Category Archives: e-commerce

What marketers can learn from 80 billion watched adult clips on one site

Pornhub, a streaming portal for porn has just recently publicised its 2014 site statistics. Despite the fact, that almost nobody on this planet would openly admit watching porn; almost 80 billion viewed clips (which statistically sums up to roughly 11 clips per human being on this planet) suggest something quite different. Before you run off now to delete your browser history, I am more amazed about what these statistics reveal about user behaviour overall and on a country level. On a side note, one has to give the Pornhub team credit for not only having established a major site in global porn consumption but for running the site and all its adjacent media on a high level of professionalism, e.g. the usage of social media, blogs and other features, that deliver funny, relevant and actually interesting content.

Here my key learnings:

1. Behaviour reveals the truth: when it comes to defining customer centric messaging, positioning strategies or product placements, observing displayed customer preferences through hard actions (no pun intended) allows marketers to not only assure high relevancy of marketing actions for the assumed target group but also an increase marketing effectiveness. Surveys, no matter how great, expensive and professional, do never reveal real underlying drivers. Digital data is thus of great help to penetrate the customers mind, understand root causes and determine action triggers. #Pornhub relevant fact: despite the steady global increase in online porn access, it is noteworthy to have a closer look at national key search terms. Despite the US, almost every country revealed a high national focus in search terms.

 2. Mobile is the king, yet privacy demands tablets: globally, particularly in less developed countries with holes in its telecommunication infrastructure, the spike in mobile usage demands an undeniable rethinking of media channels to target customers. Whilst the desktop driven Internet access was the preferred 24/7 channel a few years ago, it`s important to note how consumers change channels, screens and devices throughout the day. To reach consumers effectively, a continuous channel switch might be necessary to stay in the consumer`s mind and assure high message relevancy at the point of purchase. #Pornhub relevant fact: both mobile and tablets see an increase, which is inline with the overall global rise of mobile / tablet driven computing or media consumption. Yet, I hypothesis, that due to the nature of the consumed media (porn), tablets show a higher increase which is probably in line with their location of use (in-house), vs. on the go mobile media consumption. I am contrasting these statistics with the latest Comscore data on mobile marketing statistics.

3. Relevant content is king: Pornsites have probably the highest relevancy to its access groups, if one excludes personal preferences within the broader definition of porn, in the WWW. Why you ask? Well, they promise porn, deliver porn and people accessing them, are probably looking for… right: porn! The level of surprise is fairly low, the promise to the consumer is most likely kept and to top it off, most user probably leave the site with a positive experience. Marketers need to ask themselves, if broad content spanning sites which are created for multiple target groups can still be relevant in a generalist way. Investing in SEO and thus reducing bounce rates by increasing relevancy of the first point of contact might be one amongst many solutions. After all, one of the major access point to the web, particularly in the B2B environment are search engines. Instead of shoving traffic to your “about me” page or an index error message, linking to relevant content site on a search pattern basis is highly recommended. This becomes particularly interesting when investigating large B2B web profiles. #Pornhub relevant fact: user expectancy = site delivery = porn

4. Statistics are cool: Most companies I have yet had the chance to interact with base a lot of their actions on assumptions or the fallacy of experience. B2B, particularly industrials, are prone to assume that 20 years of industry experience make up for not having or not understanding the use of data. If it weren’t for privacy reasons, pornhub could probably go down on an IP basis to reveal all sorts of data. Thus recurring visits, duration between visits and average site duration, correlations with search terms and and and… which B2B business has access to that sort of data? Well, almost all, but data collection is still not seen as a top priority in many firms, particularly not if business practices involve a high percentage of relationship building tactics. Just ask about your web demographics and or connecting the dots from the web to your CRM… #Pornhub relevant fact: Pornhub serves not as a silo platform but links to full scale porn content on other pages, thus the more pornhub can learn about visitors, the more relevant the site can become, the higher its conversion rate for partners, the more $ it makes.

5. Glocalisation rules: We live in a globalised world, yet national interest, cultural heritage and other factors do still play a role in purchasing decision making. It is for a reason that supermarkets increasingly advertise local produce to consumers. B2B`s do yet again mostly discount this fact. Thus a costumer in China has to view the same or similar content to a customer in Europe or even North America. Although one can assume a more professional purchasing decision making process, underlying drivers, particularly non-rationals to make up for missing data in the decision making process, are being subconsciously influenced by cultural factors. #Pornhub relevant fact: Despite the US, all major countries by access numbers reveal a high level of national search terms.

6. Don`t scare customers with insights but be brave enough to make use of them: if you have continued to read this article after your cache cleanup, you will have probably noticed or thought about that Pornhub could have gone in much further detail on a country level, probably down to individual IP`s. Don`t worry, it would be a fatal error for Pornhub to go that far, as the key point of their statistical review is not to scare users away but to provide them and the general public with the confidence to access a highly professional site while having social affirmation. As the saying goes “a million flies can`t be wrong – eat shit”, social affirmation works in porn as well as in other business. Thus the key is not to tell your customer that you were surprised about his Sunday night reading habits of your email but to build necessary conclusions and work with statistical data to your advantage. Did you know, that 75% readers of this blog seem to have a very high level of education? Charming, ha? #Pornhub relevant fact: the use of both movie ranking and viewer statistics is a smart move to give other users an easy selection of good content based on social rating schemes. Every B2B should have enough data to highlight its no 1 product in 2014, Q1 or of all times – the only difference to Pornhub however is, that Pornhub is already working in porn, it has crossed the boundary of society, which I assume does also lead to quite some freedom for its marketing team in the content creation space. You won’t have that freedom in your company as product managers, sales mangers, general managers and the rest of your team will state their biased opinion, which are most likely entered around reducing risk.


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big is great but smart is better – a big data discussion

I have recently had the chance to listen to a presentation by Phil Winters at the CRM Expo in Stuttgart. Phil is an active advocate of big data theories and a very lively presenter (can only recommend to sit in if you have the chance). Anyway, one of Phil’s slides caught my attention particularly.  A slide about a very basic but yet, as it seems, mostly ignored principle about big data.


In other words, BIG DATA – NON-IDENTIFIABLE DATA SOURCES = SMART DATA. The grid used to present this is about as easy as it gets but holds all the value a smart marketer needs to step into the big data discussion.

1) Visualise your customers purchasing decision making process (or the funnel if you want)

2) Identify touchpoints (this alone is a great exercise for most marketers and even more for internal service providers – customer centricity is the key – not what you want)

3) Assess data availability per touchpoint (is data readily available, in which form, when, from whom etc)

4) Assess smart data options (can you make sense of the data or identify user groups or even single users out of a specific data set)

5) Identify the data creator (is it a customer, potential customer, noise etc)

6) Smart Data entry (can you make sense of underlying values, behaviours or motives – in other words, can you interpret the data gathered at this level)

More from Phil Winters here – enjoy the read and happy smart data mining (I am a big advocate of logical naming conventions and from that point of view, big data is a misleading term, we don’t need big data but smart data; think about it!)

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35% of offline researchers purchase offline – resulting in negative channel conversions

Multichannel marketing has been a buzz word for quite some while yet as it seems, for most stationary retailers, it still turns to the ugly side with negative channel conversions. According to a recent study by GFK and Accenture, about 35% of all online purchases made result in a prior stationary retail research (research offline, to take up Google) before a purchase is finally made online. With that, a stunning of 5.4 B EUR resulted in these negative channel conversions in 2009. In other words, 5.4 B EUR are most likely lost transactions for stationary retailers, as chances are that the online purchase is not done via their online shop (should they have one).

Think cross channel and not multichannel!

Cross channel marketing could be one solutions for retailers to look at. Instead of relying on multichannel marketing perspectives, which often result in channel centric marketing models and thus quite some linear conversion, cross-channel marketing aims to put the user in the centre of all action while using channels as supporting instruments to assist the user in the web 2.0 buying funnel. E.g. a retailer should not rely upon stationary offline (even offline rich media) initiatives but a dynamic channel conversion alongside the customers progression in the buying funnel. This is not a revolutionary idea but amazingly only a few large retailers have jumped upon that bandwagon.




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how much product choice is too much?

After having listened to Barry Schwartz TED talk (multiple times – if you haven’t CLICK HERE) as well as Dan Gilbert’s TED talk (if you haven’t – CLICK HERE) I couldn’t help it but observe how companies deal with the paradox of choice in a different manner.

From an economic perspective, increasing product choice, assumed costs can be controlled, should make a lot of sense. The economist trained mind thinks of course about a perfect world inhabited by an infinite number of homo oeconomicus, occupying an infinite number of price value points along the price curve, yet for one product class (perfect price discrimination). The marketer on the other hand, will join the discussion and argue that a finite number of heterogeneous target groups exist, displaying however homogeneous needs and wants (the basics of traditional segmentation). The mix of these two worlds is exemplified by Samsung; for the non-trained aspiring mobile phone customer, Samsung offers (at the time of writing) a staggering 145 different mobile phones. Note, this includes various carrier combinations. Switching over to TVs is even more confusing. One has to wonder, particularly after reading Schwarz’s books or listening to his talks, if brands are increasingly hurting themselves by increasing the number of product choices offered to consumers.

just a phone

According to Schwarz, increasing choice for humans lead to several negative effects but most of all a decrease in overall satisfaction which in itself sounds like a paradox, yet manifests itself in the following terms:

Opportunity cost of choice: the higher the number of options, the more attractive the 2nd, 3rd of nth option becomes to the consumer. In other words, with each option the value of opportunity costs increases until, in theory, it reaches a point of paralysed decision making.

Expectations increase: the more choice consumers’ perceive, the higher expectations become. The more unlikely however becomes, that set expectations will or can be met by the current product offering.

Doesn’t it also seem like a paradox to offer double digit product choices in one product category , assuming today’s stressed consumers have both the time and the drive to research product differences. Shouldn’t the smart marketer argue, that in the light of the ROPA effect (read here if you haven’t), diminishing cannibalisation of marketing efforts is hardly achieved with a complex and almost undistinguishable product portfolio? Is it still seen a sign of weakness in our society to reduce offering instead of enlarging the portfolio – after all, who wants to claim product offering declined under his or her reign?! Does it make sense to spread marketing budgets across +100 different product variances instead of focusing efforts to get one product message and positioning right? Wouldn’t it make much more sense to establish one dominant product design and then allow to establish alternative for price discrimination purposes instead of working the other way around: “let’s throw all we have and see what sticks the most – this will be our flagship product”?!

I argue that in the light of big data, marketers should increase their influence on the company’s product portfolio and not only emphasise on social listening post launch but also social rational pre launch.

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If the sky isn’t the limit anymore… what is?

Redbull’s Stratos campaign has set a new dimension of campaign and social engagement with consumers around the world. It also sparked discussion about the terms paid, owned, earned and shared media, however seldom has somebody questioned if the reach for the stars and current campaign scopes are still in line with resource theories of the firm. I argue they are not, excess capital holdings allow certain organizations to device campaign stunts, driven by eager agencies to score the next big thing, without establishing a full campaign to business objective link.

Watch somebody jump from space vs. going to space yourself (Axe Apollo):

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Questions that should be raised by marketers and budget holders:

> Is the race for the most extreme campaign to gain a healthy and sustainable race?

> Is it just too easy to spend money on paid media to get audience attention through extreme campaigns vs. meaningful content stimulation throughout the customer journey?

> Are campaigns like Stratos and Axe Apollo really justifiable beyond the hype of press and bloggers? Shouldn’t we as marketers not look beyond total reach and claim target audience reach in meaningful numbers? What is it that we achieve with campaigns? Can we create meaningful links towards the bottom line? I argue big media allows us to do so but at the same time introduces campaign limits.

> As most marketing organisations are setup as cost-centres, don’t we have an obligation towards stakeholders and the firm to justify our spending even more so these days?

> Are we just making use of aggregated fluffy terms like earned or shared media to hide behind walls of agency influence and ego stipulation to have the biggest campaign? Does size really matter in that sense?

Paid, earned, owned and shared isn’t what is seems like – we need to dig deeper:

If we spend $20m on a campaign and estimate to reach x number of people on the premise of paid media, y number of people on the premise of owned and z number of people on the premise of earned and zz number of people on the premise of shared; do we really measure what is meaningful to the brand, to the bottom line and to our budget responsibility? Shouldn’t we segregate reach into current customer reach and potential customer reach, furthermore increase in sustainable purchase effects and short term campaign spikes? Furthermore, I argue that smart data allows us to construct a media model which assigns values to each theoretical nod, we can start to differentiate between dead-end reach, multiplication reach and bottom line effective reach.

Dead-end reach: non current and non-potential customers, low network degrees

Multiplication reach: non-current or non-potential customer with a value generation network degree, e.g. one or more nods are either customers or potential customers

Bottom line effect reach: the total of current and potential customers reached through the conglomeration of paid, owned and shared. This number should be in a healthy relation to both substitute media spendings (e.g. $ effort) and also the total number of people reached. If we assume our target reach is equal to a sample population, the sample to total population ratio becomes a statistical ratio and the marketer’s task is to find the sweet spot instead of trying to cover the entire population to also reach the target group by intersection effects.

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6 reasons why… forget it, this is just a funny ad from Adobe

Adobe claims a top spot in online advertising with their funny Super Bowl commercial (see below) and well, their “You can measure Social Media ROI ad”. In times of smart data (I still refrain from using the word big data to specify the use of data for decision making advantages), this should come as no surprise, yet as it seems, business schools (as I currently experience the no 1 ranked business school in Europe), marketers and organisations still avoid the ROI discussion and shift to the topic of online brand building. I particularly like how Adobe uses current stereotypical personas to play in their ads – which in my opinion should put 85% of today’s marketers (see their Super Bowl ad), advertising agencies and “consultants” to shame.


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Adobe’s Super Bowl commercial… wondering who the “winner” is now:

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As always, if you made it this far, check out the Adobe blog for more – worth a visit.

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Algorithmic location modelling to enhance customer behaviour profiling for marketers


Location based services (LBS) or location based marketing (LBM) has seen an increase in adoption based on ongoing technology diffusion amongst consumers (e.g. GPS enabled smartphones  as well as service enablers such as Foursquare or Facebook places).

Gartner estimates about 800 Million active LBS users by the end of 2012, a figure to grow to 1.4 Billion by 2015, resulting in over $13.5 Billion LBS consumer induced spending. With overall mobile advertising to be expected to account for $20.6 Billion by 2015, consumer induced spending on LBS provides a strong indicator of future growth driven by navigation, social networks and location search. The increase of smartphone penetration in third world countries will most likely result in a 2nd growth phase for mobile marketing and with it, LBS.

Growth of LBS will be enabled by further mobile and and tablet adoption rates and thus placing location based targeting devices into the hand of consumers. LBS revenue is assumed to grow mainly on its main form of current revenue generation, advertising.

LBS development

While customer privacy remains an issue, current adoption rates of LBS suggests users starting to neglect privacy concerns and even access fully passive LBS services such as placeme. Current studies confirm this trend with 58% of consumers valuing benefits over privacy concerns.

Advantages of LBS / LBM for marketers:

> Enhanced listening features to gain access to consumer behavioural data
> Enhanced targeting features to create more relevant POS offerings
> Enhanced interaction features to elevate customer brand interaction with social, contextual and location relevant content
Emerging opportunities for higher level data mining:
> Behavioural mapping. This is something which should see strong developments. Similarly to the Google Page Rank, a location rank should gain access to algorithmic modelling of consumer journeys. E.g. with increasing data, patters are likely to emerge to attribute value to. A consumer coming from location a to location b might suddenly seem more valuable based on prior exhibited behaviour than a consumer from location c to location b.
Some examples:
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3 steps to evolve your marketing from content to contextual

Over the last 2 years, the term content marketing has not only been coined but received wide acceptance within marketing circles. Content marketing became the new mantra to engage with customers on a wide array on both digital and traditional levels. Current studies show (link to a Marketing Prof’s article on B2C content marketing trends), that content marketing still receives great attention and for the most part, rightly so. As most studies confirm, over 85% of both B2C and B2B marketers keep or even increase their content marketing efforts in 2013 based on previous years budget spending.

The content marketing matrix, shines some light on the level of content marketing management possibilities but also highlights, that a very generic customer profiling is assumed.

content marketing matrix

Why is content marketing however becoming complacent with an overflow of content from all sides to a single consumer?

> Consumers follow less traditional funnel concepts but rely on multiple sources and a more diffuse buying decision making behaviour (see ZMOT by Google for some inspiration)

> Technology enables consumers to not just for ROPO (research offline / purchase online) but currently for RMPO (research mobile / purchase online) and RMPM (research mobile / purchase mobile)

> Influence of content to consumers decreases with the increasing emphasise placed on social sharing and social recommendation (e.g. great content marketing but 2 out 5 star rating)

How can a marketer deal with these changes in consumer purchasing behaviour and the increase of technology as enabler for new purchase decision making? 

1) Utilise digital data: with digital media in place, enabling big data to become smart data is easier than ever. It is however important to differentiate between wanting to know everything and being able to distill what is really important. Don’t get overwhelmed by the flow of data but control it!

2) Enable customer journey thinking: smart data allows you to follow single customers (don’t think stalking) but to determine their need at any given time. A housewife in Massachusetts using an Android based Smartphone might follow a different decision making journey than a college freshmen in San Francisco using a laptop in a coffee chain. Customers don’t want to be spammed with content but receive the right content at the right time. Banner blindness is not a sign of too much content but non-contextual content – just because I searched for a fridge doesn’t mean I want to see fridge banners for the coming two weeks.Don’t spam with content – be smart and enable customer’s to use it!

3) Less is more: Customer’s banner blindness, which served as an example for the increasing marketing message aversion, is just one example of content being misplaced, money and resources wasted. Follow the customer journey and anticipate in real time the needs and receptiveness of customers to your content. Use digital data to model the journey and most importantly track progress – add value to the customer journey but not noise. Use your budget wisely and at important decision making stages when the customer most heavily relies on external content to progress in his or her buying cycle.

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the Kodak “social” media moment

Kodak, despite its financial struggles over the last couple of years (more here), has emerged as the golden child of social media. With 29 dedicated Facebook pages (overview on Kodak’s website), various dedicated Kodak owned blogs, twitter, flickr, google+ and youtube profiles (summary here), Kodak is actively engaging with consumers and prospects.

Kodak FB Page Screenshot

Kodak’s CMO, Jeff Hayzlett, has been very outspoken in the media about Kodak’s strong social media engagement, not just by publishing Kodak’s own social media tip booklet, but by detailing Kodak’s belief to grow its brand and thus, in theory, its market return.


How much is a Facebook like worth?

It can be hardly argued that Kodak’s social media efforts were of shortsighted nature and or didn’t reach consumers, yet sales fell short in almost every product category Kodak serves, multiple quarters in a row. One could of course argue about operational and or product inefficiencies at Kodak (e.g. the constant decline since Kodak’s $ 10 billion sales in 1981 through its struggle with the digital disruption in its markets), yet sales is what ultimately matters and this is were Kodak’s social media engagement seems to fall short.


Can you convert fans, likes, followers into buyers?

Most articles, blogs, books and papers argue to convert followers into sales by promoting follower specific sales incentives, apply like gates (e.g. you can only access certain content by liking a site) and various other methods. Unfortunately, Kodak has applied almost any of them. Kodak has been highly engaged with its followers, listened to conversations, offered specials, had consumer specific content areas and even filtered consumers into product niches to allow for highly relevant content. Based on this, one can continue to argue Kodak’s mediocre and lagging innovativeness in the digital image space is a major factor to let sales slip further downwards.


The Kodak Moment of Truth:

1) Social Media can hardly make up for a lack of product innovation. If you have 1000 fans but no product, you can impossible start to convert fans into sales. In other words, don’t over-promise but under-deliver (as it seems, traditional marketing lessons learnt do still hold true).

2) Listening to consumers is reactive, sometimes a company might need to be proactive (especially in new product development – taking lead times into account)

3) A like is worth nothing if you file for bankruptcy: this is probably the most important lesson to be learnt here! When a business is going fine, a like can be calculated in any way you want because you can actually afford to spend resources thinking about the value of a like. Most likely you will also have to spend resources to justify the resource expenditures in the first place, which is why you come up with this calculation in most cases. In a situation in which the very existence of a company is in jeopardy, all creative like value calculations come however down to one simple point: conversion. Can you or can you not convert your likes into sales.


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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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