“Data-driven” is a common buzzword in our digital world, but what does this actually mean and how can marketers use data science to increase their success? In this #ContentChat, Danielle Oberdier (@DikayoData) and the community discussed what data science means for marketing, including the type of data that matters, where to find it, and how to use data science to better connect with your audience.
Q1: Let’s start things off with some level-setting. What is data science, and how does it relate to AI and machine learning?
Data science combines various approaches and tools to understand a set of data, extract meaning from it, and to try and identify trends from it.
A1: #DataScience is a method of using tools like #AI and #MachineLearning to unearth trends that could change the way our industries operate. #ContentChat https://t.co/pUHkLSpaQi
— DiKayo Data (@dikayodata) February 3, 2020
A1. Data science it the discipline of extracting knowledge and insights from data. AI and machine learning leverages automation to streamline the process of gathering and disseminating data. #ContentChat
— GreenRope (@GreenRope) February 3, 2020
A1: Data science isn’t just about AI and machine learning, it’s using data in a scientific (collecting/experimenting/rigorous) manner to derive business, user, and product value. Many practitioners don’t work with ML #ContentChat
— Randy Au (@Randy_Au) February 3, 2020
A1: I define data science as the ability to gather meaning and interpret data that is outside of normal analytics. It is getting deeper into the what. #ContantChat
— Bernie Fussenegger #SMOS2020 Speaker (@B2the7) February 3, 2020
A1. Data science is organizing and analyzing sets of inputs (data) to report on performance. That’s my non-technical definition, anyway. #ContentChat
— allison ryder (@allisonryder) February 3, 2020
Certain tools for data science leverage artificial intelligence and machine learning to improve efficiencies in data collection, storage, and analysis. Basically, these technologies make various data science tools more efficient and enable a level of automation that frees data scientists/humans from needing to do as much manual work.
A1; What we are trying to do as #data scientists is to leverage technologies like #AI and #MachineLearning to improve efficiency. There are barriers to efficiency that are preventing innovation or even basic operations from happening. #ContentChat https://t.co/pUHkLSpaQi
— DiKayo Data (@dikayodata) February 3, 2020
Right! And using machine learning to automate that process saves so much time, and ultimately, money. It’s clearly worth the investment.
— ChipBot (@getchipbot) February 3, 2020
Q2: How can marketers use data science to inform their content strategies?
At its core, data science principles are used by marketers to better understand their audience and the unique needs of their various audience personas.
A2: Marketers need to know who their audience is before they target who they’d like to be. #DataScience can tell you who is most engaged with your content by profiling and segmenting your audience into clusters. How can you best serve and benefit from this audience? #ContentChat https://t.co/ijMsrORy8r
— DiKayo Data (@dikayodata) February 3, 2020
A2a. Using the right systems for tracking and analytics, allows marketers to gauge the effectiveness of their content and revise their process based on these insights. The part is isolating variables. #ContentChat
— GreenRope (@GreenRope) February 3, 2020
This information can help to refine almost any marketing activity along the customer journey. Using data science, marketers can more effectively identify the right channels for their audience, preferred content types and length, and even how different messaging or branding could help achieve business goals.
A2: Data science involves reading the tea leaves in your analytics to see what type of customer responds to your content assets in each channel at each stage of journey, from initial interest through to advocate / fan.#ContentChat
— Ed Alexander (@fanfoundry) February 3, 2020
A2b. Being able to implement effective contact segmentation in your distribution process helps marketers test similar content pieces against eachother. #ContentChat
— GreenRope (@GreenRope) February 3, 2020
A2: Data science can be used in finding the right customer or audience, channels and message that regular analytics wouldn’t give you – what is working and not working. #ContantChat
— Bernie Fussenegger #SMOS2020 Speaker (@B2the7) February 3, 2020
A2 #contentchat the level of data available can help inform content/media types, seasonality, length of content, topic trends, and help us with content ideas or help us tweak existing content to better funnel website visitors
— Diana Richardson (@DianaRich013) February 3, 2020
A2: Data science gives metrics on content performance. We can use that info when making editorial calendars crafted around what topics our audience responded to in the past.#ContentChat
— Danielle Bullen Love (@daniellewriter) February 3, 2020
A2. We use data science to look at what messaging and branding (text, colors, button placements) work to drive action, and also to assess which content performs best. #ContentChat
— allison ryder (@allisonryder) February 3, 2020
So important to understand what messaging—in which channel—resonates with your ideal buyer. #ContentChat
— Erika Heald | Content Marketing Consultant (@SFerika) February 3, 2020
Notably, modern data science approaches can yield insights that can be acted on immediately.
A2: Marketers can utilize the projections provided by data science to predict what content topics will be best received and when by their audience. Data science allows for a level of proactivity that gives marketers an immediate advantage. #contentchat https://t.co/Ol2fWSXpAF
— ChipBot (@getchipbot) February 3, 2020
Q3: Is there an opportunity for data science to help marketers create an emotional connection with their audience?
By implementing data science principles, marketers can more objectively understand the realities of the space they are working in and the audience they serve.
A3: This question should be asked every day in every space. I have made people cry over numbers and numbers have made me cry. The cold hard facts are not cold at all. They show us the world in a way we can’t run from. And that bonds us together. #ContentChat https://t.co/nbNno375ZF
— DiKayo Data (@dikayodata) February 3, 2020
A3: When we talk about #data, we are forced to talk about what is real and true in the world. And that really opens up a discussion of how we all see the world. It’s a vulnerable space and that kind of humanity fuels real relationships – even professionally. #ContentChat https://t.co/nbNno375ZF
— DiKayo Data (@dikayodata) February 3, 2020
The insights you glean from your data can help you to better tailor your messaging and work to strengthen an emotional connection with your audience. You just have to establish KPIs or other markers that you can track to assess whether your efforts are paying off.
A3: When I have worked with our data scientist, they are looking at the data differently to find trends, different segments etc. This have enable us to better understand the customer/audience which helps to better target and communicate. #ContentChat
— Bernie Fussenegger #SMOS2020 Speaker (@B2the7) February 3, 2020
A3 #contentchat The data you can glean from any sort of analysis can help inform any of your decisions as a marketer. If your goal is to create an emotional bond, then you’ll want to look at your data through that lens.
— Diana Richardson (@DianaRich013) February 3, 2020
Yes!!!! The better you know and understand your audiences, the better you can target and communicate to them and the better you can understand their behaviors. #ContentChat
— Bernie Fussenegger #SMOS2020 Speaker (@B2the7) February 3, 2020
Inversely, focusing on the wrong information can alienate your audience. A company’s success in leveraging data science ultimately comes down to methodology and minutiae. #ContentChat
— GreenRope (@GreenRope) February 3, 2020
# – Data science can help you uncover data insights and trends that you otherwise wouldn’t have known about.
The opportunity lies in being able to expand your data pool and think bigger so you can use that human touch to find the points of emotional connection. #contentchat— Worldmedia Interactive (@worldmediamiami) February 3, 2020
A3. There are SO many opps for data scientists and marketers to work together. If marketers can first be clear about what they want to learn, trust the data science team to do their jobs, and partner to tell a story (which each does best, through different mediums)…#ContentChat
— allison ryder (@allisonryder) February 3, 2020
A3: Absolutely. On the continuum from logically connected, to emotionally connected, to fandom (increasing relationship value), content goals vary significantly. You don’t want to act like a first date with your customer when they’re ready to be in a relationship. #contentchat.
— Ed Alexander (@fanfoundry) February 3, 2020
There are several tools to help in this process, a few of which are shared below.
A3: Monitoring and categorizing interactions are really important. Through Twitter analytics alone, you can find out who is engaging with your content and have a good profile of why that you can play with further. I’d like to hear @getchipbot’s answer on this. #ContentChat https://t.co/I9L6h3D8cr
— DiKayo Data (@dikayodata) February 3, 2020
A3: We use a wide range of data types:
Twitter analytics is definitely a key resource,but pairing it with Google analytics reveals much deeper insights.
We can learn what times users are engaging with our posts, and what path they’re taking from start to finish #contentchat
— ChipBot (@getchipbot) February 3, 2020
For us Twitter Analytics and Facebook Business Manager treasure troves of information that help inform our content marketing strategy. #ContentChat
— GreenRope (@GreenRope) February 3, 2020
Q4: Data science for content marketing clearly requires data. What internal data types and sources should marketers be collecting?
Data can come from a seemingly endless number of sources, and you could track virtually anything. Start by identifying your goals and then determining data sources and types that could track your success of those goals.
A4: In general for content, views, time of page, traffic sources, clicks, and shares.
How deep you go in the data depends on what your goals are. There is SO MUCH of it, you have to ask yourself what guides actionable insights.
#ContentChat https://t.co/qhLGn2b6t7
— Kristen McCabe (@AusmericanGirl) February 3, 2020
Our community shares a multitude of different data types and sources for you to consider. Comment below if you’d like to add to this list.
A4: The data comes from all kinds of sources…email, website, social, transactional, customer feedback etc…all brought together to understand the customer, who they are, where they are in the journey. #ContentChat
— Bernie Fussenegger #SMOS2020 Speaker (@B2the7) February 3, 2020
Yup! “Big” and “small” data (or “thick data” as @triciawang would say) so we can get at users’ behavioral trends but also their sentiment #Contentchat
— allison ryder (@allisonryder) February 3, 2020
A4) #contentchat
– the “basics” (users, pageviews, top pages, etc)
– conversions (phone calls/form submissions, etc)
– click behavior (heat mapping)
– path through the site— Diana Richardson (@DianaRich013) February 3, 2020
A4: Website, email, enriched data, event ( and e-event) behavior…THEN connect those dots among prospects and customers (tribe, affinity, IRL relation, etc.) #contentchat
— Ed Alexander (@fanfoundry) February 3, 2020
A4: With content creation in mind, we often overlook how stats from how our customers use our products have the potential to become powerful pieces of benchmarking content such as data visualizations and infographics. #ContentChat
— Erika Heald | Content Marketing Consultant (@SFerika) February 3, 2020
A4: Conversions are always important to track. Are people actually taking action after consuming your content? #ContentChat
— Express Writers | Your Content Writing Team (@ExpWriters) February 3, 2020
A4: It’s easy to assume that because we are engaging we are doing our job. But it’s a two-way street. It can be hard to reflect and ask if we are doing the right thing in terms of content but it’s important for success and growth. #ContentChat https://t.co/7Q4MAOGMmQ
— DiKayo Data (@dikayodata) February 3, 2020
Q5: Help! I don’t have a data scientist on staff. How can I learn what I need to do to take a data science approach to my content marketing and content creation?
This is a common dilemma, and some marketers are using online courses and trainings to learn the essentials.
A5: A lot of marketers are learning to double as #data scientists to some degree. @outlandosmedia of @trylately could speak on this. I have a friend Matt whose courses on https://t.co/v62Tu1ClK4 can get you prepped with the tools you need to apply but nothing more. #ContentChat https://t.co/YUeUdmpO0j
— DiKayo Data (@dikayodata) February 3, 2020
@ecornell_online have a very accessible data analytics course!
— Emma Westley ☘️ (@EmmaMoly) February 3, 2020
A5: Many marketers have some understanding of data analysis, though it may be more shallow than the knowledge of a data scientist. If marketers want to know more, @HubSpotAcademy is a great resource. There are also courses offered via @edXOnline, @udemy, and others#ContentChat
— Dr. Donald Hecht (@realDocHecht) February 3, 2020
Keep in mind that you should first have an idea of what you want to achieve with data science before attempting to put resources behind it.
A5: Each organization and brand is going to have different needs and different strategies. I would look at the approach that is best for you and do the necessary research to get there. #Contentchat
— Bernie Fussenegger #SMOS2020 Speaker (@B2the7) February 3, 2020
I agree. How detailed you need to get into the data depends on:
the magnitude of the decisions that need to be made, and
how risk adverse you are
#ContentChat— Tod Cordill (@todcordill) February 3, 2020
Google has a few options in the analytics space that are widely used and user-friendly.
A5. Learn enough about Google Analytics to connect the dots between content and achieving goals.
And if you’re heavy right brained and a little light on the left – don’t strain yourself. It’s not worth it. A little help goes a long ways. #ContentChat
— Tod Cordill (@todcordill) February 3, 2020
Google Adwords is a free certification program that will help introduce you to SEO and PPC. #ContentChat
— GreenRope (@GreenRope) February 3, 2020
If you’re ever in need, there are plenty of resources online that can help you learn the basics of data science.
A5: At this point in time, knowledge is more accessible than it’s ever been. There are podcasts like #datafemme, articles, YouTube videos, etc. created specifically to spread awareness & info about data science. The information is always there, if one needs it. #contentchat https://t.co/wM7MwdmQk5
— ChipBot (@getchipbot) February 3, 2020
A5. The internet is full of useful info. Read up as much as you can on strategy and then invest in intuitive tech with powerful tracking capabilities. #ContentChat
— GreenRope (@GreenRope) February 3, 2020
Or, when in doubt you can always bring on a freelancer.
A5. Are there freelancers you tap for help, or to teach you some basics? #ContentChat
— allison ryder (@allisonryder) February 3, 2020
Q6: Not all companies have compelling internal/proprietary data that’s of interest to their audience. What are some publicly available data sources that content marketers can access?
Do you use any of the publicly available data sources mentioned below?
https://t.co/xTQ0OJzQhj has a LOT. It can be a dangerous maze to go down but very fun and informative. A lot of open source #data can be found within city portals. And many companies post their data online for consumer viewing. It’s not everything but it’s a start. #ContentChat https://t.co/M0w9zoM2Ni
— DiKayo Data (@dikayodata) February 3, 2020
A6: My market is likely different from many of those in this chat, but I rely on several sources including @usedgov, @EdNCES, @WorldBank, and @EdDiveHigherEd #ContentChat
— Dr. Donald Hecht (@realDocHecht) February 3, 2020
A6a: One of my favorite resources for compelling data for content is @pewinternet #ContentChat https://t.co/vHcDDkfHur
— Erika Heald | Content Marketing Consultant (@SFerika) February 3, 2020
A6b: I’ve also used datasets from @usdatagov https://t.co/5BVkpGR26z #ContentChat
— Erika Heald | Content Marketing Consultant (@SFerika) February 3, 2020
I like https://t.co/e8pKzdp9ib #ContentChat
— Bernie Fussenegger #SMOS2020 Speaker (@B2the7) February 3, 2020
A6: A few generalized data resources are:
– World Bank Open Data
– Google Public Data Explorer
– FiveThirtyEight — Great for identifying personal interests#ContentChat https://t.co/xT4OG1PKtx— ChipBot (@getchipbot) February 3, 2020
Q7: What are some examples of data science-driven content marketing programs or individual pieces of content?
Check out these data science thought leaders to follow on Twitter.
There are many #data scientists with blogs and social media content to follow and get to know. I say focus on the individuals because they’re the ones shaping trends. @pip_alise @data_bayes @ZenDollData @AdamMico1 @allison_horst @WeAreRLadies @ZachBowders @AlliTorban #ContentChat https://t.co/qJbUTQ0drS
— DiKayo Data (@dikayodata) February 3, 2020
I’d add @cspenn to any list of data scientist thought leaders. #ai #ContentChat https://t.co/rvE2F4PJMd
— Tod Cordill (@todcordill) February 3, 2020
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