Mega online retailers such as Amazon and Alibaba have long afforded robust big data operations to collect, combine, and report on the extreme amount of retail data generated each day. These data-driven retailers are undeniably faster, nimbler, and more responsive to customer needs while generally more profitable at the same time. But for most retailers (the ones that aren’t spending hundreds of millions to compete), building up a big data operation and employing their data science teams has been out of reach. Recently, a significant technological shift has occurred which has leveled the data-driven playing field between small and large retailers: decision intelligence.
Scramble to hoard customer data
For years there has been a gold rush of ‘hoarding’ customer data with the advent of CRM’s and CDPs. However, the infrastructure and payroll needed to run big data operations can cost hundreds of millions of dollars. Dedicated, in-house data science teams tasked with developing insights for marketers and merchandisers from enormous datasets spend countless hours solving retail challenges like price optimization, inventory management, and supply chain control. Amazon, for example, is known to change product prices 2.5 million times a day, meaning that an average product listed on Amazon changes prices every 10 minutes. That’s 50X more often than competitors like Walmart and Best Buy. That is nimble.
The Democratization of Data Access Has Not Been Enough
Many have tried to become data-driven by simply extending data access with technology. Data from sales, customer service, inventory, and countless systems contributing to retail business operations are now generally accessible through APIs or “application programming interfaces”. These interfaces are designed for systems to efficiently pass data from one program to another.
For instance, if you wanted to analyze or use your Shopify data for insights, a data science team could leverage Shopify’s published APIs to accomplish this. And you could repeat this process to include other applications’ data, such as Google Analytics or Twitter. Internal data sources can easily surpass a dozen systems for small retailers, and you may even have more from external systems. The difficulty of combining and utilizing so many sources has limited all but the largest of retailers to build truly data-driven operations.
Even with a team, a reported two-thirds of data generated by the average retailer goes unanalyzed.
However, simply democratizing data doesn’t mean it’s useful or used well. Where to store, normalize, transform, and report out this information has been a big hurdle. And the interpretation of this daunting amount of information has remained problematic without a dedicated data science team. Even with a team, a reported two-thirds of data generated by the average retailer goes unanalyzed. There is just too much for human teams to collect, process, and utilize for decision-making manually.
Artificial Intelligence Brings a New Era of Decision Intelligence
A whole new software approach is reinventing decision-making to make data science accessible across economic tiers in the retail industry. In this new approach, data is connected automatically through integrations designed for common business systems. This greatly reduces the complexity of connecting data for analysis. And once connected, sophisticated new Artificial Intelligence (AI) churns through the collected data much faster than the old dedicated data science team – running millions of decision scenarios, constantly, all aimed at predicting the future of the retail operation given different probabilities.
This new engineering discipline is collectively referred to as “Decision Intelligence”. These new solutions focus on impact. Rather than push volumes of data and graphs to management for interpretation and action, platforms orchestrate actions and push whole decisions mapped and evaluated by the AI in real-time.
What Will the Dawn of Retail Decision Intelligence Mean for Retail?
The implications of Decision Intelligence systems for the retail industry will undoubtedly be far-reaching. Among a sample of the impacts, we can expect to see in the near term:
1. Decision Intelligence delivered as-a-service will level the playing field between small and large retailers.
2. There will be a noticeable separation between the Decision Intelligence “haves” and the “have nots” based on their speed, profit, and customer loyalty differences. It will not be possible for a human-run system to beat AI in retail operations at scale.
3. Not only will the quality of retail operations decisions improve dramatically for adopters of Decision Intelligence, but the number of decisions that a typical organization can handle will increase as dedicated personnel time shifts from collection and diagnosis to disproportionately execution.
4. Decision Intelligence as-a-service may greatly compress the cost of retail data operations at large retailers, which previously needed to build big data operations from the ground up.
5. We anticipate more organizational fluidity as Decision Intelligence accelerates actions with justifications for those decisions more universally accepted across retail teams than ever before.
6. Over time, Decision Intelligence will help drive organizational changes as these platforms increasingly become automated.
Empowering the next generation of retailers
Decision Intelligence is disrupting both the world of AI and retail, and will bring retailers to Shein’s speed and Amazon’s competency. It empowers every business to add organizational scale without complexity, and tap into the full potential of data without deploying intricate data systems.
To learn more about how smaller retailers are using Decision Intelligence today, check out our Case Study to learn how one luxury retailer re-engaged with churning VIP customers and decreased discount sales with the power of AI.