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Big Data Platforms and AI Tools for Your Small Business

Emerging technologies such as big data and AI can be daunting for the small business owner, but there’s also significant pressure to stay ahead of the curve. Small businesses are always competing to keep costs and overheads low, as they’re less able to sustain themselves through the lean times. However, McKinsey research has shown that implementing big data strategies can significantly impact the bottom line. For example, a retailer could increase operating margins up to 60 percent.

Implementing big data and AI platforms doesn’t need to involve hiring teams of data scientists or analysts. At this point in 2019, small businesses have a wealth of out-of-the-box solutions available to them, covering a variety of use cases.

Chatbots

Chatbots are helping to reduce operational costs by providing a “human” element to first-line customer services and general inquiries. Juniper Research estimates that chatbots will save businesses around $8bn by 2022. Bots can also help with lead generation and conversion, by providing a direct route from the company website to a sales representative for those who choose to engage with the bot online.

Now in 2019, many chatbots operate cross-channel, integrating seamlessly with Facebook Messenger, WhatsApp, Instagram, and more. Liveperson is one example, with a chatbot builder feature that makes it easy for anyone in the organization to build and optimize bots using industry-specific templates. Using Liveperson, a business can automate up to 70 percent of messaging conversations across various platforms.

Predictive Analytics

Predictive analytics platforms have a broad range of applications in companies, including reducing employee turnover and decreasing risks such as cyberattacks. However, the implications for sales and marketing are particularly significant. By understanding which demographics are likely to buy a product, sales and marketing teams can make more efficient use of their budgets.

Endor is one company with a predictive analytics platform that’s as easy to use as a Google search.

The user only needs to type in their question, which could be something as straightforward as “who is most likely to buy x product?” Endor’s data science methodology relies on the “social physics” discipline, to deliver fast and accurate answers based on crowd wisdom. It was developed by a team from MIT, who pioneered the concept of social physics in an academic setting and are now applying it in across a range of industry sectors including retail and finance.

Business Intelligence

Internal business systems are often fragmented, with different software for managing sales, customer services, human resources, and accounting. However, together they generate a vast amount of data that’s greater than the sum of its parts and can be converted into actionable business intelligence. 

This is the goal of Insight Squared. It takes historical data from internal company systems and analyzes it in aggregate to generate recommendations for sales, marketing, and staffing. Users have access to multi-dimensional reports and analytics to help manage pipelines, gain more accurate forecasts of sales or product usage, and clearer visibility into marketing-generated demand.

Recruitment

Recruiting new staff is a considerable cost for any business, taking up a vast amount of management time. Among a 2017 Wasp Barcode survey of 1,100 small business owners, fifty percent stated that their top challenge was hiring new employees.

Developments in AI mean that hiring managers can significantly cut down on the legwork involved in the recruiting process, particularly in the earliest stages of sifting through dozens of applications. Ideal offers a suite of AI-powered tools that will integrate with existing HR software to enable data-backed hiring decisions and make the recruiting process more efficient. Ideal can pre-screen candidates, engage with them online via a chatbot, and automate tedious tasks such as sending out interview requests and rejection letters. 

Visual Analytics

For a bricks-and-mortar business, visual analytics can provide powerful insights. Most stores these days are equipped with security cameras both front and back of house, usually to deter thieves. However, platforms such as Prism can put these cameras to work harvesting all kinds of data to help make better business decisions.

For example, Prism can produce heat maps showing how customers move around a store, helping to ensure optimal placing of merchandise for maximizing revenues. It can also analyze footfall to tell a retailer when the busiest periods will happen so they can arrange adequate staffing. Behind the scenes, the software can also assist with inventory management and help to spot theft.

These are just a few examples of the available AI and analytics tools and platforms on the market today, and none of them require extensive technical expertise to operate. Each of them illustrates how small businesses can start to seamlessly incorporate AI and big data to help reduce business overheads and increase profitability. The point is not to implement technology for its own sake but to find methods of incorporating technology into your business in such a way that it enhances your edge over the competition and helps secure the future of your business.

About the Author

Sheza Gary is a technical director at Algoworks, a global IT service provider which operates chiefly in United States from its California office. She was previously a technical manager at CloudGenix. Sheza has a MBA from California State University, Northridge and a bachelor’s from Boston University.

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Comments

  1. Well explained Sheza. Big data analytics helps organizations harness their data and use it to identify new possibilities. That leads to smarter business moves, more efficient operations for organizations.

  2. Frank Quintana says:

    We put too much hope on AI, analytics and big and fast data but the fact is that if your processes are wrong then your fast data make you err faster and your big data make your err bigger, besides you need a minimum of data literacy in order to be able to run your analytics properly and get the rights conclusions

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