Across industries, nearly everyone agrees that if it hasn’t arrived yet, the recession is coming soon. The real question is, how long will this recession or set of recession-like conditions last? Does our current economic ride end in a hard or soft landing? A growing list of indicators suggests that recession and continued economic uncertainty will span most of 2023. For previously growth-minded retailers and ecommerce vendors, this grim outlook should shift your focus to finding novel means of profit optimization.

With inflation above 8% for six consecutive months, reduced consumer spending on non-essential purchases has begun eating into top-of-the-funnel growth and new customer acquisition across the retail industry. Consumer pricing indices continue to hold at roughly 6.3% above 2021 numbers, and consumer spending is contracting in stride, eroding profits.

In the retail industry, business leaders have traditionally focused on growth as the primary success metric, and in booming conditions, this strategy delivers. However, recession changes the game, and most companies are ill-equipped technologically and ideologically to make profitable adjustments without serious downsizing and cost cutting.

Learn how data analytics powered by artificial intelligence (AI) can transform business and customer data into real-time actionable insights and give your business the data-driven maneuverability they need to drive profitability.

Key Takeaways

• Protracted recession is on the horizon. Business leaders should prepare for long-term adjustments.
• Retailers and ecommerce companies cannot rely on top-of-the-funnel growth in extended periods of restrained consumer spending. Rather, they must adopt new profit-focused business strategies enabled by data-driven internal maneuverability.
• Some businesses may find it too complicated to turn around on their own. The solution is incorporating powerful, cutting-edge AIs to unlock agile, data-driven decision-making.

The Challenges of Creating Profits Without Growth

Pivoting from top-of-the-funnel growth to data-driven maneuverability to drive profits comes with a unique set of challenges most companies have yet to face head-on.

1. Unstructured Data

While across the globe, human activity generates 2.5 quintillion bytes of data per person per day, only 10-20% of that data is structured in machine-readable formats. The vast majority of the rest is unstructured in the form of text files, application logs, emails, images, sensor data, social media data, and video files. They’re opaque to analysis and unusable.

Unstructured business data contains a wealth of potential insights companies could use to make informed operational decisions. To take just a few examples, real-time visibility into sensor data in warehouses would enable companies to identify inefficiencies and bottlenecks. Or if companies could structure and analyze sales data scattered across CRMs and software-as-a-service (SaaS) platforms and databases, they could discover patterns of up and downticks in the selling rates of different products and adjust inventories and pricing accordingly to drive profitability.

2. Insufficient Human Resources

On the heels of COVID-19 disruptions and the Great Resignation, the U.S. faces an IT labor shortage of nearly half a million jobs. Building profitability infrastructure and maneuverability into a company’s long-term operations isn’t a quick fix or one-time adjustment. Achieving the kinds of data visibility and internal response necessary to capitalize on fleeting and evolving opportunities requires significant structural and operational adjustments and investment in capable in-house IT resources.

However, for the time being, current conditions in the IT labor market simply won’t allow companies – or even most enterprises – to hire their way back to profitability,  even if they had the payroll to do it. The demand for skilled IT labor soared during the pandemic as companies struggled with adjustments to remote work and hasty cloud adoption. As a result, industry burnout spiked, and skilled IT workers left their employers in droves.

As it stands now, just 29% of IT workers report a high level of intent to stay with their current employer. Overall, long-term employer commitment in IT comes in at 10% under other fields on average.

3. Insufficient Internal Maneuverability

Even when companies have visibility into opportunities to make profit-driven internal adjustments, most lack the necessary agility in decision-making and implementation to take advantage. Concerning decision-making, the problem is often an absence of a data-driven culture within the organization.

Despite the powerful analytical tools available to most businesses, executives still tend to prefer gut-based reasoning in critical decisions. Recent studies by the Harvard Business Review found that:

• A mere 26.5% of reporting organizations claim to have a data-driven decision-making process in place.
• Among executives, 91.9% identify cultural issues of authority as a barrier to implementing data-driven processes in their organizations.
• Within organizations that have invested in creating full-time positions, such as chief data or analytics officer, only 40.2% report having achieved successful cultural adjustments toward data-driven operations.

Leveraging AI to Overcome the Challenges of Profit Optimization

If companies don’t have the data visibility, in-house IT talent, or internal agility to capitalize on profit-driving opportunities, they must look to new technology-based solutions. Today, these solutions come from AI and neural network-based deep learning algorithms that allow companies to offload intensive, intervention-heavy analytical tasks to programs capable of ingesting and structuring data to extract actionable insights in real time.

Recent developments in deep learning technologies have enabled companies to unlock more of that dark 80% of their existing data. However, developing proprietary AIs with comparable capacities is far beyond the budget of even the most tech-savvy companies. Fortunately, access to cutting-edge deep learning AIs has recently come to market.

These AIs mitigate the challenges of opaque data stores and the financial barriers to developing in-house solutions. Unlike your human analysts who work with hunch-based hypotheses to supervise analytical tools, deep learning AIs are capable of extracting their own categories and generating their own hypotheses. The testing and adjustment of these hypotheses then run day and night, evolving in step with both test results and newly ingested data. The result is up-to-date optimized hypotheses expressed in concise, executable recommendations and risk assessments in categories such as:

• Profit optimization
• Customer segments and their price elasticity
• Inventory
• Ad performance

AI-Driven Profit Optimization with Hypersonix

Hypersonix offers retailers and ecommerce businesses an industry-first profit optimization solution based on AI data modeling. With the power to deliver actionable, profit-focused insights right to the top of the decision-making chain, Hypersonix is a lifeline for businesses drifting in the current swell of uncertainty and austerity.

Request a personalized demo of Hypersonix today.