Consumers want fast solutions to their problems. With the help of unprecedented innovation in technology, digital transformation empowers businesses to improve the overall business structure and, most notably, the customer experience. While it always made sense to adopt digitization across companies, but the adaptation of digital transformation has still been slow.
Amid the pandemic, the need for transforming digitally has never been more urgent. Businesses that neglect the transformation will likely be left behind and risk losing their market position.
How can you embrace digital transformation successfully? Consider these ideas:
Switch from being product-focused to being customer-focused mindset
Embracing digital transformation holds special significance for customer experience. The primary focus should not be your product features; instead, more emphasis should be put on understanding and catering to your target customer’s wants and requirements.
If you clearly understand your customers’ problems and extend them a customized experience to resolve them, they will become your loyal customers. The key to earning loyal customers is paved by understanding their problems and offer them a customized experience that can solve their problems.
Scale-up creating innovative digital experiences
With technology pacing forward continuously, customers expect businesses to produce personalized digital content faster and cheap (or even free).
Accordingly, businesses must adapt to this trend and swiftly scale their digital designs, content production, and collaborations to keep their customers engaged, interested, and responsive.
Create your customer journey without depending on technical teams.
We know what kept you wondering, whether it’s even possible to create a digitally customized customer journey without a technical team?
It’s possible! Businesses don’t necessarily have the complete skill sets and teams required to execute the desired action when starting from scratch. However, the market now possesses plenty of new technologies that entrepreneurs and businesses can leverage.
Also, with the boom of low-code or no-code technology, it has become hassle-free to find code-free platforms. For example WordPress and Wix for hosting; Squarespace and Canva for content and website design; Hotjar and Google Analytics for analytics visualization.
Low-code or no-code technologies are designed specifically for entrepreneurs who may not have any technical or design background to efficiently create digital experiences without having to recruit a different team.
Enable remote workforces and automation
Amid the pandemic situation, businesses across the world shifted and aced the remote-work setup. Advancement in technology and adapting digital transformation across the organization thoroughly, employees no longer need to work from offices in a specific place only all the time. Robots can even substitute some responsibilities. For example, in-store robots manage transactional tasks like checking inventory in store aisles and fulfilling small orders.
To enable remote workforces, count on using project management tools, to stay connected with your team with virtual conference platforms.
In addition to an inclinedmindset that understands and implements this new management concept, it is essential to implement an effective strategyand use the right technology.
Digital Strategy:
Digital strategy requires creating a digital culture in the organizations that follow a clear and combined transformation strategy to realize the organization’s digital maturity, which begins with the vision: think digital at all levels stretched across all the departments, from the senior managers to the last employees, that includes clients, middle managers and also external stakeholders or collaborations.
This digital strategy also requires continuous restructuring and rethinking of the business model, thus maintaining a culture that constantly adapts to market trends and the transformation of products and services.
Digital Technology:
ADigitized Companypractices all possible digital technologies to optimize management and satisfy all personalities involved in the business (employees, customers, suppliers, etc.): automation process, digital work stations and mobility, Big data, electronic documentation, sensors, and the Internet of things (IoT), etc.
Moreover, these technologies must be thoroughly integrated and with business management to be truly useful (a factor usually overlooked and causes most digital transformation process failures)
Although this might seem obvious at first, yet the majority of reports and interviews by consultants and experts in digitization usually fail to consider the essentiality of task coordination.
The time for digital transformation is now!
The pandemic was also a wake-up call for businesses to embrace their digital transformation journey. It won’t be easy to transform how you’ve been running your business so far but know that the beginning is always the hardest. With the proper practices we mentioned in this article, you can successfully get ahead in your transformation journey and keep your business striving!
Worldwide the necessity of data science has become very vital in many industries, they are using it to grab valuable insights to stay ahead of the competition. Each industry has a massive amount of data that they don’t know what to do with it. The need for professionals in data science has grown immensely in all industries because only they can understand the data.
People who choose a career path in data science can prove their skills in big data platforms by doing certification programs through several learning institutions that offer certified data scientists both online as well as offline.
If one wants to get certification in data science, then there are many ways they can choose from. In this article, let’s dive deeper and know the best certifications that are in high demand to be an expert in data science.
SAS offers multiple data science certifications that are mainly focused on SAS products. One among them is SAS Certified Big Data Professional Using SAS 9 offers registrants insights into Big Data with the help of a variety of open-source tools and SAS Data Management tools. They will be using intricate ML models to create business recommendations for deploying the models at scale with the help of a robust and flexible SAS environment. To attain a SAS Certified Big Data Professional Using SAS 9 the applicant is required to pass all 5 exams that consist of short-answer, interactive questions, and a mix of multiple-choice. These are the following five exams:
· SAS Certified Big Data Professional:
SAS Big Data Programming and Loading
SAS Big Data Preparation, Statistics and Visual Exploration
· SAS Certified Advanced Analytics Professional:
SAS Advanced Predictive Modeling
Predictive Modeling Using SAS Enterprise Miner 7, 13, or 14
SAS Text Analytics, Time Series, Experimentation and Optimization
DASCA is an industry-recognized certification body, which provides certifications for senior data scientists. These certifications provide professionals best acumen and capabilities to anticipate and appreciate the requirement to deploy the latest Data Science techniques, tools, and concepts to manage as well as to harness Big Data across various verticals, environments, and markets. DASCA tests every person’s ability with the world’s most robust generic data science knowledge framework. The certification programs include a complete range of essential areas of knowledge. It approaches, initiatives and programs work toward developing every professional’s knowledge to address the challenging objectives of Big Data stakeholders globally.
Data Science professionals across 183 countries can take DASCA certification exam. Take these certification programs and study from the most advanced Big Data learning resources ever. Its certifications are based on the renowned and comprehensive Data Science Body of Knowledge (DASCA-DSBoK™) designed around the seminal Data Science Essential Knowledge Framework (DASCA-EKF™).
Being a certified data scientist professional will help you to perfect for reaching horizons of information, specially designed for big data engineers, big data analysts, and data scientists. The following are the certifications: The following certifications are:
· Data Scientist Certifications
DASCA Data Scientist Certifications address credentialing needs of senior, accomplished professionals that specialize in managing and leading big data strategies and programs for firms and have proven competence in leveraging big data technologies for generating mission-critical information for firms and businesses.
The SDS™ credential is a perfect proof that an individual has taken a massive step in mastering the field of data science. The skills and knowledge one can attain by doing this certification will set them ahead of the competition. This credential program has five tracks, which will appeal to various applicants — each track has different prerequisites in terms of degree-level, work experience and requirements to apply.
Principal Data Scientist (PDS™)
This credential consists of three tracks for professionals with 10 or more years of experience in big data. The exam covers basics to advanced data science concepts that include big data best practices, business strategies for data, developing cross-company support, ML, NLP, scholastic modeling and more.
SDS™, PDS™ credentials exam duration is 100–minutes online exam. And a complete exam preparation kit will be offered by DASCA.
The Dell EMC Education Services provides Data Science and Big Data Analytics Certification to evaluate the in-depth knowledge of a person in data analytics. The exam especially lays emphasis on analyzing and exploring data with R, data analytics lifecycle, creating statistical models, choosing accurate data visualization tools, and applying several analytic techniques, Data Science aspects like Natural Language Processing (NLP), random forests, logistic regression. There are no specific pre-requisites to enroll in this certification program.
By doing a certification program is very useful as it ensures to improve the skills and a person can be a valuable asset to the company in which they work in. Certifications are the perfect investment when an individual wants to grow in their respective careers.
Instant Grocery Delivery is the startup hype of the year in Europe. You select a few groceries via the shopping app, pay via Paypal, and 10 minutes later, a bike courier is at your door with your purchases. It’s a business model that spreads magic among the users. A few months after launch, I know friends who do almost half of their shopping this way. It’s a multi-billion dollar idea like Uber. A business model that is so easy to explain and still magical? But there are also apparent problems with highly disruptive business models like this:
Overworked bike couriers going on strike.
Issues with the districts because of noise pollution from warehouses located in the middle of residential areas.
A low margin on products and little price tolerance from customers.
Business growth is occurring geographically from district to district and city to city for companies like Gorillas.
The colossal competition (I count 12 providers in Germany alone by now).
The US company GoPuff, founded in 2013, is considered a pioneer for the startups Gorillas, Flink, Zap, or Getir. GoPuff makes data-driven decisions to minimize the risks mentioned above. To boost these ambitions, GoPuff recently acquired the data science startup RideOS for $115 million. In markets with aggressive pricing, for many direct competitors and existing substitutes building a competitive advantage quickly via technology has proven to make the business model more efficient. A bold but also expensive move by GoPuff. In this article, I will show how to integrate within a day geospatial analytics for an instant grocery delivery use case without spending multi-millions on a startup acquisition.
But how exactly can we think of data-driven decision-making for instant grocery delivery? Assets that are important to optimize are:
Where should I set up warehouses?
What is the optimal size of the drivers fleet?
What are the preferences of target customers in the region?
How big is the market potential overall?
In this article, we ask ourselves the fictitious question, should an instant grocery delivery company go to the outlying Berlin district of Pankow? We do this using external data sources that can scale globally and use the data integration framework of Kuwala (it’s open-source). With Kuwala, we can easily extract scalable and granular behavioral data in entire cities and countries. Below you see activity patterns at grocery shops in Hamburg. We will make use of some of the functionalities to derive insights from the described areas.
[embedded content]
We start our analysis by comparing the data on a neighborhood of Pankow with the neighboring part of PBerg (“Prenzlauer Berg”). The two selected areas are similar in size (square kilometers). Using the Kuwala framework, we first integrate high-resolution demographics data. On a top-level view, they are comparable to each other in total and within subgroups of gender and age.
In the next step, we analyze the current status quo of Point-of-Interests regarding groceries (e.g., supermarkets). We build the data pipeline on OpenStreetMap data and extract categorization and name as well as price level. We combine that data with hourly popularity and visitation frequency at those POIs.
We find that Pankow has significantly fewer supermarkets per square kilometer. In addition, it shows that the price level of grocery stores is much higher in PBerg. Furthermore, we identify that groceries in Pankow are +10% more visited during the evening than PBerg. In summary, we can assume now that people in Pankow…
… travel longer to supermarkets on average.
… often spend more time in the evening hours in supermarkets.
… have a lower price elasticity towards groceries.
Companies can now use that information in a market entry strategy. An aggressive cashback activation convinces people in Pankow to skip the evening shopping in a supermarket for a comfortable way of receiving the purchases right at their door.
We aggregated the high-resolution demographics data on an H3 resolution of 11 (based on raw data representing 30×30 meter areas). By that, we can analyze in-depth the distribution of people in a comparatively small district.
We can spot areas with a high population of the young target demographic and less reachable options for doing groceries.
In addition, we can spot micro-neighborhoods with a low population density, which makes those areas a perfect spot to open a warehouse, close enough to service areas and further away from people who could be disturbed by noise.
In the next part of this article, I will share some more advanced algorithms to identify over- and under-served areas and put everything at scale by comparing entire cities and the popularity of those places. If you want to discuss geospatial topics with us in the meanwhile, I recommend joining our slack community.
Automated messages like “Hi, how may I help you?” are quite familiar when a customer requires some service online. Want to know what’s that? These are the chatbots for businesses that have improved customer service by making service available round the clock, and64% of online users are satisfied with the automated system.
As a fact, chatbots will most likely take care of85% of all customer dealings by early 2022. Almost50% of businesses prefer chatbots to mobile apps, proving it a viable future ofcustomer service. Let’s see what they are!
1. Boost Customer Service
The automated messaging feature of Facebook Chatbots can garner an immediate response from the customers. A well-developedFacebook Messenger Chatbotwith an inbuilt cache of FAQs provides quick answers to customer queries. Moreover, chatbots can offer multiple-choice responses to understand the specific needs of a customer.
The quick response from chatbots permits customers to make their purchase decision faster and lessens the probability of shifting to a competitor.
Vital Statistics show that there are more than300,000active bots on Messenger. This implies that businesses can thrive well in the competition with the help of Facebook Chatbots.
Currently,conversational marketingthrough Facebook has a lead of roughly 70% higher open rate than email marketing.
A use case is Domino’s chatbots on Facebook. The chatbots allow customers to choose their favorite dish from a plethora of items and place orders. The chatbot links the customer’s Facebook account to their Domino’s account. Customers can track their orders, seek support, and do many more things. These digital innovations have helped Domino’s increase their customer base by allowing them to have a good experience on their platform.
2. Offer Personalized Recommendation
Customers can view online catalogues of your store within the Messenger application. For instance, Shopify offers e-commerce stores with The Messenger Sales Channel engineered by chatbots that enables buyers to browse products through Messenger. Once buyers make a purchase decision, they will be automatically redirected to your website. The messenger chatbots via the sales channel let companies send automated notifications regarding their orders. Such chatbots can come as a blessing for small businesses.
Few brands move beyond customers’ expectations and use chatbots to recommend during purchase. Rather than searching many products independently, the customers may ask for suggestions as per their choice of products. Conversational AI plays a significant role in this.
Babylon Health, a renowned British online subscription service, has taken the help of chatbots to provide consultation based on the patient’s medical history and can contact patients via video call from a physician.
3. Collect Feedback Seamlessly
Your Facebook chatbot can effectively conduct a brief survey for customer feedback in a conversational manner, almost like human interactions. Thus, in a few clicks, your company can gather vital information and form an idea of buyers’ response to your brand, products, or services.
Chatbots save your customers’ time, for they just need to click rather than typing out. Prepare a satisfactory scale or few statements for the customers to choose from. A meticulously-designed chatbot boosts the process of getting feedback from your customers.
Take the use case of a typicalsurvey chatbot. The Facebook chatbot asks the buyer if they would like to participate in the survey. Once the buyer gives their consent, the survey starts instantly. The buyers don’t even have to take the pain of typing anything. They can just select from the ‘options’ furnished below the question to progress through the survey. On top of that, GIFs, images, and videos displayed above the questions make the survey fun and less tedious.
WotNotprovides you with some wonderful ways to createFacebook chatbotsfor business. These chatbots are based on conversational AI, and you can deploy them for flawless feedback collection.
4. Makes Scheduling Appointments Easier
You can use Facebook Messenger chatbots to schedule appointments for your customers. Booking a slot for an appointment through a bot lets customers schedule appointments anytime, without the hassle of contacting a customer service representative.
The beauty brand Sephora enables customers to fix appointments using the Facebook Messenger chatbot. By opting for “Book A Service,” the buyer is directed to a trail of questions from the Facebook chatbot. It helps them select the location and services they would like to schedule. Finally, the chatbot generates a scheduling pop-up that lets customers select a particular slot available at the store. Once the time is fixed, Sephora collects the email and name of the user from Facebook to finalize the appointment.
An11% upsurge was seen in in-store booking conversation rates after they introduced the scheduling chatbot. Allowing customers a separate way of scheduling their appointments through Messenger also helps the in-store employees to converse and connect more with customers on the spot personally.
5. Enhance Brand Awareness
Your brand’s Facebook chatbot enables customers to know about what your company does, especially when interacting with people who have forayed into your brand’s ambit of influence. This is an impactful way to capture customer attention, moving them down your sales funnel because marketing via Facebook Messenger has 10-80 times better engagement than email and incurs70-80% open rates on an average.
You can directly present your brand as a part of your chatbot conversation by telling people about your business’s latest event, which might have been an exciting project. In this way, audiences can stick to your brand advertisement.
An interesting use case is the Upbeat Advertising Agency, whose Facebook chatbot allows users to develop awareness about the agency directly as part of its bot conversation. The agency messenger bot gets Facebook users started by letting them know about a recent event or an exciting project that Upbeat has been a part of. Such tactics are likely to capture the audience’s attention.
6. Influence Customers to Visit Your Product Page
Once you warm your audience up via Facebook Messenger, you can start directing them to your product pages. As Facebook messenger bots can be conversational and amiable while communicating with their target audience, the whole interaction looks pretty natural and not like a sales pitch. However, if you do not want to direct people to your product pages in this way, include a shop button to the menu, but a disciplined conversation does help.
Burberry, a luxury brand, has a well-organized bot conversation facility on its Facebook page that provides visitors with the option to browse its products in both the menu and the conversation.
With Facebook messenger providing highly engaging and personal communications,40million businesses have taken to this platform to set up amiable interactions with potential customers and increase their sales. Thus, Facebook Chatbots can play a crucial role insofar as effective customer engagement and conversational marketing is concerned.
Facebook messenger has been prospering exponentially over the past few years and became a well-performing mobile platform rather than simply an app. As a fact, about3,00,000chatbot developers have joined the Facebook messenger app.
7. Enable Shopping Directly Via Facebook Messenger
A “Buy Now” button lets customers enjoy a seamless buying within the Messenger app, cutting short the buyer journey and increasing conversion rates. The Facebook chatbot will fill out the form automatically with users’ data during this quick checkout process.
Beauty Gifter, the Facebook messenger chatbot for L’Oréal, aims to enhance personalization. The messenger chatbot gets to know every buyer’s needs and choices and makes customized product recommendations from 11 L’Oréal brands, integrating with L’Oréal’s e-commerce system for checkout. Beauty Gifter chatbot statistics prove27times better engagement than email, 31% detailed profiling, and 82% buyers loved the experience.
Around$8 billionwill be saved by 2022 from businesses using chatbots, as per IBM. Also, 85% of customer conversations with businesses will occur via chatbots by 2020, as per Gartner, and 53% of customers would heartily text than call a customer care agent.
8. Notify Customers With Broadcasts to Increase Customer Retention
Facebook Messenger chatbots for business can convey your brand’s message by making it effectively engaging, which can compel the target audience to make the right decision. Its high click-through and open rates will bear the right results for your brand’s marketing intent. A bland email template like “Your Cart Is Waiting” might not have what the subscriber wants. Hence, the subscriber will most likely not open the mail. However, crisp and short broadcasts automated by Facebook chatbots using friendly emojis and stickers make them more persuasive.
Observe your organization’s internal style of pitching, customer care terminology, advertising strategy, etc. This will provide you with a strong conscience of your voice of exposition and the attitude to use in your Messenger broadcasts.
A simple use case is a Facebook chatbot forging relationships by sending broadcasts to customers to educate them about your brand. For example, if you own an athletic store, your target customer base must be people who love running a marathon and you aim to sell more and more sneakers.
Use your Facebook chatbot to create a sequence of messages, with each message consisting of an actionable tip to convince them to get started. This implies your sequence gives them insightful information on how to run their first marathon with the expectation that when they need to purchase running shoes, your brand is the first one they would think of buying from.
9. Adding Augmented Reality to Customer Experience
Since customers have been opening up to chatbot-based communication, companies are going one step ahead to include Augmented Reality (AR) and conversational AI to make the customers’ experience more immersive. Companies like POND’S, Sephora, Ikea, etc., incorporate AR and conversational AI in their chatbots to make them more targeted and precise.
Advantages of using Augmented Reality and Artificial Intelligence are their extraordinary selling point, improved experience with a personalized experience to the customers, and the enhanced prospects of earning gravity-defying revenue.
One of the famous use cases is that of Victoria Beckham, who is among the many fashion designers to incorporate Augmented Reality as an integral part of her chatbot, producing impressive results. She owns one of the best Facebook chatbots we have ever seen. She uses her Facebook bot to enable users to use their camera to try on her sunglass collection to see if or not they will suit them. This is an innovative tactic to boost conversions.
10. Generate Leads
Chatbots in Facebook Messenger can add an edge to your sales approach. By communicating with users, you can know their preferences and categorize them and identify your leads. All you need to do is adjust your bot’s situations to your sales funnel and develop a positive buyer experience.
You can use your Facebook chatbots to find out the challenges your potential buyers face regarding a product or a service by asking some multiple-choice questions and then offer valuable suggestions and enable accessible contact. By entertaining consumers through a chatbot, you can engage your customers.
Bots can contribute to your business by nurturing leads. You can send regular automated messages personalized with a follow-up or an interesting new piece of content that can keep your customers hooked on. You can build a formidable bond with potential buyers and increase your leads.
Conclusion
While revising your social media policy, do not forget to includeFacebook Messenger Chatbot for businessin it. Begin with simple FAQs and automated answers to enhance the quality of customer support, and add more options over time, e.g., product recommendations, content distribution, and events. Do not flinch while interacting with your customers in a more engaging discussion, albeit through Facebook chatbots. In this way, you can develop better and more long-lasting relationships with them.
TryWotnotfor creating highly advanced no-code chatbots for business/small business. Wotnot’s state-of-the-art analytics dashboard lets your brand understand customer insights more deeply and use this knowledge to strengthen conversational marketing.
Instant Grocery Delivery is the startup hype of the year in Europe. You select a few groceries via the shopping app, pay via Paypal, and 10 minutes later, a bike courier is at your door with your purchases. It’s a business model that spreads magic among the users. A few months after launch, I know friends who do almost half of their shopping this way. It’s a multi-billion dollar idea like Uber. A business model that is so easy to explain and still magical? But there are also apparent problems with highly disruptive business models like this:
Overworked bike couriers going on strike.
Issues with the districts because of noise pollution from warehouses located in the middle of residential areas.
A low margin on products and little price tolerance from customers.
Business growth is occurring geographically from district to district and city to city for companies like Gorillas.
The colossal competition (I count 12 providers in Germany alone by now).
The US company GoPuff, founded in 2013, is considered a pioneer for the startups Gorillas, Flink, Zap, or Getir. GoPuff makes data-driven decisions to minimize the risks mentioned above. To boost these ambitions, GoPuff recently acquired the data science startup RideOS for $115 million. In markets with aggressive pricing, for many direct competitors and existing substitutes building a competitive advantage quickly via technology has proven to make the business model more efficient. A bold but also expensive move by GoPuff. In this article, I will show how to integrate within a day geospatial analytics for an instant grocery delivery use case without spending multi-millions on a startup acquisition.
But how exactly can we think of data-driven decision-making for instant grocery delivery? Assets that are important to optimize are:
Where should I set up warehouses?
What is the optimal size of the drivers fleet?
What are the preferences of target customers in the region?
How big is the market potential overall?
In this article, we ask ourselves the fictitious question, should an instant grocery delivery company go to the outlying Berlin district of Pankow? We do this using external data sources that can scale globally and use the data integration framework of Kuwala (it’s open-source). With Kuwala, we can easily extract scalable and granular behavioral data in entire cities and countries. Below you see activity patterns at grocery shops in Hamburg. We will make use of some of the functionalities to derive insights from the described areas.
[embedded content]
We start our analysis by comparing the data on a neighborhood of Pankow with the neighboring part of PBerg (“Prenzlauer Berg”). The two selected areas are similar in size (square kilometers). Using the Kuwala framework, we first integrate high-resolution demographics data. On a top-level view, they are comparable to each other in total and within subgroups of gender and age.
In the next step, we analyze the current status quo of Point-of-Interests regarding groceries (e.g., supermarkets). We build the data pipeline on OpenStreetMap data and extract categorization and name as well as price level. We combine that data with hourly popularity and visitation frequency at those POIs.
We find that Pankow has significantly fewer supermarkets per square kilometer. In addition, it shows that the price level of grocery stores is much higher in PBerg. Furthermore, we identify that groceries in Pankow are +10% more visited during the evening than PBerg. In summary, we can assume now that people in Pankow…
… travel longer to supermarkets on average.
… often spend more time in the evening hours in supermarkets.
… have a lower price elasticity towards groceries.
Companies can now use that information in a market entry strategy. An aggressive cashback activation convinces people in Pankow to skip the evening shopping in a supermarket for a comfortable way of receiving the purchases right at their door.
We aggregated the high-resolution demographics data on an H3 resolution of 11 (based on raw data representing 30×30 meter areas). By that, we can analyze in-depth the distribution of people in a comparatively small district.
We can spot areas with a high population of the young target demographic and less reachable options for doing groceries.
In addition, we can spot micro-neighborhoods with a low population density, which makes those areas a perfect spot to open a warehouse, close enough to service areas and further away from people who could be disturbed by noise.
In the next part of this article, I will share some more advanced algorithms to identify over- and under-served areas and put everything at scale by comparing entire cities and the popularity of those places. If you want to discuss geospatial topics with us in the meanwhile, I recommend joining our slack community.
Ever thought about what would have been our world without data science? Many and many things would have been different. Understanding customers has been only possible in person; experience would have been the only critical factor to take new risks without knowing or predicting the ultimate outcomes.
Thanks to AI and machine learning, they stand today: tracking and analyzing data can never be this easy without them. With a few clicks, one can generate very accurate data using various filters to differentiate the odds.
Introduction
But the small businesses are always in the spotlight, from being the best in the locals to opening their branch in the big cities and continuing their legacy for which they are best known. Without the right set of data, they also suffer, bear a huge loss and even diminish.
Without the right team and tools, sustaining in the highly competitive business world is highly sturdy. If you own a small business, you have to become more cautious and think about leveraging data science into your small business and scale it.
Having said that, here are five expert tips to power up data science use in your small business. Let’s dive in.
Every business has its strategies, no matter how big or small they are: and that makes them stand unique in the market. With effective branding, advertisement, and customer experience, along with the quality of products they deliver, they establish their position in the market. That’s how one brand differs from the other.
The Data Science Strategies That You Need To Scale-up Your Small Business Are:
Hire A Data Scientist With 2 – 3 Years Of Experience (In Your Relevant Industry)
When you are a business, many employees work under you. Treat them so well that you let your employees become the voice of your brand to attract new and existing customers. Let’s say you run a SaaS startup; hire a data scientist who has a good 2-3 years of experience as a data scientist and has already been working in the SaaS industry.
Then he always has a good understanding of data that your companies require; just let him know your objectives and goals, and he can help you in better ways. He can find and analyze new trends, get the customers’ preferences, and do many things. However, hiring can be costly when you have the best professional in your team. But if you feel you don’t have that much budget, upskill one of your employees, or work with a consultant who can guide you in the right direction.
Using Right Set Of Data To Make Better Decisions
The right set of data matters a lot if you want your data to be accurate. And this dataset shall be packed with concrete evidence and statistics that you need for your business. For this purpose, data wrangling is necessary to differentiate the odd ones.
Therefore the best ways to look into data are:
Collecting survey reports to identify products, services, and features.
Conducting user surveys to find out how well they relate to your product.
When launching new products to understand how a product might perform in the market
Determining business threats and new opportunities
Right Tools And Softwares That Makes Your Work Super-Easy
Gathering data and analyzing them is a humongous task. It can kill all your productivity manually and even give you a headache when your work is not over on time. And when you do manually, there are high chances your results won’t come accurate, and you might miss a slice of data for the same reason.
Python and its libraries are an excellent tool for data science that can do a lot of work in minimum time. But having one data visualization tool either from Tableau or Power BI will help you understand unstructured data and make complex decisions easy-going.
Thus, you master MYSQL, Excel, Python, R, Tableau, Microsoft Azure, Apache Spark, Big Data, and Hadoop to get most of your work done.
Identify And Target New Customers Having Existing Customers
You’ll have many existing customers speaking about your business who love what you sell and come back to you for their next purchase. But what about new customers, how can you target them better, what they like most, and so many questions.
From identifying where most of your customers come from, how they interact with your products, how your products can give a permanent solution to one of the problems. And best customer service wins you many new customers through word of mouth.
The best way to get insight is by running ads for local and nearby places and diving into a google analytics dashboard that gives you a complete understanding of how your customers interact with the ads they see. Their location, area of interest, and much more. And you can get it from the marketing team, combine it with yourdata science, and produce a robust report.
Discover New Trends And Opportunities To Scale-up Your Business
To be at the top of the business, you need to follow the ongoing trend and look for the opportunities that your competitors lag. When you fill those gaps, you build trust in your customers’ minds.
As a data scientist, your primary work is to do research, come up with concrete ideas, and plan effectively. Suppose you want to sustain and be at the top of the business. When you do thorough research using advanced tools, you find better opportunities. Try them out to discover how they work for your company (necessarily a dry run at least) to collect your customers’ feedback.
If it works, then great. If not, you can look for even better ideas. business is all about taking risks, but calculated ones (so it won’t affect you much.)
Final Words
Taking new and calculated risks is a new approach to grow your business fast. But when you don’t research and invest, you face a significant loss, and it’s tough to recover. And if you run a small business, it’s not like your business can never go big.
You can make it big, but the right strategies, mindset, and team will help you achieve the same. This blog taught about five best practices to power up your data science use in your small business. Let’s know your thoughts and how you would implement data science in your small business, and which one you find most helpful.
Data is powering this century. There is an abundance of data coming from the digitized world, IoT devices, voice assistants like Alexa & Siri, fitness trackers, medical sensors to name a few. Data Science is becoming the center of growth hustling sectors like healthcare, logistics, customer service, banking, finance., etc. AI and Machine Learning are now mainstays in boardroom conversations and with this data-centricity also comes the big question around governance and ethics in data science.
Step 1. Acknowledge Bias
Are we ethically responsible for handling data?
Everyone is responsible for handling data with utmost care. Bypassing ethical data science just for monetary gain fosters bias and stereotyping. Similarly, cross-validating real-world data against the biased data results in an inconsiderate business decision reducing not just monetary gain but most importantly its reputation and customer loyalty.Every enterprise is responsible to grow its business bycultivating togethernessamong communities by being more inclusive and filtering out any unconscious bias.
What are the effects of unethical data science?
Data Privacyis becoming a major concern with more and more machine learning models learning our digital footprint and predicting our future necessities whether we like it or not. Legislations like GDPR(Europe), Personal Data Protection Act(India), California Consumer Privacy Act (CCPA) stresses the importance of data privacy, protecting digital citizens from dangerous consequences of misused data.
Micro-targetingbased on consumer data and demographics is influencing the action of the targeted consumer segments. With an abundance of data, it is becoming harder and harder to differentiate truth from falsehood. Micro-targeting without the proper understanding of data and its source leads to more harm than good.
Healthcare prediction failures,like IBM Watson, leads to irreversible consequences. Right now, the healthcare industry is undergoing a major revolution with Artificial Intelligence. The success of AI in healthcare depends on a one-team approach with transparent discussion from a diverse set of leaders from both healthcare and data science.
Facial RecognitionSoftwares are known to falsely classifying people with criminal intent based on one’s skin color as the ML models are trained with predominantly white faces.Multiple facial recognition applications are available in the market. But the success of the application depends on the diverse set of data used in training the facial recognition models.
Step II. Understand Bias
1. Know the Bias Types
It is very crucial to understand the different bias types and be conscious of their existence to handle data ethically. Bias in Machine Learning can be classified into Sample, Prejudice, Measurement, Algorithm, and, Exclusion Bias
a. Sample Bias
Sample Bias arises from misinformed information where training data contains either partial information or incorrect information. For instance, predicting the spending activity of a customer based on their social feeds and not from relevant payment platforms leads to sample bias.
b. Prejudice Bias
“Our environment, the world in which we live and work is a mirror of our attitudes and expectations –Earl Nightingale“
Being prejudice with preconceived opinion cause more harm not just to the business, but also to the society and well-being of our future mankind. It takes immense strength to acknowledge and eradicate any unconscious bias.
c. Exclusion Bias
Everyone is unique with their own abilities and strength. Just because some of us do not follow the norm, are by no means subjectable to exclusion. Each one of us has our own unique qualities to contribute. Enterprises not adopting inclusive policies will be out of the market in a short time.
d. Algorithm Bias
Machines do not understand bias. The erroneous assumptions often made when selecting the datasets and algorithms either consciously or unconsciously, lead to algorithm bias.
e. Measurement Bias
Measurement Bias usually happens when a model favors certain outcomes over others. A model predicting the sales target of consumer products that will double in the next quarter based on past sales history will favor items whose prices were marked low over others.
Step 3. Eliminate Bias
Eliminating Bias is not a one-time activity, rather a continuous process. Bias elimination starts from selecting the right algorithm and setting the data governance team with all the members involved in the ML project lifecycle including the business team, data scientists, and MLOps team.
Models are less prejudiced if the test datasets are from the real world rather than from the sample set. Real-world data also offers the advantage of being diverse and inclusive in nature as the data is from real customers. But at the same time, including data from active customers alone will not solve the inclusion problem. Such unconscious bias can be detected by having Human-in-the-loop along with continuous monitoring.
Summary
With data growing exponentially and legislation controlling data usage, it becomes crucial to exercise data consumption for common goodness. Fostering togetherness by collaborating with people from different sectors, being socially responsible and accountable for ethically using data will become the foundation for the successful AI revolution.
Data ingestion is required for organizations and businesses to make better decisions in their operations and provide better customer service. Businesses can understand the needs of their stakeholders, consumers, and partners through data ingestions, allowing them to stay competitive. Data ingestion is the most effective way for businesses to deal with tons of inaccurate and unreliable data.
How is data ingestion done?
It is performed in various ways. Top of these ways include;
Real-time – Ingesting data in real-time is also known as streaming data. It is the most crucial method of ingesting data, especially when the information is time-sensitive. In this method, data is retrieved, processed, and stored in real-time for real-time applications, such as decision making.
Batch – The batch approach entails shifting data at predetermined times. This method is excellent for recurring processes, such as reports that must be generated on a regular basis, such as daily.
Lambda Architecture – The lambda architecture is a method that combines real-time and batch procedures. This strategy combines the advantages of the two methods. It makes use of real-time ingestion to extract information from time-sensitive data. It also makes use of batch ingestion to provide a broad view of recurring data.
Best Practices:
Self-service data ingestion
Many organizations have multiple data sources. All of this data must be ingested before it is stored and processed. Data continues to grow in size and metrics, requiring enterprises to continue to add the resources required to manage it. If the ingestion process is self-service, it relieves the pressure to constantly expand resources through methods such as automation, and the focus is now switched to processing and analysis. The ingestion process becomes very simple, requiring little to no assistance from technical personnel.
Automating the process
As organizational data continues to grow, both in volume and complexity, manual techniques of handling and processing it can no longer be depended on. The need to automate every process along the way increases to see that you save time, reduce manual interventions, minimize system downtimes, and increase the productivity of the technical personnel.
Automating the ingestion process offers additional benefits including; architectural consistency, error management, consolidated management, and safety. These benefits come in handy to reduce the time taken to process data.
Anticipate challenges and planning appropriately
The imperative of any data analysis is to transform it into a usable format. As data continues to grow in volumes and type, so do the complexities of data analysis. When there is a process that can help you anticipate these challenges in advance, you will have an easier time completing the whole data processing task successfully. Data ingestion is one big process that helps you anticipate these challenges, plan accordingly in advance, and work on them efficiently as they come, without necessarily having to incur any loss of time and output.
Use of Artificial Intelligence
Making use of Artificial Intelligence concepts such as statistical algorithms and machine learning eliminates the need for manual interventions in the ingestion process. Manual intervention increases the number and frequency of errors in the process. Employing Artificial Intelligence not only eliminates these errors but also makes the whole process faster and increases the accuracy levels.
Data ingestion reduces the complexities involved in gathering data from multiple sources and frees up the time and resources for subsequent data processing steps. The emergence ofdata ingestion tools such as DQLabs has seen the creation of efficient options that can help businesses improve their performance and results by easing the decision-making process from their data.
There’s a much maligned topic in web scraping – data parsing. Building scrapers would be a lot easier if the data presented through HTML wasn’t intended for browsers. However, that is the case, which means that the data extraction process has to go through several hoops before delivering results.
Parsing is part of the process. Unfortunately, it’s one of the most resource-intensive parts of the entire web scraping chain. In fact, developing a parser for a specific website is not enough. Maintaining it over time is required. Even then, that might not be the end as some complex websites might need numerous parsers to work the data out of the source.
The dilemma
Any sufficiently large scraping project has to develop their own parsers. That means dedicated time and resources to a, comparatively, low-skill task. Most of the time, developing and maintaining parsers is a task for junior developers.
However, junior developers are a highly valuable resource. Spending time maintaining and writing parsers usually barely improves their skills. In fact, it might even bring a certain level of annoyance.
On the other hand, parsing is a critical part of the scraping process. Most of the time, the data acquired is messy and unusable without intervention. Since the end goal of all web scraping, whether for personal or commercial use, is to provide data for analysis, parsing is a necessity.
In short, we have an essentially necessary process that takes up a significant portion of resources and time while not being significantly challenging or useful to the individual. In other words, it’s a resource sink. Solving such a challenge would free up a lot of highly skilled hands and brains to do greater work.
A look towards automation
If you were to approach any sensible CXO or businessperson in general with an idea to save significant time for developers, they would accept the suggestion with open arms. There’s rarely anything better than saving resources through automation.
However, automating parsing isn’t as simple as it may seem. Partly, the reason is the frequent maintenance required. Usually, the requirement arises because websites change their layouts. If they do so, the parser breaks.
Yet, predicting future layout and coding changes is simply impossible. Therefore, no rule-based approach is truly viable. Classical programming is of little help here. Manual work, as mentioned previously, is a huge time and resource sink.
There’s one option remaining that has built up a lot of hype over the past decade or so. That is machine learning applications. Parsing seems to be the perfect way to test the mettle of machine learning engineers.
Since all of HTML has a similar structure across certain categories of pages, the visual changes are decidedly small. Additionally, layout changes aren’t usually massive overhauls of an entire website. They’re mostly incremental UX and UI improvements that are implemented. While that may add to the annoyance of a developer, it’s a great candidate for a stochastic algorithm looking for similarities between trained data and new data.
Preparing for adaptive parsing
Before engaging into any machine learning project, at least these questions should be answered beforehand:
What will be the limits of the model?
What type of learning will be needed?
What type (labeled/unlabeled) data will be used?
How will the data be acquired?
Luckily, for ourAdaptive Parser project at Oxylabs, we had the easiest answers to the last three questions. Since we already knew what we were looking at and for (data from specific pages), we could use labeled data. That meant supervised learning, one of the most practical and easy to execute models, can be used.
However, the true difficulty lies in answering the first question as the rest, at least partly, depend on it. Since all resources are finite, the machine learning model should be as narrow as required and as wide as possible. For us, it meant looking at how our clients are using our solutions (e.g. Real-Time Crawler) and making a decision based on data.
As we discovered through our research, e-commerce product pages were the most painful ones to parse. Generally, the source can be a bit wonky for parsing purposes. Additionally, there’s usually almost identical fields that are only sometimes available (e.g. “new price”/“old price”).
These fields can be confusing to machine learning models as well due to their similarity. However, answering the question about limits lets us set proper expectations for accuracy and the amount of data required. Clearly, we’ll need quite a bit of labeled data as we will haveat leastone problematic field.
Answering the final question was somewhat easier. We already knew where to pick up our examples. In fact, we could quite quickly collect a large amount of e-commerce pages. However, the strenuous part is labeling. It’s quite easy to get your hands on large amounts of unlabeled data.
Labeling data and training
Every supervised learning dataset has to be labeled. In our case that meant providing labels for most fields in every e-commerce page and it had to be done at least partly manually. If it could be automated, someone would have already created an adaptive parser.
In order to save time and in-house resources, we took a two-pronged approach. First, we hired a few helping hands that would label fields from our soon-to-be training set. Second, we spent some time developing a GUI-based labeling application to speed up the process. The idea is simple – we spend more financial resources on manual repetitive tasks to save up time for cognitive tasks for our machine learning engineers.
After getting our hands on enough labeled data to start training our Adaptive Parser, the process is really a lot of trial and error with some strategizing peppered in between. Sometimes, the model will struggle with specific parts and some logic-based nudging will be required (or it will at least speed up the process).
Many months and hundreds of tests later, we have a solution that is able to automatically parse fields in e-commerce product pages, which can adapt to changes with reasonable accuracy. Of course, now maintenance will be the challenge, but we have shown that it’s possible to automate parsing.
Conclusion
Automating parsing in web scraping isn’t just about saving resources. It’s also about increasing the speed, efficiency, and accuracy of data over time. All of these factors influence the way businesses engage with external data. Primarily, there’s less time dedicated to working around the data and more time to working with data.
More discussions on the pressing topics around web scraping, industry trends and expert tips will be shared in an annual web scraping conference Oxycon. It will take place online on August 25-26th and theregistrationis free of charge.
Mobile application development is among the most consistently growing sectors in software production. There has been an increasing demand for fast and user-friendly apps in recent years. A certain statistic reveals that in the year 2020 alone it is calculated users spend an average of 87% of the total time online on mobile apps. When you are opting for any app development there are two platforms to choose from-Android and iOS. These two platforms are the leading platforms worldwide. Both iOS and Android have incredible development options. Before making the decision as to which platform you would choose to build your app you must thoroughly go through the comparison between the two. This article will give you an overview of both the platform’s perks and perils and also point out the differences.
After you have decided which platform you will go ahead with, the next step is to choose the developers. Whether you hire ios app developer or you hire an android app developer, make sure they are technically sound and have proper knowledge for your app development.
The Benefits and Drawbacks of iOS App Development
iOS app development is always high in demand. This is because the iOS apps all the time perform extremely well. The platform is very fast, the reliability factor is very high, very user-friendly, and very few bugs to be found in the final output of the developed app.
An Experience that is sleek and flawless
The iOS platform provides the developers with detailed guidelines. So when you hire an ios app developer they get the detailed guidelines from the iOS platform. These detailed guidelines help the developers in the creation of a user interface. So when you hire an ios app developer they can easily create a user interface for the applications. Though this interface may sometimes be limited to a few. But on the other hand, this approach usually guarantees the security of an exceptional user experience.
The Drawbacks of iOS development
For native iOS app development, the developers require software like XCode which runs on Mac. So when you hire an ios app developer for app development for iOS Smartphones he/she will always need at least one piece of Apple technology.
Extra Demanding requirements for App release
The Apple App Store is a bit extra demanding in comparison to Google Play Store. Even if your app did not break any rules as per the Apple store guidelines still your app can be rejected if your app is found to be not relevant or is found to be of less use. So when you hire iOS app developer make sure that he/she is well versed with the guidelines of the Apple Play Store and the app he/she develops is well enough to be accepted on the Apple Play Store.
Less Options for Customization
In a later stage after your app gets developed and published on Apple Play Store and you feel like customizing the app’s interface it gets restricted, so you lose the option of customizing your app. And also it would be difficult to add some new features if at any stage the app requires interaction with some other third-party software.
The Benefits and Drawbacks of Android App Development
Flexibility
Normally, Android has a less restricted environment than iOS. Regarding distribution, these applications run on any Android device. Also, the issues with hardware compatibility are not there. Thus the development process is much more flexible for Android.
Availability of huge and elaborate learning resources
The Android platform also allows a smoother development process by depending on the Java language. Java is an extremely versatile programming language that supports Windows, Linux, and Mac OS. This feature allows the developers to develop Android applications regardless of the operating system the machine is running on. Google provides a vast knowledge base for beginners, provides interactive materials, exercises whole training programs for the different levels of Android developers.
Easy Publishing of app
In regards to publishing the apps, Google is less lenient. Google allows the developers to post on the Google play store. Previously the review process was performed automatically within 7 hours, but now it takes up to a week for the new app developers. In spite of this new rule almost all the Android apps that are not violating the policies get approved. Moreover, the app developers pay a very small registration fee.
The ability to go beyond the Smartphones
Developing any Android application means building software for the complete ecosystem of devices. Thus you can expand your app’s functionality. The app runs on Wear OS devices, Daydream and Cardboard VR headsets, Android Auto, and various other platforms. It also gives you the power to integrate your application into cars, smartwatches, TVs with mobile phones.
Issues with quality assurance
Fragmentation is very beneficial. It allows the developer to develop for different Android platforms simultaneously. But it makes the testing process very complicated. For the simplest of the apps, the app developers have to deliver fixes at frequent intervals. This is because the majority of users keep using the older version of the OS even after the upgrades. So hire an android app developer who is well equipped with this above feature.
Higher Cost and time requirement
Developing an android app is more time-consuming than an iOS app. Thus as the Android app takes more time in development and quality assurance the cost increases too.
Availability of more free apps affecting in-app purchases
Android app users look for free apps, so they spend less on in-app purchases than an iOS app user does. So the return on investment is not always high.
Security issues
Android platform is an open-source platform, so there is always a chance of becoming a victim of cyber fraud. Whereas the iOS platform is a much more closed counterpart and cyber attacks are rare.
A breif comparison of iOS vs. Android development
Both Android app development and iOS are gaining popularity over time. Both have brighter future prospects for the next few years. Both the platforms will hold the market with the present strength and none is going to lose popularity.
If you are planning to develop an app that provides extensive additional content or a retail app that you may buy, then iOS will give you more opportunities for making a profit.
Android apps are more popular among users belonging to medical or technical fields. Whereas, iOS-based technology is popular among high-end business professionals, sales experts, and senior managers, and also the iOS built technology is preferred by the audience of higher household income along with the strive to keep up with the trends in technology.
Android development has greater global coverage; the audience is from Africa, Latin America, and parts of Europe. Whereas the iOS has the targeted audience is from Australia, North America, or Western Europe.
So which platform to choose first?
When you are deciding which platform to choose between iOS development or Android development, the following factors should be kept in account:
The location of the User
The budget you are willing to spend for the development of the app and also development time requirements.
You should also keep in account how much unique interface you want for the app development.
To Conclude
Both Android operating systems and iOS operating systems dominate the market. Both have a good future prospect and outlook. Both the operating systems have an extremely large user in all existing fields. It can be judiciously mentioned that to hire android app developer your application will solely be directed by the application you are developing and the future plans you have for it.
