Brands are finding data practices to become increasingly beneficial to their future growth and their bottom line, which has changed hiring practices in many organizations.
By 2020, the number of jobs for data analysts will grow by 16 percent, and jobs around machine learning, big data, and data science skills are among the most challenging to recruit for. For these reasons and others, agencies and consultancies have also emerged that provide data analysis as a third-party offering, while the larger brands are now bringing this practice in-house and hiring these roles internally. Even with the increases in employment and interest for these programmatic individuals, there is some ambiguity about the role.
So, what do they do? A data analyst's job is to collect and analyze the data coming from digital platforms to find trends, red flags, important points of impact, etc. They should have the goal of moving away from the anecdotal and toward the analytical.
Data analysts are often compared to data scientists as both are curious, asking questions of the organization to help try to find answers behind the data they are seeing. Both seek the goal of finding insights from the pool of data they have access to in order to give direction back to the organization.
While data scientists are focused on advanced statistics and theory (looking at data acquisition, movement and manipulation), data analysts are traditionally more of the executors (the ones managing the databases, using the tools like Power BI and analytics platforms as well as performing intermedia statistics to find results). A scientist could typically be an analyst, but an analyst isn't always as versed or interested in the theory behind the execution to become a scientist.
The question organizations often have about the field is whether they need to hire a data analyst. While data analytics should become a major part of all marketing and content practices, a company's level of investment will dictate the size and scope of resources. If organizations identify a higher need, they could find a partner of record who provides data analytics as a service, or even contract a third-party service that can provide this to the team. For businesses that have the resources, hiring a data analyst can help shape the role to the company's specific needs to optimize their investment. There are, of course, smaller businesses looking for their marketing team members to embody the qualities of a data analyst to improve operations and save themselves from taking on another salary. In this case marketers should:
Be inquisitive. Ask the questions like, "What don't we know about our marketing practices that we could use to make better decisions?"
Find the trends and causality of actions. What specific kinds of users purchased products and are these the same users as before? What was the impact of the last campaign we ran, besides click rate or revenue? Was there an impact after the campaign stopped over time? What is the result of including our vanity phone number in advertising?
Along with these qualities, organizations can move toward a data-driven culture by valuing analysis over anecdote. Anecdotal thinking can create a confidence about decision making in an organization, but the only true indicator of past and future success is to attribute data to decisions. If this is emphasized from the top-down, an organization can make employee, technology and everyday decisions based on making this a reality. Culture based on data and analysis can improve confidence in long-term success by replicating decisions attributed through data rather than hope that trends will continue positively. It should be known, however, that some marketers resist collecting, evaluating and reporting data.
THE RESISTANCE IS STRONG
The practice of data analysis is expensive and time consuming. The extra investment in people and technology hits a company's bottom line and requires them to stop daily operations to establish processes. It also factors in extra steps in marketing execution to collect and analyze data. For many, this extra time and money is not worth the perceived value, but this is a misconception. The lack of buy-in for reporting puts a short-term value on the status quo rather than seeing the long-term value in improved growth as additional investments pay for themselves with increased revenue through existing channels and/or new ones.
One of the problems is that marketing teams do not have the right time, tools or skillsets to help in data analysis. Basic analytic tools are perfect for small teams looking for site trends and analysis. This could be a very simple integration to a site, and many cost models plus basic reporting can help teams start with a great baseline to see site trends. Also, built-in tools to a technology platform can have huge benefits. Content management and customer relationship management platforms often provide off-the-shelf reporting that fits a company's needs or can with a bit of customization. In other words, teams that do not have the budget to hire a data analyst or take on extra solutions should look within, as many of the reporting tools and people they need already exist.
THE REPORTING CAN BE STRONGER With all of this, the best-case scenario of how data is reported to the rest of the organization is that every marketing decision has a data reporting component.
These components are relative to their desired result. If a company runs an email campaign, it can identify in advance the types of data points that are important to them (e.g., revenue generated by the campaign, demographic breakdown of those that responded). These reports are also generated with a feedback loop to other teams in the organziation. Merchandising teams, for example, need to see a possible correlation from the marketing team showing types of creative used and products purchased. If they put a lawn mower in a prominent placement visually on the site, they might find it drives more interest in the moths of April and May and can reproduce these results to great success.
Larger components should also be considered for the organization at-large. Company-wide marketing meetings should contain analysis on successful trends such as geo-locations where marketing had the biggest impact, or types of products or content that push the best results. This should be shared in a timeframe that the organization can effectively use to help drive decisions. If we share once a year, we avoid the nuances of how seasons or trends can affect our decision making. If we share daily, its information overload. If we share in an optimal frame such as monthly, this can help drive our departmental meetings to be more agile and shape next-step solutions.
PURPOSEFUL ANALYSIS
Collecting data for the sake of collecting data will not suffice. If an organization is responding to the data effectively, then it is being collected in a useful manner. If they are not seeing results perpetuate throughout their organization, then they will not be able to justify the expense to collect this data from the decision makers or they will not analyze the right metrics. This all shows a lack of proper goal setting and establishing of feedback loops to assure the right questions are being asked to get relevant answers. With a little bit of interreflection, marketers can embody the qualities of data scientists and shed light on departmental and organizational wins and losses.
About the Author: Jeff Cheal is the Director of Product Strategy for Personalization, Campaign & Analytics at Episerver. He serves the North American market as an ambassador for the Episerver product suite, staying connected with both the partner network and customer base.
