In the world of digital marketing, data is king. As a marketer, harnessing the mountains of data generated by your website and other marketing efforts to gain actionable insights can literally make or break your business.
Data management is important because it allows marketers to make better use of their customer data. However, the traditional data management systems can’t cope with today’s amount of data, so new approaches are needed. Thus, new trends are expected to arise.
Understanding these trends will help every digital marketing company be better prepared to lead their digital marketing efforts in the right direction.
The Rise of Sales Automation
One of the biggest challenges that companies face is getting sales and marketing teams to collaborate on their buyer’s journey. Unfortunately, sales teams are often siloed off from marketing efforts, leaving both teams to work alone on their respective segments of the buyer’s journey without any cross-pollination.
Data attributes will play an increasingly important role in helping marketers bridge this gap by improving alignment between sales and marketing. This is creating a growing need for technologies that support marketing automation.
Data Quality and Ownership Standards
Another problem that marketers often face is having access to the right data on which they can act at each stage of their buyer’s journey. This is especially true with first-party enterprise data. However, some companies are starting to address data quality issues by creating specific guidelines for collecting, using, and sharing data across the organization.
This trend is expected to have a powerful effect over the next five years as more organizations start to develop standardized data quality protocols across the enterprise.
Machine Learning Will Allow Marketers To Automatically Adapt Rules For Customer Segmentation And Filtering
Every digital marketing company often has a large amount of data that they want to use in their communications, so it makes sense to divide them into segments or groups. These segments can be used for targeting purposes, such as sending relevant content to your customers. Traditional segmentation used fixed rules that were hardcoded into software, but this approach doesn’t work well with today’s amounts of customer data.
A more scalable method is machine learning, where software can determine the best division of customer data based on what it has learned from examples of correct outcomes. If marketers provide enough examples of what a good segment for targeting looks like, the software will be able to figure out the best division on its own.
This approach results in better outcomes because it uses all of the available information rather than just a small number of fixed rules. It also scales well because machine learning is very efficient at finding patterns and making predictions from a large amount of data.
Adopting A Model-Driven Approach To Personalization Will Increase The Response Rates For Messages.
As more companies use machine learning to automatically generate personalized messages, they have started to see a return on their investment through increased revenue and customer retention. In fact, some marketers reported that they were able to increase their response rates by up to 14% using this approach.
The idea behind model-driven personalization is that marketers can use machine learning models to generate customer communications. Since these models take into account a large amount of information, such as historical data and demographics, they can determine what types of messages will be most relevant for each customer.
The drawback to this approach is that it requires a lot of resources and expertise since the software depends on the quality of machine learning models. In order for marketers to get started with model-driven personalization, they will need to work with their data scientists or hire an expert from a data consulting company.
Expanding the Role of Data Scientists
In a survey taken by IBM, 84% of respondents agreed that “significant business advantages are possible when applying analytics to Big Data,” however, less than 1% of these respondents said they were fully satisfied with their current ability to capitalize on this potential.
This lack of satisfaction can be attributed, in part, to the dearth of available talent. Due to the highly technical nature of big data analysis, it’s expected that demand for data scientists will exceed supply over the next five years.
As a result, companies are looking to hire “citizen data scientists”, which can be defined as “employees who have some IT skills but are not formal statisticians, data engineers or machine learning experts. This trend is happening now across many organizations, and it’s expected that by 2022, companies will more than triple the number of citizen data scientists they hire across the enterprise.
Marketers can prepare for this surge in demand by training their teams on machine learning skills or hiring an outside consultant to provide these services.