Deep Learning
This year alone, the U.S. is projected to absorb a shortfall of 190,000 data scientists — and that’s not even counting the 1.5 million more analysts and leaders needed to make use of the information big data supplies.
This is an especially terrifying prospect in the marketing world, where data science provides the signals that let marketers know their decisions have paid off. “In the end, the analytics won’t tell you the next big creative idea,” Elea Feit, assistant professor of marketing at Drexel University, says. “It will tell you when the next big creative idea is working.”
Data scientists can use data points and trends to help strategize content, tweak content to meet demand, and measure the outcomes of the actions taken by marketers. They combine the science of statistical models with the art of creative work to go past the “gut feelings” of the “Mad Men” era and into a space where marketers can not only see a payoff today, but also a payoff tomorrow.
What You Don’t Know Can Hurt You
Having a wealth of knowledge is a huge advantage — until your knowledge surpasses others’ understanding. If people don’t know how to apply a significant piece of information, that data is useless.
This is why data science is so essential to the marketing equation. “ The most powerful data scientists are those who act as bridges between insights and people ,” says Kirill Eremenko, the founder and CEO of SuperDataScience, an online educational portal for data scientists and data science enthusiasts. “There’s a science behind analytics; however, communicating insights is an art.”
Straddling that line is important because data science insights are connected to marketing results. Marketing departments are expected to quantify their results as justification for keeping their budgets and strategies intact. Marketers handle digital information within their campaigns and collect it to improve their tactics, increasing the demand for data science.
Data science is responsible for mapping social networks and illustrating customer personas. It also identifies demographics and locations, in addition to tracking target audience responses and moods. Data science has enabled companies to customize their customer experiences. It also helps develop new approaches to long-held marketing challenges.
“Data is massively complex and comprehensive, which makes it difficult even for experts to understand,” says Eremenko. “Extracting insights is the first step, but the crucial follow-up is finding ways to communicate and contextualize those insights so they’re accessible to all.”
Application As Inspiration
If there’s a shortage of data scientists, what does this mean for marketers? Marketers have to learn how to use data science for their work on a global scale, and they need to position themselves for success, regardless of how accessible data scientists may be on any given day. 91% of senior marketers indicated that customer data was essential to making decisions. Here’s how marketing teams can take advantage of every piece of that data.
1. Break Down Departmental Silos
Data science can’t take into account data it doesn’t have. Department- or division-wide silos put up barriers where they shouldn’t exist, blocking one department from receiving data from another that could be valuable. How many times have you heard about content marketing teams having to start newsletter subscriber lists from scratch because the sales team wouldn’t share its email lists?
The same idea applies here. Find ways to allow your platforms to integrate and share data; at any rate, build systems to report data from one segment to another. Something seemingly small — such as your company Facebook page’s demographics — could influence not just your social media marketers, but also your SEO team, your affiliate marketers, even your R&D department.
2. Keep Your Streams of Data Current
Data has to be timely to be actionable — or, at the very least, it needs to include information from the past through the present to highlight patterns and trends. As big data analytics and visualization firm Zoomdata explains, real-time data analytics are the optimal option because they allow marketers to act on information as it’s happening. Streaming analytics, which occur nearly in real time, are a close second.
The focus is on fresh data so your decisions are made based on what’s best for your current market. But context is important, too. Creating entire trails or streams of data will allow your marketing team to see that a product that sold well last winter and dismally this winter may be influenced by bigger factors you’re also tracking, such as economic downturns or a declining audience segment.
3. Invest in Tools and Technologies, Particularly for Visualization
Data can only be gathered if you have the technology to do so. If you’ve been putting off investing in a data platform because you figure your team can do it manually, or you assume the information will sit in a database never to see the light, think again. Data not only showcases ROI, but it’s also ROI itself — you need numbers to justify numbers. Remember that you’re only as good as the information you have.
Visualization is an especially important tool to have in your data-gathering belt. Dynamic visualizations can simplify complex data and capture numbers in a graphic representation, which will speak more clearly to a wide swath of people. Most importantly, visualizations unlock collaborative opportunities for marketers and data scientists to discuss data together and interpret the data’s meaning for future campaigns and marketing efforts.
While we’re looking at a dearth of data scientists in the near future, that doesn’t diminish the importance of data science for marketing. If anything, it should compel marketers to set their systems up to benefit from data science and empower themselves by learning to broadly analyze data alongside data scientists. What you don’t know can hold you back — and what you do know can drive your company’s ROI.
Written by: ,
January 12, 2018
What is Deep Learning? Here's Everything Marketers Need to Know
The machines are here.
You may have heard rumors about artificial intelligence (AI) potentially taking over our jobs. And the question is: Should you be concerned?
In my opinion, we should be excited.
AI -- especially “deep learning” technology -- brings new opportunities and innovation in the way digital marketing, sales, and customer support are handled.
But what is deep learning? How does it work? And how can it be applied to marketing and sales in your company?
What Is Deep Learning?
Deep learning is a discipline within AI that uses algorithms mimicking the human brain. Deep learning algorithms use neural networks to learn a certain task. Neural networks consist of interconnected neurons that process data in both the human brain and computers.
Neural Networks in Advertising
Let’s assume we are an online car dealership, and we want to use real-time bidding (RTB) as a mechanism to buy ad space for our product on other websites -- for retargeting purposes.
RTB is an automated process that takes place in a short time frame of under 100 milliseconds. When a user visits a website, an advertiser is alerted, and a series of actions determines whether or not that advertiser bids for an ad display. Have a look at the image below:
Source: Periscopix
In RTB, we use software to decide if we want to bid for a certain ad -- the software will make a decision by predicting how likely the website visitor is to buy one of our products. We call that "buying propensity."
In this instance, we'll use deep learning to make this prediction. That means our RTB software will use a neural network to predict the buying propensity.
The neural network inside our RTB software consists of neurons and the connections between them. The neural network on the above image has only a handful of neurons. In reality, a digital neural network has thousands -- or even millions -- of neurons and connections.
In this scenario, we want to find out if a certain website visitor is likely to buy a car, and if we should pay for an ad to target her. The result will depend on the interests and actions of the website visitor.
To predict the buying propensity, we first choose several “features” that are key to defining this person’s digital behavior. In our example, those features will consists of which of the following four web pages were visited:
- Pricing.
- Car Configurator.
- Specifications.
- Financing.
Those features will influence the output of our neural network -- or, essentially, our conclusion. That output can have one of two values:
- The website visitor is interested in the product, or “ready to buy.” Conclusion: We should display an ad.
- The website visitor is not interested in the product, or "not ready." Conclusion: Do not show an ad.
How the Neural Network Functions
Let's have a closer look:
For each input, we use “0” or “1”.
“1” means the user has visited the webpage. The neurons in the middle will add the values of their connected neurons using weights -- or, more simply put, they define the importance of each visited webpage.
This process continues from left to right, until we reach the “output” neurons -- “ready to buy” or “not ready,” as per our earlier list.
The higher the value of the output, the higher the probability that this output is the correct one -- or the more accurately the network predicts the user’s behavior.
In this example, a website visitor looked at the Pricing and Car Configurator pages, but she skipped Specifications and Financing. Using the numerical system above, we get a “score” of 0.7, which means that there is a 70% chance this user is “ready to buy” our product.
So, if we look at our original formula, that score indicates the conclusion that we should buy the RTB ad placement.
Training of the Neural Network
Now that we know how a neural network functions, let's have a look at how to make sure our output neurons are calculated correctly, in order to make the right decision.
The challenge is to come up with the correct “weight” factors for all the connections inside the neural network, which is why it needs to be trained.
In this context, “training” means that we feed the neural network data from multiple website visitors -- things like visitor features (which web pages users have visited), as well as indicators of their eventual purchase decisions from us (which are labeled as "yes" or "no").
The neural network processes all these data, adjusting the weights of each neuron until the neural network makes appropriate calculations for each person within the training data. Once that step is done, the weights are fixed, and the neural network can more accurately predict the outcome for new website visitors.
The Future of Deep Learning
Democratization of AI
AI is quickly finding its way into marketing tools that we use every day. Take, for example, the AI-powered Chatbot builder by Motion.ai (part of HubSpot), which allows you to easily create and publish your own chatbot.
Another example is Dialogflow, a platform from Google that lets you build a chatbot for your company or service.
It certainly doesn't stop there. AI can assist with the setup of advertising campaigns, hyper-personalize emails, optimize lead scoring, categorize and escalate customer issues, and actually help you with anything that requires data processing or orchestration.
Deep learning can be applied in any area of digital marketing, provided that you have a sufficient amount of “training” data. The challenge is typically to extract data from your various marketing tools -- that's where data integration platforms like Blendr.io will be crucial in connecting your data silos when you start experimenting with deep learning and AI.
The Future: AI ... That Builds New AI
Google explains that the process of designing neural networks often takes a significant amount of time for development and experimentation, because all of the neural network layers have to be crafted by people. That's why Google invented AutoML: AI that can build new and better AI algorithms.
Imagine what that type of technology can bring to something like marketing automation, for example. The AI will be able to build additional, customized AI algorithms that will learn and automatically optimize nurturing campaigns, for example.
Though deep learning may sound complicated, it's a process that, much of the time, boils down to math. Neural networks “learn” in a manner similar to humans: by viewing many examples, and discovering the commonalities among them.
Once the neural network is trained, it can perform complex tasks and a certain level of reasoning. Deep learning and AI can be integrated into many aspects of digital marketing and sales automation. The machines are not coming -- they are already here.
Originally published January 05 2018, updated January 08 2018
Written by Niko Nelissen
photo by: H Heyerlein - unsplash
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