In a survey of nearly 200 business leaders by consulting firm McKinsey, 43 percent said they do not get enough sleep at least four nights a week, and yet 46 percent said that lack of sleep didnt play a significant role in leadership performance.
Research is now confirming everywhere, points towards the opposite.
Years of bad sleep can negatively impact memory, decision making skills, creativity and social interactions, of which are crucial for those in top corporate positions. Your brain basically goes through a cleanse when you’re not awake: It flushes out harmful toxins while going over information you acquired during that day.
Power naps during the day offer a complete turbocharge for your memory and boost productivity - and now most major companies, in particular in Silicon Valley ones are offering ‘nap’ rooms for their workers.
Now for the next step - ensuring you calendar has slotted the ‘nap’ hour. Perhaps you can list that as an Calendar invite with yourself. Block it out now, or someone will use that hour since you are ‘free’ in the schedule. Your growth in your career demands it.
Coronavirus has cut a large and uneven swathe through the world’s businesses. While some fields — hospitality, transportation, restaurants — have been crushed, other fields — grocery delivery, e-commerce — have been crushed in a very different way: with too much demand from sheltering consumers. This is the story of how Tolstoy, a NLP company based in San Francisco, helped an online garden retailer handle a massive inflow of customer emails as they waged their way through the pandemic.
Pandemic-level demand
While some businesses have seen their demand dry up overnight, other businesses have experienced record inflow. One of the activities consumers stuck at home have turned to is gardening. Garden retailers both in the US and Europe have seen orders skyrocket hundreds, or thousands, in percent growth.
One of these garden retailers approached Tolstoy with a dilemma. The coronavirus spigot had sent them several times more orders than they had capacity to fulfill. Correspondingly, they had received over 50,000 customer emails since the start of March — most of them angry emails inquiring on order status.
The company usually routed customer inquires through Freshdesk, an email platform, that helped categorize inbound mail. But now they were receiving correspondence from many sources, including direct email, which had no categorization applied. They were confronted with the prospect of reading through more than 50,000 emails — with thousands added per week — just to triage high priority emails, such as cancellation requests.
They estimated that if they hired 4–5 staff, it would take about a month to read the emails — during which tens of thousands more emails would arrive.
AI to process emails
Tolstoy took a look at the company’s email data. Email text data can often be messy — everything from the sender fields to legal disclaimers are included, so we cleaned it up with some preprocessing. This meant stripping out all forwarded emails and auto-email generated text, after which we normalized the text by removing punctuation and capitalization.
Next, we considered the training set of about 3,000 categorized emails. These emails were classified through Freshdesk, which meant that customers picked the category. While most of them were useful categories, customers often picked wrong categories — for example, some selected “Cancel” when they actually wanted to inquire about a new product, or “Delivery query” when they meant to cancel. We manually corrected and labelled a set of more than 5,000 emails to assert “ground truth”.
We also balanced the limitations of the training data by hard-coding certain heuristics into the predictions.
After testing different models, we settled on a logistic regression, which is based on the frequency of words found in the email text. To help the model focus on the more important features, we only included words that surpassed a fixed number of appearances across the dataset and incorporated elastic net regularization.
Ultimately, we configured the model to read and categorize emails with ~98% accuracy, and 97–99% precision/recall (a measure of true/false positives captured), based on a set of 1,300 manually corrected emails. This was far more accurate than even the existing categorized emails via Freshdesk.
Record time
The garden retailer sent over their 50,000+ batch of emails on a Friday. We tuned the model on the following Monday, and returned the categorized results back on Tuesday.
This enabled the retailer to immediately start processing and responding to all their customer emails, which they had not been able to do for months. Finally, customers could get their answers and orders fulfilled or refunded.
From Tolstoy to the gardeners of the world, Here’s to hoping you get your gardening in during shelter-in-place!attack vectors and general confusion as to who is doing what now.
More info on Tolstoy: www.tolstoy.ai
Just when we thought we had a good lay of the land with software publishers in Legal Automation, we became very impressed with a new start up in San Francisco called Agreemint (www.agreemint.com). Agreemint is framing the problem conversation entirely - and with a very convincing story and results. And we must admit we are swayed with the approach.
Agreemint is challenging the market to rethink how business stakeholders send a document for signature.
It is wildly expected that most businesses are pressed to sell more, each quarter. And do so in a very customer friendly way (with proof in the rising spend in Customer Success budgets), but sales teams are lacking in additional resource spend in Legal help or Operational help for these closes.
Therein lies the mismatch. And technology can help here.
In modern day business, people tend to negotiate things with some level of heirachy. Very few items of negotiation are just plain yes or no. They generally have weight to them, or additional context. For example, some are ‘no go’ and some tradable for something else. This goes for internal negotiation and external.
In an Agreemint document format, users are presented an intuitive and friendly interface that allows them to set additional context or weight to a redlined item. Those weights are captured through a clever drag and drop interface within the document. And when users drag drop items by priority, Agreemint is able to catalog those patterns for future intelligence and historically aided suggestions.
The result is less time stuck in redline limbo and faster close times.
Users seem to love it as well, with less friction in the entire close process.
Agreemint has a whole slew of usefull features including Smart Forms for which make the contract nearly self reporting in a CRM when signed. Another huge time saver.
More can be reviewed on their website www.agreemint.com
They have our vote and a company we can recommend to our users
Researchers are no longer focused solely on building a quantum computer that could carry out Shor’s algorithm and break encryption codes. For many, an intermediate goal is to achieve “quantum supremacy,” a term coined by Caltech’s John Preskill. Thus when quantum computers can carry out tasks that are not possible or practical for a normal computer this would be reached, often deemed interesting in optimisation problems.“There’s ample reason to believe that quantum computers would be good at solving optimization problems.
For one, nature seems to have no trouble with them. Plants solve them all the time when they turn sunlight into energy using photosynthesis.”
Forward-thinking organizations recognize the synergistic boost that the combination of quantum computing and artificial intelligence may herald. Microsoft CEO Satya Nadella stated in a WSJ Magazine interview, “What’s the next breakthrough that will allow us to keep up this exponential growth in computing power and to solve problems—whether it’s about climate or food production or drug discovery? I think that’s where quantum plays a role." Per Nadella, artificial intelligence and quantum computing are "going to shape a lot of the technology going forward.”
So the short answer is yes. As long as humanity can be lead towards solving impactful societal problems, this combination may allow mankind to flourish beyond anyones practical expectations.
Adobe isn’t playing around with their latest in Creative Cloud from what was learned at the Adobe Max this month in Los Angeles.
The big updates include major advancements in Adobe After Effects, Adobe Premiere, Photoshop, Lightroom, Audition and Bridge.
One of the biggest features of the year included Adobe After Effects Content-Aware Fill feature, and we expect the new updates to expand the accuracy and performance of this ground breaking feature for video editors.
Rulers and Guides was a very nice advancement in Adobe Premiere over 2019, which allows for easier placement of objects, title, and general harmonious balance in a video render.
We also expect major advancements in Adobe Illustrator, with particular new support of IPAD, mirroring all the same features as in a desktop version.
Adobe Fresco, previously called Project Gemini, would have new advancements as well. In particular for a new set of users that want the feel and simplicity of regular style painting and drawing without using a bezier tool, native in Adobe Illustrator.
Employers are increasingly using non traditional employee monitoring tools and employees are growing more comfortable with it - if you tell them what you are doing and why.
New tools are being provided to listen to employees in non traditional ways.
Last year of 239 large corporations surveyed by Gartner, nearly 50% are using some kind of non traditional monitoring software. That is up from only 30% in 2015, and we expect by 2020, this will be at 80% will montior regularly.
When it comes to non-traditional monitoring, we are referring to analyzing the text of emails, and social media messages, scrutinizing who’s meeting with whom and gathering biometric data while understanding how employees are using their workspace.
Employee surveys have their limits.
They key to an effective monitoring solution within an enterprise lays foundationally in how transparent the organization is as to the how and why. As long as those are communicated in advance and understood that this is to the benefit of the health of the organization then most of the time this rolls out quite successfully.
Delta Airlines used gamification to reach the heart of New Yorkers.
To put it simply, gamification incorporates fun and an element of competition to a marketing strategy. It also works with all brand fans and people who want to participate – not just the ones with a ton of followers.
The theory behind implementing these types of techniques is that they make an emotional connection with the audience and lead to a longer relationship as opposed to simple brand awareness.
An easy way to work with brand advocates and potential consumers is through tactics such as contests, free product giveaways with bloggers, and games that have a thematic relevance with the brand being promoted.
So some stats:
More than 70% of the worlds largest 2,000 companies are expected to have one gamified appliation by the year end of 2020.
Vendors now are seeing a 100-150% increase in engage on applications that are gamified to some degree.I
In the same way a hollywood movie or soap operat can ‘hook’ a member in to continue to watching the climax, this same concept when applied in marketing can hook a user to feel an emotional connection to follow the brands personal story, through some kind of direct engagement.
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