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Utilizing AI And Big Data To Reduce Costs And Increase Profits In Departments Across An Organization

Utilizing AI And Big Data To Reduce Costs And Increase Profits In Departments Across An Organization

Research reveals that the combination of AI and Big Data technologies can automate almost 80% of all physical work, 70% of data processing, and 64% of data collection tasks.

In this article, we explore the major indirect cost origins in departments that demand immediate attention through AI and data-led cost optimization, and speak with experts who are implementing and creating these AI technologies to optimize the respective processes within a business.

We’ll start with an often overlooked, but very interesting, department when it comes to data and AI utilization to reduce costs: fulfillment.

Shipping & Fulfillment 

AI has huge potential when it comes to changing the way supply chains are run by consolidating services, offering an amazing customer experience, and ensuring that businesses know what they pay before they ship.

Frazer Kinsley is the CEO of Hook Logistics. Hook Logistics is an end-to-end 3PL and Fulfillment startup based in New York City. Kinsley notes that automation and tech are at the core of what allows them to deliver on their promise of a flate rate, single fee structure; something not seen anywhere else in the industry.

“The supply shain/logistics industry is a prime candidate for AI disruption,” says Kinsley.

“Fulfillment and supply chain as a whole are both extremely data driven, but at the same time are far behind technologically compared to other industries. In order to adapt to incredibly dynamic supply chains, our customers are demanding cutting edge tech and thus we are demanding cutting edge tech to serve them properly.”

With the current volume of shipments moving throughout the world at any given time, it is imperative that shipping and fulfillment departments have access to real time insights for customer experience, financial, strategic, and planning purposes.

Kinsley explains, “it’s extremely important to be able to offer data insights in real time.” He continues, “Shipping rates, inventory levels, order volume, etc. are especially important to new brands, as these data all impact cash flow severely.”

If a company’s fulfillment department can begin to predict the cycle for production runs or the average cost of each shipment, then that company is suddenly much closer to their ideal margins and balance sheet risk. For example, by offering an end-to-end service, and all of the data associated with those services, Hook Logistics is able to offer its customers visibility into their supply chain, while making reconciliation of those services as much as 50% quicker in some instances.

There is a big difference between how AI is best used for ecommerce fulfillment vs. retail fulfillment. It’s worth addressing the different challenges and how AI is used differently for each. At the core, both warrant very granular data, as it’s incredibly important to access COGS for each SKU, for each channel. However, there are some key differences.

For e-commerce fulfillment, data analytics as a whole is increasingly important in delivering an amazing customer experience through constant communication as their (end users) order moves through the supply chain.

“End users are getting closer and closer to the brands they love and are closer to the source than ever before,” says Kinsley. “It’s this level of connectivity that founders and brands are looking for, as it creates the most buy in and thus, higher LTV for each customer of theirs. The expectation for speed is becoming faster and faster, so in order to buffer any potential lapses, communication to customers on tracking and deliveries is incredibly important.”

For retail fulfillment, data analytics advances may be even more significant. “At the retail level,” Kinsley explains, “it’s all about efficiency and ‘trimming the fat,’ so to speak. The margins are thin enough in retail, so in order to pursue margin expansion, it’s imperative to know what your COGS is for each and every SKU.”

The best way to get the right COGS, is to have integrated, automated systems that, when aggregated together, can offer insights throughout the entire supply chain that will then lead to a very accurate cost to serve.

It’s at this point that brands can look at spacing out production runs further and with deeper buys (driving down unit cost at the factory level), they can assess shipment frequency to their respective retailers, they can assess length to haul to each distribution center, etc. All of these seemingly minor details coalesce into what becomes cost savings, improved experience, and increased overall satisfaction for end consumers.

With both of these in mind, AI will play an increasingly important role in shipping and fulfillment departments. Kinsley adds, “I’m already seeing it now with companies that are utilizing very granular data and machine learning to help brands with the points that I outlined above; both on the ecommerce and retail levels.”

Internal Communications 

AI is often discussed when it comes to customer communications, but what about employee communications?

The challenge for the average company—large or small—is they don’t know if they can reach all employees with their communications or know they can’t. Communicators are using email as a primary communication channel but can’t measure open rates/if emails are received or action is taken.

The intranet is the second comms channel and again “intranets are generally outdated in usability, design, and content relevance with little to no analytics to help them understand how their content performs,” explains Gregg Apirian, VP Customer Experience at Korbyt, a workplace experience platform.

Communicators have some major transformation opportunities in front of them, and while AI plays a role in their future, it is a bit early as they first need to advance their skills and capabilities and begin to use targeting more often so they can drive the engagement they are after.

“Once they can begin to create more targeted content and personalize the employee experience more, they will have the key insights they need to use AI more effectively to help them scale,” says Apirian.

AI’s future role in Internal Communications will be discussed during the upcoming WorkplaceEX, a virtual conference where professionals with a common interest or responsibility to manage the workplace experience will be brought together to share ideas through interactive think sprints, as well as watch live keynotes from industry leaders.

Industry vetran and Korbyt CEO Ankur Ahlowalia, is among the list of prominent speakers at the event. Ahlowalia was appointed CEO in 2020 to help the company double-down on innovative features like AI that help companies communicate better with their audiences. So for those looking to improve internal comms with AI, his fireside chat at the event is not to be missed.

Marketing 

No discussion of improving department efficiencies is complete without addressing your organization’s marketing team. Often the department found utilizing the latest technologies first, marketing and AI go hand-in-hand when it comes to improving the ROI for your marketing dollars.

Reducing cost acquisition directly correlates to the amount of sales a company can make, which in turns results in increasing their profits to scale their business even more and expand to heights never seen before. Understanding market data is key to scaling forward and being able to pinpoint different hooks and angles that speak best to your target audience.

Some of the major challenges companies face when it comes to marketing is scaling their business and understanding true market strategies that speak to their end customer.

“Without this understanding, a lot of companies end up flying blind and wastefully spend their precious ad dollars,” says Michael Fances, Vice President of Instaboost Media.

Fances was one of the early pioneers of Social Media Marketing and a veteran in the ecommerce industry, assisting social media influencers curate their own personal brands and companies, reducing the barrier to entry and closing the gap of big retail.

Each company’s data points are unique to their business model and process. Importing and coupling these metrics with AI allows marketing departments to curate custom campaigns and strategies that speak to a specific hook or angle for a particular customer avatar. With distinctive data points, each business has different indicators to support their capability to scale healthily.

“Having a robust system for data and customer behavior analysis is one of the most effective ways to reduce costs and increase profits,” says Fances. He advises companies accomplish this through detailed analysis and ad audits to consistently optimize campaigns.

These data points vary from COGs, fixed costs, variable costs, sales cycle, average order value, lifetime value, churn rates, attribution windows, CTR, view rates, engagement rates, bounce rates, and many more. Each one of these metrics are needed to be applied to each product, service, and campaign to fully comprehend not only breakeven points but as a criterion for scale. Once these points are established, the growth process can be automated and projected for the upcoming season, quarter or year.

“Our clients rely on these data points to not only project a healthy return, but to also maintain production projections, in addition to supplier communication” says Van Dennis, President of Marketing at Instaboost Media and self made entrepreneur that paved his way to success through Mastering Google Ads and helping other companies grow and scale their businesses.

“As time progresses, you’ll be sure to see many more businesses take an approach to utilizing AI to better harvest customer data with ridiculously speed.” says Dennis.

“This allows a more catered marketing approach that best communicates with a business’ customer.”

On average, companies who utilize data optimization and enhance their marketing with AI  see a growth of 3-4x their ad spend and increase their bottom line with more accuracy.

Capital

In most industries, you need capital to launch before you can even begin to put different departments in place. For this reason, it is important to discuss how AI is playing a role in modern-day lending practices.

While the technology in this sector is still evolving, it is perhaps unsurprising that the AI innovations in capital lending are being born of the mortgage industry.

“Banks are notoriously late to the tech party as they play catchup, struggling to connect their legacy platforms with the demands of today’s digitally-minded consumer,” explains Patrick Buckner, Chief Strategy Officer at Informative Research Inc. He continues, “The integration of disparate systems or crafting nifty new ways to package products occupied much of this space’s attention.”

However, lenders with advanced technological innovations such as Quicken Loans and loanDepot have shown the way to market share leadership by mastering process optimization and automation – all enabled by technology. Like these two industry behemoths, Informative Research is doing it so part to propel the mortgage industry into the future.

To achieve this, they currently deploy Machine Learning based propensity models on top of a consumer database composed of everyone in the US’s credit, public record, property, demographic and marketing data.

“In addition to our Machine Learning models,” Buckner adds, “we deploy an AI called IRIQ – an efficiency engine that scores files and routes and optimizes loan files for the best possible path to funding.”

In lending, there has traditionally been a focus on product platforms that have disparate systems and technology. Using the latest AIs, lenders can now consolidate the disparate parts of the loan process and get the individual product applications to speak to each other.

One top 5 bank that continues to save $11 million every year on credit, verifications, and underwriting. This is due to the abovementioned machine learning-based efficiency engine. Another example, one large personal lending company increased new account openings by up to 700 percent using a customer data platform, which enabled them to compare their accounts to all national accounts in near-real-time.

Borrowers in many cases do not know how much they can afford or how much they should spend. Introducing ML propensity models on top of the three C’s (credit, capacity and collateral) allows lenders to take a holistic view at a consumer and determine earlier on in the process who is qualified and who is not.

According to Buckner, the biggest challenge remains bringing awareness to the ways in which AI can be and is already being used in the capital lending space. “It’s fresh and growing with a lot of innovation happening in AI, ML and Platform Strategy.”

It is especially important for departments and sectors who historically have been slow to adopt new technology, to have innovators in their fields driving innovation forward. It is also important that human capital is never overlooked.

As Kinsley concludes, “Tech can fill a lot of the holes left uncovered by humans, but there will always be room for a human touch in delivering better value and better service.”

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