The concept of “big data” is already 20 years old, and until now it was defined mainly by size and computational problems, with less focus on the opportunities it creates.
The amount of data in the world is growing exponentially, and most of this is because every Internet user, both human and non-human (IoT) is a data creator, willingly or unaware.
Most of this will never be used or looked at again, yet the trend is to store it…just in case.
A report by IBM released in late 2016, shows that 90% of existing data at that moment was at most two years old. Most of it comes from YouTube videos, images, Tweets, Google search queries, and e-mails.
Add the metadata attached to the human-created content, throw in the data created by wearables, beacons, and sensors and you have a more complete the picture.
The real question is: “How can a marketer use all these bits and pieces to drive growth for their business?”
How Is Big Data Different?
The best characterization of big data is the 5Vs: volume, velocity, variety, veracity, and value.
The difference between traditional data and big data is not just size, as the structure also plays a significant role in assessing the dissimilarities.
Big data includes a small part of structured data, carefully organized in tables that could be analyzed using conventional techniques, but the largest part is unstructured or semi-structured.
The unstructured part includes images, comments, and reviews while labels, tags, and keywords define the semi-structured portion.
Big data allows marketers to be accurate and address the habits of the consumer – all in real time.
It is a paradigm shift from the traditional approach of marketing, which used historical data to compute general trends.
Enter the era of the long tail marketing, where niche products are becoming more important than generic products.
Applications of Big Data in Marketing
Personalized Consumer Experiences
Consumers are becoming more connected and distracted at the same time. Companies are competing for a bit of their attention and are adapting the traditional sales techniques to the online environment.
Using the name and details of the person is a way to make them feel important and stop for a while, just enough to hear your pitch.
Customization should go far beyond this and provide relevant options, taking into consideration the previous actions of the consumer such as items they searched for, goods added to the basket but not purchased, or even comments on social media.
Users create data which powers real-time marketing by allowing cookies in the browser, checking in at various locations through social media or just having their location tracker activated.
Clients welcome on the spot marketing opportunities, although these are intrusive, if they come as coupons or discounts.
Providing guidance or responses to unsatisfied customers is another way of taking advantage of real-time interaction. Adjust your suggestions based on their search keywords.
Simply put, this means to be in the right place, at the right time, ready to make the customer an offer he or she can’t refuse.
Suggest alternative or complementary products to their choice, welcome first-time visitors and greet returning ones while thanking them for their loyalty.
Add a free delivery option when the value of the goods from the basket is approaching a threshold. Make the customer spend as much time as possible on your site due to engagement.
Offer personal support and call center alternatives for clients struggling to find what they need. Take the relationship off-line with a call or an appointment in a brick and mortar store, if possible.
Optimising Price Strategies
The online environment offers the opportunity to compare prices instantly. Keep triggers on the competition’s price strategy, show variation graphs to customers and enable them to set price alert targets.
These alarms give a valuable insight into the amount they are ready to spend on the product – most of all, they help you make a sale before a competitor does.
Consider retargeting an interested customer when the price has dropped to a value close to their alarm.
If a client searches for something that is not on offer – although it would make sense for it to be – use this information to fill in your stocks or create the service.
List your products and think about the niches that could benefit from each of them, and are not currently engaged.
Design campaigns to target these segments and analyze results. Use big data tools to evaluate your competitor’s moves, including AdWords purchasing or keywords considered for ranking.
Reducing Costs & Increasing Revenue
Big data allows you to do more with fewer resources. Use the insights provided by the analysis to identify your 80/20 rule (which small niche is driving your growth) and invest in that.
Companies can save on publicity costs by engaging only with interested prospects. Optimize delivery by being present on the right channels, at the right times for your audience.
Learn from Amazon and think about the lifetime value of a client, not only short-term gains.
Big data helps you build a personal relation to each customer, in a way that is similar to how friendships develop; don’t let that go to waste.
Data and tests are a perfect match, and big data is bringing the game to a whole new level. You can learn about what your customers like, peek into online trends to see what is hot, and measure the shareability of your content.
It is possible to use big data to evaluate the potential of a thing to go viral.
Additionally, perform tests to optimize your landing pages and identify the specific problems of your website by looking at behaviors and bounce rates.
It’s best to make a map of your customer’s interactions with your site and redesign it to guide them to a buy.
Create different versions of an ad and let the automated algorithms pick the best version.
Challenges of Big Data for Marketing
Their lack of structure makes Excel sheets, and SQL commands powerless in front of big data.
The variety of the unstructured data and the presence of natural language are serious challenges to overcome and usually require dedicated solutions. Big data consulting services use clustering techniques, including fuzzy clusters methods to help marketers understand and define consumer groups.
This approach is closer to reality and further away from the traditional demographic/sociographic segmentations.
Although data is abundant, time and resources are limited and expensive. Therefore, the main problem of any marketer is selecting the most relevant items.
Business strategy should be the driving force behind the collection process. The marketing department should first define KPIs and attach proper measures to these to harness the power of big data.
Automation can help analyze data, but the machine needs clear instructions from humans. For each KPI, the data scientists will select a few data sets to be recorded.
Tests will reveal that only a handful of indicators should be kept in the dashboard in the long run.
Aggregating data in indicators or compound sets is a way of capturing more information while remaining with a small number of final results.
Privacy concerns are growing as big data is considered an invasion of one’s personal life by conservators. Identity theft, intrusiveness, and lack of control over personal data are all concerns of clients.
The personal data is necessary to power artificial intelligence algorithms and make them learn about users to provide dedicated responses. Anonymous and aggregated data do not pose any direct security threat to users.
The real problems and opportunity for crime arise when data can be traced back to a single user, revealing behavioral patterns and preferences that a hacker could use.
Big data does not lead to better marketing automatically, it is just another powerful tool. The real change remains in the hands of marketing strategists.
Their mission is harder in this new context since simple forecasting tools based on demographic and sociographic characteristics no longer work alone. The solution is to pair census results with robust algorithms to create user personas, not consumer groups.
The hardest part is not analyzing, but selecting the most relevant indicators for the job from the ocean of possibilities. It is a problem of quality versus quantity.
Not all organizations are mature enough to harness the power of big data, although almost all can recognize the commercial potential behind it.
Marketers should think backward to get the right steps in implementing big data. Start by defining the desired outcomes, study the clients’ behaviors that lead to such results and determine the triggers.
Finally, deliver the incentive at the most convenient moment by using the customer’s preferred online channel and cash on the results.