Points Worth Noting to Raise the Digital Maturity of Your Brand in 2018
Talking about trends for the coming year can often be risky, as we can simply talk about subjects that are either not new or are still very distant to today’s reality. Taking this into consideration, we prefer to deal with themes that are important for the digital marketing scene, which will certainly contribute to raising the digital maturity of companies, as these initiatives are extremely relevant to the current and future context, as well as being realistic and consistent with the evolutionary stage of our country.
Integration of On and Offline Strategies and Ability to Measure Impacts
We have long talked about the importance of companies adopting a unified and coherent strategy, treating the on and offline worlds as continuous and complementary realities. Although this can be put into practice in a variety of ways (consistent communication and experience in Point of Sale and online shopping, facilitating customer interaction in stores with specific applications, using information compiled through all customer experiences with the brand to provide personalized communication and delivery, etc.), changing strategic thinking and processes that are so deeply rooted in companies’ day-to-day operations can be a great challenge.
In general, brands hire different advertising agencies for their online and offline media. This alone can be a big problem, from the separate views of these media to the use of different suppliers. Ensuring a single, coherent line of communication is not a simple task.
Another point is to take advantage of all the available data of the consumer experience with the brand through different forms, ensuring good knowledge of the customer and activating in a personalized and highly relevant way. There are some tools and technologies that can help in this integration measurement and quantification of these impacts. Therefore, when planning an action or campaign, defining the objectives, strategies, tactics and indicators that will be the thermometer and the driver of good results, the vision and integrated strategic thinking should permeate the reasoning of the decision makers from the beginning to the measuring of these actions.
Real Use of Machine Learning and Data-Driven Decision Making
Talking about machine learning sounds very futuristic and it is high time to demystify the concept. This is because even when we consider the Internet of Things or any other subject related to artificial intelligence, we see that this it is not so far away — from a user point of view, for example, chatbots have been a reality for some time now.
More than that, bringing it to the digital marketing scene and its use in strategies, machine learning is becoming even more prevalent. For example, one of the most commonly used media tools in the digital universe, Google Adwords, already makes use of this, especially in the configuration of smart bidding, where it is possible to define the desired goal (ROAS, best CPC, CPA target). This does not require any type of micro-adjustment or manual monitoring — artificial intelligence plays its role by analyzing the whole history and performance of the campaigns, understanding the behavior of users and taking into account a series of parameters to achieve the goal defined in the tool. Compared with manual segmentation and optimization, the use of intelligence brings greater agility and speed and the ability to consider several factors that would be manually impossible, delivering the ideal message at the most opportune moment.
Still talking about marketing strategies, the vision of media attribution is more than essential on the path to greater digital maturity for any company. And this doesn’t simply involve looking at the last click. Looking at the consumer journey, analyzing the assistance and the contribution of each marketing effort to the whole. Attribution model tools as prominent as Google Attribution or Visual IQ, for example, have as their main pillar the change of a company’s strategy to data-driven media allocation through the use of machine learning, which recommends the ideal weights and credits for each media and every step of the process and the consumer journey up to the final conversion.
So when we talk about machine learning, we want to open the door to something more than technology: we want to emphasize the importance of making decisions based on data, hone strategy and thinking in the construction of marketing efforts, use statistics by making correlations and predictions to better predict results, to customize the delivery of communication to users, to make the most of the information generated and available to provide better experiences for consumers.
Understanding Consumers using Data Intelligence
We have already gone through the phase of understanding and accepting that the consumer should be the focus. The challenge now is to incorporate the philosophy that governs the definitions of marketing, communication and media strategies (which are the areas normally responsible for direct contact with the brand consumer).
When we talk about the importance of understanding the consumer, we now want to define that in a deeper way: to understand who they are, to chart their journey, to understand their customs, tastes, habits, preferences and routines, anticipate their needs, establish their personalities as accurately as possible and provide good experiences. Translating this into a more business-oriented vocabulary, the consumer interacts with the brand in a variety of ways, through numerous contact points, generating a huge amount of data. Knowing how to measure this, interpret it and analyze it in the right way, provides a series of differentials, including the personalization of the message and the communication delivered, the browsing experience and content and greater assertiveness when trying to expand a base of clients by identifying similarities and affinities in interests and profiles.
For this, in many cases technology can help a lot. A Data Onboarding initiative, for example, ends up permeating the other two themes we mentioned above, as it consists of the ability to enrich user data (offline information joining the data online, for example), providing greater knowledge of this customer base. Consequently, this creates a more intelligent activation on both sides: for the company, which can assert the maxim of ‘delivering the right message, to the right person, at the right time ‘, as well as for the user, who is impacted by communication that fits their interests.
Another well-known platform that is still the object of desire of many large companies is the Data Management Platform (DMP). With it, you can build a large unified database of your consumer information from a variety of sources (online and offline, media, CRM, database, web analytics etc.), create segmentation and audience through relevant similarities and characteristics. In this way you can enable them in media channels in the most personalized way possible. Again, here we are talking about a capacity for centralization and data management for maximum utilization.
It may seem repetitive and even sound very basic and old-fashioned to many, but it is worth emphasizing: the use of (a lot of!) data to identify, organize, integrate then deliver a personalized message and experience, understanding and even predicting what the consumer is looking for, is the key to a smarter, more mature and more innovative marketing strategy.
Author: Monica Fukumoto | Over five years of experience in Digital Analytics, working on navigation and behavior analysis, market studies, media optimization and performance and also planning and gathering of demands regarding data intelligence and analysis.
