Technology

How Can Machine Learning Boost Your Brand? Check Out These Key Ideas

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Artificial intelligence is now playing a greater role in capturing and developing relationships with consumers, and machine learning is one field of AI that has marketing potential to harness. To truly understand machine learning's potential, deeper insight is required to fully execute its impressive applications.

Building Trust and Improving Experiences With Chatbots

Conversational interfaces continue to improve consumer relations, which ultimately opens the door for marketers to use chatbots in an array of applications. An intuitive form of conversational AI, chatbots are used across sales and customer support to provide customers with 24/7 self-service on their own terms. Not only does this machine learning technology align with the millennial mindset, but it's also hailed by industry research firms as a dominant force amongst decision makers in the purchase process.

Chatbots extract and sift through data quicker than humans can. The result? Better delivery of more personalized consumer experiences. This is especially true for customers desiring self-service. As such, the need to fulfill a task independently now outweighs the need for another person when technology is the desired choice. Instead of being instructed to "press three to speak with a representative," chatbots are quick to extract details to direct prospective customers without delay. This allows users to naturally communicate based on personal preference. 

Brands can effectively implement chatbots to serve as the first point of contact for engagement. What better way to drive up interactions and generate leads to funnel into the appropriate channels? Moreover, it's a definitive resource with which to operate more efficiently, add value, and build brand loyalty.

Prioritizing Personalized Care

Top brands are adopting machine learning practices to identify, predict, and take action on trends affecting product or service development. With more insight into leveraging machine learning, brands are better equipped to improve upon existing products and services.

Savvy brands use machine learning to launch innovative strategies for better customer experiences. It's the orchestrated efforts to attract, sell, and serve that win out when enhancing customer satisfaction. Another competitive strategy for market leadership hinges on customer intimacy — provide unique methods to personalize service and customize products to meet differing needs. AI is better at learning what's effective for prospects and consumers.

Improving Targeting Accuracy

As with any marketing strategy, there's always room for improvement. An investment in machine learning allows marketers to create targeted advertising effortlessly and effectively. The value of doing so (besides giving marketers valuable data on consumer behavior) is that advertising decisions are carried out in real time. With an AI-based model in place, the decision on when to display targeted ads is entirely up to you.

There's a heavy reliance on data, but as it increases, so does the challenge to manage data effectively. Machine learning streamlines the processes of data analysis in advertising. AI is the go-to process when brands want to target potential customers with precision. For example, Facebook Business Manager is an important advertising tool. When a target audience is created based on interests, machine learning enhances the strategy. The targeted interests are all generated via machine learning, which is the same algorithm that analyzes data users voluntarily input into their profiles.

Optimization of Pricing

For brands struggling to set appropriate prices, machine learning can help. Since consumers have a wealth of options with which to compare prices, brands must factor in specific competitor criteria to accurately set prices. Machine learning's algorithms generate data to better detect emerging trends or new demands. Brands benefit most from predictive models to best price products and services.

Next to autonomy and speed, the biggest advantage of using machine learning to optimize prices is globalization. Imagine manually inputting pricing parameters into multiple channels. Then there's price changing, which can impact product sales in other areas. Machine learning crunches the numbers more accurately. It even anticipates market trends when brands need to make appropriate decisions to adjust prices.

Lastly, when competitive pricing strategies are paramount to success, it is possible to use machine learning to crawl the web and social platforms to collect essential information about competitor prices for identical or similar products. You can also analyze customer feedback, popular deals, and pricing history over a period of days or weeks. An intelligent system like this will constantly learn the digital landscape to give brands more leverage over the competition, which leads to wiser pricing decisions.

Determining Incentives and Sale Offers

Brands can further optimize marketing efforts by defining which sale offers, promotions, and incentives to present to prospective customers. This is accomplished through the use of multiple channels, which is yet another machine learning strategy that's transforming marketing.

Specialized sale offers are created through contextual information, offers, and incentives. Each is systematically structured using machine learning. In turn, AI regularly predicts the best possible combinations of marketing elements to generate an upsell, cross-sell, or new sale entirely.

As brands work vigorously to ensure products are exposed to the right consumers, advances in AI continue to emerge to help reach marketing goals. With proper planning and execution, machine learning has the potential to have an exponential impact on advertising.

7 Tech Tools Every Marketer Needs to Know

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Today's digital marketers need help on all sides to achieve their goals, and marketing services providers offer that support. In order to reach consumers online more effectively, you'll need the best tools for the best engagement. Fortunately, we've got you covered.

The tech companies below all have tools that can be utilized to refine and improve your marketing strategy. We've skipped a few of the obvious ones, such Google Analytics, because it's likely already entrenched in your marketing toolkit. There are other tools that help streamline a variety of tasks for marketers and increase the effectiveness and return on investment (ROI) of your campaigns.

1. Pardot for B2B Marketing Automation

If your company uses Salesforce, you've likely heard about Pardot, a marketing automation tool for Salesforce users. Pardot can be used to reach clients via social media, email campaigns, or paid marketing. Pardot's email templates are used in mobile responsive campaigns and custom drip campaigns, among others. Pardot is a great way to streamline your marketing efforts and track results from lead generation to client nurturing in one system.

2. Zapier for Automating Connections in Platforms

Zapier helps customers automate and connect apps during the workday. Imagine your workday when you receive an email filled with attachments of ad proofs that must be reviewed. Instead of opening these individually, Zapier performs a number of processes that use trigger actions to shorten your time spent on these tasks. Zapier can detect when emails with vital information come in and automatically transfer the attachment images into Dropbox for easy sharing with your team. If you need to start workflows multiple apps or finish routine tasks automatically, try Zapier for speedy automation.

3. SimilarWeb.com for Tracking Competitors' Site Traffic

SimilarWeb.com is a web software analytics company whose employees help thousands of clients with competitive website analysis. If you're looking for top-line analytics on your competitor's websites, then SimilarWeb is for you. You can view and analyze exactly which sources are generating traffic to your competitor's site. But SimilarWeb does more than that; it analyzes industry data and apps for clients and provides reports that mix marketing stats for export use.

4. Kickbox for Email Verification/List Maintenance

Kickbox is an email technology company that utilizes software products to help customers verify email addresses and prevent fraudulent user information to a company's applications. It verifies that an email address is valid and deliverable. If you're working in a field that needs official email address verification, then use Kickbox in your email platform.

5. Marketo Digital Marketing Automation Software

Marketo has taken marketing automation to new heights in recent years, warranting its market-leading position in digital marketing. Now, with its sale to Adobe for nearly $5 billion, Marketo's tools will be available to many more marketers. Marketo's account-based marketing tech tools and lead management will join with the Adobe Experience cloud tools for a powerhouse B2B-B2C digital marketing engagement experience.  

6. Buffer for Managing Social Posts and Accounts

If you're running several social media accounts across brands, it helps to have tech tools like Buffer to help improve your game. Buffer is an social media management platform used by more than 80,000 companies for managing social media content. The company operates in a crowded vendor space that includes Zoho Social, Sprout Social, and Crowdfire.

Besides its primary task of helping users share content across Facebook, Instagram, Twitter, and other leading social sites, Buffer lets users distribute content from one primary log-in. There is also the ability to create seamless integration with Chrome, WordPress, and other tools to find shareable content.

7. Marchex for Call Analytics

If you're doing any kind of online advertising, from display to mobile, working with Marchex for your call analytics is a no-brainer. The company helps analyze, quantify, and measure the performance of mobile, online, and offline advertising for small to medium-sized businesses, resellers, and advertisers. Marchex helps you identify what's really driving new customers to call? Which ads? Which keywords? When they call, what type of consistency is in place? Marchex ad tech products help you to measure and analyze calls for quality, origination, and how to measure call trends.

 

Harnessing Artificial Intelligence, Machine Learning, and Predictive Modeling in Digital Marketing

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Artificial intelligence, machine learning, and predictive modeling offer a clear path for digital marketers to deliver personalized service efficiently and with ever-increasing reliability. But they're not the same thing. Here are the differences, and how they can be used.

What Is Artificial Intelligence (AI)?

As humans, we're able to harness our natural intelligence to make predictions based on experiences we've had in the past. Say, for example, you've allotted 15 minutes for your morning commute, but in the last week you've actually spent closer to 30 minutes navigating through traffic and construction. You predict that next week will be the same based on these experiences, and modify your schedule to allow for 30 minutes in the future.

Artificial intelligence allows for a computer to make these predictions without your input. Based on the dynamic traffic and construction data available to it, an AI system can estimate, often with greater accuracy than human intelligence, the amount of time your commute may take, and then rely on traditional programming to prompt you to leave sooner for work.

How AI Is Used in Digital Marketing

AI takes a data-driven approach to digital marketing by analyzing customer patterns, profiles, and purchasing decisions to deliver personalized content that is relevant to your customer and results in conversions. Product suggestions and pricing can be based on data pulled from any of these dynamic sources for a complete customer-driven experience.

What Is Machine Learning?

Machine learning is a subset of AI that allows machines to learn and improve from experience without being programmed to do so. Returning to our commuting example, if our AI system predicted you'd need 30 minutes for your commute but your trip actually took 35, the machine could automatically change its estimate for the following day based on this new experience.

How Machine Learning Is Used in Digital Marketing

Facial recognition, natural language processing, and chatbots are three major areas where machine learning has created a marketing stronghold. Machine learning is also used to manage user-generated content (to flag or rank posts), to rank search engine results to determine which appear on the first page, and to determine which marketing activities have the highest return on investment.

It's also important to note that machine learning is responsible for filtering certain types of marketing emails to the spam folder, so it's even more important to send relevant emails that your customers want to read.

What Is Predictive Modeling?

Predictive modeling is used as a practical application of machine learning. It's a general practice that relies on using identified patterns to make informed decisions about future events. Predictive modeling existed before AI, but now can be used in tandem with these very large digital marketing data sets.

How Predictive Modeling Is Used in Digital Marketing

Predictive modeling allows a digital marketer to chart a marketing campaign's performance in real time, assign priority to sales leads, or rank product search results for relevance in real time.

With the vast amount of data now available to digital marketers, AI, machine learning, and predictive analysis offer ways to work smarter, not harder. A digital marketing strategy focused on improving the quality of customer data can result in actionable insights with ever-increasing reliability.

Getting Smarter: Here's How AI Is Changing Digital Marketing

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Artificial intelligence (AI) is changing the way marketers see interactions with consumers. It's becoming the way for products and processes to work smarter with more data and less human intervention. Put simply, AI is "intelligence" shown by machines rather than that of humans.

AI exists in making things work through a combination of machine learning, big data, and cloud computing. At AI's core are algorithms that are integrated within products and processes to solve specific problems. Over time, products using artificial intelligence can be trained in a variety of ways to learn from past examples.

AI in Digital Marketing

Each time humans interact with artificial intelligence machines, we leave more and more data to be parsed, sorted, filtered, and used to increase AI learning. With a wider range of data, algorithms can work faster on their analyses. This increased data collection is how digital marketers learn more about our searches, buying methods, among brand preferences, among other things.

More than 50 percent of marketers already are using AI in some form, according to recent Salesforce research, and another quarter (27 percent) of marketers will start using AI technology in 2019. Also, a recent Business Insider study noted that marketers are using elements of artificial intelligence to better plan and execute keyword tagging, segmenting and tracking in current campaigns.

For brands and advertisers, the big question for using AI is how it fits into their current processes. AI machines are great at sifting and sorting through incredible amounts of data for programmatic advertising, for example, but will AI learn to create stories using human emotions in ads?

AI to Anticipate Consumer Behavior

Data collected via artificial intelligence are helping marketers anticipate consumers' needs.

Natural Language Processing (NLP) is a promising area for the study of consumer behavior, and is being researched by data scientists and digital marketers. NLP machine-learning technology can find trends in patterns and behaviors, and help digital marketers to look past keywords and show online ads to people based on much more context. 

AI in Chatbots and Customer Service

Chatbots are another way that AI is changing how marketers interact with consumers using machine-based AIs. For example, HGTV launched its own chatbot named Hazel to interact with customers on Messenger or the HGTV Facebook page. HGTV uses its Hazel AI to share design-themed pictures and information to customers in the form of pictures, videos, and content. 

AI in Voice Search

AI is already being used in voice-activated digital assistants. Marketers are exploring how to use voice search for Amazon Echo or Google Home devices, and shifting their SEO strategies to include this new machine learning.

Google is also using AI technology in its search tools. When you ask Google, "How old is Drake?" the search box below automates related questions to you based on your initial query about Drake's age.

AI in Data and Demographics

Successful AI-based machine learning is dependent on large sets of data, and uses those data sets to specialize in specific demographics data collecting and targeting. As digital marketers collect more data through AI experiences, brands can use that data to gain more organic traffic from potential buyers.

AI in Image Recognition

AI is advancing the use of image recognition for easier creation of ads/social media posts.  Facebook recently experimented on Instagram with learning tools to create higher accuracy rates for image recognition. Facebook engineers were able to train image recognition networks using hashtags, and ended up achieving an 85.4 percent accuracy rate.

Google, too, is using AI to enhance image recognition with its Cloud AutoML Vision. It's an AI-assisted tool to let enterprises, brands, and agencies create custom ML models for image recognition. Brands with thousands of product images can use machine learning models for image recognition to avoid having to do this work manually.