Predictive analysis is the future of effective retail

The disruption impacting the retail space is profound and expansive. In years past, big box stores supplanted independent retailers as the power of scale outweighed the pleasantries of a small business. The Walmart effect was real, and it dominated retail sentiment until it too was superseded in the digital age.

Today, Amazon is leading the charge in online retail, offering low prices and free shipping on millions of items, while challenging the competition to make up the difference in other ways. As other online retailers try to carve out their niche, the modern retail environment is becoming ultra-competitive, and companies must be intentional and proactive to ensure that they stay in business.

Therefore, many organizations are turning to big data and machine learning to develop predictive datasets that equip them to make critical decisions about their customers and their buying habits. This information allows businesses to make strategic choices about advertising methodologies, pricing, store layout, and other tangible features that impact the shopping experience.

In an extensive study on shifting retail trends, Mckinsey & Co. concluded, “In our experience, [companies] have succeeded primarily by developing a deeper understanding of consumer and shopper behavior and embedding these insights into the way they manage every product category.”

Quality products and creative advertising just aren’t enough to close a sale, so this information can be the difference between success and failure. As the MIT Sloan Management Review notes, “Organizations that demonstrate higher levels of analytical maturity saw a marked advantage in their customer relationships.”

Big data can cause big problems

Of course, collecting customer information is more controversial now than ever before. A scourge of high-profile data breaches at companies like Equifax, Branch.io and Yahoo have made consumers wary of divulging personal information to any platform, and as Facebook executives are learning the hard way, mishandling consumer data creates cascading problems across an organization. Moreover, formal regulations like Europe’s expansive General Data Protection Regulation (GDPR) and California’s forthcoming digital privacy law make data acquisition and analysis a complicated process.

For example, GDPR, which impacts every company doing business with European nationals, requires companies to be strategic about protecting customer information and transparent about its collection, use, and protection.

The cost of failure in this regard is steep. Depending on the nature of the infraction, companies can lose up to 4% of their worldwide annual revenue for failing to comply with GDPR’s standards.

In short, data analytics can be the key to success, but the built-in detractions can make it a hassle for companies to manage, and failure to appropriately navigate this data ecosystem can harm companies more than it can help them.

Guarding the movement

Some solutions are beginning to emerge. For example, data encryption and anonymization of consumer information is a burgeoning methodology for eliciting the benefits of big data while mitigating its risks.

For instance, Endor, a data analytics platform serving an array of clients, allows companies to receive statistical insights using encrypted data. Most importantly, this information is never decrypted, so it always remains secure while keeping companies compliant with legal and social standards for privacy protection. By harvesting data derived from social physics, it’s possible to conduct a predictive analysis of numerous datasets that can help answer the most pressing questions related to growth and sales.

Several companies including Coca-Cola, Walmart, MasterCard, and Fiverr are already deploying this technology to make data-driven decisions about their customer base, and they are saving money in the process.

This significant push into data analysis and predative technology is being deployed on many fronts. Qlik, a data analytics firm focused on providing companies the insight necessary to make data-driven decisions, is also relying heavily on advancements in AI and machine learning to provide quality data analysis. Their goal is to bring analytics to every decision a company makes, which requires both a glut of information and an incredibly competent technological infrastructure.

In this arrangement, effective data analytics and consumer privacy must go hand-in-hand, which is essential in a post-GDPR world. If companies are going to benefit from big data, they need to glean its benefits without repeating the mistakes of companies who saw this approach backfire through expensive cleanup from privacy violations and continual opportunity and reputational costs that may never truly recover.

To this end, encryption and anonymization seem like natural next steps for any company striving to apply big data and machine learning to quantify their customer experience. For those without the technological capabilities to do this in-house, there are many opportunities to export those responsibilities to capable third-party platforms.

Without a doubt, the retail industry – both online and in person – is undergoing incredible change, and this change is being inspired by the analysis of big data. Companies with the right information can best navigate these difficult waters, and those who can achieve these insights while being mindful of privacy protection and data security will be positioned to succeed.