How to use AI to drive targeted customer acquisition by 2020.
Today, 80% of digital market players feel increasing pressure to meet their customer acquisition and revenue generation goals and report as if they are walking on an endless hamster wheel.
And it’s true: You may be pushing your marketing muscles too hard for your customers to find you with organic search – but it’s something you have little control over or react to. Traditional advertising media are becoming increasingly outdated, but the problem is that many companies still do not understand how technology can reverse this trend. Take the example of the automotive industry, where customer acquisition is still very reactive. Most customers always find a dealer and a car brand, not the other way around. Recent research has shown that the first point of contact of car dealers with more than half of their customers is when they physically enter the dealership, so there is essentially a chance that their dealer or brand will be chosen to enter the centre. There is no doubt that companies around the world are rapidly experimenting with the introduction of Artificial Intelligence (AI) in various departments, including business and the automation of human tasks. But it’s time to think about using it to improve your customer acquisition strategy.
Changing entire business models
Customer loyalty strategies alone are not enough unless they are targeted and proactive. We have all seen how the video rental industry has improved through the streaming business model. Imagine coming back to the video store today, if there is one, and inspecting the shelves of the cinema to find out what you are going to rent now. This is unimaginable in today’s world, but it underscores the power of proactive and personalised delivery to the customer based on what the customer wants and when he wants it. Power companies use AI and marketers can change their marketing approach.
Take Netflix, for example. After logging in, you will immediately receive personal advice based on previous actions on the site. Because Netflix knows your preferences, it can predict and recommend films or shows you are likely to like, so you can watch new and interesting programmes every day. Netflix’s SEO engine is so good that 75% of what users see can be attributed to these references.
Given these convincing results, it is surprising that the established marketing model tends to react. It depends on how your customers find you. Although the digital age has borne fruit in the form of incentive instruments, companies are still taking a step backwards. For digital marketing to be effective, customers must take the first step to show their interest or visit the website. Many companies are still dependent on this successful strike.
AI can reverse this pattern. Using the right datasets, a marketing AI can accurately predict the probability of a purchase, recommend the best time to approach customers and help create the offer that has the most impact. In short, AI helps answer the most pressing marketing questions of our time: Who, when and how to aim.
Targeted conquest thanks to artificial intelligence
Numerically speaking, once a customer has shown interest in a product, it is easy to recommend other products or new products based on the purchase history. Is this a conscious conquest? Not exactly, but it is still much further away than traditional industries.
Take the example of a traditional industry, your typical consumer electronics retailer. It publishes marketing releases for traditional media, weekly e-mails and printed brochures, as well as offers for individual products. They are intended for the general public, with the sole purpose that anyone with purchasing power can take them for a walk. If the same retailer uses targeted AI-based conquest, he could contact a customer who would likely buy a television with multiple options and make an offer tailored to their unique preferences, while another customer could contact a home theater with offers and options. Your customers already expect this level of customization and are waiting for you to catch up with them.
72% of consumers expect companies to understand their individual needs and expectations. So it’s safe to say that AI’s ability to offer targeted and proactive customer engagement beyond traditional digital channels is the only great opportunity we see in marketing today.
Powerful reference training cycle in the field of structural prediction.
Thanks to targeted customer acquisition, you will find the right customer for the right product at the right time and get in touch with the right offer and the right message. To do this, you must follow these four steps.
Start by creating a classification for each product you offer. This means you have to take five to ten characteristics that best describe it. These are not necessarily the characteristics of the product as described in the marketing literature, but the characteristics that influence the purchasing decision. Then collect historical customer lists for each product.
To obtain a 360-degree customer rating, you must look for external, publicly available data, including demographic, lifestyle, behavioral and basic life-related purchasing data.
These two datasets must then be linked and deleted. You should be aware that this last task is very time consuming, but it is an important step in making accurate predictions. This allows you to create specific main characters for specific products and categories.
Once these data are available, the next step is to create prediction models with statistical models, machine models and in-depth training models. If your overall goal is to predict target buyers for specific products, this may involve multiple mini-trainings related to the trigger point, purchasing area, buyer preferences, and other factors.
This step also includes the selection of functions for the prediction models. If you created a clean and comprehensive data universe in the previous step, you can have hundreds of attributes at your disposal. In reality only 10 to 20 minutes are needed to get the prediction models up and running.
In this phase, you apply your prediction models to appeal to your customers. You can select different filters, e.g. by geographical location, to define a wide range of customers for whom you want to run your forecasting models.
By applying the target person to the actual customers in the region, you can define and evaluate objectives based on the level of compliance. Study account trigger points and product comparison attributes to find customers exactly where they are most likely to make a purchase.
Then your true marketing spirit comes into play. The last part of this phase consists of developing personalised messages and offers for the customer and determining which channels are most suitable for him. These two elements are also taken from your mini-previsions or attributes, as defined above.
Find a solution.
Learning and improving AI skills are the main benefits of this technology. If you think Amazon and Google’s recommendations are getting better and smarter, it’s because they learn with each prediction cycle.
For a targeted commercialisation of AI, the training phase must consist of two elements:
A cycle of self-learning: Imagine the AI robot displaying its conquest marketing newsletter, which correctly represents the customers it predicts – i.e. those who bought from you. For every wrong prediction the machine punishes itself, and for every right prediction it rewards itself. It learns from the results and uses this knowledge to make further improvements.
Guided training : As a marketer, you should always do the same – see which results are better, which are not, and which attitude is needed.
The learning phase is essential and ensures that the next cycle is smarter than the previous one and that the identity of the buyer, the trigger points and the consideration of changes in buying behaviour are constantly changing.
Many companies are already carrying out the training cycle of the Construction Forecasting Recommendation with great success. A good example is Spotify with its weekly discovery function. By building the algorithm, Spotify can determine each user’s music preferences, predict what type of music is similar to what the user likes and recommend a unique playlist to hundreds of millions of subscribers each week.
The key to using AI? Overcoming calls
To fully utilize AI’s potential to attract target customers, three key challenges need to be identified: removing disruptions, completing a person’s purchase, and moving to real-time forecasting.
Removal of distortion
When it comes to fighting dependence on automation, be vigilant. Prejudice is by nature a real problem. It may therefore seem surprising that all preconceptions do not necessarily have a bad influence on the wanted conquest. The reason for this is that companies can see a brand change in their models, but it can be a real change that is necessary for the accuracy of those models.
The only way to eliminate unwanted distortions is to constantly monitor the algorithms you use. This responsibility should lie not only with the data collector, but also with a team of professionals, including economic experts. AI output and market feedback must be constantly monitored by the best marketing minds on board.
Completion of the purchase of a person
Buying a person can be difficult. Many companies lag behind in their business decisions with AI because they simply don’t know how to fill in the data and build a buyer.
A typical company has a customer’s name, address and phone number – but that’s not enough to make predictions and recommendations. Therefore, this information needs to be consolidated and collected over time in order to be able to tell each customer a more accurate story. You can enter external data to combine it with your internal data. But be prepared to spend a considerable amount of time cleaning up the noise data.
The pursuit of real-time forecasts should be your primary objective. Analyses and efforts to look back and reassess have already become commonplace – and are just a stepping stone. The power of AI lies in predicting how a customer can be attracted at the right time and won when the interaction takes place or is required, rather than analyzing the concept from a historical perspective.
The banking sector is a good example: banks have started using machine learning models to detect fraud in real time. Using a series of advanced algorithms to analyze each user’s behavior, any suspicious activity is immediately reported by the computer and the user is alerted to ensure that he is aware of possible fraud on his account.
The beginning of AI is far from over. In this decade, it is time for companies to stop looking at technology as a buzzword to prove that they are innovative and instead focus on realizing their true potential. With its ability to focus on customers, AI can enable vendors traditionally paralyzed by their dependence on customer proactivity to change entire marketing strategies – whether in real estate, automotive, interior design, electronics or other sectors.
Photo rental : Peshkova / Shatterstock
Vikrant Pathak, is the CEO and data collector of myautoIQ, the platform designed to attract buyers of AI-based cars. Vikrant is passionate about using IT training and IT to implement common business decision scenarios and create advanced real-time intelligence embedded in the business process. Prior to myautoIQ, Vikrant spent more than 20 years in the industry working with customers in sectors such as automotive, information services and media, marketing and loyalty, and financial services to accelerate the digital transformation, monetize data and create devastating opportunities.