Marketing is perhaps the original use-case for big data. The idea came about when companies like Google and Facebook, who offered free services, had to figure out how to monetize their products.
They could have followed the traditional advertising model, based on very crude (compared to what’s possible today) techniques, allowed marketers to pay to put the same ad in front of everyone who fits a certain demographic. For example, just as fishing company might advertise its wares in Outdoorsman magazine, Google and Facebook could have gone the simple route, and charged advertisers x amount of money to show one specific ad to everyone who visits Outdoorsman.com.
But, as the founders of these and similar companies realized, the sheer amount of personal data they had about their users meant so much more complexity, to say nothing of efficacy, was possible. This gave birth to the field of consumer data analytics that we know today.
As a result, the marketing techniques employed by firms today are so much more sophisticated, and this is all made possible by big data. So advanced are today’s user metrics, there have even been instances of intelligence agencies using targeted ad campaigns to identify a specific person’s location.
The exact type and sophistication level of big data analysis tools and techniques you need will vary according your particular business niche and the scale of your organization (a company that needs customer data analysis might not also need IoT industrial systems monitoring), but with so many types of tools out there, many of which can be used for free, the fact of the matter is that you can’t afford not to be using Big Data.
It’s no wonder then that according to Business Wire, all big data market segments together are poised to grow by USD 64.27 bn during 2020-2024, with a CAGR of over 30% during the forecast period.
Software for Aggregating Consumer Data
Data aggregation is the first step of any big data analysis process. It involves sourcing data from a variety of different sources, in order to paint a complete picture.
For example, when Facebook builds a persona to use in their marketing efforts, it builds a picture based off the users likes, interactions with other users, photo uploads, and other activity on Facebook. In addition, to the extent that the user has granted Facebook permission to track other interactions across the web, for example, via an app log in through the service or other cookie permissions, they integrate this information into the user profile as well.
However, most businesses don’t have the luxury of owning a globe-spanning social network that users use in all variety of transactions and interactions.
This means they often spend a lot of time manually creating spreadsheets based on data from different channels and platforms (eg. Wordpress, Adwords, etc.), and them combining them into one product on which analysis can subsequently be run. This is a meticulous and painstaking process.
Which is why third party providers have entered this space. Now organizations can save a lot of time, effort, and money, by automating data aggregation. Not only is it faster, the results are often better and more accurate than a team (especially one at a small company) would be able to get working alone.
Let’s take a look at some of the most popular options on the market.
Let’s review the example of defining complicated patterns in a collection of patient records. Each record has several discrete attributes with one target attribute among them. This target attribute has two values: abnormal for the patients with a disease or risk and non-abnormal for other records.
MX is a major player in the financial industry, having partnered with over 1800 institutions, including the top 43 banks in the US and Canada. Their mission statement: Connect. Enhance. Experience.
This last part is especially worth emphazing, in that data aggregation is not only valuable for marketers. It can also be used to take your user experience to the next level and beat out your competitors.
What? Didn’t you just spend the first part of this article trashing Excel and saying it was a waste of time?
Not exactly. You don’t want Excel to be the only tool that you use. But as part of a well defined pipeline and in conjunction with other tools on our list, especially those that leverage machine learning, it can be one of the most powerful tools at your disposal. Excel is the modern day abacus, deceptively simple, with a lot of capability.
“The proliferation of 3rd party data aggregation tools on the market is a sign of just how essential big data capabilities have become for any organization. They fill an important capability gap for companies without data analysts and enhance effectiveness of data teams that are already in place. However, no one piece of software is a replacement for understanding big data.s.”
— Vlad Medvedovsky, Founder and CEO at Proxet (ex - Rails Reactor), a software development services company
Cloudera for Hadoop
Cloudera is all about the elimination of silos. It’s advanced capabilities allow teams to access all the data they need from a single dashboard. On top of that, it’s SQL functionality allows for deep insight into the data provided.
MongoDB is a great choice for teams that don’t have strong SQL skills, as all of this system’s features can be used without it.
Sisense specializes in aggregating data and presenting it in a beautiful, clean dashboard. It’s visualisation abilities are a great choice for those who need to brief other teams and executives on their findings.
Zoho is another low-code/no-code data aggregator that empowers non-technical specialists to unlock the power of their data with aggregation. This one is especially useful in big data for marketing.
Big Data Customer Analytics
If you work with a lot of content, you can use big data to increase your customer acquisition and retention. The way this works is that people will only stay on your website if they find that the content their is interesting. If you have a big audience and market, they are likely to be interested in different things as well.
Big data can give you laser focused insight into the interest of your major customer segments. Once you’ve found them, you can produce content specifically tailored for each audiences’ needs, and if you have your cookies set up correctly, change how your website displays and what content it shows depending on who has visited the site.
This will ensure that your content speaks to your whole audience and keep them engaged. This strategy requires good customer analytics tools to work.
Online sellers have a lot of competition, and many also have more products than any one individual could possibly browse. This means it’s important to put the right product in front of the right customer, which is only possible if you’ve collected the data.
Another aspect in which ecommerce data analytics can be helpful is price optimization. For example, a price drop and exactly the right time can push somebody to buy. But knowing when that time is can be done only by aggregating lots of data sources.
Where legal, price optimization can also be used to get the best price that a specific individual will pay, based on their online behaviour. But this technique is illegal in some jurisdictions.
Big data can help you to determine if you are really getting your money’s worth out of your ad spend, and where the gaps in your campaigns might be.
Once you’ve obtained this information, you can then adjust your campaigns and spend accordingly, and get better results.
More Targeted Ads
If you find that web users aren’t clicking through your ads, you may not quite understand your personas as well as you could.
Big data allows marketers to gain insights to all aspects of their customers habits and lives, and develop more personalized ad content based on this information.
When you’ve understood your audience and start getting clicks on your ad, revenue will go up.
Data Services Companies
While data is essential to business success, implementing it can be tricky. Even with all the software options out there, to really make the most of your data you need to hire a team that includes data scientists, who don’t come cheaply.
For organizations without the resources to hire a team, or who feel that they don’t have the pre-existing expertise in house to evaluate potential hires, there is an alternative; hiring a data services company.
Partnering with a data services company is a great way to fill gaps in your organization’s capabilities without having to hire a team yourself. These companies bring their own technical acumen, sophisticated analysis tools and experienced professionals to bear on any data-related business problem you can throw at them. They know when to use big data, and when another tool would be better for the job.
At Proxet, we take great pride in helping our partners identify what data they need, and how they can best use insights gained from it to boost their business. If your organization has a data challenge it needs help solving, please get in touch.
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