Einstein Vision enables you to tap into the power of AI and train deep learning models to recognize and classify images at scale. You can use pre-trained classifiers or train your own custom classifiers to solve unique use cases.
For example, Salesforce Social Studio integrates with this service to expand a marketer’s view beyond just keyword listening. You can “visually listen” to detect attributes about an image, such as detecting your brand logo or that of your competitor in a customer’s photo. You can use these attributes to learn more about your customers' lifestyles and preferences.
Images contain contextual clues about all aspects of your business, including your customers’ preferences, your inventory levels, and the quality of your products. You can use these clues to enrich what you know about your sales, service, and marketing efforts to gain new insights about your customers and take action. The possibilities are limitless with applications that include:
- Visual search—Expand the ways that your customers can discover your products and increase sales.
- Provide customers with visual filters to find products that best match their preferences while browsing online.
- Allow customers to take photos of your products to discover where they can make purchases online or in-store.
- Brand detection—Monitor your brand across all your channels to increase your marketing reach and preserve brand integrity.
- Better understand customer preferences and lifestyle through their social media images.
- Monitor user-generated images through communities and review boards to improve products and quality of service.
- Evaluate banner advertisement exposure during broadcast events to drive higher ROI.
- Product identification—Increase the ways that you can identify your products to streamline sales processes and customer service.
- Identify product issues before sending out a field technician to increase case resolution time.
- Discover which products are out of stock or misplaced to streamline inventory restocking.
- Measure retail shelf-share to optimize product mix and represent top-selling products among competitors.
Deep learning is a branch of machine learning, so let’s first define that term. Machine learning is a type of AI that provides computers with the ability to learn without being explicitly programmed. Machine learning algorithms can tell you something interesting about a set of data without writing custom code specific to a problem. Instead, you feed data to generic algorithms, and these algorithms build their own logic as it relates to the patterns within the data.
In deep learning, you create and train a neural network in a specific way. A neural network is a set of algorithms designed to recognize patterns. In deep learning, the neural network has multiple layers. At the top layer, the network trains on a specific set of features and then sends that information to the next layer. The network takes that information, combines it with other features and passes it to the next layer, and so on.
Deep learning has increased in popularity because it has proven to outperform other methodologies for machine learning. Due to the advancement of distributed compute resources and businesses generating an influx of image, text, and voice data, deep learning can deliver insights that weren’t previously possible.
Updated about a year ago