Why marketers should think as Data Scientists (And how to do it)

December 21, 2018

Your company may not employ data scientists today. And you might be fine with that. Consider, though, that if you’re not working with a data scientist or at least thinking like one, you’re missing something: the ability to say “I know” instead of merely “I think.”


That distinction matters when you’re talking with executives, says content marketer and consultant Katrina Neal. “If you walk into a meeting in a next-generation data-driven organization and announce, ‘I think this campaign is going to work,’ you could risk being humiliated in front of your colleagues and asked to leave the room.On the other hand, if you walk in with what you know, people listen. They might even approve your budget.


Why does data science matter in content marketing?

I like this straightforward definition of data science: the practice of “surfacing hidden insight” using data in a way that helps “enable companies to make smarter business decisions.”

Smarter business decisions come from better predictions. As a marketer, when you think like a data scientist, you make predictions that keep shareholders happier, you make customers happier, and you increase respect for your profession. Your content teams make better decisions, you build support for the content initiatives you propose, and your company gets more value from its content.


Social media plays a huge part in advertising and marketing in today’s world. Each tweet, Facebook post, and Instagram picture, both from your company and from your customers, contains a goldmine of data. Using sentiment analysis, you can uncover how your customers are feeling about recent campaigns, announcements, and more, without the time-consuming process of reading through each post. Through discovery analysis, you can better understand who in the social media world is talking about your business, and which high-profile users are talking about your competitors.


This year alone, the U.S. is projected to absorb a shortfall of 190,000 data scientists — and that’s not even counting the 1.5 million more analysts and leaders needed to make use of the information big data supplies.


This is an especially terrifying prospect in the marketing world, where data science provides the signals that let marketers know their decisions have paid off. “In the end, the analytics won’t tell you the next big creative idea,” Elea Feit, assistant professor of marketing at Drexel University, says. “It will tell you when the next big creative idea is working.”


What You Don’t Know Can Hurt You

Having a wealth of knowledge is a huge advantage — until your knowledge surpasses others’ understanding. If people don’t know how to apply a significant piece of information, that data is useless.


This is why data science is so essential to the marketing equation. “ The most powerful data scientists are those who act as bridges between insights and people ,” says Kirill Eremenko, the founder and CEO of SuperDataScience, an online educational portal for data scientists and data science enthusiasts. “There’s a science behind analytics; however, communicating insights is an art.”


Straddling that line is important because data science insights are connected to marketing results. Marketing departments are expected to quantify their results as justification for keeping their budgets and strategies intact. Marketers handle digital information within their campaigns and collect it to improve their tactics, increasing the demand for data science.



Data science for marketers: 5 steps to preparing your data

December 12, 2018

Every business holds a unique key to overhauling their marketing success. That key is data. Or more precisely, unique insights that could be derived from that data and applied to optimising different marketing activities – from customer retention to search engine marketing and pricing strategies.


The wrinkle, however, is that big data analytics isn’t a point-and-click solution like your typical MarTech tool. It requires certain operational changes, backed up a solid transition plan.

So if you are already sold on the benefits of big data in marketing, here are the next steps you should take.


Step 1: Get company-wide buy-in

Marketing data analytics adoption is a big step (not just because you plan to use big data). It assumes that your organisation is ready to embrace not only technical changes but undergo an organisation-wide transformation that will tackle the people and the processes as well.

According to Big Data Executive Survey 2018, 48.5% of respondents name ‘people challenges’ as the main barrier to becoming a data-driven organisation versus 19% identifying technology as an issue. Over half of respondents also state that insufficient organisational alignment and/or cultural resistance are the two issues slowing down the adoption of new technologies.

To succeed, you will need to get the critical mass of employees on-board. Education is the simplest cure for that. Show your teams that marketing analytics isn’t a threat to their careers or skill sets. On the contrary, data science and big data analytics can help your marketers do their best work without being decelerated by mundane, repetitive chores.


Step 2: Determine the types of data you will need

Your analytics will only get as good as your data. The problem? Most businesses already feel overwhelmed with what they have. And that data is typically siloed in all the wrong places.

To determine what data to operationalise first, consider the end-game of your big data programme. What are the exact outcomes you want to achieve? Data science use cases in marketing are manifold:


  • Programmatic pricing optimisation and dynamic pricing systems;

  • Advanced customer segmentation and user profiling for personalised marketing;

  • Predictive lead scoring;

  • Search marketing foresight that enables you to determine the success of different campaigns;

  • ‘Intelligent’ PPC advertising campaigns that will self-adjust depending on changes in the advertising environment.


A clear end-goal will help you determine the primary data sources to prepare for departure. Start with defining the minimal data sources you will need to achieve the results. For most companies that would be a CRM system and online analytics tools. Though, you may later consider connecting additional data streams on an ad hoc basis.


Step 3: Arrange a consistent flow of data

Digital marketing assumes a constant inflow of dynamic data from multiple sources – social media, website analytics tools, email marketing tools, advertising platforms and so on. Your job is to ensure that new information can be seamlessly delivered for analysis.

Often this means that you will have to account for both structured and unstructured data. Structured data is delivered in a machine-friendly format; 99% of the time it’s readily available for further analysis.


Unstructured data, on the contrary, is everything we humans love to share – videos, text messages or documents, audio, images. Such assets need to be converted to other formats before they can be processed by the algorithms. Certainly, this presents additional challenges, but the trade-offs are significant. Unstructured data usually holds unique insights that cannot be retrieved any other way.


Step 4: Schedule data cleansing

Depending on your tech capabilities, you may either perform data cleansing in-house or outsource it to a data-science vendor.

Before being sent for analysis, all your proprietary and external data will have to be converted to the same format, suitable for the algorithms to comb through. Duplicate and ‘fuzzy’ data should be also removed. Data cleansing is a crucial step to ensure that your analytics will work properly and deliver objective insights. This is a time-consuming step as well: 60% of data scientists say that they spend the majority of their time on data cleansing and quality assurance.


Step 5: Launch the data consolidation process

Now all your crispy-clean data, along with the data sources should be directed towards a single destination – a data lake. Data consolidation can seem like a major technical investment, but it pays off in multiple ways:


  • Reduced total cost of ownership. A single storage unit means that you no longer have to pay for multiple software licenses used by different teams; and for storage space to accommodate ever-growing databases.

  • Increased efficiency and productivity. A single entry point means that your data becomes readily available for further analysis and easily accessible to employees in different departments.

  • Simplified compliance. By knowing exactly what kind of data you are collecting and where it resides you can comply with new data protection laws and requirements without much hassle.


The final preparation step is to find the right talent who will transform your business data into actionable insights. You will need to consider what skill sets your organisation requires to execute every step of your data programme – from determining the best use case and estimating ROI to selecting and implementing the best technology strategy – and fill in the gaps with external expertise.


credit: George Karapalidis, head of data science, Vertical Leap

Why Does Big Data in Marketing Need Visual Analytics?

June 15, 2018

It has become necessary to look for new ways of storing and indexing data, and at the same time, different methods of helping people interpret it. Using visual aids to bring context to data offers several advantages, including the following:


Emphasising valuable aspects

It’s typically difficult for people to look at numbers on a spreadsheet and understand their significance. However, visual analytics software can create bar graphs, pie charts, and other graphics-based representations of data that would otherwise be very difficult to comprehend.

For example, many marketers rely on big data when answering questions related to customers. They might want to gain a better understanding of how to generate leads, increase engagement, or urge people to keep giving companies their business.

Some marketing professionals also depend on big data to determine the specific aspects of a website that play the most significant roles in driving sales, or how long users usually spend browsing before they buy.

In all those cases, a simple strategy such as separating demographic groups with different colours on a graph makes it easier for viewers to keep the various aspects distinct and reduces confusion.


Saving time when delivering findings

Some people – particularly those at the executive level – may ask marketing teams to dive into big data and reach conclusions, then present them as efficiently as possible. That means they may not always have the patience to decipher intimidating amounts of data. Statistics indicate that people process visual data 60,000 times faster than text alone.

Moreover, they could request that marketing experts show them different representations of data on the fly, which is possible with visual analytics platforms, but not always when using other tools.


Spotting the outliers when looking for trends

When marketing professionals evaluate trends highlighted by big data, they often look for outliers – the things within a group that deviate from the expected norm. Focusing on the outliers and understanding their prevalence is especially important in A/B testing or when dealing with small sample sizes.

Fortunately, well-organised data can aid decision-making by letting marketers distinguish the outliers, sort through content quickly, and reach informed conclusions about how to accomplish goals. It may also become evident that the outliers represent a group of people with unmet needs that marketers can address.

Providing dynamic resources

There are particular cases where visual analytics are especially valuable, such as when looking at financial data or evaluating documents. Visual interpretations of data add worthiness when working with data that might often change, such as sales territory maps, complete with features showing where the most loyal customers reside.

Analysts point out that it’s necessary to think of visual analytics as being similar to software, meaning it’s easy to update. One of the reasons big data is so essential for marketers is because it profiles how things change over time.

Visual analytics should reflect those changes by being dynamic. If they do, the time it takes to make decisions often gets shorter, because people can access visuals that use updated information and interactivity to solidify comprehension.

Helping people absorb the facts

The insights big data delivers may become intimately familiar to the marketers who work with it every day. However, people who are seeing the data for the first time – such as audience members listening to a marketing presentation – may feel initially overwhelmed by the content they’re receiving. Also, some people naturally understand image-based data more than figures and statements.

In these cases, it’s critical to help individuals feel well-equipped for optimal understanding. Visual analytics can achieve that goal by not only increasing immediate and accurate comprehension but also by promoting long-term retention.

Visual analytics should not be overlooked

Regardless of the frequency with which marketers work with big data, and for what purpose, visual analytics make it more palatable. Therefore, they should prioritise incorporating it into their marketing research methods.

How data is transforming the music industry (The Conversation)

August 01, 2017

Fifteen years ago, Steve Jobs introduced the iPod. Since then, most music fans have understood this has radically changed how they listen to music.


Less understood are the ways that raw information – accumulated via downloads, apps and online searches – is influencing not only what songs are marketed and sold, but which songs become hits.


Decisions about how to market and sell music, to some extent, still hinge upon subjective assumptions about what sounds good to an executive, or which artists might be easier to market. Increasingly, however, businesses are turning to big data and the analytics that can help turn this information into actions.

Big data is a term that reflects the amount of information people generate – and it’s a lot. Some estimate that today, humans generate more information in one minute than in every moment from the earliest historical record through 2000.

Unsurprisingly, harnessing this data has shaped the music industry in radical new ways.

An intervention on marketing’s dysfunctional relationship with data - MetricVision can help! (Martech Today)

June 27, 2017

If it feels like you have a dysfunctional relationship with data, you're not alone. If this article resonates, contact us to help provide actionable marketing data analytics and dashboards to help you and your team work effectively!    

Data is the New Marketer’s Currency (DigiDay.com)

June 26, 2017

Data is the new marketer’s currency. Our job is to build meaning around the data to serve our customer more appropriately. That goes back to the basics of what marketing is: understanding consumer preferences. But rather them telling us in words and in surveys, we use our own gathered data to make decisions in real time. Ten years from now, any marketing team at any level will have those capabilities.

Actionable Metrics at Your Fingertips!

June 29, 2017

As CCO's content marketing research indicates, 33 percent of B2B marketers and 41 percent of B2C marketers cited the inability to measure as a significant challenge. MetricVision can help address all your marketing channel analytics & business intelligence, providing you with clear and actionable dashboards.    

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