Target Marketing, Email Marketing Services

Real Estate Online Marketing is a big numbers,bid data game. You need to track countless actions of countless leads across countless channels. You need to gather insights from every source possible. Whether it’s your website, IoT devices, published industry studies, public online information like traffic data, or anything along those lines. Finally, you have to combine everything that you have learned into a coherent strategy. And this is just the preparation stage—executing the strategy, tracking its progress, and reacting to emergencies can be even more demanding and costly.

So why isn’t AI (artificial intelligence) already commonplace in marketing agencies (AI is actually a broader term than machine learning, but for the sake of simplicity, we will be using them interchangeably)? According to a relatively recent study, the main reason is that marketers still largely need someone to demystify the notion of machine learning for them, to bring it closer to how they see their industry and to relate it to the practices they are already used to.

This is my humble contribution to that cause. A list of plainly explained ways to leverage machine learning in your marketing efforts. And the contributions that AI is already making in this area. While you might find some of the possible applications too pricey, infrastructure-reliant, or simply unnecessary. They will be shaping the industry you are working in, and at least being aware of them is obligatory if you want to call yourself a marketer jump into a  company already deploying this strategies.

1. Audience targeting and segmentation

Everything you need to know about how the type of product you are promoting is being perceived by the public is out there. What groups of people usually need that product, when are they the likeliest to buy it, how much are they prepared to pay, what other products do they like, etc. Data with these insights is everywhere, and its amount is growing by the second.

Even though the term big data and the notion it describes have been with for a while. They are often met with similar apprehensions as machine learning . People are sure it is bound to be useful, but don’t know how to leverage it. This is where AI comes in.

The main issue with big data is not where to find it, but how to process it and actually get something useful out of it. Regardless of the size of the team of data scientists you may have at your disposal, they won’t be able to do much with adequate machine learning utilities.

However, if you are properly equipped, or get some help from outsourced marketing analytics and big data companies, these insights could help you learn everything you need to know about the people you want to turn into your customers.

2. Programmatic advertising

Aside from informing your decisions regarding other segments of your marketing campaign, the insights gained from machine learning assisted data mining can be directly implemented in programmatic advertising. However, this is not where the contribution of machine learning to this part of your marketing efforts stops. Apart from letting you set up the initial campaign, AI can also track its performance and adjust the targets accordingly based on the observed behavior.

While this doesn’t mean that you can start taking the ‘set it and forget’ approach with your PPC campaigns, it will make them far more effective than they used to be.

3. Competitor and market analysis

Potential customers are not the only ones you should be keeping your eye on. Your competitors are as important a part of the equation as the audience you are trying to draw away from them. By tracking and analyzing data on their publishing frequency. Favorite social media platforms, incoming links and every other traceable action.  You are not only learning about their strategy, weaknesses or strengths, you are also learning a lot about the market you are catering to. You see, your competitors themselves might be engaging in some data mining of their own. By directly observing their actions, you are getting the summary of their findings, the results of their conclusions. In a way, they are doing some of the heavy lifting for you, analyzing big data and then presenting you with their findings simply by acting upon them.

4. KPI and campaign tracking

There is much more to campaign tracking than opening Google Analytics and checking if your conversions are going up. People are responding to your efforts in many different ways aside from clicking on a subscribe button or making a purchase.

For instance, they are leaving feedback on different social networks, feedback that you can use machine-assisted sentiment analysis to classify as positive or negative without ever seeing what is actually being said.

This kind of instant insight into how your campaign is progressing and how people are reacting to it gives you a chance to immediately react to any alarming signals and potentially avert an otherwise imminent disaster.

5. Predictive analytics and demand forecasting

OK, so machine learning gives you an amazing overview of the current state of things. But wouldn’t it be nifty if it could also let you take a peek into the future. Why, yes it would, and actually, yes it does. Through big data analysis, you are given a chance to observe situations similar to your current one, how those situations were handled, and which outcomes stemmed from which reactions.

This gives you the ability to, with a fair bit of certainty, predict the progress of your campaigns and potential consequences of the actions you are planning to take. Learning from your past mistakes and triumphs, or from those of others is an essential skill regardless of the industry you are in, and the more of them you observe, the more accurate will your predictions for the future be.

Naturally, aside from just letting you anticipate outcomes of your actions. This kind of predictive analytics can be used to forecast the changes in the market as well. While the number of factors that go into determining the fluctuations in the price or demand for a particular product might make an average human dizzy. Machines are having a much easier time of defining the mutual relationships between these factors. And at predicting the result of their interactions.

While the projections obtained this way are far from exact, they can still make a huge difference. Imagine knowing that everyone who tried an approach that you are currently considering has failed or finding out that a product that you could be making is about to become extremely sought after. Sure, it would make the game less interesting, but it would definitely make it safer.