How to Use AI to Increase Marketing Effectiveness
The concept of marketing has been long present in our lives, ever since the days when people began selling stuff that they didn’t need. The flow of time brought new concepts and tools that caught people’s attention. And these new ideas have been utilized to increase the effectiveness of marketing techniques.
Artificial intelligence (AI) has been such an idea in recent times. However, AI is so much more than just an Idea. It is the way forward for us. Artificial intelligence has started a revolution in our midst that is only just beginning.
“We’re still at the dawn of AI adoption,” -Jean-Luc Chatelain, CTO at Accenture Analytics
But there is no denying that artificial intelligence and machine learning has already had a significant impact on marketing and on marketers. The introduction of AI has changed the rule of the game. Using AI, the drastic increase is possible in the effectiveness in marketing techniques.
But how does someone uses this innovation to bring new life into his/her marketing strategy?
Let’s start with the most obvious use of AI in marketing, chatbots. Chatbots are there for marketing as much as for customer service. The AI learns from the interaction with the customers, analyzes their queries, needs and their preference.
Chatbots have unique access to gather knowledge directly from customers, determine patterns and spot persistent problem faced by them. This wealth of information is invaluable to marketers that are looking to get insights into the customer’s mind.
The use of chatbots has been growing steadily. Businesses understand the need to get firsthand information from the customers. This information is used by business to figure out a business strategy. And whereas surveys can be annoying, people voluntarily provide information through chatbots.
On the part of customers, chatbots allow them to get the answers they require rapidly. The AI in today’s chatbots is sophisticated enough to understand and deliver the exact answer the customers are looking for.
- Lead generation
One of the primary goals for any marketer is to find new strategies to help find quality leads for the sales team. AI is the next logical step to determine the strategy.
One of the fundamental truths of life is that a machine is able to process more data than a human can. And an intelligent machine is able to sift through millions of data to identify and isolate valuable leads.
Machine learning is also capable of analyzing human language from varied sources such as social media and CRM to create buyer personas for any situation.
An advanced AI system like Node is one of the greatest tools for lead generation. The system discovers and recommends new potential customers and advises marketers & salespeople on what strategy would be most effective in closing the deal.
“It mines the connections between people, companies, products, and places on the web,” -Falon Fatemi, CEO at Node
- Data-driven forecasting
Numbers, data, and facts, though may sound boring; they are what’s needed for pinpoint market prediction. With so much influx of data from various sources, it can be convoluted to sort through it all.
AI’s capability to handle a large volume of data and find reasons and patterns among them has given marketers unprecedented ability to make data-driven predictions and decisions.
While traditional analytics tools may become biased because of several inconsistent data, AI will learn from the history and make proper forecasting of the market. This reduces human effort and individuals can increase their effectiveness in marketing strategy.
Unlike the weather, where wrong forecasting can ruin a single day, predicting the market wrong can ruin a company. That is why data-driven forecasting is so vital and this an aspect where AI can take your business to the next level.
Artificial Intelligence is most widely used in advertising and surprisingly, a lot of people aren’t aware that it is the AI that is aiding them. Whenever someone uses Google AdWords to buy keyword ad space, it is with the help of an AI.
The whole process of programmatic advertising, where automated buying and selling of ad inventory goes through uses artificial intelligence technologies.
The AI is a great catalyst for uncovering new and fruitful advertising channels that otherwise would remain undisclosed to businesses. An AI system has the ability to test out numerous ad platforms to optimize targeting.
Another form of AI systems use in advertising is the recommendations that customers receive about products. It is the Artificial Intelligence that learns from your browsing and clicks through of products, order history to understand your type of products you need and recommend thusly.
Advertising is also the most profitable platform that uses artificial intelligence. Marketers gain a huge boost in their PPC campaigns and advertising with a little help from machine intelligence and increase their effectiveness in marketing.
- Automate marketing tasks
The introduction of AI has been one of the main reasons behind the sudden spike of marketing automation. It has made repetitive tasks easier to do, freed up time and made the whole process less bothersome.
Tasks like sending cold emails have always been a thorn for marketers, but it was still a necessary task. Automation tools lacked the human touch and made things feel like as they were in real life, machine-like.
AI has the capability to eliminate this feeling of fabrication by enabling your business to become more human. For example, Boomtrain, a marketing automation tool can create and send customized emails by learning from your previous interaction with that particular recipient.
Predictive analysis, semantic analysis and cognitive filtering, features of AI, greatly improve marketing automation. This allows the automation tools to transcend their set programming and provide unequable support to marketers.
AI is effectively making our lives much more automated and easier, and that is spilling over to the business side of our life. Artificial intelligence tools like Retention Science frees the marketers by learning from customer interaction, making decision empowered by data, predicting recommendations and providing unique customized experience to each and every client.
This undoubtedly offers new opportunities to enhance and increase effectiveness in marketing tactics and allows business to become more successful.
Everything You Need to Know About Chatbots
What is a chatbot?
“A chatbot is a software application that can complete tasks in an automated way without needing humans,” said Cliff Worley, co-founder of Bitbot.ai, a third-party chatbot builder for Facebook Messenger.
In my exploration to understand where chatbots came from, what the allure is, and how businesses can use/are using them today, I sat down with Worley to get his expert opinion and insight.
The chatbot market is experiencing rapid growth—Grand View Research predicts the market will reach $1.23 billion by 2025 with a compounded annual growth rate (CAGR) of 24.3 percent. Additionally, 80 percent of businesses said they already use chatbots or plan to deploy them by 2020, and 45 percent of end-users prefer chatbots over humans for basic customer service needs.
Before we get too far into how chatbots are used for business today, let’s take a quick trip through history to understand how chatbots as we know them today came about.
Early chatbot history
The earliest chatbot of note was ELIZA. Created in 1966, ELIZA was built to simulate a psychotherapist through text-matching capabilities. Her responses were so good that she passed the Turing test, and users even confided their most profound thoughts to her. In 1972 she was superseded by a chatbot named PARRY, which was designed to mimic a patient with schizophrenia (the two even “met” a few times).
Following ELIZA and PARRY came a handful of other predecessors to the chatbots we recognize today, but the next chatbot to make a serious impact on normalizing the technology was SmarterChild. Any millennial who spent hours on AOL Instant Messenger as a kid remembers conversing with this chatbot, but it’s likely not many knew of its impact. In fact, SmarterChild is known as the precursor to the voice assistants we know today (Siri, Alexa, Cortana, etc.).
Finally, we’d be remiss if we didn’t mention IBM’s Watson. Although initially developed to answer questions on Jeopardy!, Watson has integrated with businesses in every industry around the world and provides its intelligence to everything from e-commerce and customer service, to behavior learning for car owners.
Today, tech giants like Apple, Google, Amazon, and Microsoft all have their own version of A.I.-powered bots or assistants, but it was Facebook who really made access to chatbots easier than ever for business owners.
Facebook Messenger and chatbots
In April 2016, Facebook announced it would allow businesses with a Facebook brand page to host chatbots on its Messenger platform. This opened a new channel for customer support, e-commerce guidance, content, and interactive experiences. It also opened the door for businesses to its 1.3 million users. Today, there are more than 200,000 chatbots on Messenger. Messenger bots can also accept payments natively (meaning without sending the customer to an external website).
With this kind of reach, Worley was definitely on to something. When he and his business partner, Sir Drafton, learned Facebook was allowing businesses to host chatbots on Messenger, they decided to scrap their prior business endeavor of building Twitter bots.
“We thought, ‘Oh, let’s drop this product and put everything behind the Facebook chatbots,’” Worley said.
Facebook Messenger, he explains, is a bit like email in the early 90s: Messenger inboxes are still a fairly untapped channel of communication, and there’s still enough novelty to it that people look forward to the messages they receive. Because of this, open rates are extremely high (80-90 percent). It’s also worth mentioning that Messenger lets businesses “enjoy fewer competitors, less ad fatigue, and potentially exponential returns on the marketing investment.”
Worley and Drafton saw an immediate need for a third-party system with Facebook’s chatbots. Facebook offered the application programming interface (API) to allow businesses to set up a send-and-receive program, but businesses needed to build their own chatbots in order to take advantage of the program.
Bitbot.ai is a third-party platform that lets marketers of all experience levels build their own chatbot in minutes by choosing from pre-made templates, interactive add-ons, like quizzes, polls, contests and giveaways, and event guides. Users can also integrate their preferred business apps (like their CRM platform or e-payment service).
Chatbots for the rest of us
The integration feature of third-party chatbot programs like Bitbot.ai are extremely valuable for businesses.
Chatbot works with Infusionsoft:
“We have this one-click integration to Infusionsoft. You literally go in, sign in to your Infusionsoft account, connect your account, then we can basically make sure that you can turn on lead capture to any message.” This function then tells the chatbot that before they see a certain message, the chatbot must capture their email. The integration then pushes that information to Infusionsoft (or any other integrated CRM service).
While this type of service may sound like a big investment, most are actually pretty inexpensive—the maximum subscription costs range between $50 and $100 a month.
You can also opt for a free chatbot developer service (and there are many out there), but what you don’t pay for you make up in time investment.
“When you see something is free, it’s usually just the basic features,” Worley said. “So you’re not going to get anything like the CRM integration or polls, content, quizzes.” While some of these services may provide you with a chatbot that greets people or provides automatic responses, you’ll still have to manually transfer all that contact and interaction data to your business apps, or you’ll have to pay for an an additional third-party service that does it for you.
So, how can I create a chatbot of my own?
Before you start designing your own chatbot, decide where you want to host it: natively, on your website, or on a hosted platform, like Facebook Messenger. Each have their pros and cons, but Worley’s drawn to Messenger because it’s an easier way to reach potential customers who are already using the app. With a native chatbot, prospects have to find your website before they can interact with it.
Next, Chatbots Magazine recommends creating a mockup of what you want your chatbot to look like, both in design and in dialogue. This can help work through any initial bugs that might occur, and provide a visual explanation to additional stakeholders.
Botsociety.io has a tool that lets users create a mockup chatbot by entering the general questions customers and visitors might ask, and the desired answers from the chatbot.
Now you’re ready to build your chatbot. Tools like Dialogflow and Flow XO make creating a chatbot as easy as building a flowchart. Botsify takes users through the entire building journey. It helps them design their chatbot, develop it (without needing to know how to code), launch it, and use it to grow their leads.
If you prefer to go the route of Facebook Messenger, the idea is still the same. You’ll still want to create a mockup, work through potential bugs, decide what the general script should be for your chatbot, and set up your preferred integrations.
Bitbot.ai simplifies this process with its drag-and-drop feature for a super quick turnaround, one-click CRM integration, and lets users include interactive modules, like quizzes and polls, sweepstakes and contests, and event guides. Users can even run Facebook ads to your chatbot.
Cliff Worley’s parting advice
“My biggest advice is, the earlier you get in there, the better,” Cliff says. “Imagine if in 1990 you knew that email was going to be where it’s at today, and open rates were at 80-90 percent. You’d have been really successful at that time to be an early adopter.”
How to Advertise Your Website to Your Target Market
Building a website is only the first step in your digital marketing process. Next challenge is to get people to visit your website. Luckily, there are many different online advertising platforms you can use to speed up this process.
Online advertising is a multi-billion dollar industry. According to Statista, the global digital advertising expenditure will amount to over 335 billion by 2020. However, not many marketers succeed in developing effective and efficient digital ad campaigns.
Improve your email marketing
Most marketers fail to generate better ROI (Return on Investment) because they lack the necessary data to properly target the right audience for the ads. Don’t make the same mistake.
If you’re thinking about promoting your website with digital advertising, do it properly. Begin your online advertising campaigns by first conducting marketing research and then follow these steps.
Do your market research
You shouldn’t blindly create ad campaigns targeting random people on the internet. Before you create the ad, you should learn more about your target audience and find out which platforms they spend their most time on. This process is called market research.
There are several ways you can conduct a market research. You can create a survey and ask questions directly from your audience. You can spy on your competitors to find where their website visitors come from and what kind of content they produce to generate traffic. And you can track your own website visitors to study their behavior as well.
Create buyer personas
Once you learn more about your audience and where they are at, you can then use the data you’ve gathered to identify your ideal customer. It’s also known as creating buyer personas.
A buyer persona is a complete profile of your perfect customer that you create to help craft better content, a better marketing strategy, and create more effective ad campaigns.
For example, if your business is related to dog food, your buyer persona should include all the information related to the ideal dog owner, including their age, gender, employment, location, etc. This will allow you to figure out if they would be interested in your product or not.
Create your buyer persona as detailed as possible to target the right people through your ad campaigns. Create surveys, conduct tests, use Twitter analytics and Twitter lists, and use data from Facebook Insights to learn the right information about your customers and create an effective buyer persona.
Which advertising platform is best for you?
Now that you have a buyer persona, you should now be well-educated on your target audience and what kind of people to target with your ads.
You now need to pick a platform to create your ad campaigns based on your buyer persona to target your ideal customers. Whether they’re business professionals who mostly use LinkedIn, teenagers who enjoy posting pictures on Instagram, or academics who use Google Search for studies, there are many different platforms you can use.
The good news is, almost all these platforms offer advertising opportunities at affordable costs.
- Google AdWords
Google AdWords is the first choice of online advertising among marketers and for a good reason as well. It’s most effective and versatile platform for targeting customers.
According to a study conducted by the University of Pittsburgh Marketing, a small business managed to generate a sale of $20,000 as a result of a $100 AdWords campaign created by a student. Ask any marketer, that’s an incredible ROI worth investing on.
What makes AdWords great is that it’s open to all types and sizes of businesses. From big brands such as Fiat, Colgate, and John Deere to small startups, Google AdWords allows you to show ads to your potential customers by targeting specific keywords and search terms.
Setting up an AdWords campaign is easy, but it requires careful research and planning. For example, you need to do proper keyword research to find the most effective and low-competition keywords to target through your ads. You can also create ads as both text and display ad formats as well.
- Facebook Ads
If your target audience spends most of their time on social media, you can target them through Facebook Ads. In terms of creating cost-effective ad campaigns, Facebook is the best.
Facebook Ads not only allows you to create ad campaigns to target your audience on both Facebook and Instagram at the same time, it also helps you to target your ideal customer and gather more data as well.
For example, Facebook allows you to create custom audiences by uploading your email list segments to find your email subscribers on the social network. Then, you can create effective ad campaigns to target those people. Facebook also allows you to create lookalike audiences based on your custom audience to find more people with similar interests for a wider reach.
Creating ads with custom audiences will also give more insights into your sales and learn more about the customers who are actually interested in your products.
- Influencer marketing
Influencer marketing is another effective strategy you can use to reach new audiences. Using this strategy, you can ask influencers in your industry to promote your products and services to their followers to quickly generate traffic and build brand awareness.
You don’t have to pay big celebrities to get a better reach. In fact, according to a survey, 30 percent of consumers are more likely to buy a product recommended by a non-celebrity influencer.
This is a great strategy to not only reach massive audiences with a limited budget, but it’s also a great way to overcome the big obstacles placed by Ad-Blockers.
Other ways to promote your website
Online advertising is a shortcut you can take to quickly promote your business and website. However, it will only generate results for a very short time.
Consider investing in long-term marketing strategies, such as developing a blog to grow your email list, optimizing the website for SEO, guest blogging on popular publications, and creating effective social media campaigns.
Whichever method you use to promote your business, keep collecting data to refine and optimize your campaigns to generate better results each time.
Crafting Your Customer Avatar
These are the phrases that are used interchangeably to describe the fictional, generalized representations of the persona that is most likely to buy from you.
It is critically important to the success of your marketing, sales, product development, and delivery of services that you have a deep understanding of who your Customer Avatar is. You’ve likely heard the phrase, “You can’t hit a target you haven’t set” this applies beautifully to the importance of having a clearly defined Customer Avatar.
Having a deep understanding a clearly defined Customer Avatar will help you:
Determine what social platforms they are spending their time on so that you know where your business should be present and active.
Be more effective in your advertising. Your marketing dollars will be well spent when you know where to advertise and who to target to maximize your exposure.
Better connect with your Avatar with your copy because you will have an understanding of their pains, pleasures, desires
Deliver and develop better
Sally is a solopreneur who is age 35 and older who has been in business for 1 year or more. Sally works alone and runs all part of her business.
Sally has a passion for serving others and loves that she does, but she is starting to see that her dream of freedom, flexibility and control are getting pushed farther out each day. She loves the fact that she owns her own business and that she does have some flexibility but she feels like her business owns and controls her (instead of the other way around).
Sally is successful enough that she is earning close to $100k a year but she is starting to find herself spending less time doing what she loves and more time dealing with the business side of her business. Sally is at the point where she is overwhelmed by the day-to-day activities of running the business – yet she wants to grow. Her business is no longer rewarding because she is doing things that she is not good at.
Her vision is to become an entrepreneur with the intention of growing her business by hiring a team that can do the things she isn’t good at and doesn’t want to do and also by automating the mundane tasks in her business that are important but suck up a lot of her time.
She is ready to take on the role of marketing as her full focus. Sally is keenly aware that marketing and systems is the key to taking her business to the next level. Her focus is in growing revenue, creating systems, and positioning her business to scale. By implementing these strategies she will create the cash flow in her business that she needs to hire and add stability.
AI process automation offers benefits, but challenges remain
Enterprises are starting to employ machine learning tools as part of their AI automation strategies, but several key challenges stand in the way of effective usage.
Thanks to rapid improvements in machine learning tools, AI applications are just now starting to make inroads in industrial processes, promising to improve older industrial automation protocols built around expert systems.
AI process automation tools that simplify the workflow of front-line employees present a big opportunity for businesses, but several challenges remain. Enterprises are still grappling with making vast stores of existing data available to AI platforms. There are also several challenges when bringing agility to AI application development and improving training data quality for machine learning algorithms.
Speakers at the Re-Work AI in Industrial Automation Summit in San Francisco discussed how enterprises are taking on these challenges.
Focus on pain points
Broken systems and downtime are among the biggest drivers of AI adoption in industrial automation.
“One of the keys for our customers is that they have experienced some incident, like a failure,” said Drew Conway, CEO of Alluvium, a company that makes machine learning tools for analyzing the performance of industrial equipment.
In many cases, data that could predict large-scale equipment failures in industrial settings is available prior to the failure, but the human experts viewing it don’t recognize key signals indicating it is likely.
“All of this data is falling to the ground,” Conway said. “A big problem is figuring out how to build tools that work with that data in a way that is valuable.”
Building a better algorithm to detect problems involves more than simply analyzing sensor data. It’s important to capture expert operator feedback and institutional knowledge to identify potential issues and alert the operators when a problem occurs. Conway said industrial automation is in need of better ways to blend existing approaches to machine learning with expert-driven systems that can provide operators with more actionable feedback.
“We realized that if we could get people in the control room to use software they trusted, it would grow usage,” Conway said.
This involves not just predicting problems, but relating these predictions to operators’ understanding of how machines work and their different potential failures so operators can take preventative measures.
Streamlining manufacturing processes
The core principles behind Agile development started in the manufacturing sector as part of lean production processes. For the most part, this has been driven by people identifying sources of waste in manufacturing processes.
Now, enterprises are starting to use AI applications and Agile software development practices to develop AI process automation strategies, said Greg Kinsey, vice president at Hitachi’s Insight Group. This is being driven in large part by the rise of industrial IoT and better data management practices.
Traditional lean manufacturing processes work well to optimize highly standardized production lines that don’t change much. But they can suffer problems when a production line is constantly adapting in response to market pressures, said Kinsey.
For example, Hitachi has been working with one company that produces polymers. The marketing department found it could significantly increase sales by making custom blends for a variety of uses. The problem was yield would drop by 30-40% each time the production line changed the blend it made.
Hitachi worked with the company to use machine learning algorithms to figure out how to adjust the settings for the equipment for each new production run, which reduced the drop in yield to less than 10%.
Agile machine learning for new data sets
The hard part of this AI process automation wasn’t not finding the right data; Hitachi worked with the polymer company to identify almost 300 different data streams that might relate to yield. But it wasn’t as simple as compiling all these data sets to train algorithms. Each data set had to be cleaned, calibrated and synchronized with other data sources to produce useful results.
Hitachi worked with the company on an Agile development process that started with the minimum viable data sets, Kinsey said. In the discovery phase, they assessed the predictive value of a few critical variables.
“Once you have a hypothesis, you can think about the data you need and then do the hard work of cleansing, labeling, ingesting and aligning that with the tasks that engineers need to do,” Kinsey said.
Hitachi representatives typically spend a month or two on the discovery phase, during which they deliberately try to avoid talking about applications. In the second phase, they begin to look at specific applications. This is done to formulate a hypothesis and create a minimum viable data set for a potentially larger AI process automation use case.
One of the biggest challenges is making sure you have the right mix of personalities on your team to tackle the different aspects of process automation problems. Highly innovative people are creative, and even though they may make some mistakes, they frequently bring new perspectives to problems. Solution-oriented people look for a stable process. The creative types play a stronger role in the discovery phase, while the solution-oriented types play a stronger role in the deployment phase.
Filling in the data gaps
In many cases, the data required to identify rare but expensive failures does not exist, said Dragos Margineantu, AI chief technologist at Boeing. Airplanes and the maintenance crews that service them collect vast troves of data. But airplanes are rarely grounded or breakdown in flight, so there is not much recorded data about what to look for related to edge cases that might cause a plane to break.
“No matter how much data you collect from real-world processes, it is typically incomplete,” Margineantu said. “We have data sets from customers that operate that have not had a single rejected takeoff in four years. This is an event that happens rarely.”
Building better algorithms for industrial automation sometimes requires finding ways to make sense of data stored in manuals and tapping into the knowledge of experts. It frequently demands a broad survey of potential sources of knowledge rather than simply building a bigger data set.
AI architecture required
Going forward, Margineantu believes AI process automation will require the development of special application architectures designed for other types of enterprise applications. These could be built using components that can be switched out, like microservices running on Docker containers. The beginnings of these kinds of architectures are already being used in domains like autonomous cars that use the Robotic Operating System framework.
An AI architecture can make it easy to develop and deploy a machine learning algorithm and then quickly switch it out when a better algorithm comes along. Today, Margineantu finds Boeing spends a lot of time developing the application infrastructure that wraps around each new machine learning algorithm.
Robustness is important
It’s also important to focus on robustness rather than just accuracy. Systems should be designed to alert humans when an algorithm has trouble reaching a conclusive prediction or recommendation, especially when AI decisions impact industrial equipment.
For example, if an algorithm is trained to identify cats and dogs in pictures, it may struggle with an edge case that includes a picture of a bear. AI systems will have to know how to respond when challenged by new classes.
“If you see a bear, you would like the systems to respond, ‘I don’t know,’ or ‘give me more information,'” Margineantu said.
In the long run, this kind of robustness is likely to be built by groups of algorithms that work together.
“I want to remind you that all competitions in machine learning are won by ensembles, since they provide for more robust outputs,” Margineantu said.