it provides them suggestions. As a result, it enhances the customer experience. And when the customer feels connected to the brand, they are bound to return. Thus, the incorporation of AI in marketing brings out the best customer retention.
How does AI impacts digital marketing?
AI marketing uses predictive analysis and provides a personalized customer experience. As a result, they help target the right audience and create successful campaigns which ensure guaranteed ROI with minimum investment.
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"text": "AI studies each customer individually. Then, according to their behavior and shopping patterns, it provides them suggestions. As a result, it enhances the customer experience. And when the customer feels connected to the brand, they are bound to return. Thus, the incorporation of AI in marketing brings out the best customer retention."
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"text": "AI marketing uses predictive analysis and provides a personalized customer experience. As a result, they help target the right audience and create successful campaigns which ensure guaranteed ROI with minimum investment."
Article | August 25, 2021
The Python programming language has been topping virtually every tech trend list for the past two years, so it was no surprise to see it earn another "most popular" ranking in O'Reilly's annual analysis of the most-used topics and the top search terms from its online learning platform. But the reason for Python's latest blue ribbon is worth noting: according to O'Reilly, it was demand among data scientists and artificial intelligence (AI) and machine learning (ML) engineers.
Article | August 25, 2021
Storytelling is an art. It brings out the best of the teller and the listener. For centuries storytelling has been proved to be a successful way to reach out to the masses. However, a myth about storytelling is that it is considered only as a subject of literature. Well, we are here to break this myth.
The art of telling a story belongs to every field. Be it literature or science, electronics or computers, every field has its own story, even astronomy. Likewise, every little particle has a story to tell.
There is a story in every line of code written by software developers. The user tells a story, and the software developer writes it in a computer code language. Read further to understand more about storytelling in software development.
The Art of Storytelling in Software Development
When a user comes across a problem that needs to be simplified digitally, he explains the problem to the developer or salesperson.
For example, a shopkeeper needs to digitalize his inventory; he will talk about the problems he is facing while manually managing the inventory. This manual management could be handwritten or a basic excel sheet.
Now, when he tells his issue, he says that “I need software that easily tells me about the things I have in my inventory, goods that are to be stocked and also the goods that have and do not have demand.”
When the shopkeeper elaborates his problem and tells them in a story-like manner, the concerned person understands it better. Now imagine if they would say that, “I need software to know about the goods in the inventory.” Unfortunately, this simple sentence does not convey what exactly they are looking for. As a result, the salesperson would not be able to give them appropriate solutions.
Once the story behind the work is understood, the entire team puts in the effort and comes up with perfect solutions. From coding to the visuals of the software, everything works in synchronization. Thus, the story behind the work is of great importance.
Storytelling in software development has a structure. It has a beginning, middle part, climax, end, visuals, and imagination. The only difference in the storytelling tool is that it is known as understanding the need of the user (beginning), designing the solution (middle part), CTAs and ways to reach the target audience (climax), visually appealing and adequately working software delivery (end).
The success of storytelling in software development depends on how well the developer connects with the user’s problem. Once the entire team is on the same page, emotionally and practically, they deliver the most reliable solutions.
The Process of Storytelling in Software Development
Storyteller software is a tool that listens to the thoughts and ideas of the customer, understands their audience, and then transforms them into concrete solutions. Storytelling software uses the following steps.
● Understanding the user story
● Implementation of the story
Understanding the Story
As described earlier, understanding the story is learning the background of why the solution is being developed. Once the digital storytelling software is understood, the implementation of the solution becomes more apparent.
When the user tells his problem, there is always a story in it. When this story is told to the team, they give a set of solutions to the user. It is like giving them the menu to select the dish they like to be served.
Once they select the dish, it becomes an easy job to make it. But you need to ask the user why he is selecting the particular solution. Because the ‘why’ will answer almost every question that is needed to design the appropriate solution.
Implementation of the Story
The implementation of the story is the ‘show, don’t tell’ part. You need to display the solution on the screen for the user to understand. The user does not know the coding language, but he will understand that if I click this, this happens. And that understanding for the user is the implementation of the story.
We can also say that the implementation of the story is the architecture of the software. A well-built architecture will convey the story to the users. For example, when HRIS software is developed, the HR departments will know that the story behind the development was to ease out their tasks. These tasks could be anything from attendance to salary management.
Always remember, only a well-understood story can be implemented most dependably.
The conclusion of the story is the delivery and installation of the visual storytelling software. The user should be able to find all the answers to his problems in the best possible way. Their story that started with ‘once upon a time’ should end happily ever after’. And this is possible only when the entire team is connected emotionally and practically while working towards the solution.
The conclusion should also convey the climax of the story. The climax implies the CTAs or the final work that the software does after getting the desired data.
Only after you have reached a conclusion and the user is satisfied you will know that you have written an excellent story. This good story is the incredible software you built by gathering the plot, characters, problems, and other raw material from the user!
Benefits of Storytelling in Software Development
We know the process of storytelling in software development, but what do we gain from it? You must be thinking, can’t we listen to the demands, design a solution, and give the user what he wants? So, what is the need to understand the story behind the software?
Well, software delivered without understanding the story is merely a puzzle created. In the future, the software would not be able to answer the most important question -WHY? And that could prove that the software is a failure.
Here are some benefits of storytelling in software development.
● Straightforward Approach
Instead of beating around the bush or designing complicated solutions you will deliver the solutions in simpler steps. And this is because you have well understood the story behind creating the software.
● Understand the Big Picture
The user's story will let you understand what outcomes or expectations the user has from the software. The vision of the user and their expectations from the product can be understood only when the whole team has clarity on what and why they are working.
● Emotional Attachment
When software is developed practically and empathetically, the user also connects well with the software. People have to be given an emotion to get clicks on CTAs or use the software.
A survey was taken wherein people had to donate to a cause. As a result, two types of causes were created. One group was asked to donate food for an underdeveloped country, and the other group was asked to donate for a hungry child. The second group donated more, and many of them stated why they felt emotionally connected to the child.
Thus, the emotional connection of the team to design software is essential.
● Give Better Outcomes
Stories assure better results and excellent outcomes. They make sure that the team works best and the customers are motivated to take action. The story behind the software helps build the software in precisely the way it is asked for.
Thus, You Need to Tell a Story
Building a great story and connecting it with the user’s pain points is the best way to develop a creative solution. This helps paint a picture in the team's minds as to what is being built and why.
Storytelling in software development is essential as each software should convey an incredible story.
Frequently Asked Questions
What is storytelling in software development?
Storytelling in software development understands the story as a tool to design the software more accurately. The thoughts and requirements of the user are used as raw materials and utilized to create an efficient solution.
The story also helps in understanding the audience for whom the software is being developed.
What is the importance of storytelling in software?
Stories create better solutions and excellent outcomes. They motivate the audience to react in a certain way. They connect with the user and the audience on an emotional level. They are impactful and even let developers learn a lot.
They help create better software by keeping the team connected through the story.
What are the software storytelling tools?
Software storytelling tools are the elements that help implement the solution most productively. These are the user's point of view, emotionally connect, purpose, and the answers to the questions- what and why.
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The story also helps in understanding the audience for whom the software is being developed."
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"text": "Stories create better solutions and excellent outcomes. They motivate the audience to react in a certain way. They connect with the user and the audience on an emotional level. They are impactful and even let developers learn a lot.
They help create better software by keeping the team connected through the story."
"name": "What are the software storytelling tools?",
"text": "Software storytelling tools are the elements that help implement the solution most productively. These are the user's point of view, emotionally connect, purpose, and the answers to the questions- what and why."
Article | August 25, 2021
Depicted as a natural predisposition to form groups of work, teamwork has been popularized through history as a central feature of organizational change programs that advocates empowerment and disruptiveness. The suasive force of discourse regarding the ineluctable essence of teamwork as a tradition and custom founded on some inclination for humans to work cooperatively, create a set of “rituals”, conventions and practices which invite to innovation, flexibility and creativity.
Teamwork as “human nature” was a common thread all through history and management literature. The team-based nature of early human activities can be traced to hunter-gathering in societies where orality was the prime source of communication. The locus communis was the collective memory (facts, rules, code of conduct, religious beliefs and practical knowledge). The pneumonic function of the verse will fulfil a didactic function as a way of memorizing any content in order to systematize a conceptual theoretical primitive language. In preliteracy times, doctrines and their conservation were highly dependent of the spoken word and memory (Havelock, 1957, 1992). Thus, in an oral culture experience is “intellectualized” mnemonically (Ong, 1982). In a sociobiological perspective, aspects of teamwork behaviour allude to a biologically determined “natural history of species”.
According to Katzenbach and Smith (1993) “teams- real teams and not just groups that management call “teams” should be the basic form of performance for most organizations, regardless the size”. This statement clearly sets the basis for the team as a natural building block of any organizational design. Buford (1972) in a comprehensive study of Ancient Greek and Roman craftmanship interpreted teamwork in a very familiar approach we understand it today: collaborative work, multiskilling, mutually interdependent tasks. There were technical divisions of labour based on skills, the relationship between mentor and apprentice and so on. The greatest craftsmen were expected to be versatile in different skills, but the coordination of work efforts was left to the so-called professional cadre of engineers, architects and masters.
With the advent of Capitalism, the massive growth of the economic activity claimed for reorganization. A new form of discourse emerged, our prehuman origins and modes of communication becoming codified and formalized as the scientific disciplines of evolutionary biology, economics and linguistics respectively (Foucault, 1972). Within the economic discourse, there was a creation of a distinct managerial object, which opened new domains of knowledge and professional practice.
The mythical traditions of teamwork replicated in today’s contexts and the “tribal” notion of team popularized by Codin (2008) paves the way to concrete changes in the form we perceived our working environment. The analogy of team as “family” so common in the corporate world which in its essence represents our first experiences as a community is not a happy term anymore, since in a manner it could go against the interests of today’s organizations. Therefore, in building a healthy sustainable workplace culture teams cannot be perceived as family. Teams have a commitment to a common goal, clear expectations and performance.
The MetaQuant: From siloed work to interdisciplinary collaboration
With the paradigm shift to automation, organizations are taking actions that promote scale in AI through the creation of a virtuous circle.
The central overarching question is: Are traditional ML teams good enough to develop models able to achieve long lasting competitive advantage?
“In a world spinning around AI, competition among institutions seems to be fierce while mayor obstacles appear on the way: recruiting top talents is not only time-consuming but also high-priced, or just trying to find a balanced approach to talent, meaning "reshaping" the old-school computer scientists into quants, is critical in terms of AI implementations. The big winners: those firms that integrate AI with human talent” (Litterio, 2020: 167).
Successful machine learning (ML) projects require professionals beyond engineering expertise. AI has the biggest impact when it is developed by dynamic creative cross-functional teams. The move from functional to interdisciplinary teams initially brings together the diverse skills and perspectives to build effective tools.
In order to bring theory into practice, and in the need of a novel conceptual framework design, I have coined the term MetaQuant.
The MetaQuant is a new breed of market players, who “translates human language into signals” and "reads" the data from a holistic perspective identifying patterns within the linguistic and symbolic constructs. The MetaQuant is the linguist, the semiologist, the sociologist, the cognitive psychologist and the philosopher or rather a combination of these intertwined profiles which will fuel the potential for information advantage providing a unique core differentiator transforming data into knowledge. In this sense, the MetaQuant has emerged as a crucial component of any AI model paving the way for a novel insight where hybridization is critical. The formula for a successful organization in a discovery-driven environment is the MetaQuant + The ML team. And eventually the Quantum Computing Expert. Finding the needle in the haystack can be a competitive difference maker.
Creative thinking, actionable insights, collaboration, proficiency, flexibility, shared vision and training are the ingredients for an elite team.
It is vital for organizations to establish workflows that empower everyone to play a role in order to move projects from test to deployed AI/ML. Yet, knowing how to do ML is not the same as being proficient with it and knowing how to implement a ML model end-to-end is not the same as using ML creatively to build solutions to real-world problems, to explore and assess potential applications specific in competitive contexts.
Ideally, when selecting members for your elite team, it is advisable to make a first distinction between those who wish to do research in ML from the ones who wish to apply ML to your business problems. Both are of major importance alike. The instreaming of new talent brings in novel ideas which can positively impact the work culture.
Demonstrating flexibility is a significant asset. Since ML projects may encounter all kinds of roadblocks, being able to easily change tactics to overcome obstacles without getting frustrated or losing sight of the end goal is key to deliver projects.
Mentoring and inspirational leaders is greatly valued when designing a ML team. An exceptional team leader is the one who shares a unique perspective and knowledge. Experience in the field is a substantial source of wisdom within the organization. Having a passion for diversity of input and fostering a healthy culture of support distinguishes average from excellent ML teamwork.
Educating everyone is the dictum to become an AI-first institution.
To ensure the adoption of AI, organizations need to educate everyone, from top leaders down. To this end most are launching in-house programs which typically incorporate workshops, on-the-job training to build in capabilities. Some others, and which reflects a common trend today, opt for partnerships with renowned academies or prefer the outsourced modality “training as a service” program or a bootcamp.
For an A-team, it is critical to make a mark in the ecosystem through journal publications, book chapters, white papers or lecturing in conferences. Disseminating their work and findings through meetups, workshops, and seminars is a must for building a thriving culture that promotes exchange and cross-fertilization of new ideas and technologies in a substantial way. Systematicity and coding belong to the ritualistic change of conscience.