It’s no surprise that big data is expected to have a sizeable impact across many industries.
In healthcare, big data will allow for earlier detection of diseases and more accurate association of risk factors. Big data and advanced analytics in the financial services industry will lead to a faster detection of fraudulent activity.
But one industry, in particular, will apply big data in a variety of ways to gain deeper insight about its customers. That industry is marketing.
Quick Rundown of Big Data
If you’re not caught up on big data, here are a few quick things you should know.
With the digital universe expected to reach 163 trillion gigabytes by 2025, according to IDC and Seagate, big data is expanding faster than we can keep up with. But what exactly is big data?
Big data is essentially all forms of structured, unstructured, and semi-structured data. In simpler terms, it’s every type of data generated by both humans and machines – and the speed at which it is generated.
This means every social media post, text message, email, video, and much more is a piece of the big data puzzle.
You can understand why big data is seen as valuable for businesses everywhere, especially in marketing.
Marketing and Big Data
Marketing is about finding new ways to introduce prospective customers to your product or service. It’s about nurturing leads along the funnel, ultimately converting them into lasting customers. Finally, marketing helps increase brand awareness through events, demos, content, social media, and more.
In today’s data-driven business landscape, it’d be very difficult to lead a successful marketing strategy without the use of data. Why is this? Data assists marketers in personalizing content and targeting the right audience. This, in turn, should lead to higher engagement rates with more budget in-tact.
The data most commonly used for a marketing strategy includes demographic data like gender, age, race, location, profession, estimated income, etc.
Prospective customers can also be targeted based on previous engagements with your brand. Marketers are then able to make informed decisions based on the collection of this data.
This is why marketing and big data are uniquely linked. With more data at a marketer’s disposal, they’ll get an even clearer picture of customers, their intent and brand sentiment, and have hyper-calculated next steps through the use of big data analytics.
The Big Data Market
Roughly 77 per cent of businesses consider big data analytics a priority, according to a survey by QuinStreet, Inc. Many businesses have, in some way, devised a plan to adopt big data soon.
The widespread acceptance of big data is why Wikibon estimates $92 billion in revenue is expected to be generated from it by 2026 – and this number could easily rise as big data software becomes more accessible.
For those wondering, “Wow, that’s a lot of money tied to big data,” let’s look at some of the ways it’ll be applied in regards to marketing to help make sense of its value.
- Targeted Marketing Campaigns
One of the most common ways marketers target their campaigns today is through the creation of customer profiles and buyer personas. These profiles are curated through the data we mentioned before.While factors like age, gender, customer location, and others are fairly good at building a targeted audience, other factors can certainly bolster your marketing campaign. This is where big data comes in.Big data, more specifically big data analytics, can harness massive data sets not just within a business, but outside one as well.
For example, valuable data can be acquired from social media, search engine history, and customer reviews on external websites. What’s the purpose of this data? It gives us a clearer view of customer behaviour and swaying trends.
Advertising firm 360i put big data into action to target its advertisement during the 2013 Super Bowl. When a power failure occurred and temporarily halted the game, 360i ran a “Power Out? No Problem” advertisement within minutes.
By analyzing behaviour across various channels, Oreo was able to boost its social capital immediately – making them the brand-winners of Super Bowl 47. The above advertisement resulted in over 10,000 retweets and 20,000 likes on Facebook.
This wasn’t just a targeted marketing campaign, but a timely one as well.
- Customer Predictions and PersonalizationOne of the most attractive features of big data analytics is its ability to generate extremely complex predictive analyses through the use of AI and machine learning.What this means in layman’s terms is that your customers’ next step can be predicted based on massive amounts of data fed through an artificial neural network. The results are then visualized for business end-users and decision-makers.A brand that we’re probably all aware of, Netflix, applies predictive analytics to learn more about its customers’ viewing tendencies. Here’s how.
Netflix currently has 130 million subscribers globally – so you can imagine how much data Netflix has at its disposal.
Some of the data Netflix sifts through is:
- When a viewer pauses or leaves a show, and if they ever come back to it.
- The time and date a viewer is likely to watch a show.
- Browsing and scrolling behaviour, and how long a user views a show’s trailer.
- Which device someone commonly uses to watch a show.
- Ratings, search history, and behaviour after a show has ended.
Running this data through advanced analytic programs, Netflix personalizes content for each subscriber in a way that is likely to keep their accounts open. Predictive intelligence helps marketers stay one step ahead of their customers.
- Tracking and Boosting ROI
You’re only as good as the information you have, and in marketing, having access to the right numbers and information is key to keeping track of and boosting ROI.
Since advanced analytics harness big data, this means your results will be much more robust and allow for a greater overview of ROI. Let’s look at an example of this in action.In 2004, data analysts at Walmart mined transaction and inventory data and found a unique link that could boost ROI. What was this link? They discovered there were a seven times increase in strawberry Pop-Tart sales right before hurricanes would reach land. Beer sales had also sharply spiked upward.
With this information in hand, Walmart was sure to stock up. Big data and advanced analytics helped Walmart unveil a new product opportunity by utilizing internal and external data. What may have looked like a simple tactic on the surface was actually possible thanks to the application of big data.
It’s clear that big data is highly valuable to businesses, however, it does pose a few challenges for those looking to adopt it in the near future.
Big Data Challenges
One of the most prominent big data challenges stems from the ways we find value out of the three types of data.
Most businesses today are making decisions based on the structured data they possess. This is essentially any data that can be easily quantified – like age, email addresses, most recent purchases, spending amounts, and more. It is highly organized and structured in relational databases.
Structured data, however, accounts for just a portion of all data that is generated.
Unstructured and semi-structured data account for more than 80 per cent of big data. This data is seemingly random in nature, and cannot be collected, processed, and analyzed in conventional ways.
Harnessing unstructured and semi-structured data is currently time-consuming and expensive. Thus, big data isn’t the easiest entry point for many businesses – which is why you really only hear of enterprise-level organizations utilizing it today.
Accessible Big Data
Another challenge is visualizing and optimizing data for business end-users. No matter how much big data is analyzed, it can’t be put to good use until decision-makers within a company know what they’re looking at.
Big data can be downright overwhelming. Those who aren’t technically inclined will require the help of data analysts and scientists to make sense of the results. This is why data science is one of the fastest growing fields of study today – with an estimated 2.72 million jobs expected to be posted by 2020, according to IBM’s Quant Crunch.
Silencing the Noise around Big Data
Believe it or not, not all data is actually valuable to a businesses’ bottom line. This is what is referred to as “noise.”
As the digital universe continues to expand, it’ll become more difficult for businesses to decipher which data is relevant and which data is noise. Again, this is why there is a high demand for data science professionals.
Organizations that make it a priority to discover and analyze relevant data could generate an extra $430 billion in productivity by 2020, IDC estimates. Without a strategy to distinguish the good data from the bad, that could lead to some serious productivity losses.
Once big data becomes more accessible for smaller and mid-market businesses, you can expect marketers everywhere to get extremely creative.
Ads will be hyper-targeted, content will be curated in a way that’s proactive instead of reactive, and marketers will get a better understanding of what customers actually want – instead of what we think they want.
Big data, essentially, will lead to more customer-centric and refined marketing efforts in the future. This doesn’t just benefit businesses by helping them understand their customers on a deeper level but opens up customers to new product opportunities.