Effective digital onboarding is the beginning of a good and long-term relationship with the customer
In 2002, Steven Spielberg directed the film Minority Report, starring Tom Cruise, which imagined what personalized marketing would look like in the year 2054. During the film, one particular scene is repeated several times: the main character enters a shop innovatively equipped with cameras and screens. The cameras follow him and collect information, which is then utilized to choose advertisements to display on the nearest screen. The whole scene takes just a few seconds. Today, the question that arises is this: when will companies be able to put this scene into practice and begin to use personalized marketing as the primary means of communication with the consumer?
Personalized marketing offers targeted content to consumers based on the information that a company can obtain, process, and utilize. Personalized marketing often makes use of the following techniques:
Product recommendations (for products, goods, and services)
Thanks to a better consumer experience, personalized marketing enables companies to sell more, at a higher price, and faster. At the same time, the company also ensures or even increases customer loyalty when it comes to the next purchase of goods or services. An alternative approach is carpet bombing, where you offer content to all consumers or specified groups without taking their preferences into account. Nowadays, both of these methods are often combined. However, what is currently being developed is fully automated personalized marketing, in which campaign preparation, selection, and application is all carried out in the same way as in Minority Report.
As a practical illustration, let us take the example of a fictitious clothing company. The company has several stores in the city, and allows consumers to register and consent to the use of their data in exchange for the opportunity to purchase clothes at a discount and gain access to special events. The company then often sends registered customers personalized messages or videos.
In addition, the company has several smart screens installed in its stores that allow consumers to choose a product tailored specifically to them (in the available/correct clothing size, preferred material, favorite color). They can also serve to display advertising, to collect customer feedback and reactions, to register new customers, and as a tool for direct communication.
Returning to our example, a consumer may come to a store and decide to use one of these screens to get advice about where to find linen shirts. On the screen, he notices an option labelled “Let yourself be recommended a product just for you”, and chooses to try it. He turns on several smart devices next to the screen, which scan him and offer him the specific pieces from the new collection of men’s trousers that would fit him. The consumer can respond immediately if the collection interests him or, alternatively, he can register and receive special offers. All this in one place.
If we want to use automated personalized marketing, then we need to focus on three areas of development. The first of these is Data Capture, which represents the ways to collect data. Data can come in a variety of forms, such as purchase history, video recordings of store visits, audio recordings of calls, completed questionnaires, feedback, or registration forms. This phases should ensure that all the data are ready for use in the second phases, and do so as quickly as possible.
The second phases is a Smart DWH, which focuses on data storage, processing, and further use. DWH stands for data warehouse, which is where the data is stored and processed. Smart refers to the tools that use the data from the data warehouse to prepare personalized marketing for Direct Contact. This is the phases that is used to establish contact with the consumer and to receive feedback. It thus covers two-way communication, and evaluates all interactions on all channels (omnichannel communication).
We should develop these three areas in parallel, each requiring a conceptual approach and seamless integration. What should we focus on specifically for each one?
Data Capture concerns the systems that collect data. This data can be structured (personal details, purchase history, questionnaires, feedback) or unstructured (audio recordings of telephone communications, video recordings of store visits, photos of consumers). Data collection is purely technological, and is covered primarily by technologies from the Internet of Things (IoT). These technologies collect data and send them to the Smart DWH, or they evaluate the data as it is collected. Then it is about a hot trend called Edge Computing. Data collection should ideally take place in real time. There are already solutions on the market today that can be used for this. They are either open source technologies that are free but need to be customized, or paid ready-made solutions (available immediately).
A Smart DWH represents all the “magic” that happens in the background. Data processing includes data storage and analysis. Data storage is handled using Big Data or cloud technologies, while data analysis is taken care of using Artificial Intelligence tools or Advanced Analytics. The Smart DWH must ensure that the incoming data is categorized and assigned to the consumer as quickly as possible, as data about one consumer can come from several Data Capture sources (communication channels) at the same time. Data analysis differs according to the type of data, and each of these might contain a different kind of information:
● Personal data (gender, age, address)
● Feedback (satisfaction, engagement, complaints)
● Purchase history (product preferences, social status)
● Sound (content, mood, satisfaction, calls)
● Video (gender, age, height, weight, social status, speed)
● Interaction (navigation through the store/website, time spent in the store/on the website, preferred parts of the store/website, frequency of visits, geolocation)
● Contact history (satisfaction, product preferences, mood, engagement)
The collected data is combined and automatically determines whether the communication scenario has been met. The communication scenario describes what should happen when its prerequisites are met. The Smart DWH must evaluate which scenario should play out. These scenarios can have many variations, such as offering a menu on the screen or generating an e-mail with an offer. In any case, the scenario generates personalized content for Direct Contact.
Direct Contact refers to all the communication channels that ensure that personalized marketing reaches the consumer (omnichannel). In this regard, companies can use common means of communication such as e-mail and social media. Adaptive websites can also be used for these purposes, as they adjust according to the consumer. An adaptive website personalizes part or all of the look and offer of products and services. Last but not least, screens and terminals located in the company’s stores or branches can be used as well. Communication channels may be chosen according to the company’s preferences or based on the personal preferences of the user (or similar users). It should not be forgotten that these channels are another source of information for Direct Capture.
Personalized marketing requires companies to adjust their portfolio of products and services and to be able to tailor their goods to consumers. For example, an online grocery store might offer a special look for a registered user that automatically adapts according to what they buy and what feedback they give (for vegetarians, for instance, this store will display vegetarian products along with a special website design). Even regular food shopping can thus become interesting for consumers.
Let us go back to our example from Minority Report. Today, companies such as Amazon and Alza are experimenting with stores of the future, and the 2002 vision is already technologically possible. However, companies wishing to follow this path ought not to forget that personalized marketing should be not only to their own benefit but also, above all, to their customers’.
The author of this article is a Big Data specialist at Adastra