Unless you know someone who can buy upwards of a hundred grand everyday for a few months just to get prices up, the closest thing that we can do today is to repeatedly engage people to ask them to buy.
That is how advertising (and re-advertising) is done today. They are not the result of savvy ad-buyers picking up the phone and calling financial publishers, but instead is procured through advertising done programmatically, a process by which codes are punched to procure ads automatically. Ads are then served up based on a bidding system driven by algorithms.
Programmatic spending above TV ad spending
Despite being subjected to criticism of over Cambridge Analytica scandal, analysts say there is little sign so far that a boycott of a size to commercially hurt the social network will take place.
“The social media juggernaut shows no signs of slowing down commercially,” said Bill Fisher, UK senior analyst at eMarketer. “ It is difficult to know right now whether the privacy issue will have a fundamental impact on user numbers. Until we see significant numbers of users coming off we are not going to see any drop in ad revenues. Advertisers follow eyeballs and there are plenty of eyeballs on social media.”
Emarketer’s report predicts growth in social media advertising will continue to surge, despite wider issues of potential advertiser boycotts over measurement, transparency and content issues such as fake news dogging Facebook.
Growth in social media ad spend will rocket 40%, some £1.3bn, between 2018 and 2020 from £3.29bn to £4.59bn, the report predicts.
By 2020, social media advertising will have passed traditional TV advertising by about £500m – £4.59bn compared to £4.04bn, it forecast. However, TV broadcasters are likely to increase revenue from their online TV services in the next few years, figures which are not covered by the eMarketer report.
What is Programmatic Advertising on Social Media
Programmatic advertising on Social Media is done by gathering and analysing social media user data contained in browser cookies, which tracks a user’s engagements towards the Advertising Content specifically shown to him/her, including click-throughs, likes, shares, clicks to url in the content, viewing time, and ultimately purchases in the Stock. This data is analysed and matched to the hobbies, interests, geographical location, keywords, age and dozens more behavioural and demographical characteristics to create a digital profile of a person most likely to purchase a stock on-market. Users matching this profile are collected and grouped into larger sample sizes of 10,000 or more, to create a statistically relevant profile based on a smaller number of variables, such as day-parting, keywords, gender, age, geographic location, income, and so forth.
What can we do with this grouped data?
Marketers can create algorithms that automatically purchase ads across Social Media channels targeting these variables, instead of having to pay for a specific number of ads, as was the practice with traditional ads. While a campaign is ongoing, these algorithms can evaluate what is working best, in terms of geographic segmentation and audience segments, to help marketers narrow their target so they are showing Ads to specifically smaller groups of audience.
For e.g, marketers can know from historical data that people in Ontario age 30-45 who like to watch the Marvel series are likely to click-through and buy into a stock.
However, attribution algorithms, which detects what color, image, size etc works, are more difficult to create and manage.
"In order to build strong targeting, you need to build a really strong attribution model because otherwise, you’re assigning incorrectly credit to stuff that really didn’t make a difference," says Jay Friedman, COO of Goodway Group, a nationwide firm that has worked on digital campaigns with Fortune 500 companies such as Subaru, McDonalds, and General Motors.
"Now if you have 10,000 people who have clicked-through, and the same 10,000 people have been clicking through to different company content thrown at them for a number of days consecutively, we can then cross-check with the stock-buying volumes and look at where the similarities of those 10,000 users are."
Retargeting is key in Programmatic Advertising
To have the same 10,000 people see a content over a number of days or weeks, we need to install tags and cookies on their computers or phones, so that these people get to see Ads published by us consecutively, just as though the company is speaking to them. For example, if a company has published an Ad to a group of audience segments informing them about their recent company announcement, the company can then explicitly send another Ad to the same people the next day to remind them that there will be more news coming up from their current exploration program.
Another example of a campaign is mergers and acquisitions, knowing how sensitive M&A related news and rumours are, a company can send an Ad to evidence that its trading at a discount against its peers and how the peers are already mergers and acquisition targets for Chinese companies (with verified links to source).
Finding out what makes people Click and Buy is easier with Programmatic Advertising
Publishings Ads directly to people who fits the company's investor profile is a direct way to engage, communicate and inform them of the company's investment highlights. Companies in the investment space can publish different versions of content that highlights the people, projects, financials and industry fundamentals.
Algos to increase attributions are difficult to create and manage as mentioned previously, but programmers can post a few forms of advertisements to the same group segments to find out what work and what doesn't. Algos will pick up the type of content that works (Fonts, Size, Explicitness of the content) and understand the exact type of banner advertisements that makes people tick and take notice.
By examining data contained in each cookie across a large set of users, it is possible to uncover other unrelated insights that can be used to inform another Ad Buy. For example, if a marketer had a set of data consisting of females who clicked-through and purchase an energy stock, perhaps a close, automated scan of these users would uncover that this segment over-represents in the purchase of say, a cannabis stock. By developing an algorithm that can quickly identify new data patterns, and then targeting new ads based on this data, marketers can make the most of any data in more ways than they have encountered.
By examining the data contained in each cookie across a large set of users, it is possible to uncover other, unrelated insights that can be then used to inform another ad buy. For example, if a marketer had a set of data consisting of females who purchased makeup and also frequented salons to get their hair done, perhaps a close, automated scan of these users would uncover that this segment over-represents in the purchase of, say, Snickers candy bars. By developing an algorithm that can quickly identify new data patterns, and then targeting new ads based on this data, marketers can make the most of any data they encounter.
Algorithms that co-relates to physical world
Algorithms are being used to do more than just serve up relevant ads online. Ori Stitelman, vice president of data science at Dstillery, a New York-based provider of programmatic advertising and creative services, notes a lot work is being done to link the virtual ad world with the physical world.
"More recently, we’re doing digital intelligence to understand what’s happening in the physical world," Stitelman says.
What Mr Stitelman is saying is that by using the data captured on a social media channel like Facebook, and "cross-walking" that data with their browsing data from online stock brokers or another financial publications, Marketers can now glean more information on investor preferences and buying activities on the stock markets, leading to more relevant and better-targeted ads in both the digital and physical worlds.
Marketers are the ones deciding the type, extent of algorithmic modeling to use.
Investor Relations Officers and C-levels at Public Listed companies tend to think of anyone who has ever bought their company stock as their shareholder, and they will grasp any opportunity to put a message in front of an existing shareholder.
Hence Ad frequency is another issue that should be addressed by marketers, since there is technology available to manage the process.
At what point is showing another ad to a user not worth is anymore?
Our algorithm can just cuts off users once they've hit a certain amount, and we see that the stock volumes have not changed, because we've deemed it not worth it anymore. We have to be careful that if people have been shown the ad 10 times and they still don't buy then they probably never will. It's beneficial for the marketer and user to mind the frequency.
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