Every marketer knows about the allure of the “hotfix.” Something on the site needs to be changed, but the dev team can’t do it until the next release, which is two weeks away. Enter the hotfix option.

Originally, engineering reserved a hotfix for critical functionality and not for making quick updates that are important to marketers. But, hotfixes are not always good for overall customer experience and can create chaos internally if things are done wrong. It’s best for marketers to run through a checklist before they make a new request.  

When to use a hotfix: 1) Easy image or text changes for active promotions or to update content to be current; 2) Updating things like stock levels or shipping deadlines during holiday seasons; 3) Using pre-QA templates within a personalization or content platform.

When not to use a hotfix: 1) Anything that involves the checkout transaction process; 2) Anything that involves updated custom logic (such as JavaScript); 3) Anytime there is not a pre-built, approved template available. 

Ensuring the changes are simple, needed and safe is the key. If anything gets too close to important transactions, or is very complicated, things can go wrong – in real time. It’s best to create a broader strategy for more flexible site updates than constantly deal with backlash from too many hotfixes.

– Greg Giletto, senior platform consultant at Monetate

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Salesforce takes the wraps off of Einstein Search. Salesforce has released Einstein Search for Commerce and Einstein Search Recommendations. The new Einstein capabilities are expected to bring personalized and actionable search to its Sales and Service Cloud platforms. “If you’re like 93% of people connected to the internet, you use a search engine to find, purchase, or learn about pretty much anything that comes to mind. Consumer search engines provide a seamless way for us to make sense of our complex world. And consumers are used to a search experience that is fast, accurate, and constantly improving,” said Salesforce VP of Product Management Will Breetz. “In building this feature, we had to completely rethink the search experience so that it would scale to our hundreds of thousands of customers. Search should help you quickly find critical information and drive productivity to help resolve customer issues faster.”

Tealium launches machine learning tech for CDP. Real-time customer data orchestration provider Tealium has launched Tealium Predict, a built-in machine learning technology for its customer data platform, Tealium AudienceStream. Tealium Predict activates machine learning insights from across the entire tech stack. “Organizations spend a vast amount of their time wrangling data and figuring out how to deploy and activate insights,” said Mike Anderson, founder and CTO of Tealium. “With our continued focus on data orchestration, we’re in the perfect position to offer a machine learning product integrated right into our CDP solution. We can deliver machine learning data into a customer’s entire downstream tech stack, whether any of those stack tools have machine learning capabilities or not.”

Washington Post builds ad network for publishers to take on Big Tech – Axios

Facebook Partners With Ray-Ban to Help Make Its Smart Glasses Happen: Report – Gizmodo

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7 things to know about the augmented reality landscape – VentureBeat

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