Vancouver-based Hootsuite lets companies track social-network channels to see what people are saying about their brands and respond in real time. Hootsuite bought Montreal-based Heyday because companies have expanded their use of social media beyond marketing to include commerce and one-on-one messaging, said Tom Keiser, Hootsuite's chief executive. "You really can do the entire customer life cycle now, all the way through selling and support, on the social and messaging platforms," Mr. Keiser said. "With the acquisition of Heyday, we get a really strong base of AI capabilities that we'll be able to use in social care and across the other aspects of our business," he added, referring to customer service via social media. The deal is Hootsuite's second acquisition this year, following its January purchase of Sparkcentral, a provider of digital customer-support software-as-a-service.

Artificial intelligence and cloud computing are changing the direction of modern business. Countless entrepreneurs are discovering the benefits of AI in their business models. The market for AI technology was $39.9 billion last year. It is growing at a phenomenal pace of 42.2% a year. But what AI and cloud-based tools are in the greatest demand among business owners?

There is no shortage of options for social media management tools; there are more than 300 listed on the marketing technology landscape. And HelloWoofy isn't on it yet, but the start-up is aiming to bring the assistance of automation to social content creation for small businesses. The company announced a new integration with social media management platform Hootsuite Friday. As with any martech solution, integrations are key to user growth and retention. HelloWoofy has scheduling features, too, but it doesn't have the massive user base of Hootsuite.

The decade belongs to the phenomenal rise of social media marketing and advertising. Thanks to Mary Meeker's insights on internet trends in 2018, marketing teams have a fair idea of how social media companies impact the way customers interact with their brands of choice. Social media marketing companies now rely on data that are collected and analyzed to create revenue opportunities across multiple marketing channels. Charged by the ever-widening expanse of social media platforms, channels, and applications-- the role of AI has grown many times over within a decade. AI is one of the fastest-growing technological segments, which has been used since 1990- and is still gaining rapid appreciation.

Here at Hootsuite we're always looking for better ways to utilize our time and new technology. That's why we're looking at building an environment that helps developers leverage machine learning (ML) in production and minimize the overhead (i.e. Currently, ML is often done on a very ad-hoc basis. That's because there is no standardized workflow--as of yet--to deal with many of the unique difficulties associated with ML. Given the complexity of ML systems, it's unsurprising that they can contain some areas of technical debt. What was surprising to me however, was the sheer number of ways that that technical debt can arise.

In this article, we discuss various strategies used to generate exponential traffic growth, while preserving traffic quality, and user loyalty. Raw data science: getting the right data sets, leveraging them, Playing with various tools and API's: designing an automated machine-to-machine communication service between Hootsuite and Twitter / LinkedIn based on insights automatically distilled from the following data sources: (1) data obtained via the Google Analytics API (traffic statistics about 50,000 live DSC articles), and (2) data collected via a web crawler written in Python A blend of high-level (strategic) data science and low-level (tactical or operational) data science. In the end, relatively little coding is involved in the process. Domain expertise and smart innovation play a critical role. Optimizing parameters of the statistical process used to select articles, create tweets, and schedule them, using experimental design and A/B testing Artificial intelligence: detection and removal of articles that are time-sensitive, automated creation of relevant hash-tags for selected tweets, and creation of a taxonomy of all our articles using simple indexing classification scheme Smart analytic-driven advertising on Twitter, using a good list of data science thought leaders worth following, as our core data set for advertising purposes. The creation of this list is an interesting data science project in itself.