Data analytics has helped businesses and organizations to generate more incremental revenues quickly by leveraging data. Data analysis comes in handy when it comes to identifying trends, interpreting customer behaviors and developing effective customer experiences for success that can be measured.
Analytics has the potential to provide objectives to marketing agencies and marketing companies to find the most impactful and cost-effective campaigns together. So, overall, we can say that data analytics has succeeded in providing business leaders with valuable insights, helping businesses innovate and deliver exceptional products, services, and experiences to both their customers and their employees.
There is no doubt that creativity and stunning visuals do trigger powerful emotions, but at times they fail to quantify the monetary value of marketing & advertising. At the same time, data analytics has the potential to deliver a systematic study of what works and what will not. It draws a clear line from campaigns to company profits.
Today, there exists one critical differentiator between success and failure – customer experience. But is great customer experience even possible with only innovative technology? There’s no doubt that technology is becoming faster, smarter, better – but to-date there’s no technology that is not powered by DATA. Today industries often talk about scaling up big data architecture, but none of them even think of scaling up data collection down to things they really need and understand.
Every time, everyone forgets that the most important aspect of data analysis is data collection. If that would not have been the case, why would marketers collect customer information for decades? Regardless of the field of study or preference for defining data (quantitative, qualitative), accurate data collection is essential for maintaining the integrity of analytics. Both the selection of appropriate data collection instruments (existing, modified, or newly developed) and experienced data collection experts reduce the likelihood of errors occurring.
- Data collection and analysis of collected data has ventured into avenues and methods of:
- Data collection and report preparation for acts, behavior or events for both personal and focus groups.
- Collecting economic, organizational, demographic & self-identity data for psychological scales and their kind.
- Data collection of personal feelings, opinions, and attitudes, both shallow & deeply held.
- Expert & cultural knowledge data collection.
- Personal and psychological traits followed with experience as it presents itself to consciousness.
- Data collection for analysis of hidden social patterns & detached observations.
- Analysis based on ethnography with help of collected data from public & private records.
- Collecting data facts, figures, and observations from in-depth interviews, surveys/questionnaires, phenomenological interviews & critical incident interviews.
Data collection is amplified by automation, cloud storage, and location independent collaboration tools. To the extent that electronic media has features to trace interactions, including opening emails, or looking at a specific post for a certain length of time, etc. As marketing continues to evolve, companies with advertising platforms such as Google and Facebook add more powerful features for analysis and projection. But all that they need to survive is data which only data collection solution experts are capable of doing as of now.
Next to come into the picture is the quality of data. Yes, quality of data is an aspect which cannot and should not be compromised; before, during, and after data collection. Preparation and adherence to data collection plans is more than important to ensure the data analytics team gets what they expect.
The way companies perform and the way they plan their strategies has changed tremendously in the last decade. This is to an extent that people in the business since last few years are defining their careers as BBD and ABD; Before Big Data and After Big Data.
As statisticians, economists, and developers collaborate, data analytics is set to provide organizations with powerful market forecasts, and mathematical modeling for studying statistical correlation between marketing campaigns and consumer behavior. But all this is likely to succeed if one and all understand the importance of data collection, data processing, and data storage.