I write about Tech, Cyber and Marketing. Not in the exact order.
There is a ton of data points generated from each of your business activities today. A simple email blast to a few thousand recipients generates data pertaining to the open rates, click-through rates and conversion. These data points can further be distilled to infer specific information about the audience demographics that find your message appealing, the subject lines that trigger the user to open your emails, the CTAs that work, and so on.
Such data analysis can be performed for every component of business activity and all of these campaigns can be perfected with the help of data science.
Structuring of data
Metrics like CTR or Conversion rate only tell part of the story. One of the major challenges that marketers face while deriving marketing insights is the structure of the data they have available. Imagine a B2B transaction where the seller pitches their product with the help of PowerPoint presentations, word documents and PDF files. Now, it is not always possible to hypothesize the impact of a specific document on the buyer. Interpreting such unstructured pieces of information while making a marketing decision can be challenging.
The structuring of data in such cases can be handled in two ways. One way to do this would be extract pieces of data from your unstructured document and use these data points to create a structured data pool. This is useful in cases where your organization shares custom brochures with your prospective clients and you want to know if the pricing details or case studies included in the document contribute to a buying decision.
This does not work if you want to assess the emotional response of a prospect to a document that you shared with them. In such cases, you may make use of surveys and questionnaires at every stage to assess the recipient’s emotional response. Marketers typically use tools like the Likert scale for this purpose. Such tools help translate an intangible metric like emotional response to a structured data point that marketers may use for their analysis.
A deeper, more insightful way to structure data can be through data science tools like NLP and machine learning. This is typically done with the help of ETL tools that help business users transform data into meaningful reports for their business. You may, for instance, study the tone and tenor of an email subject line or your ad copy to analyze conversion rates depending on what kind of emotion this triggers in your target user. Your readers are perhaps more likely to convert if your pitch hinges on creating fear and anxiety (“Don’t get stranded in the desert without our camping equipment”) as opposed to triggering a sense of thrill and excitement (“Explore the wilderness with our camping tools”).
Improving marketing ROI
Any marketing strategy — organic or paid — costs a business money, time and operational resources. Marketers pick strategies based on the resource that is most abundantly available. A startup that is flush with VC funding is likely to invest heavily in PPC advertising since money is not a constraint. On the other hand, a bootstrapped business might invest in SEO, content marketing and cold emails that do not cost a lot of money, but can consume a significantly large amount of time to deliver results. A realistic marketing ROI pegs a monetary value for every hour and resource consumed as well as accounts for the opportunity costs that arise from the use of techniques like SEO that take time in delivering results.
Data science can be of terrific help in maximizing these marketing investments. Let’s take the example of a social media marketing project — more specifically Instagram marketing. A successful campaign here involves a lot of manual and mundane tasks like joining relevant Instagram groups, reposting others’ content, adding hashtags to content, following people who like competing pages, offering giveaways, and so on.
While tools like HootSuite and Buffer can help you automate some of these tasks, they may not be adequate in creating a strategy. Data analytics can help a marketer analyze each of the relevant Instagram groups, hashtags and users to come up with the right groups to target, the right hashtags to deploy and the right people to follow from competing pages. This way, the returns from your campaigns are dramatically improved and this helps you optimize your budget for more value-adding tasks.
Such forms of data analytics can also enable marketers to pick the right subject lines of ad copies to use for their multivariate campaigns and this improves marketing turnaround.
Traditionally, marketing success has been pinned on a marketer’s unique ability to come up with ‘out of the box’ ideas. However, data science has shown that any campaign could be made successful if the right data is used and is analyzed in the right way. While automation transformed how marketing is done in the past decade, the future will be defined by how data science shapes it.