Retailers Can Use AI to Unlock — and Capitalize on — Their Treasure Trove of Data
Data is the oil that’s lubricating the sales machines at huge online retailers like Amazon.com and is exploring user behavior for tech giants like Google and Facebook. According to Amazon Web Services (AWS), its payments data engineering team alone is responsible for data ingestion, transformation and storage of a growing dataset of more than 750 TB. That enormous volume will dwarf that of most other organizations, but this doesn’t mean that their data is any less valuable or that there isn’t room left to compete.
The insight that a retailer can gain from good quality data isn't determined by how much there is of it, but rather by how it's collected, analyzed and used to meet customers’ requirements. Where will demand be particularly high next weekend? How much influence will the weather have on online sales? Under what circumstances is the probability of fraud or returns particularly high? Why does the customer behave like this and not differently? The answer to all of these questions is in the data.
As data volumes continue to grow, the next consideration is how best to manage it. Can we still rely on good old-fashioned statistics, or should we be harnessing artificial intelligence (AI) and big data? Many questions can be answered by a combination of big data and statistics, particularly in companies that are very familiar with their data and the insights it’s providing. The challenges start if there's no statistician capacity in-house or a suitable big data tool.
At this point, AI needs to be considered because it’s the best way to help retailers evaluate their data and relationships to gain a better understanding of buyer preferences and predict future behaviors. The danger of not doing this in today’s fast-moving commerce environment is that customer expectations aren't met, and competitors quickly move into the available space.
Retailers shouldn’t worry about the amount of data they have. Size isn’t an issue when it comes to AI. The right questions coupled with the right data mean that a medium-sized retailer, whether online or offline, can achieve just as successful results as even the largest players in the market.
Where to start? AI today is at the point that IT was back in the 1960s — still very much in its infancy. In truth, only a few companies have the internal expertise, data competence and technical staff available to manage its implementation. For this reason, many retailers are outsourcing the management of their data to a service provider.
The advantage of this is that the expertise, particularly in AI, has already been built, which means that the data is in good hands. Often the company is already providing a service to the retailer, so the data is familiar to the provider. This allows insights to be drawn more quickly and more accurately, resulting in faster results that can be implemented.
Before rushing in, however, retailers that are new to AI should ensure that any third party they use is able to provide AI-prepared data that can then be transferred to AI tools such as Python and TensorFlow to test suitable mathematical models. If the provider is able to automate it on their platform, this further reduces the workload for the retailer.
AI is transforming the way we do business, helping retailers to realize the treasure they have at their disposal hidden deep inside their databases. To compete successfully, they just need to find the right partner, or expert, to tease it out and turn it into meaningful insight.
Ralf Gladis is the co-founder and CEO of Computop, Inc., a global payment service provider.
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