Data has been a cornerstone of marketing for many years; however, in more recent times, the sheer volume of data that is available to marketers has exponentially increased. While this is all well and good, the data that is available is largely unstructured. This means that the amazing insights it holds are often hidden and inaccessible.
So how can marketers harness the powers of big data?
The answer to this question lies in artificial intelligence. The machine learning techniques can iteratively learn from the data that is available. For example, machine learning algorithms allow computer programs to detect hidden insights by identifying patterns in the data. Without any human intervention.
Within the marketing context, identifying these patterns can help marketers to produce meaningful insights. They can be related to such factors as consumer behaviors, preferences, and market conditions.
If you still have any doubt about how useful machine learning and big data can be for marketers, take a look at these statistics:
- Gartner predicts that 30% of all organizations will use AI to augment at least one of their sales processes.
- Research and Markets predict that the total market value of cognitive computing-related goods and services will reach $12.5 billion by 2019.
So, now we’ve established that big data and machine learning can hold exceptional promise for the future of marketing, let’s get into to the detail of exact developments we can expect.
Optimized user journeys
At one time, it was pretty straightforward for marketers to map customer journeys. However, the increase in online activity has made things much more complicated. Now, organizations have to worry about consumers leaving negative reviews online and/or sharing their views with others via social media. As a result of online channels, the user journey has become much different to what it once was. Again, let’s turn to some facts to get an understanding of what’s happening:
- Just under two-thirds of adults in the United States spend at least 12 hours a week using social media platforms.
- 55% of buyers now turn to online channels to perform research before buying a product or service.
- Consumers expect brands to respond to any tweets within just two hours.
The long and short of it is that the consumer journey is no longer linear. It is a complex spiral of touchpoints through which consumers assess, choose, and evaluate products and services before going online to share their experiences with the world. Your challenge is to meet consumers at points along these journeys as opposed to relying on one-and-one interactions.
The Help of Machine Learning
Machine learning will allow marketers to get more complex information about the contexts and conditions. Here they can more precisely target their customers. The days of manually identifying which offers should be focused on which audience and how will go to the past.
The more sophisticated the software and machine learning algorithms, the more insights we can expect to gain into the factors that drive a user’s online journey. Some organizations are already putting this to good use. For example, SessionCam employs a machine learning algorithm to pinpoint the areas in which a visitor experiences problems using a website. The software then allocates a customer struggle score to the interaction, allowing marketers to identify areas for improvement.
Automated customer service will become common practice
Automation is by no means new. In the contemporary world, you can book a salon visit online, order a takeaway from a choice of 60 restaurants using an app, and order a taxi, all without interacting with another human. By the way, it is proved that the free apps are the biggest earners. However, automation is set to increase thanks to machine learning. Through analyzing big data to predict customer behaviors and queries, machine learning will improve a number of offerings, including the following:
An increasing number of the tasks that were once performed by humans will be performed without any human intervention whatsoever. One company that is putting the power of machine learning to use is Mastercard. Through its Mastercard KAI offering, Mastercard will be able to predict consumer’s spending habits and use the big data that is available to recommend appropriate financial tools and services. This kind of apps will gain more money than others ever earned.
Netflix has its recommendation system nailed. The more users watch videos, the more accurately the organization’s algorithms are able to identify consumer behaviors and present useful recommendations. Think about what the future may hold if you are able to apply the ideas that underpin this recommendation system to a sales-orientated business. Applying machine learning to drive insights from big data will allow even the most novice salesperson to cross-sell and upsell products.
Enhanced real-time bidding
Online advertising is now big business, and everyone is vying for an ad spot on the websites of the major publishers. Real-time bidding and programmed ad buying have become pretty standard for major organizations in recent years. However, machine learning has the potential to take things up a notch. Machine learning algorithms can enhance bidding performance and help advertisers to access more impression opportunities. For example, advertisers may be able to pinpoint the optimal bid amount per impression through the use of data related to ideal timing for an ad placement, place in which the ad is located, and the content of the web page on which it is placed.
Lookalike marketing will gain traction
A lookalike audience is a group of people who exhibit very similar traits to your existing customers. One platform in which lookalike audience-based marketing is in common use is Facebook. Facebook uses a pixel, which is a small script powered by machine learning algorithms. This script can be installed on landing pages. It tracks who visits and when, and subsequently uses this information to create and target similar audiences. Through pixeling visitors to a site, marketers can reach out customers who share a similar profile to those they know are already buying. The whole process achieves two things:
- It automates targeted marketing processes
- It makes marketing more intuitive
Marketers are frequently using pixels to collate data related to thousands of customers. They can then use machine learning algorithms to harvest information related to customer’s interests and behaviors and find users who are just like them. The faster the machine learning algorithms can produce these insights, the more valuable they will be.
Through lookalike targeting, marketers can create and show ads to the people who are the most likely to respond positively to them on the basis that these people resemble their existing customer base. This can ensure ad campaigns are well-done and ad budgets reach their full potential.
Predictive analytics will take off
Marketers can use analytics algorithms to generate insights from big data by distinguishing patterns within it and then use these insights to predict how consumers will behave and what they may do next. Amazon uses predictive analytics to offer product recommendations to customers based on their past purchase behaviors. While methods such as this have been in use for quite some time, they are set to become more sophisticated in the future by informing advertisement campaigns before they are even created, thereby making ads smarter.
Let’s look at an example.
Say you want to promote a holiday business. You can use Google Adwords to find out what words your target audience are using to find holidays and when they are using them. However, there’s a whole ton of data you don’t have access to. For example, the weather conditions. Perhaps a cold spell will motivate customers to book holidays to warm locations. If you are able to anticipate a cold spell is coming, you can target your ads at the right people at the better time. This will make more informed marketing decisions and ensure that your ads are profitable and cost effective by targeting the people that are most likely to respond to them.
But what about data that you can’t see, like weather conditions? It doesn’t snow in every state which can affect ticket purchases amongst audiences scattered in different states.
Need some help?
We’ve known for quite some time that data is worth its weight in gold to marketers. Now, with big data and machine learning, marketers can access enhanced insights that make marketing campaigns much more effective.
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