Why Marketers Should Think Like Data Scientists
Your business may not employ a data scientist at this time and that may be fine.
However, if you are not thinking like one then you are missing something.
As Jack Ma, Founder of AliBaba said we are leaving the age of Information Technology and entering the age of Data.
All marketing insights today need to be backed up by data.
All business solutions and projections must be backed up by data.
We no longer live in an age where opinion is all that matters….
The distinction comes when you walk into an executive meeting. It could be with investors, shareholders or just with the next level up the payscale.
According to Katrina Neal in a speech at the Intelligent Content Conference:
“If you walk into a meeting in a next-generation data-driven organisation 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.”
However, if you walk in with what you know backed up by the statistics to prove it, people will be engaged.
Better still — they may even approve your budget!
In this article we take a look at the ever increasing significance of data science and why data scientists need to be marketers’ new best friends.
Data Science and Content Marketing
Data Science is 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.
Marketers that think like data scientists are able to make informed predictions that keep shareholders happier, make customers happier, and that increase respect for the industry.
Your content teams make better decisions, you build support for the content initiatives you propose, and your company gets more value from its content.
As Neil Barlow of NewsCred puts it — predictability is:
“how the world’s largest brands continuously delight Wall Street investors and increase stock prices. Within many businesses, CMOs are under particular scrutiny to transform marketing from a cost center to a predictable profit center.”
By working like, or at least thinking like, a data scientist you will be building predictive models that enable you to make educated predictions instead of thinking something will work and then undertaking a process of trial and error.
Consequently your marketing will be transformed into a profit centre.
What is a Data Scientist?
When talking about Machine Learning, Predictive Analytics, Predictive Modelling and Artificial Intelligence you will more than likely come across the term Data Scientist and Data Science.
Data science is not a science as Physics, Biology or Chemistry are. Instead it is a description of the application of machine learning techniques.
A Data Scientist is someone who is able to apply machine learning in a practical way.
Usually Data Scientists are skilled in math, tech, and business.
A good Data Scientist will have a good understanding of IT systems and data, and is able to understand the business context in which their solutions will be applied.
Data scientists are concerned with three types of analytics:
- Descriptive
- Predictive
- Prescriptive
Data and Marketing
Descriptive Analytics
Most marketers work with Descriptive Analytics.
This kind of data provides information of what has already happened.
Google Analytics, for instance, presents historical results such as impressions, click through rates and cost per click.
Predictive Analytics
Fewer marketers use Predictive analytics (or Predictive Modelling).
This type of data enables marketers to predict likely outcomes based upon cross referencing historical and real-time data.
For example, lead scores can be applied to customers based on their interactions with a website. Then, by applying an algorithm, it is possible to accurately predict how offers or incentives will influence customer response, spending and interactions in the future.
Predictive Analytics works rather like a navigation app that predicts your car’s arrival time, updating the prediction on the fly as circumstances change.
Prescriptive Analytics
The next level up again is Prescriptive Analytics. This is beyond where the majority of marketers are today.
These analytics tell us not only what is likely to happen, but what should be done to capitalise on what will happen.
Imagine that Prescriptive Analytics function a little bit like an autonomous car. As well as predicting the destination arrival time, the car will also do the driving to the destination.
Another example is the simulated interaction between a marketing professional and IBM’s artificial-intelligence agent Watson.
The marketer tells Watson to set up a social media advertising campaign.
The ads run and the data is collected for a number of weeks. After sufficient data is collected Watson recommends replacing an image with a lower click through rate “based on the performance of similar images for this category in other channels.”
Now that’s a serious tool for marketing.
Take a look at how the conversation between the Marketer and IBM Watson panned out here:
What Data Science Can Do For Your Business
A data scientist, or a data science mindset, can assist with marketing strategy.
For example, with a content marketing strategy, content can be planned, refined and created. Results can be measured and predictive models and can be compiled using machine learning techniques and statistical models.
Content Planning
When planning a content marketing strategy here is a list of what the models can predict:
- Total addressable market (TAM)
- Segmentation and account selection
- Demand generation
- Lead scoring
Content Refining
In any field testing is essential. Content marketing should be no different.
Testing content can include A/B testing, serial testing, article posting times, or whatever kind of testing gives you the feedback to determine what’s working and how well.
For example, LinkedIn ran A/B testing on sponsored content in the feed to see which words performed better, “guide” or “e-book.” The post that used “guide” had a 95% higher click-through rate. In a similar test, “register” outperformed “join” by 165%.
With Predictive and Prescriptive Analytics marketers can quantify with certainty what content is out performing the others. Simply altering one word can have a massive bearing the overall success of any marketing campaign.
Data science relies upon a test and learn mentality. Marketing blueprints can be established based on the results.
The examples cited above provide valuable insight into the way a data scientist might work with a marketing team.
How to Measure Results
Working with a Data Scientist to build an algorithmic content-attribution model will help to ascertain which content is generating revenue. The model will display which content was read on the customer journey and which were the most effective in terms of the business marketing goals.
Results will now be quantified in terms of numbers and projections will now be measured on a more precise ROI calculation.
This is the language that CEOs, Investors and Shareholders understand.
Whilst working at Cisco Katrina Neil worked with a metrics dashboard based on insights gleaned from the Cisco data-scientist team through a Bayesian network analysis. The team used website data to predict which activities or pieces of content would result in a sales-qualified lead (SQL).
She states that:
“When you show up to a board meeting, take a marketing-sourced revenue goal, maybe 20% or 10% of the overall revenue goal. Then walk into future meetings and talk about pacing. How am I pacing against that revenue goal? How many marketing-qualified leads have I got? How many sales-qualified leads have I got? How many bookings have I got? And map that success into a multi-touch content-attribution model. That’s the kind of conversation we need to be accountable for to get a grown-up seat at the table.”
JavaScript enables the “magic” responsible for tracking those online touchpoints. HubSpot, LinkedIn, SlideShare, and Facebook, among others, use JavaScript tracking code to enable you to follow people “from your content through to your website.”
Pawan Deshpande describes the reason that marketing attribution is integral to the success of content marketing:
“The vast majority of touchpoints (instances where potential customers interact with your brand) happen online. They occur when someone reads your blog post, e-book, or infographic, or watches your video. Marketing attribution models enable content marketers to more accurately understand how their content is influencing buyers, and to get full credit for their work.”
Getting Started
Typically, although this is not always the case, Data Scientists hold Graduate Degrees in Engineering, Mathematics or in a similar field. Many hold a Graduate Degree, and there are of course cases where experience is sufficient to be a good data scientist.
If you don’t have that kind of data science expertise don’t worry. Possessing knowledge of what is involved is enough to understand the potential of incorporating data science into marketing.
Here are three ways to get started:
#1 — Understand What Numbers Matter to your C-suite
The language of business is the language of numbers.
If you really want to get executives on your side you must be able to justify how you are going to get a good Return on Investment.
Here are two terms that all marketers should now be familiar with:
ROMI (return on marketing investment): ROI technically applies only when a business asset is sold. See this online ROMI calculator. Talk in terms of ROMI any time you have costs and sales that you can tie directly to a marketing campaign.
ROMI = incremental net profit (additional income minus additional costs) ÷ cost of campaign x 100%
CLTV (customer lifetime value): indicates how much a client or customer is worth to your company over his or her life. This number indicates the maximum amount your company should invest in acquiring each customer. Katrina shares this formula:
CLTV = average revenue per user x lifetime x gross margin (the ratio of total revenue to cost of goods sold — cost of providing services)
To come up with ROMI, CLTV, and similar numbers, engage a data scientist or calculate them for yourself. “It can get complicated,” Katrina says. “What I want to embed is a mindset change. We can all go into our jobs with a mindset of being accountable.”
#2 — Choose Ways to Measure Your Content Performance
When it comes to measuring content performance, Katrina proposes that marketers strive to create a multi-touch algorithmic attribution model as discussed above. And she assures you that this kind of model is within your reach even though it’s not easy to decide which content-related numbers to track.
She also touches on UTM parameters, which are components (codes or tags) you build into a URL that appear in a long string after a question mark. UTM parameters enable you to track user behavior. They give you a way to see how many visitors come to one page from another page — be it one of your own pages or a page on a third-party site you’ve provided the link for. When your link is clicked, the parameters are sent to Google Analytics.
In devising your metrics, the main thing to consider is what you’ll want to do with the results.
#3 — Hire a Data Scientist or Explore a Predictive-Modeling SaaS Platform
If you can hire a data scientist then do so.
If this is not an option then explore a SaaS platform that supports Predictive Modelling.
These platforms act as a data scientist in a box — data science as a service. Optimove, for example, is a Predictive Modelling application that works by integrating with an existing client data base, CRM and/or sending application.
Here are some of other companies in this space:
Conclusion
CMWorld presenter Alicianne Rand essentially summed up this message when she said in her own talk (How the Estée Lauder Companies Drive Sales Through Content Marketing):
“All the best modern marketers I know know how to look at data. They don’t need to be data scientists but they know how to ask the right questions.”
Here’s an excerpt from Katrina’s talk:
In what ways do you think like a data scientist? Let us know in a comment.
Originally published at www.affexpro.com on January 4, 2018.
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