Four Applications of AI in Marketing
All marketers are now familiar with the concept of personalisation and its benefits. Besides being a time-consuming task, it’s also known how complex and inaccurate the results can be. Here is where AI comes in; one of the great strengths of the collaboration between Machine Learning systems and Data Scientists is the ability to reduce uncertainty by organising, analysing and making sense of data. AI solutions also interpret sentiment and communication as a human would, this means that they can identify concepts and topic discussions across huge data sets, incredibly fast.
Where can you find data? Customers are constantly producing data by talking about products and services everywhere in the web space, you just need to listen and use what you gathered to make smart decisions for your business, and to ensure your customers are happy and engaged.
Data can be unstructured and structured, it’s important to first understand its different forms. The majority of data available (80%) is in unstructured form, it’s chaotic, messy, and hard for a machine to understand. Turning this data into machine-readable information will unlock the hidden value for your business to innovate and stay ahead of the game. 72% of marketers consider AI as a business advantage because of the insights that it’s able to provide.
4 Applications of AI in Marketing
- Customer Profiling
- Churn Prediction
- Content Recommendation
- AdTech
Customer profiling is a way to create a portrait of your customers to help you understand their behaviour. Profiles sharing similar attributes are broken down into customers groups: segmentations. The extent to which marketers can profile and segment their customers comes down to the quality and quantity of the data they can utilise. Typical user segmentation from broad demographic types of data (i.e. gender, age, location), limits the knowledge and understanding of a customer profile and therefore groups.
As previously mentioned, the majority of data out there is unstructured, hidden somewhere traditional technologies cannot reach. Our Skim Engine can help you to achieve your greater goals, it provides additional data from sources you may have not considered, such as social media, webpages, news, and forums for a deeper understanding of your customers.
By applying Machine Learning using unstructured web data to your customer profiling process, you will gain a clear understanding of a customer’s habits, spending motives, needs and wants. You will be able to understand the root cause and the likelihood of certain transactions to occur. AI uncovers once-hidden data insights, optimizing outreach to target only the most relevant users.
AI never stops learning, it will capture the trends and give you results adapted to the changes or shift in preference or behaviour of your segments, especially as those changes occur in real-time on the web.
2. Churn Prediction
Losing a customer is not only a competitive loss, it’s also a financial loss for your business ( up to 25 times more than retaining an existing one). Customer loyalty in 2019 is a real challenge for businesses across all industries. To ensure they are aware of the ever-changing customer dynamics, businesses are now turning to Machine Learning for Churn Prediction in real-time.
Predicting the future behaviour of individuals is what people usually associate with Machine Learning. Churn prediction models generated by Machine Learning processes, compute for you the likelihood of a customer to discontinue their transaction with your business so that you can react and prevent this happening at the right time instead of depending on red flags found here and there.
AI-powered tools can help you identify those profiles prone to churn risk, it can help indicate what stage of churn a customer is in, what is causing the churn and how to approach each case. Early-stage of churn customers (usually identified as one-off customers) are difficult to keep, engage and retain. However, late-churn customers leave for a deeper reason. If you are able to understand their reason to churn then you can provide a solution and take a proactive step to prevent it.
Data gathered from the web can help you understand what customers are feeling and predict what will be their next step (i.e. stop all the transactions). You can harness the data and then quickly make effective decisions on how to approach these individuals, leading to a higher lifetime value of each customer.
To ensure that these tools produce accurate predictions of churn rate and deliver the best solutions, businesses must integrate all data that is available from various sources inside and outside the organisation. Discerning the best data to use is our Data Scientist’s speciality; they will handpick the best churn prediction model for your organisation and industry. Our Skim Engine is able to reach a variety of sources, it provides endless possibilities by gathering and making sense of additional external data that can add additional insights beyond traditional internal churn data methods.
An average adult spends 5.9 hours per day engaging with content online, however, that’s not enough time to engage to all the content available to them. Although the web is overloaded with content, 57% of marketers are planning to invest in their content creation in 2019.
AI solutions provide you with deeper knowledge about your customers’ preferences, enabling you to deliver the right content, to the right person, at the right time.
Customers are now used to hyper-personalisation in content and product recommendation from companies like Netflix, Amazon or Spotify. As customers are used to this level of personalised experience, they are seeking and expecting more brands to provide the same tailored experience.
Content optimisation is based on key insights and in-depth analyses to better connect with your audience and show them more relevant content. Relevancy through personalisation approaches is extremely important for your customers. By showing your interest in their preferences and by sending relevant content or products tailored to their needs; if customers prone to churn are provided with a solution to their needs and wants, they won’t have a reason to stop engaging with your business nor to switch to a competitor.
The digital advertising industry is experiencing a period of boom due to the increase in time spent on digital media by customers. This is positively improving the interactions between businesses and their users, through different digital channels.
Due to the increase in demand for adverts, the implementation of technology solutions is a logical choice to increase the efficiency of ad spend, hence an increase in profits. However, digital advertising can be costly, it involves a huge amount of data and computing capacity to truly understand things like attribution.
To overcome the challenges in digital advertising, businesses are increasingly turning to AI-powered Advertising Technology (AdTech) to join the dots.
Aggregating 1st party and 3rd party data is the role of Data Management Platform (DMP), which cleans and scores user ID’s for mapping across a number of Adtech models to produce a coherent Single Customer View (SCV). By combining these data types into one coherent overview, it makes second-guessing an anonymous user sequence, for propensity to buy, a lot easier, as there’s so much historical data to use. However, there’s one major data class that’s often ignored, but that could make those models even more accurate — referrals.
If you know the contextual information from the referrer page or pages that a buyer viewed before entering your site, then you have an even greater chance of modelling that behaviour to new visitors, thereby increasing your ability to convert a visitor into a customer.
The cost savings within RTB systems are enormous. You only have to bid for spots you know have a higher chance of conversion. Therefore improving overall ROI on ad spend and increasing profits.
A recent Salesforce study found that customers are willing to give away more data in return for more personalized experiences. The challenge is getting people to appreciate that the data used for personalization is non-invasive and could improve their use of the web with recommendations for products they actually need vs products they bought 2 weeks ago.
However, the data you give to an Adtech or Martech system should be transparent, and it’s down to data science and analytics companies like ours, to strive to improve the awareness and understanding of how a consumer’s data is processed, through clear data policies and obvious GDPR compliance with rights to be forgotten.
Your Return on Investment
No business is the same. Firstly, you need to understand your goals, what you would like to see from the implementation of AI-powered tools in your Marketing function. Once you’ve understood your goals, a Data Scientists can help you harness the power of data to build AI solutions to help your business grow.
Typically, AI increases your workflow efficiency, by introducing automation in many of the time-consuming tasks. By becoming data-driven you will have more insights at your disposal that will facilitate decision making. These tools will process more data than humanly possible; AI can see and consider more data in a minute than the lifetime of a human, it can consider unlimited variables and determine significance in seconds. The fusion of AI into advertising and marketing will only improve the lives of consumers being offered products they want, and reducing the continuous friction between ad buyers and sellers with guessing games on ad spend and attribution.
Originally published at https://www.skimtechnologies.com on March 8, 2019.
