Tag Archives: digital marketing

advertising + context = information

The advertising age has just published their latest edition about big data and consumer shopping. In other words, companies, both online and offline, ramp up their data mining efforts to learn about consumer’s preferences, needs, wants and overall behaviour. Whilst marketers strive to improve behavioural targeting to reduce the invasiveness of advertising and increase the contextually meaningful placed information (advertising + context = information), it becomes clear that the privacy – vs. advertising data verge is continually growing.

Marketers need to understand, that consumers start to exercise their right for privacy stronger than in the past. Apps like Ghostery enable consumer to spot trackers online and with that gain some control over what they aim to disclose and what not. According to ghostery, knowledge + control = privacy, which is certainly well received in times of continuous NSA scandals and the flurry of big data discussions.

Marketers need to understand that with the increasing awareness of consumers about marketing or advertising tactics, contextually placed information becomes the corner stone to successfully engage with consumers throughout their customer journey. At the same time, one could think of an educational campaign to let consumers know about trackers, opt out possibilities and also the benefits of contextually placed information; e.g. to reduce advertising noise.

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Only 26% of marketers are able to measure their social engagement

According to a recent Social Media Examiner study about the social media usage of both B2B and B2C organisations, resulting in over 3000 respondents; the overarching majority of marketers (86%) claim an importance of social media to their business. Whilst this result comes as no surprise and seems to correlate with other surveys, it is interesting to note that a mere 26% of marketers claim a proficiency in measuring the social media engagement’s impact on their business. This numbers becomes even more staggering if the social media marketing experience is taking into consideration. 45% of responding marketers have been using social media tactics for 2 -5 years, 5% of this group even claims a social media experience of over 5 years.

With agencies pushing hard to have their clients engage in Social Media Campaigns (particularly in B2C), it is interesting that despite big data and ongoing digitalisation, the majority of organisations is still willing to invest money without being able to measure its ROI. To proof Henry Ford’s famous quote about misguided marketing spending wrong, it is inevitable to not only be brave enough to try new things (e.g. social media efforts are largely trial and error based) but to continuously work on defining bottom line relations to increase attributable marketing spendings to business results.

Some efforts to define the social media marketing ROI exist, such as MDG’s ROI of social media or the famous HBR blog about the calculation of a “like value” in Facebook but most efforts seem to lack consistency in measurement and a lack of bottom line integration. Engagement and conversation measures are being frequently reported and often lead to wild claims about social media campaign success with high reported engagement numbers, yet they too lack to correlate social media engagement to to bottom line results or business objectives. This is were the smart, engaged and eager marketer needs to start to employ digital metrics to track multivariate correlations and subsequently develop smart social media cause effect models.



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A thought on the revival of the Trojan Horse of advertising: advergaming…

Nostalgia has caught up with modern marketing:

Over the last years, with the steep increase in casual gaming, heavily enforced through mobile and tablet technology diffusion, companies have started to increase marketing spending on contextual marketing. One of the most valuable contextual marketing instruments, which however still struggles to find its way into many industries is advergaming.

At marketing conventions and conferences, many marketers seem to treat advergaming as a new concept, but fail to acknowledge the early work from marketers at McDonalds or at Coca-Cola in 1980 and 1983 respectively to create the first company sponsored game and the first advergame ever.


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Over the years, with increasing computing power, Commodore has further been a platform or early advergames, such as Painterboy in 1986 which lead players to paint a house for a Finnish paint company called Tikkurila.

Modern home and mobile computing enabled new immersive customer experiences:

Years later, Volkswagen, RedBull, General Mills or Frito-Lay (a PepsiCo. division) have come out with advergames based on visually stunning graphics or a very intense customer experience. Success rates speak for selected forms of advertising; Volkswagen has generated over 5 million visitors to its GTI project with players being on the site for an average of 8 minutes. Frito-Lay’s hotel626, a Dorito revival of 2 flavors has even elevated this number to 13 minutes on average per player. No other form of advertising allows a more intense customer immersion. Granted, production costs for these advergames are likely to exceed a basic CPC campaign but visitor numbers and duration times suggest a much lower cost on a customer level.

How much messaging is however healthy and sustainable in advergames?

To me, this is the key question to be answered and reflected upon. Pumping out an advergame is a relatively easy task with enough budget and thus advertising and coding partners to come up with experience rich or graphically rich games to spend (or “waste”) a few minutes with.

But marketers have to think about how deep their advertising message will be engrained into the game. One option is of course to sponsor a game as premiered by McDonalds in 1980. Hotel626 from Frito-Lay followed this early trend with a very subtle message placement for its more adult target group. Another option is to create a full-blown product and messaging centric game such as Honey Defender by General Mills or the GTI project by Volkswagen.

2 factors seem to stand out to decide upon level advertising message placement to the consumer:

1) Contextually translatable brand or product attributes

2) Advertising aversion of the target group

The first one determines how subtle and contextually meaningful brand attributes can be transformed into the gaming experience. The GTI Project for example did a great job utilizing Volkswagen notion of German engineering, paired with the nostalgic racing character of the Golf GTI to create its miniature racer. Looking at Volkswagen’s most likely target groups, prior Golf or GTI owners and thus brand affiliates, the message aversion of the target group can be likely considered as being low (in relation to the chosen medium). In contrast, how many adults would have played a Dorito game for 13 minutes in which you chase Doritos or create Doritos? Most likely not that many as contextual attribute translation and advertising aversion would have shown an inverse relation. General Mills Honey Defender however is faced with similar translation issues as the Dorito brand, despite it having a slightly easier stand to translate to honey and bees. The main point of contrast is however the target group and thus the target group’s message aversion. General Mills targets mainly kids with its game; who granted are not going to buy the sugary cereal but most likely nag their parents during the next shopping trip to get a new box to unlock in-game specials.

Below a first go to cluster some examples into a likely grid.

messaging in advergaming

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How to truly digitalise your digital marketing

Most marketing efforts these days, digital marketing efforts that is, are based on multichannel strategies, applying social media strategies to traditional marketing problems in a more or less planned fashion. What many companies miss so far is however to adapt their data mining efforts from pure web analytics to modern day digital intelligence frameworks.

As shown by Forrester Research, the evolution of digital intelligence has surpassed data mining efforts and moved on to close the gap between today’s multiple customer touch-points and the company’s intelligence frameworks.

Evolution of Digital Intelligence

Evolution of Digital Intelligence

Why haven’t companies moved on or benefited from Digital Intelligence frameworks?

For starters, digital intelligence surpasses web marketing efforts not only by complexity but also by the lack of reporting simplicity based on the sheer amount of data at one’s hand. Many marketing departments and agencies do lack the manpower to deal with this new flow of data in both quantity and quality.

The paradox of choice comes in second. Digital data is almost infinite and real time. A day however remains at 24 hours and the human capacity to deal with data complexity hasn’t changed either. Thus selecting data sets of importance is still an issue many marketers face to create meaningful data reports to drive – and that is the most important part – change!

Silo thinking prevails! Even if marketing has moved on and implemented the most efficient, tactical and real time digital intelligence framework ever seen by mankind, does that mean sales, R&D and the rest of the organisation’s top management are likely to change directions based on marketing’s new stream of data reportings? Unlikely!

The 6 steps to implementing a functioning Digital Intelligence framework:

1) Understand your companies business model and strategy! Data mining, intelligence and reports are great but only if they track and measure the right metrics to help intelligent management decision making to drive the company forward to reaching its set goals.

2) Define a set of relevant KPI’s! KPI’s are indicator’s for what has been defined as a state of success of advancement. If you end up with a list of 50 relevant KPI’s – think it over and start again. For many organisations anything from 5-10 is most likely enough at this level.

3) Derive measures to track your progress! Divide these measures into lead and lag measures to not shift from pure historical data mining effort to a framework to enable educated management decision making. E.g it is great to know that in the last quarter 7% of your customers have increased their mobile spendings but it might be to late to prepare your backend to handle the change in customer spent! The more meaningful lead measures, the more enjoyable lag reporting you will have.

4) Get other managers on board! This could well be step number 1 or even step 0 before you start any effort. Knowing key decision makers and influencers in your organisation will help to drive a high key implementation of a customer centric digital intelligence system. Product development needs to be as much on board as your sales teams, in-house support and other departments.

5) Built meaningful reporting and not an all in one dashboard! With all the world’s data at your hands, select data sets that are relevant to key decision making processes. This data will also help to get others on board and built up use cases. Less is more!

6) Revise your data handling efforts! Assure your data sets are flawless, protected and up to all legal data handling standards within your area of operation. The last thing you want is a disgruntled intern to blog your new most valuable assets for a few clicks, likes and big open eyes of your competition.






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