Data analysis a tool for success in business and how to use it

Data analysis a tool for success in business and how to use it

Is it time to put money into machine learning now that more and more companies are realising its potential? Three companies tell us how they’ve used this tactic to help their customers expand.
Data analytics and the application of AI have become commonplace in many different types of businesses.

Executives need to adopt and integrate new technologies into their operations if their companies are to thrive in today’s dynamic and competitive market.

AI techniques like machine learning, which employ algorithms to analyse data, are increasingly being used by industry leaders to set themselves apart from their less technologically advanced competitors.

Machine learning stands out from other data analysis tools because it can automatically learn from the data it is fed and improve its accuracy and decision-making capabilities without human intervention by spotting patterns and outliers.

All businesses can gain a lot from properly implementing machine learning, and some of those benefits are:

  • Facilitates work flow and productivity by automating routines.
  • Finds answers to data problems that have been holding back your company.
  • Boosts online safety.
  • Without resorting to external sources of data, this method successfully implements personalization.
  • Facilitates a smoother interaction with customers.
  • Boosts revenue and financial success.
  • Facilitates better handling of potential dangers.
  • Here are three successful startup founders who have embraced machine learning.

Consolidating information

When Stephany Lapierre saw a client in 2014 searching through a massive binder of business cards for a crucial supplier, she realised how badly businesses needed easy access to standardised, up-to-date information about their supply chains.

As she explains to The CEO Magazine, her initial reaction was one of shock: “I found it shocking that organisations were spending so much money on suppliers, but very little information was available to them.”

“We’re talking about millions of dollars annually by the time they find suppliers, source and run requests for proposals, onboard them, pay them, and manage and maintain their information. Additionally, I believe there are even greater risks and opportunities that have been foregone.

In 2014, Lapierre had the idea to establish a central repository for all supplier information, and thus TealBook was founded in Toronto.

The CEO explains that his initial plan was to create a platform similar to LinkedIn, where suppliers could upload their information and have it shared with all of their customers. To paraphrase, “But we quickly learned that suppliers wouldn’t do it because we didn’t have the monopoly.”

Real time supplier data was unlocked when Lapierre and TealBook’s CTO realised they could build a new platform on Google Cloud Platform and take advantage of its machine learning services.

After receiving its initial seed funding in 2017, TealBook re-platformed again in 2021 after receiving a series A funding round. In 2021, the company’s revenue increased by a whopping 350 percent, and it has raised a total of US$73 million so far.

“Today, we have 5.5 million digital profiles that are continuously enriched by new data sources we’re adding to include more and more attributes,” explains Lapierre.

There are currently 73 attributes per supplier, each with varying degrees of quality. Initially, the primary concern was answering the following: “How do we define quality using machines?”

Whether an attribute has a high trust score or is too contradictory and our customers shouldn’t rely on it, we can have machines tell us with sufficient confidence.

Additionally, we are currently implementing increased levels of openness, scalability, and oversight to give our customers more control over which data they decide to put their faith in. High trust at 90% accuracy may not be adequate for some clients. They might require one hundred percent of my attention.”

Realizing the Big Picture

Realizing the Big Picture

On the other side of the world in Sydney, a company called Particular Audience uses machine learning to help e-commerce sites personalise customers’ purchases without collecting any personally identifiable information.

With the impending deprecation of third-party cookies, merchants are scrambling to find alternatives to using customers’ personal information for advertising purposes.

“Machine learning is effectively predictions, and to make good predictions you need robust data,” says James Taylor, founder and CEO of Particular Audience. Perhaps counterintuitively, personalization can be achieved without access to sensitive information.

You are more than just a number, a zip code, or a browsing time. Your situation is always evolving. Shopping for jeans and a Mother’s Day present for your mother-in-law are two very different experiences.

There is no shortage of information available to online merchants; all they need is a strategy for using it to create a personalised shopping experience that increases sales and profits by directing customers to the items they are most interested in buying.

According to Taylor, “we can apply a different algorithm to a retailer’s product metadata, product images, interaction, and purchasing data.”

“We use natural language processing to parse product metadata and find similar products to recommend to the customer.

Then there’s computer vision, which is typically applied to the furnishings and apparel industries, and which lets us analyse visual similarities between objects down to the pixel level. This is how related product recommendations are generated.

As the author puts it, “Finally, there’s the behavioural data that tells us which items get compared and end up in the same baskets, and allows us to identify comparable, alternative, and complementary items.”

One way in which Particular Audience stands out from competing e-commerce personalization platforms is that it enables multi-brand retailers to charge suppliers for advertising space within relevant search recommendations.

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“Your retail media platform and your search and recommendations personalization shouldn’t be separate,” says Taylor.

You’ve put the cart before the horse if launching ads on your retail website has a negative effect on sales. The fact that it is part of a unified system is a selling point for us.

Digital River, The Good Guys, and Target have all reported increases in their unit sales, AOV, and profit margins after stocking up on Particular Audience’s wares. Taylor claims, “The outcomes for our Price Beat product are the best we’ve seen.”

It’s an add-on for Google Chrome that enables shoppers to easily compare prices across multiple online merchants in real time, and it helps our stores undercut the competition. There was a 102 percent increase in sales for items that didn’t already have the lowest price because of this.

Error suppression

Tiliter, based in Sydney, is another company that uses computer vision to improve the grocery store self-checkout experience for customers while simultaneously lowering the incidence of costly human errors and fraudulent activity.

Using camera images, “our AI quickly identifies different varieties of produce in grocery stores for shoppers and cashiers,” explains Co-Founder and CEO Marcel Herz.

“With our technology, customers can speed through the checkout process by a factor of five because of the increased scanning speed.

Customers are less likely to make the mistake of selecting the wrong item, whether by accident or design, saving stores millions of dollars annually in returns.

One of our best-sellers is a self-contained AI scale that can recognise various produce without the use of barcodes or a menu. We offer AI-powered software that is compatible with both self-service and traditional teller-based registers.

Using its revolutionary technology, Woolworths, Countdown, Netto, and IGA are just a few of the major international supermarket chains that have benefited from Tiliter, saving customers over 200 days of wait time.

According to Herz, “over the past couple of years, the user experience has become more elegant and user-friendly.

The addition of computer vision has made sales of fresh produce even more streamlined. One day, all of your grocery-shopping needs will be effortlessly supported by computer vision.

Tiliter plans to stay ahead of the competition in the machine learning market by consistently innovating in the face of growing competition.

According to Herz, “innovation is in the DNA of our company.” Chris (our CTO) and I researched machine learning applications like cancer cell detection in MRI scans using computer vision before we founded Tiliter in 2017. The boundless potential of computer vision was incredibly exciting to consider.

Since then, more businesses have used machine learning to boost productivity. Simply put, this is fantastic for humanity, and we hope to aid in highlighting its many obvious benefits.

Daniel Harrison

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