Every marketer wishes they knew more about their clients. Data analytics in marketing is no longer just an added value or competitive advantage. It is becoming a do-or-die thing for all B2C companies in all of their marketing efforts. What are the current trends in monetizing big data?
by Beata Socha
Online advertising spending has been growing for years. In Poland it increased by 7.7 percent y/y in the first six months of 2017, reaching PLN 1.858 billion, according to a study conducted by PwC for industry association IAB Polska. “You can clearly see that online advertising spending has been growing the quickest in video, social media and classifieds. Ads sold in the Programmatic model are also recording strong growth,” Włodzimierz Schmidt, CEO of IAB Polska told PAP.
The industries with the highest share in online advertising market were retail (18 percent), automotive (11 percent), telecoms (8 percent), finance (7 percent) and foodstuffs (7 percent). “The list of industries with the highest online ad budgets doesn’t change much. There are no huge moves. It’s more about constant trends, and changes are caused by the seasonal nature of marketing and sales activity,” Schmidt added.
What is changing, however, is how the effectiveness of online ads will be assessed. Recently, IAB Polska introduced a new viewability standard for internet ads, where “an ad is considered as viewed if at least 50 percent of its pixels are displayed for at least one second for a graphic ad and for two seconds for video ads,” Schmidt explained.
I know what you’re doing
Given how competitive the online ad market is becoming, it comes as no surprise that 81 percent of companies already declare that they employ user data in their marketing activities, according to a recent report titled “Data-driven marketing benchmarks for success” prepared by Ascend2 and Zoominfo. Big data marketing ranges from profiling clients in online advertising, through direct marketing and sales calls, to the use of social media in marketing. All of these activities are aimed at driving up revenue, and all of them need to be used wisely.
There is no doubt that companies already have tons of data at their disposal. The abundance of information is one thing, but learning how to put all the pieces of the puzzle together is what really matters. “The key to success is not only an analysis of relevant data sets, but above all, an appropriate combination of its own data with external data,” said Paweł Mazurek, Board Member at TogetherData. Collating data from multiple sources allows companies to implement new marketing strategies, analyze customers’ behavior and find out what their interests, preferences and purchase intentions are.
“We’ve noticed that companies are beginning to invest in their own Data Management Platforms (DMP). These platforms allow them to integrate data from multiple sources – websites, media activities, CRM, sales systems. A lot of the data from these sources can also be used in advertising,” Mazurek said. According to IAB Europe’s “The economic value of data-driven marketing” report, 86 percent of behavioral data is already used in programmatic advertising.
Call centers are another example of where behavioral data analytics can become a huge help. Who isn’t annoyed by cold calls that catch you in the middle of a meeting or when you’re trying to write an important and compelling email. But there are ways to know if your potential client has the time to take your call. You can check when they are idle or looking at funny memes websites. That’s when you call them. This simple trick managed to get the response rate up by 8 percentage points for the call center in question. If your call center agent makes, say, 200 calls a day, it could generate 16 more “leads” every day.
Likes, clicks, and ambassadors
Social media undoubtedly is a cornucopia of information for marketers. Take Facebook for instance, where every minute users post 510,000 comments, update their statuses 293,000 times, and upload 136,000 photos (source: The Social Skinny). It comes as no surprise that Facebook users alone generate some 100 TB of data each day. Twitter users send 6,000 tweets every minute (data for 2016), which is still less than a half of the traffic recorded in the service’s peak month of August 2014.
According to different studies, 42-50 percent of marketers already considered social media critical or very important to their business. This means that social media is definitely crowded – you can bet that most of your competitors are already there. Marketers are therefore being taught not to overload their social media with content but to use them wisely. For instance, Facebook sees the most traffic mid-week between 1 and 3 pm, according to Bit.ly blog, but posts published later in the day, at around 7 pm, usually garner more clicks. It’s a trade-off between reaching a wider audience and getting through to fewer people but keeping them more engaged.
This information alone can be useful for marketers but making your posts stand out from the crowd of other content is becoming a real challenge. And companies do want to get their money’s worth. Imagine you are about to publish a post about your latest winter collection. Based on your previous track record you know that posts published around 6 pm have been the most successful at getting clicks. You also know that the most successful posts were those that contained between four and six short sentences. There, you have a very general recipe. But if you want to tailor your social media marketing even more, you can go one step further and use predictive analytics to foresee how successful your social media communication will be before it is even posted.
“You can predict which words make your posts more visible, for instance. We also check for sentence length and time when the post is published,” said Łukasz Trębicki, head of Social Media Analytics at Valkea, which recently launched its new online analytics tool called Viewell. Big data algorithms can also help you identify trendsetters among your target group: people who are followed the most by your target client base and whose likes generate the most traffic. “You could approach such individuals and make them ambassadors of your brand, for instance,” said Trębicki.
Whether you sell shoes, cars or financial services, if you know your client’s daily routine, his or her favorite pastimes and favorite websites, you can definitely reach them more effectively and increase your visibility. The pressure to increase efficiency of your marketing efforts, be it online ads, sales calls or social media activity, will almost definitely continue to grow in the future.
How much data is big data?
One of the most commonly used definitions of big data encompasses anything that is calculated in petabytes. According to McKinsey, big data refers to datasets that exceed the capacity of standard data storage, analysis and management. In addition to that, IBM also emphasizes that big data requires that the data come from diverse sources and be generated in real-time (or close to real-time).
Scouring the web
100 milliseconds is not a lot of time, yet that is how long an auction for an advertising spot takes. With Big Data analytics, advertisers no longer have to choose randomly which spots to bid for. And as many examples show, intuition can be flawed: if you want to sell men’s deodorant, don’t advertise to a sports fan but to his wife. WBJ sat down with Piotr Prajsnar, CEO of Cloud Technologies, to talk about the real life applications and the surprising effects of BigData in advertising… Read more