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Online advertising algorithms thrive on a high number of signals. Industries like e-commerce and mobile gaming provide sufficient data to train these algorithms. Therefore, the approaches popularly employed by advertising platforms such as Google and Facebook allow efficient targeting and engagement of desired audiences.
However, in many other industries where the number of signals is lower, the target conversion rate is far below the channels’ benchmarks. If you find yourself in this situation, it is vital to make the switch and leverage your data with the power of artificial intelligence and machine learning, which are transforming sales and marketing by automating repetitive tasks and enhancing productivity.
We have teamed up with Konstantin Bayandin, the founder of the adTech startup Tomi.ai, to discuss online marketing opportunities that can be unlocked with machine learning.
Tomi.ai is an AI-powered platform that transforms behavioral data into actionable insights for better ad targeting. It was developed to solve inefficiencies in traditional ad metrics and improve marketing ROI. Now, let's get into the questions we asked the CMO.
As CMO and Head of a large marketplace's Data Science & Analytics Department, I lead the company's data-driven marketing development. In e-commerce,e there are valuable conversions (purchases) galore and lots of data to slice and dice. Upon relocating to the US to work for Compass, a high-tech real estate brokerage, I discovered a problem. Having switched to real estate from online advertising and e-commerce, I was shocked by how challenging online marketing was for this industry. The main issue was the low final conversions since people rarely buy houses or apartments. To put it into perspective, the US has just 5 million real estate transactions annually.
Because of this, conversions are extremely rare. Roughly speaking, you are lucky to have even one transaction per 10,000 website visitors. The rate of conversions into transactions amounts to 0.01% or less. Therefore, real estate marketers cannot employ smart bidding, the automated bidding strategy offered by Google and Facebook’s advertising platforms.
So this begs the question, why do some industries have a more challenging time with online marketing? Why do they have low conversion rates and poor ROI? I believe there are five key reasons for this:
You probably have a low conversion rate if your business has at least three of the five components mentioned above. In this case, you must be very careful with managing your data. Consider implementing machine learning to your advantage: it can help close the feedback loop for future transactions by linking them to the demand at the top of the funnel.
Inspired by this challenge, I created a solution that predicts the probability of future purchases using machine learning and website visitors’ behavioral data. Tomi.ai is a platform helping online marketers today more than double their advertising ROI using machine learning, behavioral 1st party data, and API integrations.
It opens possibilities for industries where online marketing is challenging, such as real estate, banking, insurance, EdTech, and SaaS. Tomi.ai also helps predict future sales by analyzing past sales data and market trends, refining sales strategies, and improving forecasting accuracy.
For our customers from complex industries, we activate predictive scores into their advertising accounts as custom conversions with an expected value amount. That is, we calculate the anticipated income that could be generated by a website visitor by looking at their level of website interaction.
We treat it as a completed purchase and then maximize this metric. As a result, our customers are able to show their ads to highly engaged users with a high expected lifetime value.
Machine learning revolutionizes sales teams' operations by providing deep insights into customer behavior and sales performance. By analyzing vast amounts of historical sales data, machine learning models can uncover patterns and trends that would otherwise remain hidden. This powerful tool can accurately predict customer behavior, making it an invaluable asset in sales and marketing.
One of the key applications of machine learning in sales is sales forecasting. By training machine learning models on historical data, businesses can generate accurate sales forecasting that helps planning and decision-making. These models can also predict the time-to-close for deals, allowing sales teams to prioritize their efforts effectively.
Moreover, machine learning in sales can identify which customers are most likely to respond positively to new products or services. By analyzing customer data and behavior, sales teams can tailor their approach to meet the specific needs and preferences of each customer. This personalized approach improves customer satisfaction and increases the likelihood of closing deals.
In addition to these benefits, machine learning in sales can automate routine tasks, freeing up sales reps to focus on high-value activities such as building relationships and closing deals. By leveraging machine learning, sales teams can enhance their sales strategies, improve performance, and drive more revenue.
Machine learning is the engine powering today’s online marketing. Marketers need to evolve into analysts or data scientists because marketing problems frequently have mathematical solutions. Online marketing operations resemble an optimization problem in mathematics, where a specific parameter (e.g. revenue) needs to be increased in a budget, channel, and other constraints.
Machine learning in online marketing aims to close the feedback loop from purchases and the top-of-the-funnel events.
It is possible to build predictive models across all funnel stages. We do this by predicting the likelihood of conversion based on users’ early behavior. Then, we express it in terms of purchase probability and the predicted value of the purchase. Understanding market trends can enhance the effectiveness of these models by identifying valuable prospects and improving sales forecasting accuracy.
The challenge presented to Martech professionals is to think of a way to close this feedback loop and create value-based look-alike audiences based on the LTV produced by such models. They also need to find an efficient way to inform their ad platform bidding decisions using early signals and micro conversions that happen early in the process.
Then, they are blended with machine learning algorithms to create a uniform metric of predicted conversions and sales, rather than using final conversions (which occur rarely).
If Google and Facebook already have machine learning on their side, why would marketers need to implement machine learning on their own?
Typically, a low-conversion website will have many user sessions. For example, an average e-commerce project will have a conversion rate of 2%, meaning that only 2 out of 100 sessions will have a nonzero value. Their signal strength is equal to 100%.
Machine learning helps us to understand the likelihood of conversion from early events in each session by analyzing customer behaviors, and assigning a nonzero value to it. We can identify 25% of website visitors with a signal strength varying from 2% to 10%. This approach gives us a wider audience with a weaker signal that can be used for segment creation.
This helps you understand how well the campaign performs and whether it will reach its target value in the future – even before the actual conversions occur.
You can optimize your bidding using predictive attribution powered by machine learning to understand your campaign’s expected performance.
Let’s assume you are launching an ad on Google with many keywords. You have spent a certain amount on each keyword, achieved a specific sales figure, and divided your sales by the ROI. You have sorted these keywords by arranging them in descending order by their ROI, then plotted 100% of the costs (cumulative) on the X-axis, and plotted all business outcomes in the form of sales or profit on the Y-axis. Having done so, this will give you your ‘performance curve’:
The purpose of using marketing technology is to remove unnecessary controls. In this case, we need to remove poor keywords that negatively impact your ROI. You cannot afford to invest in these keywords, therefore, their respective budgets can be cut. Optimizing sales operations with machine learning can also lead to better ad performance.
You can change the performance curve by employing new and more efficient methods. You can achieve higher performance with the same budget and keywords. In 2016, we used machine learning-powered bid modifiers for search ads, which helped Google attract higher-quality repeat audiences.
As a result, your revenue grows, you earn a higher marginal ROI than your target figure, and it helps you unlock new opportunities. For instance, by investing in new keywords and increasing spending, you can achieve higher performance on your target ROI figure.
Perhaps we are machine learning pioneers for predictive marketing in real estate, banking, and SaaS industries. The very idea of scoring users and using this score for optimization purposes comes from the gaming industry. Mobile games are often free but come with options to buy in-game. “Whales,” as they are referred to in the gaming industry, comprise only 1% of the player population, yet are capable of paying the cost of all players through their in-game purchases.
All games of this sort are designed to grow their share of high spenders and, most importantly, identify prospective high spenders. Product managers have used data to predict LTV and inform their decisions for a long time in the industry.
Mobile gaming apps have already integrated with Google and later with Facebook to transfer online event data, revolutionizing sales. To assess the likelihood of a player becoming a “whale,” marketers use data from a player’s first few days of gameplay. They then produce a “quality install” synthetic conversion, submit this event to Google UAC, and then optimize for quality rather than regular installs. Natural language processing can enhance predictive marketing by analyzing user interactions and providing real-time insights for better decision-making.
We implement the following standard integrations for our customers:
The most crucial factor for machine learning is the number of positive outcomes that took place – how many conversions we have captured. Theoretically, 20 purchases are enough, but practically; we attempt to pinpoint at least 100 purchases that we can link to online behavior.
Generally, we aim to train the model using several hundred or thousand purchases. We must achieve at least 100 positive outcomes during our data collection period, typically in the first month of work (or longer if conversions are scarce).
From our experience, the main criteria for data quality is the share of offline orders that we can link to customers’ online activity. The main idea is to integrate the website and the CRM system and collect anonymized cookie identifiers. Google Analytics ID is usually used for this. You must collect users’ Google Analytics IDs and then plug this identifier into the CRM at all subsequent stages: lead qualification, sales support, contract execution, etc.
The value of machine learning does not lie in the data contained in a single table. It lies in the quality of the link between multiple tables.
The value of data is not in the data but between the data.
This means that the value is not simply in having the tables but connecting several tables to unlock extra value. Linking these online behavior tables with order tables is extremely important for marketers. Data quality can significantly enhance sales forecasting, making predictions more accurate and efficient.
To work with machine learning, collecting high-quality data and pooling it correctly is vital. This is why OWOX BI places special significance on data completeness and quality, which is so that our customers can trust such data.
When importing costs, OWOX BI analyses UTM tracks in the campaigns and reports possible errors, recognizes dynamic parameters, converts the costs into a single currency, monitors data relevance, and provides automatic monitoring. You will have exhaustive data from advertising accounts, the website, and the app in your project and under your control.
Machine learning offers a powerful way to optimize the sales process by identifying bottlenecks and areas for improvement. By analyzing sales data, machine learning models can reveal patterns and trends that inform sales strategies and help sales teams better understand their customers.
For instance, machine learning can identify the most effective channels for reaching customers, the most impactful messaging and content, and the optimal timing for outreach. This data-driven approach ensures sales teams use the most effective methods to engage with their audience.
Personalization is another significant advantage of using machine learning. By analyzing customer data and behavior, machine learning models can determine the most effective approach for each customer.
This includes personalized messaging, content, and offers, as well as tailored timing and frequency of outreach. By delivering a personalized experience, sales teams can build stronger customer relationships and increase the likelihood of closing deals.
If advertising campaigns never progress beyond the learning phase (and this can be the case with extremely rare conversions), you can do something about it with the power of machine learning.
If you are still maximizing the number of clicks on the website, you're already behind the times. If you optimize your ads for contact collection, and these contacts are poor quality (only 5-10% of contacts converted into a sale), machine learning is highly likely to improve that.
Implementing machine learning in your sales strategy can be a game-changer but requires careful planning and execution. Here are a few steps to get started:
By following these steps, you can successfully implement machine learning in your sales strategy, improving sales performance and driving better results.
Measuring the success of your machine learning implementation is crucial to ensure its effectiveness. Here are a few metrics to consider:
Overcoming challenges is also essential to the success of your machine learning implementation. Here are a few common challenges and how to address them:
By measuring success and addressing challenges, you can ensure the effectiveness of your machine learning implementation, driving better sales performance and achieving your goals.
You don't have to be a data scientist to get started. Get your feet wet and see what machine learning can do for your marketing campaigns. As industries become more interconnected, now is the time to bring new tech innovations into your toolkit.
