Using Machine Learning To Understand Your Customers Better
When retailers aim to collect more data about their customers, they follow larger sources of market information. Here are some notable examples of technological advancement in machine learning allowing retailers to understand their customers better.
As the global retail market is expanding, the industry has become capable of growing and competing with challenges, such as infrastructure, competition, and above all, the absence of efficient analysis tools and tracking retail execution methods in the stores.
Here are some notable examples of technological advancement in machine learning that has allowed retailers to understand their customers better:
Thanks to this technology, retailers, and manufacturers are now capable of grasping an accurate picture of their existing marketplace to react in real time. Data collected by the manufacturer’s sales reps can be transformed into actionable intelligence in real time. This converts sales execution into a new science.
By employing image recognition, retailers can save up to 60% of audit time in stores with 98% auditing accuracy. This implies retailers will be able to get their hands on accurate and reliable data on their distribution, knowing which items are out of stock. It also makes the wealth of other actionable insights readily available and accessible by retailers.
Through the output from predictive analytics and machine learning, the image recognition technology has come a long way in the retail world. Many retail stores use technology to track customers when they are in the store as it contributes to a positive shopping experience. Moreover, the fine-grained image recognition engine evaluates the image or video capture of any product on the shelf and successfully differentiates between minute design changes in brands and SKUs (stock-keeping units).
For instance, consider all the different Coca-Cola bottles that were promoted and exclusively branded for the World Cup. The technology will update its SKU in the database automatically and will not require any manual updates from the sales reps.
With geo-fencing, a retailer successfully targets location-specific information to a set of ZIP codes. This allows more chances of accessing premium inventory. The audience and marketing content will be more relevant to trigger higher conversion rates. The actual purpose of creating a geofence is to direct all communications towards a given zone, in a particular context. It is similar to geo-targeting, but with increased accuracy. Retailers use geo-fencing to attract shoppers when they are passing by their stores.
Beacons serve as little physical objects placed in specific locations. The sole purpose is to detect consumers as they move into the range of retailers. The beacons are not designed to transmit any content. They only trigger a signal a consumer is nearby and the server sends an in-app text or a push.
Beacons are also termed as BLEs or Bluetooth Low Energy. This is why consumers and retailers like the technology. The low energy bit implies the batteries in a beacon will sustain for a significant time. The Bluetooth bit suggests the beacons can serve as great marketing tools in areas with no Wi-Fi.
How Machine Learning Is Used By Industry Leaders:
IBM has turned its Watson AI system into a consumable service with a range of tools that are powered by machine learning. IBM has included tools, such as language translation, weather prediction, image recognition, sentiment and tone analysis and more on its Bluemix PaaS. IBM's roster is considered the most ambitious attempt to accomplish maximum benefits of machine learning. IBM has also been developing pie-in-the-sky tools with other components that revolve around reporting and analytics. Growing Watson appears to be the motive behind dozens of IBM's strategic acquisitions across various fields, including healthcare, weather and so on. IBM has Watson to flaunt but Microsoft offers Project Oxford to maintain its authority. It is a set of curated high-level APIs that cover language analysis, speech recognition, and machine vision. Although the list of APIs may not be as diverse as Watson's, Microsoft is heading in the right direction to help retailers. Moreover, Amazon Machine Learning works like Google Prediction API. This means the models can be trained against data to deliver accurate predictions. The service is simplified deliberately for appealing developers.
The highly competitive retail landscape today allows retail giants to enhance their retail execution at the point of sale. However, only a small percentage of conventionally conservative FMCG companies have embraced machine learning to benefit from it.
This article was originally published here. Re-published with permission from Hughes Systique Corporation.