Artificial Intelligence in Human Resource Management
That's the theme of this month's online conference from HR.com. If you thought the human part of human resources was broken for many companies, could AI be making it a better or worse experience for candidates, employers, and potential partners?
Marketing Technology Stack
Been seeing a lot of "marketing data science" positions appear in the Pacific Northwest on job boards lately and many of the job postings look like employer wish lists. The pay scale for such a mythical position varies as well, from entry level wages to all the way past $150K/year for larger, global firms. This is not the same as a Marketing Technology Stack (or, the tools and resources used that a marketer uses to deploy digital or mixed media campaigns).
A typical technology stack for marketers involves the following examples of software:
General Marketing Tool Categories
Is there a "data stack" for marketers? Yes and no. It depends on how complex and/or robust analytics data is. Are you reporting on enterprise data where you need to show campaign performance by sales region, territory, product groups, etc.? You might need something more than Excel's Pivot Charts and a flashy Powerpoint.
Is it possible to find a marketing technologist with a data analysis stack? It's possible, but not very likely. Though, marketing job requirements have been trending in this direction since the end of the great Recession (circa 2010) when people are expected to do more work with fewer resources. This trend was born out of industry need not because it actually helps companies make better pivots with marketing spend in their budgets.
Most companies want a software developer with marketing experience; while, a marketer with analytics experience doesn't offer the same level of technical skill (for software or methodology implementation). How often is a digital marketing (e.g., SEO specialist, email marketer, paid ad buyer) going to do their own campaign analysis and compare it to historic spend in order to create predictive models for future campaign spending?
What's in a typical data stack for marketers, if they are inclined towards software development?
A typical technology stack for marketers involves the following examples of software:
General Marketing Tool Categories
- Project Planning: Wrike, Trello, Asana, Basecamp
- Social Media / Group Social Media: HootSuite, SproutSocial, Buffer, MeetEdgar
- Social Listening: mention, talkwalker, buzzsumo, brandwatch
- SEO: ahrefs, moz, serps.com, SEMRush
- Video: vimeo, brightcove, wistia
- Content Management Systems (CMS): WordPress, HubSpot, contentful
- Email Marketing / Marketing Automation: MailChim, emma, aweber, GetResponse, Campaign Monitor, Pardot/ExactTarget
- Customer Relationship Management (CRM): salesforce, infusionsoft, HubSpot, Microsoft Dynamics
- Analytics: Google Analytics, kissmetrics, Adobe Analytics (formerly Omniture), Google Data Studio
Is there a "data stack" for marketers? Yes and no. It depends on how complex and/or robust analytics data is. Are you reporting on enterprise data where you need to show campaign performance by sales region, territory, product groups, etc.? You might need something more than Excel's Pivot Charts and a flashy Powerpoint.
Is it possible to find a marketing technologist with a data analysis stack? It's possible, but not very likely. Though, marketing job requirements have been trending in this direction since the end of the great Recession (circa 2010) when people are expected to do more work with fewer resources. This trend was born out of industry need not because it actually helps companies make better pivots with marketing spend in their budgets.
Most companies want a software developer with marketing experience; while, a marketer with analytics experience doesn't offer the same level of technical skill (for software or methodology implementation). How often is a digital marketing (e.g., SEO specialist, email marketer, paid ad buyer) going to do their own campaign analysis and compare it to historic spend in order to create predictive models for future campaign spending?
What's in a typical data stack for marketers, if they are inclined towards software development?
- R or SPSS
- Python
- Tableau (for data visualization - makes pretty pictures from data)
- Excel
- Google Analytics / Google Data Studio
In my digital marketing career, how many times have any of these come up as tools for marketers to use while doing marketing campaign analysis? Tableau, and only at one company.
Startup Weekend: An organizer's recap
Startup Weekend Vancouver happened at the start of June. People came out from all across the west coast to attend, as far north as Vancouver, BC and as far south as San Luis Obispo, CA. I'd like to say that it was a blast and fun to put on this event but it wasn't. Like most non-profit events, we had a lot of moving parts and at one point had setup a GoFundMe page because we thought we had more expenses than actual revenue from sponsors and ticket sales. Fortunately we were in the black with some budget money leftover for future events.
Hello Alexa, part 2
It's been a while since I last posted about Amazon's Alexa being accessible through the Amazon retail shopping app. And while Amazon's generic web search is fine and comes nowhere near the voice-to-search recognition that Google search offers, Amazon is missing the point about monetizing the index system that they have for the millions of products listed on their shopping exchange.
Wouldn't it be better if instead of matching to keywords (mostly nouns) in a user's speech search, that Amazon served up relevant recommendations instead.
Say for example, you ask Alexa (in the Amazon app):
"recommended wines for dinner" or
"recommended red wines" or
"recommended fruity wines"
Alexa currently offers no recommended product searches for any of the wines or wineries that sell on Amazon. Well, it certainly can't recommend wines that's for sure. But it could if Amazon incorporated product label text, certified wine reviews, or wine manufacturer descriptions in what can be searched. The words "recommend" and "recommended" are not in Alexa's lexicon of search knowledge. Perhaps this is too advanced a concept for Amazon's AI.
You can still just say "red wine" or "white wine" and those options will show up with valid results in the Amazon app.
Voice searching the Amazon product engine should be no different than typing in the search query.
The results are mixed, however.
You can say "services for window washing near me" and Amazon's app will show for "Hire a Window Cleaner" (Amazon Home Services) as the top result. That's spot on. The third result (same screen on a smartphone) shows "Window Cleaning" (Amazon Home Services), also a valid result to what I was voice searching for.
Maybe this is a phased rollout for voice search queries.
Wouldn't it be better if instead of matching to keywords (mostly nouns) in a user's speech search, that Amazon served up relevant recommendations instead.
Say for example, you ask Alexa (in the Amazon app):
"recommended wines for dinner" or
"recommended red wines" or
"recommended fruity wines"
Alexa currently offers no recommended product searches for any of the wines or wineries that sell on Amazon. Well, it certainly can't recommend wines that's for sure. But it could if Amazon incorporated product label text, certified wine reviews, or wine manufacturer descriptions in what can be searched. The words "recommend" and "recommended" are not in Alexa's lexicon of search knowledge. Perhaps this is too advanced a concept for Amazon's AI.
You can still just say "red wine" or "white wine" and those options will show up with valid results in the Amazon app.
Voice searching the Amazon product engine should be no different than typing in the search query.
The results are mixed, however.
You can say "services for window washing near me" and Amazon's app will show for "Hire a Window Cleaner" (Amazon Home Services) as the top result. That's spot on. The third result (same screen on a smartphone) shows "Window Cleaning" (Amazon Home Services), also a valid result to what I was voice searching for.
Maybe this is a phased rollout for voice search queries.
Deep Learning
Deep learning has its origins in the early days of artificial intelligence, when researchers began to explore the use of artificial neural networks to learn from data. However, it wasn't until the early 2000s that deep learning began to gain popularity as a field of study. This was due in part to the development of new algorithms that made it possible to train deep neural networks on large datasets. Additionally, the availability of high-performance computing resources made it possible to train deep neural networks in a reasonable amount of time.
In 2012, Geoffrey Hinton and his team at the University of Toronto used deep learning to achieve a breakthrough in image recognition. Their algorithm, called AlexNet, won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) by a significant margin. This victory helped to spark a renewed interest in deep learning, and the field has since exploded in popularity.
Today, deep learning is used in a wide variety of applications, including image recognition, natural language processing, speech recognition, and machine translation. Deep learning is also being used to develop new drugs, create self-driving cars, and improve the accuracy of weather forecasts.
As deep learning continues to develop, it is likely to have a major impact on a wide range of industries. It is already being used to solve some of the world's most challenging problems, and it is only going to become more powerful in the years to come.
The Age of Automation
I have mixed feelings about automation. The word even appears in my job title from time to time. The myth about automation is that somehow doing so will allow us to have more time to do other things. I've encountered many scenarios where a consultant or subject matter expert is brought into a workplace situation to help a company build out its marketing automation, only to not retain that talent for the long term.
But, this post isn't about marketing automation or my aforementioned rant about companies that fail to use it to build 1-to-1 relationships with their customers. Instead, I'd like to point out the concerns addressing automation's impact on the US trucking industry.
But, this post isn't about marketing automation or my aforementioned rant about companies that fail to use it to build 1-to-1 relationships with their customers. Instead, I'd like to point out the concerns addressing automation's impact on the US trucking industry.
Hello Alexa
Not sure how long ago this feature was added, but it looks like someone just replaced the default microphone app with Alexa's voice and mannerisms on the Amazon app. You'd think that if you were accessing Alexa from within Amazon's shopping app, that the default search would be for items listed in Amazon's eCommerce ecosystem. Sadly, this is not the case.
My first query: "weather tracking for the home", followed by "weather tracking apps"
I don't like Alexa's color bar acknowledgement followed by its electronic beep. For the few seconds it takes to execute these robotic response commands, it is an unnecessary feature. Alexa responds by verbally giving me the weather forecast for Salem Oregon.
The response is puzzling because I was just adding/removing items from my wish lists in the app which one could assume that I am already logged into my account which has my mailing address in it (and I don't live in Oregon). Even if location services were turned on for this app, surely the developers would have programmed that into Alexa -- to be able to give regional information based on already known criteria.
My next query: "search Amazon for home weather tracking"
That brought up a relevant search list on Amazon's store.
Artificial Intelligence is only as good as the team that builds it.
I can just visualize the disconnect between the business user story and what got implemented by the development team. Maybe I'm just disappointed because I'm so used to Google search providing accurate, relevant results from text or voice queries.
At least Alexa can tell jokes (Siri cannot):
"Tell me a funny cat joke"
Alexa: What does a cat say when it gets hurt? Me-ow.
 |
| Screenshot of Alexa's Intro Screen on Amazon App |
My first query: "weather tracking for the home", followed by "weather tracking apps"
I don't like Alexa's color bar acknowledgement followed by its electronic beep. For the few seconds it takes to execute these robotic response commands, it is an unnecessary feature. Alexa responds by verbally giving me the weather forecast for Salem Oregon.
The response is puzzling because I was just adding/removing items from my wish lists in the app which one could assume that I am already logged into my account which has my mailing address in it (and I don't live in Oregon). Even if location services were turned on for this app, surely the developers would have programmed that into Alexa -- to be able to give regional information based on already known criteria.
My next query: "search Amazon for home weather tracking"
That brought up a relevant search list on Amazon's store.
Artificial Intelligence is only as good as the team that builds it.
I can just visualize the disconnect between the business user story and what got implemented by the development team. Maybe I'm just disappointed because I'm so used to Google search providing accurate, relevant results from text or voice queries.
At least Alexa can tell jokes (Siri cannot):
"Tell me a funny cat joke"
Alexa: What does a cat say when it gets hurt? Me-ow.
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