Category: Marketing

Previously we discussed the not-so-subtle relationship between branding and sales. Now we’ll see 3 ways how data analysis serves marketers, namely plugging their spendings, achieving precise segmentation and targeting, and assisting in creating personalized offerings for customers.

We also propose integrating your datasets into one standardized, consolidated dataset that helps you conduct data analysis much faster and better than before. We’ll discuss ‘how’ later in this blog, under the heading ‘How to consolidate the Data Analysis process’.

Let’s begin:

How Data Analysis Helps Marketers: 

1. Plugging Spendings

Marketers previously had little idea where they spent their budget. They just did so on the basis of their experience and intuition, with little data to back it up. If the campaign failed, the corrective course of action too was based on assumptions, experience and intuition, since the manager of the past 

1. Did not have access to the data points he needed
2. Could not / would not utilize technology to analyze the right data points

Hold on, are we describing the marketers of today?

Unfortunately, we are partially doing so. This is because while access to the data points is no longer as big an issue, the inability or unwillingness to analyze the data to make data-backed decisions is still something prevalent amongst marketers. One of the primary reasons found is along the lines of ‘Oh, marketing is not an exact science, so we cannot treat it as such. Marketing is equally an art as it is a science’. And while the creative element will always remain, the fact is that marketing is increasingly turning into a science. The best marketers no longer rely on just themselves; they let the data influence their decisions. 

Data analysis helps marketers know where they are spending their dollar, so they can avoid ineffective spending. The need to spend budgets to test hypotheses that may prove to be baseless is eliminated. Additionally, marketers can, within the same budget, conduct A/B Testing and undertake more campaigns than before to increase their reach, awareness and revenue. 

Mapping out and optimizing the logistical costs of making products available in stores is done by modeling KPIs in Excel. Similarly, data availability and analysis drive budget allocation, for example, allocation for LinkedIn advertising

2. Helping Achieve Precise Segmentation & Targeting

The rise of omnichannel marketing is a clear indication that customers are more distributed than ever. The opportunities for reaching the target customer have increased, but so has the difficulty. Not to mention the competition. 

The fundamental questions of ‘who are we selling to’ and ‘how are we going to approach them’ are answerable only when the right data is present at the right time. Marketers have to use data to answer questions such as

1. Who is our target audience?
2. Where does our target audience lie / what channels do they consistently use/are exposed to? How many channels do we want to be present on?
3. Based on our product, budget, and other influential factors such as logistical reach and size of workforce, what type of targeting should we adopt? Mass targeting, differentiated, niche, or microtargeting being some options
4. What is the competition doing in the market? What parameters does he work on?

3. Helping Provide Personalized Offerings

Precise targeting also helps marketers create a database over time that they can use as a reference for providing personalized products, services and experiences to the customer. 

With consistent maintenance and upgradation, this database can serve as a pivotal point where the customer prefers your brand and your brand only. 

There has been ample research showing that customers are more satisfied, more likely to complete a purchase, and even willing to pay more when offered a personalized product/service as compared to a non-personalized one. The trend is only growing with Generation Z, who are more willing to share their personal details to receive personalized recommendations in return.

Leveraging both big and small data to achieve this is the way to create a lasting and authentic bond with customers.

How To Consolidate The Data Analysis Process

Our proposal is very simple: gather all of your present and incoming data under a single platform – Explorazor. Conduct queries on the integrated, standardized dataset, and you will receive instant data pivots. It’s a no hassle process that accelerates data analysis, empowering marketers with high-quality information and leaving them with so much more time on their hands to do stuff more productive than extracting insights from multiple datasets stored in different files. There’s so much more to it, like downloading of required data pivots as CSV files if needed, ability to drill-through and drill-across any metric of interest, customization options, and other features built especially for Brand & Sales Teams and Managers.

We encourage marketers to do more with their marketing strategies and campaigns by leaning more on data analysis than intuition and experience, and we encourage using Explorazor to expedite the process of extracting data pivots from data. 

Take an Interactive Product Tour of Explorazor today!