AI and Machine Learning are emerging technologies and will add $15 trillion to the world economy by 2030. But what does this mean when it comes to marketing? And what are the opportunities for marketers right now?
It's a brave new world. AI is revolutionizing every aspect of digital marketing, from attribution to customer journey mapping and into marketing automation. But how can marketers best take advantage of this new world order?
In this guide, we’ll look at the primary factors driving the use of AI in marketing and the exciting potential of its benefits, as well as some key considerations in any effective AI marketing strategy. We’ll then show you how you can get started using AI for your marketing efforts today - it’s simpler than you might think.
The world is generating exponentially more data than in the past. This data includes everything from social media posts to interactions users have with an application. In 2018, more than 2.5 quintillion bytes of data were created every day. With such enormous volume, it’s easy to see why the utilization of this data has become a significant challenge.
This magnitude of data, known as “big data,” has become too much for marketers to analyze and interpret manually. It often is so overwhelming that marketers can no longer have a “feel” for the data. Fortunately, today we have an answer: AI marketing. AI can analyze massive amounts of data practically without limit. Want to add another thousand or even a million data points of training data? AI is up for the task, as additional computing resources can be added in the background to scale up operations.
Machine learning is one technology that has been used to turn this enormous amount of data into insights and actions. This form of artificial intelligence trains an algorithm to recognize patterns in data, thereby learning from information like a human. With this technology, teams can use data from their business’s history to make predictions about the future. Predictions can optimize marketing actions, flow directly into marketing automations, or inform marketing strategies.
New AI tools open technology that was once reserved for large corporations to now become accessible to almost any marketing team. Marketers can now make quick decisions from large quantities of data without the time, cost, and expertise barriers previously in place.
If the complexity of bespoke, code-based AI systems is holding your company back, then it’s time to look at platform solutions. Platform solutions make implementing the deep learning power of AI far simpler than was previously possible, often without the need for a team of software engineers and data scientists. There are new AI platforms that offer more accessible solutions for everything from time-series forecasting to natural language processing to computer vision.
AI is allowing marketers to leverage the power of big data and predictive analytics for their work without needing expensive technical resources. You can now start applying machine learning to your marketing activities with no coding or specialist expertise required.
The challenges of marketing in the digital age have left people looking for ways to make it easier. Companies are now using artificial intelligence to optimize marketing operations, increase ROI, and ensure brand consistency - all towards the goal of faster growth.
AI offers benefits such as automation, marketing measurement, personalization, reducing errors, and making faster decisions that human marketers can't or don’t have the time for. AI has limitless potential to automate tasks and save time across a number of powerful use-cases.
Automation frees up time from repetitive tasks so that marketers can spend more time on developing creative campaigns and strategies to help grow your business.
Practically every business has sections of the customer journey and user acquisition funnel that are brute-forced - places where conversion rates reach just high enough to make sense for the company to invest in driving more volume.
You can usually identify these brute-force choke points because someone in the workflow process is complaining. The finance team is complaining about the customer acquisition cost and its proximity to lifetime value. Business Development Reps (the first touch from the sales team) are frustrated and spend a considerable percentage of their time filtering through leads that will never buy the product. The sales operations team is working nights and weekends to adapt legacy workflows as the business environment rapidly changes. The list goes on and on.
AI and automation can help marketers with almost all of these issues by doing the two things it does best - sorting and automating. Using AI, marketers can automatically identify the best leads for a business and route them directly to the right people. Marketers can easily measure the quality of each advertisement channel and turn on and off spend automatically as the ROI approaches a critical threshold. Operations can parse open-ended text (both customer application input and sales team notes) to automate and optimize each engagement.
Thanks to leveraging AI automation, salespeople can focus their time and energy on the best contacts that will generate the most revenue - making their jobs massively more efficient.
Chatbots are another popular example of AI automation. They are increasingly being used to automate everything from booking appointments to upgrading a subscription to providing customer support in real-time. From Amazon Alexa to Apple’s Siri to help buttons on websites, chatbots have processed billions of minutes of voice and text conversation. Countless apps are leveraging these types of AI technology to improve their customer experience.
One common criticism against using automation is that salespeople's jobs will be handed over to machines. This concern tends to disappear as revenue targets are met and exceeded, and accelerated bonus compensation is achieved.
The benefits of AI in marketing don't stop at reducing workloads: AI also helps you measure your business' success and metrics more effectively.
AI makes the process of finding new customers massively more efficient by optimizing your advertising spend through channel optimization and audience targeting. Once you have established a pattern of what your ideal customer looks like (i.e. product market fit), AI can sort through the world of possible customers to surface the ones that are closest to your target. And you can track the performance of individual ads over time, knowing when to rotate out oversaturated campaigns that are no longer producing leads with sufficient quality.
AI makes understanding customer behavior quicker and less costly for you as a business owner. Using data points gathered from interactions with customers, AI can help create reports that show trends in an individual customer's behavior and be more accurate than traditional market and customer research.
And of course AI can tell you when to act on that data. It can highlight which users are ready to upgrade to the next product tier given their usage and engagement with your product. This helps you quickly target the right customers for an upsell without annoying the rest of your installed base. It can also identify customers at risk of churn so you can act to retain them.
Personalization is another primary use-case of AI in marketing departments. AI-powered personalization will help you find the right message for your target audience on a granular level, leading to better user experiences and higher conversion rates.
AI models can make it easier for marketers and advertisers to personalize content, from product recommendations in email marketing to demographic and firmographic targeted messaging on social media. With AI, digital marketers can create campaigns they know their customers will engage with and, as a result, reduce wasted time and money spent on unproductive channels. The bonus to personalizing your content with AI - an overwhelming majority of consumers respond better to personalized marketing messages than non-personalized ones.
By using algorithms to analyze data about customers’ decisions (ranging from their time on an e-commerce site to their purchasing history), marketers can predict which next piece of content is most likely to result in a customer making a purchase.
As Bill Gates said, “content is king,” so creating effective content strategies based on your business data is a no-brainer to help marketing teams reach the next level of content marketing.
Identifying key patterns in your data is challenging and easy to get wrong when done manually, with potentially dramatic consequences. Drawing conclusions from manual data analysis is subject to human bias - and it is very common to use the specific lens that most supports an agenda when presenting findings. These errors in understanding and decision making compound over time and drag down your growth trajectory.
Similarly, it’s easy to make single-customer level mistakes when dealing with a huge volume of data, such as in classifying customer support tickets. This can lead to presenting a solution to a customer that’s entirely unrelated to their actual problem, leaving customers frustrated and subject to churn.
Mistakes are human, that’s what makes AI such a great candidate to help. Artificial intelligence marketing can both surface the actual underlying patterns from business data and also automate and streamline repetitive tasks and eliminate all-too-common human error.
These benefits together add up to faster decision-making. Using the power of automation and algorithms, businesses can implement changes more quickly, stamp out process inefficiency and, most importantly, maximize revenue capture.
Picture this: A sales team has 1,000 leads to get through. If these leads aren’t scored, it’s hard to decide which leads to tackle first, and how to assign leads. With augmented lead scoring powered by machine learning algorithms, the sales team would know exactly how to tackle the list. And research shows that the sooner you reach out to a customer the more likely they are to accept a meeting and convert.
With all this talk of AI and its potential, let’s take a closer look at what considerations should be made when it comes to AI for marketers.
While the potential applications for artificial intelligence are limitless, one of the essential aspects of any AI endeavor is to ensure that any predictions it makes are aligned with the values of your company and of society. This is just as instrumental in the future of digital marketing.
There are also considerations surrounding data quantity and quality, consent and privacy, bias, and technical details that need to be taken into account when developing marketing automation systems or applications.
The quantity and quality of information fed to train AI algorithms correlates directly to the quality of the output predictions. In other words, bad data means bad predictions.
The main thing to know is if you want to create an ML model to predict an outcome in the future, you need to have data from the past of that outcome happening and the data that led up to it.
If your team doesn’t have a decent amount of high-quality data, it’s worth spending the time to look into acquiring more data. For example, suppose you want to build a model to classify incoming support tickets as “urgent” or “not urgent.” One option would be to export data from your current support system, such as Intercom or Zendesk, and label the tickets as “urgent” or “not urgent.” Then, you can upload this “training data” to your ML platform software to build a model.
Generally, the more data you have, the better. For simple or very specific models, you can create a model with less -- sometimes as few as 100 records for some tasks. But more data will yield a model that better captures the wide range of inputs and outcomes.
Companies can also enrich or augment the data they have by combining the data they collect with data from a third party. An example of this is purchasing data on weather patterns to augment data on seasonal retail sales. Or often companies enrich the lead data in their CRM with information on the company the prospect is from. Often, ML platforms have features that allow a company to combine or merge data from multiple sources together.
Companies that collect, analyze, and use data and ML to operate their business have to maintain the trust of their users. They can use the data they have to make better products, operate more efficiently, and improve customer journeys. But they have to take new measures of care to maintain and build trust with customers and users that they are operating responsibly.
Leading companies using ML believe individuals should have control and transparency over their data. Their data should not be sent or disclosed to another individual or organization unless the user has given explicit consent to do so. And personally identifiable data should not be used for purposes outside of benefiting the user without permission from the user.
Machine learning systems work by pattern matching data from what’s happened in the past into future outcomes. It is critical that marketers putting AI into practice pay attention to the biases this can perpetuate when deploying an automation. For instance, as Amazon learned, if the historical data you use to create a hiring model favors hiring men, an ML model will likely steer you to hire more men.
When creating ML models, marketers must take steps to look for and eliminate biases that adversely impact groups based on ethnicity, gender, or sexual identity. ML platforms can now often show you the fields that carry the most weight in making predictions, so a marketer can think about whether those factors should play a role in their model, and then choose to ignore parts of the underlying data when creating a model as necessary.
Eventually the technical tools used to create models will improve in their ability to identify, highlight, and correct for bias. In the meantime, AI marketers must keep it top of mind when doing their work.
Companies should consider which types of AI models they want to invest in for their predictive analytics and marketing processes. There are endless opportunities when it comes to AI and data science. The most common types of models that marketers choose are:
Both of those models can take numerical data and free form text as an input. With natural language processing, a machine is able to process text and convert it into data. It provides a company with insights about any text at hand, at scale.
Companies should decide how they want to implement these AI models into their marketing tools. They should consider whether they want to enable it at an individual or enterprise level, as well as what specific goal they want to focus on using the AI model for - whether it's conversions, engagements, or clicks. These considerations may inform how often you want to retrain the ML model and what new data should be captured going forward to improve the performance of the model. And if the model is used in automations, a company should decide a schedule for a person to review the predictions the model is making to ensure that it is operating as intended.
Leading companies around the globe are already deploying artificial intelligence to help their marketing campaigns succeed. AI in marketing enables sales funnel optimization, forecasting, churn prediction, augmented lead scoring, and more.
Many businesses have large sales funnels that allow them to expansively capture leads and nurture them into more sales. With the wealth of customer data available, it’s possible to customize the entire process based on previous purchase history. Many businesses are using AI in marketing to take customers through a personalized journey down the sales funnel.
Sales funnel optimization (SFO) is a great way to make your sales job easier, find sticking points in the funnel, and ultimately close more deals. Companies armed with AI-powered SFO will naturally have an advantage over their competitors that are stuck using inefficient manual or often incorrect rules-based methods.
Predicting what and when your customers will purchase helps with your planning at all levels of a business. This level of foresight allows you to focus your business accordingly.
Traditional forecasting takes a lot of time-consuming manual work, data engineering, and needs statistical expertise. AI makes forecasting effortless, even for non-technical teams, allowing marketers to plan ahead, predict campaign performance, and build better budgets.
Many businesses focus on generating leads and closing new business while forgetting that minimizing churn is just as important. If lead generation is plateauing, but churn is still a problem (as it is for most businesses), then your business will inevitably start losing capital.
With Akkio, marketing teams can build churn prediction models to find which customers are most likely to churn next and turn that into action. Teams can reach out with targeted attention or solutions to save those customers. And in the longer term, the marketing analytics they gain from this activity can help businesses fix the underlying causes of churn.
With augmented lead scoring, marketers can effortlessly discover which sources are generating leads with the highest purchase intent (or other desired action).
This enables marketers to adjust the marketing funnel and spend quickly and efficiently, rather than waiting for infrequent and out-of-date reviews and recaps. Ultimately, this lets both marketing and sales teams prioritize the most important leads.
Artificial Intelligence is on the rise, and it’s not going away. It is indisputably the future of marketing. As more and more companies and industries catch on to the potential of AI, they are also realizing that they need to start using it now or risk being surpassed. After all, you can't ignore such a rapidly growing new technology that's already being used by the most successful firms.
But as we all know, AI is complicated stuff! This isn't something that mere mortals can do - at least not without a team of data scientists to back them up.
With Akkio's no-code AI platform, this assumption couldn't be further from the truth.
Akkio is a cloud-based AI solution designed with simplicity in mind. It's easy to learn and use, as there’s no coding necessary, and it scales endlessly as your needs grow. Akkio has been used successfully across a wide range of industries and niches, from marketing applications like lead scoring all the way to financial fraud prediction and sentiment analysis of free form text.
With Akkio, you can create custom machine learning models in minutes. Machine learning has traditionally been the domain of engineers - but not anymore. Akkio makes AI accessible for marketers who may not have a technical background but still want access to powerful technology.
So what does this mean? It means that even if your company doesn't have a big software engineering team, you can still build AI models that match your needs.
One final question we often hear: "Isn't AI expensive?" Not with Akkio. You also don't need massive datasets or data engineering expertise; with Akkio, any team can build models. And because there's no cost for training models, you don’t need to pay hefty up-front costs just to see if you have a process you can predict and automate.
With every Akkio plan, there are no limits on users, data uploads, deployments, or integrations. Pricing is based on usage, so you only pay when you’re getting actionable value from the platform.
Traditional artificial intelligence software solutions require a lot of time and technical expertise. With Akkio, marketing teams get access to cutting-edge technology in an easy, affordable way. Now, even non-technical marketing teams can grow their businesses with AI beyond what they ever thought was possible.
AI helps marketers optimize their ads, score leads, forecast sales, and more. Perhaps even more importantly, AI helps marketers automate the boring work, allowing teams to focus on strategic and creative tasks.
Sign up for a free trial of Akkio to experience first-hand how AI can transform your marketing efforts.