Home > Blogs > Transitioning Data Scientists to Managerial Roles: Building effective data leadership
For 80% of companies, investing in data science talent alone does not generate the required return on investment (ROI), unless it is accompanied by the ability to connect data with business outcomes. With organizations increasingly depending on data to etch out competitive advantage, the data scientist’s role is evolving. From being cooped up in office corners crunching numbers, the role is assuming expanded responsibility – one of supporting businesses in making smart decisions to drive growth and expansion.
However, building effective data leadership is a major challenge for organizations today, given the acute shortage of skilled data science talent in the country. According to Analytics India Magazine, there will be a 45% spike in the number of data science job openings in 2019, with the banking and financial services industry creating the maximum demand. This puts the onus on organizations to proactively transition their data scientists to managerial roles. This is easier said than done as data scientists usually have extensive background in areas like mathematics, science and statistics and analytics but not in business or management. So, how can forward-looking businesses help their data scientists successfully transition to managerial roles? The answer lies in implementing focused learning interventions, starting with the following three skills.
Communicating effectively with technical and non-technical teams
Humanizing mathematical results into actionable business insights is the most critical aspect of the role of data scientists as they interact with stakeholders across the organization. Often, business leaders may not understand the import of a particular set of data. As data scientists move up the ladder, a large portion of their work will not only involve analyzing structured and unstructured information to detect patterns and make predictions but also helping business stakeholders understand its implications. This is where data storytelling comes in.
Acknowledged as one of the top skills required in the industry, a study by Stanford University states that storytelling with statistics results in better understanding, with a retention rate of 65%-70%. For data scientists to acquire communication and storytelling skills, training in skills such as data visualization and usage of relevant anecdotes and interactive dashboards that can convert complex information into consumable narratives is a must.
Aligning data science goals with organizational goals
Data analytics assumes relevance and meaning only in the context of a business. For organizations to benefit from the data they have access to, data scientists must be coached on how to connect complex information back to what the business is trying to achieve. This requires an understanding of the company’s unique offerings and how it stacks up in the market vis-à-vis its competitors. With the right understanding of business, data scientists can leverage data to identify and analyze potential business risks, relate expenditure to process improvement, or identify ways to create customer delight.
At the Rising 2019 event, Sohini Mehta, Global Service Delivery Head of Analytics at Wipro said that technical expertise has almost become redundant with so many automated machine learning platforms making most of the jobs easy and simple to perform. But there is a lot of business thinking that cannot be performed by machines. A data scientist, therefore, needs to be trained on cultivating strong flair for viewing information from multiple perspectives and then narrowing it down to the most useful insights for a given business.
Effectively managing inter-personal relationships
While data scientists are capable of churning out relevant insights, the question is: do they have the skills to deal with friction in the form of multiple perspectives from a diverse set of stakeholders? According to LinkedIn’s 2019 Global Talent Trends Report, the top five in-demand soft skills include persuasion and collaboration.
Clearly, data scientists must acquire skills that will enable them to cultivate, manage and nurture interpersonal relationships - given the diverse set of stakeholders they interact with across functions. One way to build this capability is to design programs focusing on building empathy where the data scientists are coached on being mindful of pitching a solution as an alternative recommendation or helping stakeholders understand that a unique viewpoint can also be constructive. The result: reduced friction and improved cohesion within teams.
Organizations that take a strategic approach to equipping their data scientists with requisite hard and soft skills will create successful teams that are able to synchronize their understanding of technology, business, and human assets to drive growth.
Home > Blogs > 3 Ways Data Science Fuels Fintechs
Earlier this year, news of the launch of revolutionary Apple Card drove the over-crowded credit card market into a frenzy. The new kind of credit card reimagines the concept of credit cards using the iPhone. This is just one example of how fintechs are transforming the world of financial services. Fintech organizations that are able to quickly draw relevant insights and successfully monetize the data will achieve unbeatable advantage in the marketplace.
Here we explore three use cases on how data science empowers fintechs to provide superior solutions.
Lending and credit scoring
Traditionally, the lending has been a tedious and time consuming process, as lenders take days and sometimes even weeks to approve loan applications. A majority of the time is spent on verifying applicant information and income statements, bank accounts and credit ratings of the individual. This process is biased against individuals without adequate credit history such as young adults, self-employed individuals, and the underbanked.
Fintechs are transforming lending by increasingly relying on data science to analyze and classify loan applicants without human intervention, while eliminating the biases of traditional models. Take the case of Credolab, a Singapore-based fintech that uses smart-phone metadata to generate credit scores for people with thin credit histories. It applies an algorithmic credit scoring engine that emulates the human intelligence of underwriters. As a result, they have seen credit approvals grow without increasing risk exposure.
Customer acquisition and retention
For financial institutions (FIs), customer acquisition and retention are major challenges. FIs are therefore constantly seeking ways to make their acquisition and retention efforts more efficient and effective. Data science is coming to their rescue through predictive models that help organizations better understand consumer behavior, drive sales and cultivate loyalty. A good example is Cardlytics, an Atlanta-based fintech, that applies data science to provide FIs with relevant insights to drive client acquisition and retention. The company uses insights from historical purchase data across stores, products, categories and geographies to identify, reach and influence likely buyers and drive loyalty programs.
Offering data science backed investment management services not only increases user engagement but greatly improves the overall experience of the user with the financial product they are interacting with. Algorithmic trading, for instance, is gaining ground as algorithms can analyze humungous volumes of data to spot patterns that can go undetected by humans, react faster than human traders, and trade automatically based on available insights. All that is needed is enough data to train the model. Sarwa, a Dubai-based fintech combines technology and human advice to make expert investing “available for everyone.” This helps clients: open an account in minutes via facial recognition, track their goals via an intuitive dashboard, and rebalance their portfolios by tracking market movements.
The fintech world is rapidly taking advantage of the power that data science can potentially unleash in combination with Artificial Intelligence, machine learning and predictive analytics. Fintech enterprises are employing armies of data scientists in addition to financial specialists and machine learning experts to develop disruptive financial solutions. Given the data science talent crunch, many forward-looking fintechs are providing access to data training, analytical tools and reputed data science courses to create a healthy pipeline of data scientists and ensure sustained competitive advantage.
Home > Blogs > Empowering Employees: Four Critical Capabilities for a Solutioning Mindset
Customer experience (CX) has emerged as the single most exciting opportunity for businesses in 2019 and beyond, with investments in CX offering the potential to double revenues in 36 months. To deliver exceptional customer experiences today, organizations need employees to practice empathy in understanding customer challenges, go the extra mile in delivering solutions to nagging customer problems, and develop a customer-centric solutioning mindset.
The good news is evolving business models allow employees at every level to contribute - by extending their influence to win customers. To create emotionally intelligent individuals who display a solutioning mindset, organizations are evolving their L&D programs to focus on employee development across four core areas:
Traditional approaches to employee development that focus narrowly on skill development is giving way to a more holistic approach of developing business savvy employees. A sound knowledge of - the market dynamics, the competitive landscape in which the business operates, and client imperatives that determine business success - are critical for employees to meet heightened customer expectations.
Consider Choice Hotels, a leading travel and leisure company that has been recognized by Forbes for its outstanding workplace culture. As part of its team development initiatives, the hotel organizes frequent talks by the heads of diverse business units and functions. Finance leaders, for instance, hold sessions on how they earn revenue, where it goes, how the excess cash is re-invested, etc. This stems from the management’s belief that if the team at large understands the business objectives and the growth areas, they can contribute better and drive competitive advantage. Such initiatives have helped drive brand loyalty, resulting in more stays for the hotel group.
The traditional approach of limiting strategic thinking to senior leaders has led to missed opportunities for businesses, something businesses can no longer afford today. By training employees at all levels of the organization to apply strategic thinking across different aspects of their work, enterprises are deploying a collaborative environment – one that encourages diverse thinking by blending aspects of sales, marketing, delivery and solutioning with the larger goals of the organization.
At Accenture, everyone from entry level employees to senior leaders goes through an extensive program to learn about the company’s corporate goals and priorities, and how these translate into respective IT initiatives. Regular meetings and discussions are held to orient people towards what is happening within the organization, the company’s performance, and key future plans. Creating such a culture of strategic thinking and alignment has led to time saving innovations by Accenture employees.
The future of work is all about creating a more agile yet collaborative organization. As employees grapple with the constantly evolving demands of the new work environment, an integrated view of customer experience, business processes and technology becomes critical.
Amazon is an example of a company that has successfully trained their technology specialists and engineers to apply technology to create unique customer experiences. Amazon employees undergo a two-day training at a call center just to understand what the customer goes through and be more sensitive to client requirements. This enables their technology and solution experts to think creatively as they generate ideas and visualize solutions keeping the customer in mind.
The most successful companies don’t recruit leaders, they grow their own. These organizations boast of L&D teams that proactively nurture employees who deliver excellence, influence product decisions, and establish their thought-leadership in every engagement - within as well as outside the organization.
Google’s success at transforming employees into leaders is visible from the number of former Googlers who rule the corporate world today. Marissa Mayer joined Google as a software engineer and then went on to become the CEO of Yahoo. Facebook’s COO Sheryl Sandberg, Dropbox COO Dennis Woodside, Pinterest's Ben Silbermann, Twitter's Evan Williams, are a few others. From early times, Google’s characteristic flat corporate structure has been integrated into its human resources development programs – training programs that motivate workers to continually improve their performance and strive for excellence.
A recent research by Gartner reveals that 70% of employees have not mastered the skills they need for their jobs today, and 80% of employees do not have the skills needed for their current and future roles. Unsurprisingly, the focus is slowly shifting from jumping headlong into skills training to instilling the right mindset first. This means, in addition to training employees in domain skills, successful organizations are inculcating a solutioning mindset in order to stay ahead of the customer experience game.
Home > Blogs > Data Analytics, AI & Cloud Computing: New Skills for Indian Techies
The Digital Transformation Landscape
According to a report published by IDC and Microsoft, approximately 60% of India’s GDP will come from digital products or services. By 2021, digital transformation is expected to add an estimated $154B to India’s economy. Apart from ISV’s, Indian enterprises themselves have realized the huge potential of using digital to transform the way they do business. They are increasingly adopting tools like Artificial Intelligence (AI), data analytics, and cloud computing to harness the power of intelligent decision making.
Why Indian Technology Professionals Upskill
With this backdrop, it is not much of a surprise that Indian technology professionals are keen to upgrade their skills in the digital transformation space. Be it learning the new or unlearning the old, they are keen to add to their repertoire of existing skills to cash in on this economic opportunity. Some of the main reasons that techies are looking at data analytics, AI, and cloud computing are:
Easy availability of AI and machine learning (ML) courses and online training modules that could be taken in their free time.
‘Hot’ projects to work on after upskilling.
Promotions, rewards, and recognition.
Better salary choices.
The fear of job skill obsolescence.
Hottest Areas of Upskilling
The choice of Indian techies is clear. Data analytics, AI, and cloud computing are the favourite areas to upgrade their skills in. Organizations are now demanding highly skilled tech workers, as the focus in these companies shift towards digital transformation. Big data is being used extensively and people who have skills in these areas are in high demand. 2018 continued the trend of adoption of big data technologies, pushing them to become more data driven.
Companies are now moving from prototypes and pilot projects to mainstream implementation of AI. Machine Language and NLP are gaining traction as businesses strive to get a competitive edge. Businesses are looking to find the right mix of people and machine or “digital labor” to optimize their core strengths. Robotic automation, process mining, and machine based intelligent business rules require techies proficient in AI technologies.
There is tremendous scope for growth in disruptive industries like autonomous driving vehicles, drones, Smart Utility Grids, large scale retailing, pharmaceuticals and drug research, and of course social marketing and advertising. The approximate salary of an ML scientist is more than ₹30L per year.
The explosion in data generated on a daily basis is now well documented. Each swipe on a Smartphone has an underlying context for marketers and advertisers. Every response from an Internet of Things (IoT) device connected to the Internet needs to be captured. Edge computing technology and extremely cheap storage prices have led to large swathes of data being collected. Thus, companies are now looking to derive useful information from this raw data to help make more informed decisions.
Predictive analytics, identification of trends and patterns, customer insights, and predictive equipment maintenance are just a few areas in the realm of Data Analytics which companies are exploring.
The on-demand availability of computing power and storage has paved the way for faster, efficient, and cheaper means for companies to invest in and develop new technologies. Cloud incumbents like Amazon Web Services, Microsoft Azure, and Google Cloud offer extremely low-priced entry points for even small and medium sized companies to have access to the latest technology.
According to a Global CIO survey conducted by Deloitte, CIO’s spent 22% of their IT budget on cloud services in 2018, which is expected to double in the next three years. Thus, companies are constantly on the lookout for techies with good cloud computing skills. The average salary of a cloud architect in India is upwards of INR 27L.
Due to the dynamic nature of the technology industry, techies need to keep themselves abreast of the latest and greatest. Upskilling one’s capabilities is the best way to move forward professionally. Given the current scenario, it makes sense to upskill oneself in Data analytics, cloud, or AI to ensure a brighter tomorrow.
Saheli Roy Chowdhuri
Home > Blogs > What is the career scope in Search Engine Marketing?
In an era of ever-evolving digital landscapes, all traditional channels of marketing have begun to undergo a significant change. Organizations, ranging from start-ups and small enterprises to medium-sized companies and multi-national corporations, have stopped being exclusively reliant on print and electronic media.
Instead, most business verticals, whether operating in a B2B or a B2C platform, have started employing digital marketing tools in order to bolster their overall marketing plans. Although an effective digital marketing strategy is comprised of a multi-faceted approach, one of the most important instruments in its kitty is the highly competent technique of - search engine marketing (SEM).
Largely considered to be a type of digital marketing, SEM is an efficient and productive way of increasing website visibility, reaching new customers, trimming acquisition costs and generating consistent revenues by undertaking targeted website promotion on search engine result pages. Owing to its popularity, several professional digital marketing courses have started including SEM as their core constituent.
Skills Required for A Career In SEM
If you are aiming to be a digital marketing executive with a specialization in SEM, you will need to possess a diverse skill set. Apart from a certification obtained by completing a digital marketing course, you should be able to:
- Analyse various SEM methodologies.
- Understand paid/organic enhancement metrics.
- Support different optimization (SEO) techniques.
- Possess detailed knowledge of HTML and CSS.
- Conduct contextual and competitive keyword research.
- Apprehend newer versions of search engine algorithms.
- Integrate services with advertisement vendors.
With expertise in these, it would become possible for you to devise a customized SEM campaign that focuses on both, brand promotion and ROI augmentation.
The Scope of Search Engine Marketing
Having firmly established itself as a distinctive field of study, the scope of SEM has expanded beyond the usual digital marketing jobs. Be it the post of a junior assistant, a project manager, a freelance consultant or a senior executive, digital marketing companies have started opening their coffers for SEM specialists in areas like:
- Business development
- Event management
- Social media
- Web designing
- Pay-per-click (PPC) advertising
- Public Relations
- Web programming
- Paid searches
- Link building
And this is just the beginning! With an expected remuneration of about Rs. 2 L to Rs. 10 L per annum, the SEM industry is headed for an exponential growth that would provide enough opportunity for advancing the careers of both, entry-level and experienced executives.
- Search engines are bound to emerge as the primary source of gaining credible information about various marketable products and services.
- Motivated customers who are prepared to make quick purchases will be increasingly dependent on web-based mediums.
- The international presence of digital marketing platforms, especially search engine marketing, will begin witnessing a steady rise.
- With internet connections and mobile phones improving access, search engines would turn into the basic tool of bolstering conversions.
- Technological leaps will make online advertisements more targeted in terms of generating specific leads and preparing demographic profiles.
As compared to the other, more conventional marketing channels, the accuracy, and trustworthiness of search engines would remain unhindered.
In a highly complex environment, the demand for SEM experts armed with comprehensive background training in digital marketing basics will continue to grow by leaps and bounds.
In fact, the future of digital marketing will be determined by the ability of SEM professionals to provide measurable outcomes, enhance search engine results page (SERP) rankings and place relevant advertisements strategically. The scalability and sustainability of their solutions would provide the fundamentals upon which entire marketing campaigns would be built.
However, in order to carve out a niche for yourself in the search engine marketing arena, you would have to create a perfect balance of knowledge, skill, and experience. After all, SEM operates across the different parts of a marketing funnel and gaining proficiency in each one of its operations, is the only way to score a decisive edge over your competitors!
Saheli Roy Chowdhuri
Home > Blogs > 4 Benefits Of Edge Computing
As the Internet of Things, Artificial Intelligence, and Machine Learning technologies mature, and advanced use cases emerge, organizations are embracing Edge Computing to bridge the physical-digital divide. Here are 4 benefits that prove why Edge is the future of computing.
Home > Blogs > How AI is Powering Digital Marketing?
Despite the complexity and the fair share of difficulties, digital marketers are welcoming artificial intelligence as deep learning, machine learning, and natural language processing integrate to routine tasks. With AI-driven strategies, marketing teams are able to drive advanced customisations, cutting-edge personalisation, and simplified analysis. Using the colossal amount of data achieved through digital channels, AI holds the power to inspire target marketing, enhance customer experience, and garner a high ROI.
To help digital marketers understand the true potential of artificial intelligence in digital marketing, Manipal ProLearn organized a webinar to explore the impact of artificial intelligence on digital marketing.
The webinar - How AI is powering digital marketing? – was a live session by Gokuldas K, a digital marketing expert in FMCG, e-commerce, food tech, advertising, training, and e-learning industries, currently senior marketing manager at Swiggy.
Understanding the Role of AI and ML in Digital Marketing
Mr. Gokuldas initiated the session by explaining how artificial intelligence and machine learning can bring about a disruption in traditional marketing. Since modern processes are tasks fuelled by computers and programs, traditional marketing doesn’t serve the purpose well. He then, revealed some reassuring statistics – we can automate 50 percent of work tasks, processes, and activities with the help of technology and 80 percent of these processes can be automated using machine learning alone.
He further deciphered the processes that go into the carrying out of several marketing tasks by marketers. Today, marketers utilise machine learning and artificial intelligence for the following purposes:
Customer acquisition and retention.
When we receive a notification, customised discounts, and personalised feeds, what actually goes into this personalisation is a series of intelligent processes. A huge amount of consumer data is cleaned, processed, and analyzed to understand who the customer is, what they want, and what they are looking for.
How Artificial Intelligence is Transforming Digital Marketing?
Role of Artificial Intelligence in the Universal App Campaign
In 2015, Google launched its Universal App Campaign and we saw a new digital wave empowering digital marketing. It is an ad campaign form that allows businesses and developers to easily launch campaigns on Google’s ad networks including YouTube, Google Play Store, AdWord’s search, and display. In 2017, it was recorded that Google was successfully able to provide 6 billion installs to various developers.
The UAC campaign can be set up and executed in less than 10 minutes. However, the processes that form the backend of the campaign stays unapproachable and unknown to the marketer. Now, this is a matter of grave concern to a learned marketer who wants to control and optimise the processes that form the campaign.
Understanding the processes that run behind UAC can palliate many of these concerns.
During manual targeting in digital marketing, a marketer usually develops 10-15 different campaigns just to grasp the essence of the customer requirements. Here, manually optimising the campaign, acknowledging the factors to scale and descale, and moving towards the desired outcome is another herculean task. However, for machines, UAC backend in our case, it is achievable.
UAC is completely built on machine learning, which indicates that the ad campaign, though running at the backend, understands your user. The system recognises the customer, what they want, where to find them, and their differentiating characteristics. Knowing the past behavior of the users allows the system to automatically run the algorithm after optimising the required processes.
When setting up graphics, videos, and images for a campaign, every marketer optimises creatives to suit the requirements of the project. With UAC, you have the autonomy of uploading multiple videos, images, or creatives. The ML and AI processes executing at the backend will automatically optimise and align the requirements of the creatives with the ad campaign.
Thousands of differentiators, aspects, and features run along with these machine learning algorithms at the backend of the UAC. From the type of customer to devices used, every bit of information is gathered to further optimise the campaign for advanced campaign results.
However, one crucial question in front of the marketers is, ‘How does the machine understand your exact requirements?’
To make machines or algorithms understand the requirements, marketers need to acknowledge how machine learning works. For instance, a toy-store retailer is required to pursue parents to buy toys for their children as the child can’t do it himself or herself.
How do you make an algorithm understand this requirement?
It is through the knowledgeable inputs of a skilled marketer.
What are these inputs?
The goal of the campaign. For instance, installs or in-app events.
The KPI of the campaign.
The budget of the campaign.
The creatives and visual communication of the campaign.
The conversion tracking related to the campaign.
The input offered by the marketer forms the foundation of the UAC campaign. Take conversion tracking, for instance. If the conversion tracking is not enabled in the system, you would never be able to draw upon the knowledge of past user behavior. You would not be able to know the users who are signing up for your campaign.
Role of Artificial Intelligence in Programmatic Advertising
Delivering the right message to the right user at the correct time is the basis of programmatic advertising. It is a technology-driven automated method that allows buying and selling of online ad spaces.
Traditionally, this lifecycle involved personally contacting the ad-space providers for an ad campaign. Needless to say, it is an inefficient method as it would become humanly impossible to host an ad on various platforms.
Programmatic advertising is an ad-tool that integrates ads from various vendors at one place. With the help of DSP or Demand-Side Platform and SSP or Supply-Side Platform, digital advertisements are exchanged or purchased.
But, programmatic advertising also raises several questions regarding the execution of the ad purchase.
How does programmatic advertising work?
How to track the ads?
Who is able to observe the ads?
How ads can be purchased?
This is where AI and ML helps.
With the assistance of multiple market DSPs, the buyers can carry out several exchanges and reach out to a relevant audience. The DSPs utilise multiple data management platforms along with data incoming from SSPs, partners, ad-exchanges, and publishers. This data is analysed using an algorithm to answer all the above questions.
Below we have mentioned the tasks achieved by DSPs.
Prediction and planning.
Identification of the target audience.
AI-driven analytics and insights.
Moving Forward: Importance of Artificial Intelligence in Digital Marketing
The computers, machines, and processes utilised by machine learning understand the users and other aspects of the campaign through past data. It extracts data related to customer purchase, demographics, buying patterns, and other customer details to predict target audience, campaign budget, and end-goal.
Just like a human mind that learns when changes are observed in the environmental factors, a machine learning algorithm learns whenever a change is observed in the processes and inputs. These modifications become a source for constant learning and relevant future implementations of marketing campaigns.
However, human expertise is key to a successful campaign as the data lies with us. Deciding the goal, target audience, and budget of the project is rightly achieved with creative human processing. The machines can achieve these tasks, but human intelligence has the ability to consider several other factors and aspects that machines can’t. Hence, machine learning can transform digital marketing with the help of expert human guidance.
Saheli Roy Chowdhuri
Home > Blogs > Why the Future of Social Media Will Depend on Artificial Intelligence
Not having a social media account is more of an anomaly than a norm in the modern world. With close to 2.77 billion users on different social networks around the globe, these platforms have transcended their primary objective of connecting people in a virtual space and emerged as gold mines for marketing opportunities. On the flip side, however, the ever-expanding outreach of social networks is generating monumental mounds of unstructured data which hides a sea of information in its folds.
Businesses and marketing gurus need to make sense of this data and draw actionable insights from it to be able to leverage social media to its full potential. That’s where artificial intelligence (AI) comes in. With its ability to fuse cognitive technologies with machine learning, AI can make sense of big data. This would, in turn, make social network algorithms capable of delivering specific and relevant information to users, going well beyond the practice of working with targeted keywords. For this reason, anyone aiming to make a career in marketing, digital media, and social media marketing must learn AI to enhance their skill set.
Impact of AI on Social Media
Whether it is client outreach, image building, or nurturing business relationship, social networks are the king. As the business-influencer-customer network gains a stronger footing, social media has become an integral part of running businesses. Therefore, effective social media management is crucial to build up a brand and enhance its visibility. One of the key benefits of artificial intelligence for social media is its ability to segregate voluminous data as per trending topics, tags, and hashtags (depending on the platform) and then analyse it to reflect user behaviour trends.
AI algorithms are capable of monitoring millions of user comments, likes, shares, retweets and so on to understand users’ behaviour toward a post or trend, and thus, carve out personalised experiences that are in line with the end-user’s sensibilities.
If you learn AI and master its application effectively, you can use algorithms to generate content based on demographics and online activity. It is for this reason that most leading social networks today have collaborated with AI businesses to add more finesse to their platforms and catapult user experience to the next level. In this environment, an artificial intelligence course can set you up for impeccable professional growth. A large section of online marketing businesses and organisations today are exploring a way to leverage the benefits of artificial intelligence for social media growth. Some of the top ways AI tools are being used to extrapolate information on social media include:
AI-powered image recognition software is being used as an effective tool to gather actionable insights that can help decipher user patterns with the help of millions of images circulating on different social networks.
Virtual assistants and chatbots are one of the most impressive outcomes of AI. As you learn AI and get adept at its ways, you can use these highly sophisticated tools to facilitate seamless interactions between brands and users. The evolution of chatbots has been one of the biggest benefits of artificial intelligence as it has turned social media into a viable space of conducting business and increasing outreach.
The progress in the field of AI has also positively impacted the growing trend of influencer marketing. By using actionable insights from social media data, businesses can predict exact incentives to motivate influences to endorse a product or service as well as assess their performance through social media post engagements and conversions.
It is already nearly impossible to build up a brand without a relevant social media presence. Going forward, a mere social media presence will not be enough to create brand value and recognition. Optimising the visibility of the things offered by social networks with help of the right tools to maximise output, customers, and ROI (Return on Investment) will be the key. With many small and medium businesses lacking the resources required to be able to sustain a staff large enough to monitor social media trends, patterns, and insights, AI emerges as a viable alternative for finding the right answers in a cost-effective manner. With the help of third-party collaborations with AI organisations, even the smallest start-ups can leverage the benefits of artificial intelligence in tapping into vast pools of social media data.
The bottom line is, when it comes to expanding business outreach on social media, AI is the go-to.
Saheli Roy Chowdhuri
Home > Blogs > How Voice Search is going to impact your SEO
Gartner predicts that the average person will converse more with bots than with their spouse and that around 30% of website sessions will be conducted without a screen by 2020. If you've seen the movie "Her", you know what we're talking about. Furthermore, research by Comscore suggests that by 2020, 50% of all searches will be voice driven.
It goes without saying that the "Voice Search" technology is making its voice heard (pun intended) in the AI and SEO circles - quite literally. So, let's look at how voice search is reshaping the future of digital marketing and SEO-driven strategy simultaneously.
The first speech recognition system, IBM Shoebox, was introduced by IBM in 1961.
According to Buzzfeed, Google's AI went through 2,865 romance novels to enhance its conversational search abilities!
Top-4 Factors About How Voice Search Can Level up Your SEO Game
Make no mistake that voice search SEO requires lateral and strategic thinking in equal measure. Simply put, by understanding the intricacies of voice search optimization, you can address your target audience' concerns in a more intuitive manner.
How so? By enabling searchers to get exactly what they're looking for through conversational voice commands. Add to the mix, the use of SEO and voila - you've got yourself a winning combination. Let's explore this concept further and understand the top-4 benefits of voice search in the SEO big picture:
1. Improved User Experience
Voice search helps improve user experience as users can get answers quickly, accurately, and effortlessly. By extension, more and more people are beginning to warm up to the idea of using voice commands for a seamless search experience. This is compounded by the fact that 40% of adults are now using voice search at least once a day.
Key SEO-centric takeaway: If you wish to drive traffic to your website, integrate voice search across your branding platforms (mobile, website, etc.) for optimized results and greater footfalls.
2. Organic Climbing of Google Rankings
Over the years, Google has been placing greater emphasis on voice search optimization after witnessing its increased usage - one in six Americans are using a smart speaker to address their daily, localized needs.
Key SEO-centric takeaway: It only makes sense to optimize your brand's interface as per voice search requirements if you wish to stay on the "good side" of Google's web page (pun intended). Additionally, it is important to remember that customers go through the "first few" websites on a page expecting their queries to be addressed accurately and swiftly. So if you're not among the top-10, you might as well be the last one - it doesn't really matter.
3. Staying Ahead of the Competition
It's important to stay on top of the current and futuristic trends, as well as your competitors of course. With more than a billion voice searches taking place on Google every month (January 2018), you don't want to lag behind in the race.
Key SEO-centric takeaway: SEO strategies keep changing all the time, so it's important to integrate and balance the two technological strategies and get the most out of your buck.
4. Increased Footfall & Sales
In a study, Amazon recorded a month's sale for 2,000 products. The results were outstanding: Voice-powered commerce accounted for $1.8 billion in U.S. retail revenues in 2017 - a number expected to reach $40 billion by 2022. Clearly, using voice search can help boost sales.
Additionally, it is common knowledge that Google, and other voice assistants read results from voice search listings referencing other sites (your brand in this case) through simplistic phrases, such as “According to ___” or "As per ". As you may have guessed, this helps increase your brand's awareness.
Key SEO-centric takeaway: Naturally, both these factors - brand visibility and traffic, and sales - are integral components of standing out in the Google Analytic Report. So, integrate voice search to stand tall on your SEO claims!
Top Voice Search SEO Best Practices for Actionable Results
Here are some professional tips from research done by Backlinko to keep in mind if you're looking to implement voice-search SEO as an integral part of your marketing strategy:
Use long-tail keywords (phrases highly specific to your niche) to contextualize content.
Use "Question format" keywords which work best as most voice queries come in the form of questions.
Ensure great page speed and responsiveness. Research tells us that the average voice search result page loads 52 percent faster than the average page. Simply, focus on creating compressed files, optimized images, etc. for an agile platform.
Integrate short, conversational, and concise answers (not more than 29 words) to address voice search queries.
Infuse simple, easy-to-read content. Research shows that the average Google voice search result is written at the 9th grade level.
Avoid creating single pages dedicated to each voice query.
Use long-form content that focuses on local searches (22% of voice search queries focus on location-based content). Also, keep in mind that the word count of a voice search result page is typically 2,312 words.
Aim for creating content that ranks high in desktop search.
Strive to appear in a "Featured Snippet". Research suggests that 40.7% of all voice search answers came from a Featured Snippet.
Armed with all the relevant and proven facts and tips, we hope you can now use voice search to your advantage - SEO or otherwise. If you have more queries, reach us at <insert email ID> and we'll get in touch.
Saheli Roy Chowdhuri
Home > Blogs > Data Science and Analytics: Similarities and differences
The world is using data like never, and the terms Data Science and Data Analysis are used almost interchangeably today. In fact, many people think that a Data Scientist is just a fancy name for a Data Analyst. However, while they do sound similar and both deal with big data sets, they are inherently different. Let us first define each one and then look a little deeper into the similarities and differences between Data Science and Data Analytics.
To put data generation in today’s world into perspective, let us look at the following graph, which depicts how much data was generated every minute in 2018.
Data Science is a multidisciplinary field which has a much broader scope when dealing with data, where several techniques and tools are used to extract insights from data. In most cases, Data Science is used to scope out the right questions from the data set. It works at the raw level of data (structured, unstructured, or a combination of both) to build data models, to create more efficient machine learning algorithms, make predictions, and identify patterns and trends.
Some of the tools and techniques involved are clustering analysis, anomaly detection, association analysis, regression analysis, and classification analysis. Data Science works in the realm of the unknown, trying to find new insights and relationships in big data.
Data Analysis is a subset of Data Science. It can be defined as the process of applying statistical, logical, and analytical techniques to data sets to discover information that helps in making informed decisions. A data analyst can use several tools like visualizations, Business Intelligence (BI), data mining, and textual data analysis.
The information gleaned from data analysis is highly dependent on the quality of the data. Data analysis merely curates’ meaningful insights from past data but is generally not used for predictions. It is typically driven by business goals.
Similarities between Data Science and Data Analytics
Both work with big data to get better outcomes for business or society.
Differences between Data Science and Data Analysis
Data Science is used to formulate the right (unknown) questions, likely to be beneficial to the business.
Data analysis is used to solve questions that come from a business perspective.
A Data Scientist is required to have business acumen and the ability to create a story from the data.
A Data Analyst will likely be required to find straightforward answers to questions posed by the business.
Data Science is used to prepare the data for analysis by Cleansing, Processing, Massaging and Organizing the data.
Data Analysis is used to mine data to discover correlations and identify patterns.
Data Science uses data from different data sets to solve real world problems.
Data Analysis identifies data quality issues and generally uses a single data set.
Typical uses include Fraud Detection, Personalized Marketing, Social & Customer Analysis, Gaming, Weather Prediction, Dynamic Pricing, Mental Health Research, etc.
Typical uses include recommendation engines, loyalty programs, on-the-fly detection of trends, targeted advertising, etc.
Data Scientists use tools like Python, SAS, R, SPSS, Matlab, Scala, Hadoop, PIG and Hive.
While differences do exist between data science and data analysis, together they form the future of our data driven world. Be it for business, personal, social, medical, or naturally occurring phenomena, embracing these technologies will make a significant difference in our lives. Their contributions have already started being felt in our daily lives and further advancement in areas like machine learning and artificial intelligence should truly prove to be of great use.