The demand for data scientists is higher than
the supply, and the reason is simple: Data scientists don't just work in
business intelligence, but in business strategy. They can transform your
marketing strategy, too, if you know what to look for.
Chris Warnock Specialist Analyst, 23 OKT 2019, getapp.com
Demand for data scientists continues to outstrip
supply, with the field topping LinkedIn’s list of “Most Promising Jobs of 2019” (up from
ninth last year), thanks to 4,000+ openings
(+56% YoY) and a median base salary of $130,000. Likewise, Gartner research (available to
clients) states marketing analytics now accounts for
the largest share of the marketing expense budget at 9.2%, contributing to
intensifying demand for high-quality analytics talent.
Is it a fool’s errand to try to hire data scientists who don’t exist and invest in marketing analytics despite stagnating performance?
Gartner’s 2018 Hype Cycle for Data Science and Machine Learning (available to clients) states 77% of senior managers view data science as delivering significant value or as being essential to the success of their organization. However, much of the current and future workforce lacks the skillset employers need to fill data scientist positions. Additionally, research suggests the effect of marketing analytics on company-wide performance over the past five years has failed to live up to expectations.
Is it a fool’s errand to try to hire data scientists who don’t exist and invest in marketing analytics despite stagnating performance?
Gartner’s 2018 Hype Cycle for Data Science and Machine Learning (available to clients) states 77% of senior managers view data science as delivering significant value or as being essential to the success of their organization. However, much of the current and future workforce lacks the skillset employers need to fill data scientist positions. Additionally, research suggests the effect of marketing analytics on company-wide performance over the past five years has failed to live up to expectations.
Not
necessarily.
To
overcome the issues facing data science and analytics in marketing we must
clarify answers to three questions.
1.
What does a marketing data scientist do?
Gartner research (available to clients)
provides a useful framework for understanding the core responsibilities of a
marketing data scientist:
·
Measurement: Determining the impact
of marketing efforts and ad campaigns.
·
Optimization: Recommending changes in
tactics or spending to improve results.
·
Experiments: Designing and executing
tests to isolate causes.
·
Segmentation: Identifying groups and
subgroups of customers and prospects.
·
Predictive
modeling: Building computer models to improve response rates.
·
Storytelling: Communicating messages
derived from data to inspire better decisions.
If
your business is looking to hire a data scientist, use this framework to scope
out the job position. As The Next Web points out, business
success or failure can depend on how well a company interprets and acts on its
data. However, a shallow understanding of what exactly data scientists do,
coupled with analytics becoming mandatory for a variety of business functions
(not just marketing), has led to companies filling seats with people lacking
education or experience in the field.
In
the long term, this quick fix might actually work: people with questionable
qualifications will fill data scientist positions, and some of them will become
experts over time as they learn on the job. Forbes estimates that by 2029 data
scientists jobs will no longer exist. Rather than the current hiring frenzy
being a flash in the pan, data science expertise will increasingly complement
communication, domain knowledge, and business-strategy skills—becoming an
indispensable requirement for productive workers across departments.
In
the short term, this may cause headaches as people without data science and
analytics expertise fill positions and then inevitably make mistakes, fail to
meet employer expectations, or both. Understanding what the job really demands
and your own company’s ability to nurture someone new to the field is essential
to successfully hiring data scientists and analysts.
2.
What determines marketing analytics success?
Marketing
analytics success begins with clean and reliable data. According to the Harvard Business Review, a shortage of
experienced data scientists is compounded by vast amounts of messy, inscrutable
data. Too often, marketing analytics efforts are hampered by large, poorly
organized data sets that are difficult for analysts to extract insights from.
Most
companies collect data across departments using different systems and by
defining different variables. This makes it difficult to easily combine and
analyze company data holistically. Businesses must have a plan for integrating
data prior to collecting it, or grapple with the costly and time-consuming
processing of retroactively standardizing data for analysis.
Accepting
that more isn’t always better from the beginning can help mitigate these
issues. Rather than capture everything, determine your business strategy first
then decide what data is required to monitor its success.
Additionally,
create a detailed customer journey map that outlines
every potential engagement point between your business and its customers. Tying
data to customer touch points will give marketing analytics the context it
requires to inform business strategy.
3.
How does software enable marketing analytics?
Abundant marketing analytics software choices
can easily become overwhelming. Rather than get caught up in an unending
platform comparison, aim to select a single tool that will enable the core
responsibilities of a marketing data scientist (even if you haven’t hired one
yet).
As
you evaluate products, the following questions can help determine which
marketing analytics software has the right features for your business:
·
Measurement: What metrics does my
company use to measure marketing campaign success (e.g. click-through rate,
bounce rate and page views)? Does this marketing analytics tool allow me to
track our success metrics?
·
Optimization: A data scientist or
analyst will be responsible for recommending changes to marketing strategy
however, software should provide tools for measurement, experiments and
predictive modeling to help facilitate this process. If you anticipate finding
a full-time data scientist will be difficult for your business, ask vendors:
does your software offer? This emerging technology attempts to automate insight
discovery.
·
Experiments: Does this marketing platform
offer A/B testing to ensure the best version of a campaign receives investment?
Will this solution allow me to test all the campaign types my business will be
running (e.g. email, landing pages, CTAs)?
·
Segmentation: Will this marketing
analytics tool help me define and target audience segments to improve the ROI
of my campaigns? How are audience segments created and maintained? Does this
platform offer dynamic lists that are updated depending on predefined rules as prospect
data changes?
·
Predictive
modeling: Does this platform offer predictive modeling to help my business
prioritize contacts based on their likelihood of turning from prospect to
customer? Can this tool use predictive marketing to provide users with personalized
experiences (e.g. content, offers, pricing) that increase the likelihood of
conversion?
·
Storytelling: Does this marketing
analytics tool offer data visualization and reporting features to help
communicate messages buried in data to less technically minded stakeholders?
Are reports and dashboards generated automatically or do they require manual
configuration?
As
is the case with data, more software isn’t necessarily better. The questions
above can help you determine which marketing analytics software features are
the most useful for your specific business needs. Additionally, if you’re able
to successfully onboard a qualified (or not so qualified) data scientist,
leverage their expertise and preferences to help inform your software
purchasing decision.
Compare
marketing analytics software
Ultimately,
you should strive to run a single unified marketing analytics platform and
maintain a clean database that has been optimized for analysis. If you’re ready
to enable marketing analytics using software, try
GetApp’s marketing analytics software comparison tool for help
finding a platform that suits your business’s data science needs.
GetApp’s software
comparison tool offers product details including platform support, typical
customers, customer support, product screenshots, reviews, features,
integrations, security and more
