Blog Abstract: Tackling Bias in Artificial Intelligence

From time to time, team members will share their views stimulated by content from an industry thought leader. Here, our CEO, Lisa Maier, discusses the recent McKinsey & Company article, “Tackling bias in artificial intelligence (and in humans)” by Jake Silberg and James Manyika.

In the last year or so, I saw a documentary about Google and the inherent bias in their search algorithm, which basically showed how it baked in the bias of the programmers, probably without any intention of doing so. Then, in this last week, I saw an AllSides blog post, “Audit Finds Evidence of Google’s Bias Toward These Media Outlets” that starts out with this paragraph:

“A new audit shows Google is biased toward a small number of major media outlets — and most of them have a Left-leaning political bias. One-sided information isn’t healthy for democracy, yet the world’s most popular source of news and information displays a major bias. Continue reading “Blog Abstract: Tackling Bias in Artificial Intelligence”

Artificial Intelligence: How AI Will Boost Marketing in 2019

Still in its formative years in the marketing realm, artificial intelligence (AI) is developing and growing at an exponential rate. Over half of all current marketers use AI in some way in their campaigns with another 27% planning to learn more about it or implement it within the next couple of years. The use of AI marketing is growing.

While it’s hindered slightly by a decided lack of education and transparency pertaining to its implementation, many see its potential value in the analysis, data collation, and customer service which makes AI trends the ones to watch in 2019.

AI Trends to Watch For in 2019

  1. Helping Humans, Not Replacing Them

When thinking of artificial intelligence in marketing, many go directly to the notion of AI as replacing human involvement in different areas of marketing like customer service. After giving it some thought, one comes to realize that AI can certainly … Continue reading

Is your data working for you… or are YOU working for your data?

We live in the age where information and data analysis has become key to good business practice. The goal is simple: Gain understanding of precise attribution, in a timely fashion, to learn which marketing campaigns lead to higher conversions and lower acquisition costs.

What is true attribution?

True attribution is the holy grail for digital marketers. Many say it is the most pressing issue today.

Attribution ensures that you can learn from your data and every unique visitor’s content consumption pathway beginning at their first contact all the way through to conversion.

The usual outcome most marketers are mired in instead?

A virtual avalanche of data, much of which is incomplete, out of date and/or insignificant. Working through this overwhelming amount of data to find something clear and actionable can take weeks of valuable time better spent elsewhere.

Without full and “true attribution” marketers are at a loss on what … Continue reading

How Businesses Can Use Bots As Part Of Their Facebook Marketing Strategy

The next technology revolution has already arrived in the form of Artificial Intelligence. For marketers and businesses, it offers more flexibility, deeper insights on consumers based on faster data retrieval, the ability to automate tasks and also vast improvement in customer service with fewer human interventions. In fact by 2020, 80% of brands will be using chatbots for customer service and interactions. Continue reading “How Businesses Can Use Bots As Part Of Their Facebook Marketing Strategy”

What’s New On Google Ads Automation

Google Ads is the new moniker of the erstwhile Google Adwords. But the aim of the rebranded Google paid search ad service remains the same- to “help businesses of all sizes connect with relevant customers across all of our channels and partner sites,” through the introduction of Smart Ads and other automated features.

So, why the change and what does it really mean for users?
With mobile becoming a huge part of daily life, most people jump from device to device or channel to channel. The consumer journey is highly fragmented, as people switch from clicking on display ads to performing brand searches, then discovering and watching videos or digging deeper through the content, to going back to the website to purchase maybe. Marketers have multiple opportunities to reach consumers where they want to be reached across different channels, screens and formats. Google has a treasure trove of consumers’ personal … Continue reading

Ad Fraud: Brand War Against Bots

Ad fraud on apps and the web is an endemic problem. Advertisers, publishers and agencies are engaged in a war against digital ad fraud bots. In a reaction to ad frauds, recently P&G cut $140 million and Chase slashed the number of websites carrying their advertisements by almost 99% without experiencing any change in business outcomes.

Ad fraud is the practice of serving digital ads that have zero chance of being seen by a human user or are intentionally misrepresented. Nonhuman, or bot, traffic use tactics like rotating user agents, random proxies to generate fake pageviews and fake clicks, even fake form submissions and, more for paid ad impressions. In 2018, nonhuman fraud traffic in digital advertising is estimated to cause losses of around 19 billion U.S. dollars to advertisers worldwide. Continue reading “Ad Fraud: Brand War Against Bots”

Artificial Intelligence and Your Email Strategy

Artificial Intelligence is now becoming widely available and is prized for the power it has to change the way we conduct business. Unfortunately, email marketing is still akin to a crapshoot for even experienced marketers who struggle to create content that is personalized for the specific needs and behaviors of the individuals on their lists.

Thankfully, Artificial Intelligence has arrived and is more than capable of closing that gap and offering the 1 on 1 personalized feel each of us desire when we open an email. Our email campaigns no longer need to be limited by one size fits all emails which understandably suffer from dismal open and CTR rates. Someone cue the hallelujah chorus, please!

In addition, we are now able to dissect our email campaigns, identify which strategies are truly working for us and modify methods that are not performing well. This level of personalization and behavior data … Continue reading

Full Digital Marketing Attribution and Choosing the Right MarTech Solution

Marketing Technology is just exploding these days. It is amazing to hear the statistic that on average, B2B companies with robust marketing groups use over 90 MarTech tools. Wow!

Marketing technology has the purpose of furthering the attainment of marketing goals. However, just as with many new tools and processes, there can be a subtle shift from reaching the goal that MarTech tool is designed to help with to fully and robustly using the MarTech tool. This is certainly true with tools that are designed to help a marketing manager make sense of the attribution data generated from digital marketing such that it can be actionable. So once a CMO or marketing leader has obtained these tools, he or she probably has some new problems.

This problem had originally cropped up because marketers had engaged in digital marketing, which held great promise to not only deliver results, but also to … Continue reading

Types of Machine Learning

There are three major types of machine learning that exists today. Within each type, there can be hundreds of different techniques for machine learning based on a statistical or mathematical principle, which makes them highly effective in certain circumstances.  1) Supervised machine learning, which is task driven, 2) Unsupervised machine learning, which is data driven, and 3) Reinforcement machine learning, which is feedback driven.

1. Supervised Machine Learning

Supervised machine learning algorithms and software are trained to recognize something using labeled examples. It uses patterns through methods like classification, regression, prediction and gradient boosting, to predict the values of unlabeled data. It receives a known training library of inputs along with the corresponding correct outputs and the algorithm modifies the model by comparing its actual output with correct outputs to find errors.

For example, machine learning algorithms help sift messages as spam in your inbox through training received from users … Continue reading