4 Technologies to Scale your Business with Limited Budget

Different founders define the word “scaling” differently. Therefore, it is necessary to identify which definition to zero in on. If scaling means using your existing data to close deals more predictably, then this article’s for you.

An ideal way to scale your business is by leveraging technology. In 2020, we expect to see a surge of 50% in small business tech spending due to increase in affordability of today’s cloud-based services and mobile devices.

Small businesses are confident about accessing new markets and targeting new customers at relatively lower costs using digital tools.

According to a Deloitte research, with the help of technology, small businesses, with less than 250 employees can:

  • Create 3X more jobs than the previous year
  • Earn 2X as much revenue per employee
  • Experience 4X as growth over the past year

Despite the potential gains and openness, 80% of small businesses are failing to take the full advantage of technology. Adding to that, here are four technologies that can scale your small business efficiently on a limited budget:

  1. Using Artificial Intelligence for higher control and visibility of your sales performance

87% of small businesses said they were using or considering to use AI to forecast sales. [Statista]



Many small businesses tend to prefer the human touch in sales as it involves direct interactions with clients. Blend it with automation and machine learning to make your sales team more effective and productive.

For instance, there are virtual sales assistants powered by artificial intelligence that predicts the number of customers who would churn as well as algorithms that tell you the kind of customers who would churn. For such segments, you could create specific reminders or promotions, to keep them engaged.

Datoin is an automated AI-based platform that helps small businesses improve the performance of their sales teams. Datoin helps businesses get visibility of the following metrics:

  • Churn rate for the month
  • The customers likely to churn
  • Predicted demand for products
  • Price predictions for products that might fetch higher transactions
  • Forecasting daily sales

Datoin is designed to be used by business owners. For businesses who do not have data scientists, Datoin is a plug and play platform where you can enter the data. The platform starts analysing this data and shares the insights.

Click here to sign-up for using AI for free, with Datoin.

Some tools compile data from previously won and lost deals and also gather data from sales calls, meetings, and emails to determine what factors are likelier to lead to a successful sale. All of these can help your sales team close deals more predictably and boost their confidence by providing them with data-backed inputs.

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  1. Let technology streamline your onboarding process.

36% of HR professionals agree that lack of technology is why they are unable to organize and automate onboarding programs. [Kronos]

As your business grows, you will be hiring new people more often. Completing all the paperwork and collating all the documents for those recruits can be a tedious task, both for your small business and for the people joining.

You can thus make things easier for your human resources team by using technology to streamline onboarding. For instance, there are several apps such as Gusto and HROnboard that allow new employees to finish all their paperwork and get connected online to the rest of the team even before they join work. They give the recruits an in-depth view of the company’s culture and vision, set up an agenda for their first day, and help them set work goals for the initial 90 days.

Many apps can also sync with other HR and communication software solutions, such as by automatically adding new employees to Slack groups, Gmail accounts, or messaging groups. With technology, you can make more time for interactions with the team – which is the essence of any onboarding program.

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  1. Use machine learning to automate new projects and daily tasks.

97% of companies agree that project management is critical to business performance. However, 44% of project managers use no software to manage tasks. [PwC]

While your existing process frameworks may be robust enough to handle day-to-day requirements, starting on a blank slate with each new project is time-consuming. This is where machine learning plays a significant role.

By training itself based on historical performance data and automating essential design decisions based on stipulated parameters, machine learning can eliminate repeated work and thus cut project-design costs for your small business.

The technology can also handle repetitive day-to-day tasks such as billing and invoicing, managing social media, handling customer support communications, and more.

That takes off a significant portion of performing the mundane tasks from your employees’ shoulders, to allow them to focus better on high-end tasks that call for more creativity and critical thinking. This leads to overall productivity enhancement for your small business.

Artificial Intelligence in eCommerce: 4 Tasks worth Automating

  1. Use chatbots to respond quickly to customer service queries

80% of businesses are expected to have chatbot automation by 2020. [Oracle]

When you are just starting, a small team of customer service executives are capable enough to handle all the queries that come through. As you expand your business, customers would want their issues to be solved immediately, around the clock.

That is where chatbots play a crucial role. They can efficiently handle a large variety of customer queries and complaints while maintaining a conversational tone and redirecting to a support executive when it fails to grasp the contact or when asked specifically by the customer.

Chatbot use cases


They can also engage with the customers by redirecting them to specific pages, answering the frequently asked questions, requesting product reviews, or providing short questionnaires for feedback.

Particularly, if you are planning to reach a global audience, a chatbot can engage website visitors from around the world and in different time zones, which an in-house team would be unable to do all the time.

6 Applications of Artificial Intelligence for Small Businesses on a Budget

Summing it up

The modernization of technology has supported small businesses in many ways, and their use will only increase in the coming years.

There’s no denying that artificial intelligence is going to be a fundamental element in the future, with 72% of industry leaders calling it a “business advantage.”

Small businesses are leveraging in their day-to-day functioning.

Start using Artificial Intelligence for free, with Datoin. Leverage the power of machine learning, for your business. Sign-up for a trial today!

5 Pitfalls to Avoid when Implementing Machine Learning for your Small Business

Small businesses are rapidly implementing artificial intelligence to gain a competitive edge over competitors. Are they doing it right? Let’s talk about the common pitfalls to avoid while adopting machine learning for your SMB.

There has been a lot of excitement about the advancements in machine learning algorithms in the last few years. There are a number of problems that can be solved in sectors such as financial services, healthcare, retail and others.

According to BCG, 84% of businesses believe that investing in AI and ML leads to greater competitive advantages – whether it comes to expanding business offerings or making substantial process improvements.

5 Pitfalls to Avoid when Implementing Machine Learning for your Small Business

No wonder, small businesses are rapidly implementing artificial intelligence technologies to maximize their potential. While the benefits of deploying machine learning are numerous, ensure that you stay away from these 5 mistakes when introducing your business to AI or ML. 

1. Deploying unnecessarily complicated ML technologies

Given that machine learning is not widely adopted by small businesses, many SMB owners are not fully aware of the different ML technologies out there. They risk ending up with tools that need large volumes of data to understand even the most basic use cases of the technology.

In the language of ML, such a scenario is called overfitting. In other words, you can have the technology without sophisticated algorithms, but without good data, it will give a poor predictive performance.

you can have the technology without sophisticated algorithms, but without good data, it will give a poor predictive performance.


Therefore, be sure to do your research and choose a machine learning tool that needs only a small quantity of data and can be set up to run in full production in just a few hours. A Data Dilemma Report states that 12.5% of staff time is lost during data collection. That’s five hours a week in a 40-hour workweek.

You can feed your existing data into Datoin, an automated AI-platform. Using machine learning, it helps you predict the customer churn rate, the customers likely to churn, the daily sales and the demand for each product in your inventory.  This way, you save valuable time and resources and start gaining much faster returns on the investment you made on the ML technology.

To start leveraging machine learning for free, for your business, sign up for a trial here –

2. Assuming where to use ML technology

When it comes to organizational process knowledge, most businesses – big and small – are compartmentalized. The top management team is usually not involved in the day-to-day processes, and neither do they have access to process documentation.

As a result, the processes that you start applying machine learning to are not necessarily the most appropriate processes to work with. Business execs are using artificial intelligence to automate repetitive tasks such as timesheets (78%), scheduling (79%), and paperwork (82%).

Business execs are using the technology to automate repetitive tasks such as timesheets (78%), scheduling (79%), and paperwork (82%).
Artificia; intelligence in decision making

It is thus crucial to incorporate process intelligence to determine which areas of work are genuinely ready for automation before you go ahead with a project.

A comprehensive understanding – based on facts, not opinion – of where machine learning will work and the kind of value-add and savings these technologies will bring to your business is critical.

3. Forgetting that it is a continuous process

Merely planning out your machine learning activities, deploying digital workers, and training your business algorithms is not enough. It is imperative to monitor every step of the process post-implementation and to assess your digital workforce regularly.

Machine learning is not a self-sufficient process. It calls for continuous measurement of the impact of automation at each stage to:

  • Ensure protocol compliance at each stage
  • Prevent bottlenecks from arising
  • Ensure that automation is not causing any adverse effects at any point along the way

4. Missing out on high-value business cases

Most businesses tend to go for conventional options that center on what has worked before. They are thus likely to apply the technology to the task that recurs most frequently because such a task has the appearance of giving reliable results.

However, this kind of ad hoc approach to process choosing is likely to end up leaving out other business opportunities that might recur less often but which have high ROI. As a business, you should have a clear plan in mind of how you want to “land and expand” with machine learning throughout your company.

What are your organisation's priorities in 2020?


While it is understandable that you would want to start with processes that involve minimal disruption to the business or the way you interact with end-users, your ultimate organizational goals should be automating tasks to bring in the maximum ROI.

5. Relying too much on Robotic Process Automation (RPA)

RPA is easy to deploy, and its digital workers can be configured without trouble and can work just like humans once they are in place. Besides, it can considerably boost your company’s efficiencies by connecting with external data sources and legacy systems.

It is crucial to remember that RPA is focused on structured, repetitive work. On the other hand, machine learning can be applied to both structured and unstructured work.

When you add machine learning technology to RPA, the digital workers gain the cognitive skills necessary to extract useful information from different types of content, understand the intent behind and meaning of different kinds of documents, and enhance their decision-making capabilities. Your business should thus avoid relying on RPA alone.

Over to you

Machine learning has immense potential, if used correctly. This includes boosting sales, attracting more customers, forecasting demand and ultimately driving profits.  

Luckily, Datoin, an ML-driven AI platform can help you experiment with data, for free, to know how you can use AI and ML for your small business.  

Sign up for a trial here, and see how the platform can add value to your business, for free.

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