Marketing automation uses sophisticated data analytics techniques to cut data discovery times and match content to specific customers.
Sophisticated capabilities such as machine learning and predictive analytics speed up and automate marketing services that used to be provided by humans.
What’s happening? Is any marketer’s job safe?
Should we just give up and start growing hydroponic produce?
2013, When Everything Started to Change
Digital tools and methods are not new to marketing departments. CRM platforms and automated data collection and storage apps have been around for decades. Until recently, finding, updating and analyzing marketing data were manual tasks for humans.
The big data analytics saga started with a trickle of IT trade press items. They described automated data analytics programs, which plowed through mountains of data in a flash. Then, data analytics programmers developed products with complex algorithms, which went far beyond automating simple data discovery and analysis.
Primed with machine learning to make programs adaptable and in-memory processing to boost speed, BDA burst on the market and the consciousness of business people everywhere.
Enter Our Anti-Hero, the Data Analyst
High-speed, high-volume data analysis and visualization became easier than ever to find and afford. Marketers started using big data analytics to measure and analyze many types of data to improve marketing processes.
As big data analytics started proving its value, attention moved from apps to data analysts, the ultimate data wranglers. These specialists had been the go-between of programmers and business users. Their role: to ensure that the stream of relevant, high-quality marketing data kept flowing.
After the initial success of this type of analysis — and data analyst — success, marketers are beginning to wonder: how far can analytical automation go? Will the technical aspects of analytics make non-technical knowledge and experience less valuable? Should marketers start polishing their resumes to find a new line of work?
Data and Text Analytics Today
No one denies that the role of data analytics as a decision-making aid will grow significantly. Prominent marketers already use the full range of descriptive, predictive and prescriptive analytics that are available now using various business intelligence tools for marketing as well as predictive analysis platforms that crunch massive datasets to generate automated insights.
Here are some examples of how they use big data analytics to improve their already-formidable marketing efforts:
Match sales reps to ideal customers. Predictive analytics can be used to match the most qualified sales reps to improve close rates and customer satisfaction for highly focused customer groups.
Tailor marketing initiatives to most likely customers. This involves connecting lead behavior and customer behavior data to identify the core audience of specific products and services.
Learn exactly where the best leads are coming from by combining click-to-call with call tracking analytics.
Learn which combination of assets results in the greatest converted value. For example, data analysis enables marketers to use various types of marketing dashboards to identify:
- Which combination of content, offers and channels is most likely to convert a new customer.
- Incremental improvements to an existing marketing process, which results in the highest total sales
- Which types of blog post can be linked to the most profitable leads.
These are rather powerful capabilities. Do they translate into pink slips for marketers?
Obsolescence Is Not an Outcome
There are two reasons why marketers aren’t an endangered species.
First, there are about 4.4 million unfilled job openings for straight-ahead, tech-loving data analysts. Second, to replace marketers, these already rare specialists must have skills and knowledge that analysts are usually less versed in.
Some of the unique skills marketers bring to the table include:
Marketing problem solving: Automated or semi-automated data analysis can help marketers identify customer problems with little effort. But only marketers familiar with the pains, concerns and priorities of specific customers or buyer groups can take the next step and communicate a relevant solution that closes the sale.
Business knowledge: Sophisticated big data algorithms can link product capabilities with specific customer problems. But experienced marketers know which type of product or solution can provide the most business value (cost savings, customer satisfaction, etc.) that specific customers want.
People knowledge: Analytics lets you easily link what customers want with what your company offers. But marketers know that sometimes, specific buyers won’t accept a specific solution, no matter how effective it might be. They know when it’s time for Plan B.
In other words, data analysts can link specific information with specific customer groups or desired outcomes.
But they don’t have the skills, knowledge or experience to know which of several bits of relevant information will give the best results for specific customers. That is and will likely remain domain knowledge that cannot be replaced by algorithms (yet).
Don’t Worry, But Look to the Future
OK, marketers don’t’ have to worry about becoming obsolete in the foreseeable future. But that doesn’t mean that they can lean back with a “What, me worry?” grin. There’s always work they can do to maximize their professional value.
Marketers can improve their reputation and value by embracing, not avoiding digital tools and methods. It won’t go away, so why not stay ahead of the curve? Use digital marketing assets to the max.
Yes, keeping current means having to learn the latest tools, technologies and digital marketing best practices. But in-depth data analysis also helps marketers provide higher-value work, such as:
Recognizing new opportunities: Identifying gaps between what customers want and you offer requires only careful tweaking of analytical platform settings.
Giving high-caliber ammunition to the sales team: Fast, accurate identification of opportunities has to be good news to sales professionals. That’s because marketers can now use big data analytics to hand off data-rich opportunity descriptions, not just bare-bones lead information.
Making quick connections to sales experts: Having access to a user’s previous search history means that a call can be routed to an agent best suited to deal with a specific call or customer. For example, connecting customers wanting a Greek Island vacation to an agent who knows about Greece and Greek travel.
Taking a more customer-centered approach to marketing and sales: More than ever, customers know more about your company and offerings before they contact you. And they like it that way. Analytical methodologies can provide your sales pros the detailed data that respects your prospects and helps you start sales on the right foot.