Roadmap to Voice Technology Domination

  • Many are quite pessimistic about the prospects of voice recognition technology, and claim that its inferiority vs. existing interfaces make the privacy trade-off not worth it
  • I believe that voice recognition has a bright future, and that the privacy trade-off will become more acceptable as the technology is enhanced and its use cases are expanded
  • Technical enhancements will be driven by competitive pressure to seize the intrinsic and strategic value of voice data, and control the ownership of voice channels
  • New use cases will come from third-party developers that contribute to voice providers’ open ecosystems

Voice recognition technology is a hot topic these days. Surprisingly though, we don’t seem to witness the irrational exuberance that typically goes hand in hand with the diffusion of new technologies. I would even argue that it’s quite the opposite: most people I have interacted with, seem to believe voice recognition is just a fad.

Typical claims I heard revolve around this technology being inaccurate, useless and threatening privacy. More precisely:

  • The current strong growth of adoption is mainly driven by short-lived curiosity and aggressive marketing from voice recognition technology providers (e.g., Cortana defaulted on Windows 10, Google’s and Amazon’s aggressive pricing of their smart speaker lines)
  • Voice recognition interfaces are inferior to existing ones for most use cases (e.g., search, messaging, etc.)
  • Privacy concerns will hold consumers back, as the privacy trade-off is not worth it for most people due to how limited the use cases are.

All these claims are true, but still – I believe that voice recognition technology has a bright future, and the potential to create tremendous value for those able to control and leverage it.

Here, I’ll look mainly at why I believe that the claims above are not incompatible with generalized adoption of voice technology, and how competition and open ecosystems will drive innovation in the field.

Limited functionality is part of the process

Skeptics’ claims are compelling because they are intuitive and get a lot of things right. Yes, it is true that voice recognition technology currently performs well on just a few use cases. It is also true that adoption is largely driven by aggressive marketing. and even more true are the public’s privacy concerns.

However, there is a key point that they are missing. Innovation always starts with a few use cases, and typically involves unbalanced trade-offs that only very few early adopters are willing to take at first.1

In other words, the fact that voice recognition technology under-performs existing interfaces for most use cases is not an anomaly that suggests the technology is heading nowhere – it’s a normal part of the innovation process.

Think of current voice technology interfaces as the equivalent of the first WAP mobile browsers2 They were inferior in all aspects to desktop browsers: slow, expensive and uncomfortable. Just like voice recognition technology today, WAP browsers only made sense for very few use cases (e.g., checking your emails when no computer was around). 15 years down the line, the technology has evolved, and I now quite often find myself preferring to use my phone over a desktop to browse the internet.

I believe that the exact same pattern will repeat with voice technology. As the technology improves, it will become more useful and more prevalent. In concrete terms, consumers are now mainly interested in using voice recognition in their cars (cf. figure below). In the future, demand will rise for other situations as new use cases develop, and consumers become used to the technology. Hotels and offices seem like the most immediate expansion areas, with Marriott piloting Alexa in select hotel rooms, and Amazon rolling out Alexa for Business.

Top Situations In Which Voice Assistants Are Used - Jan 2017 - Amazon Alexa, Google Assistant, Microsoft Cortana

Finally, a big part of the equation that we, Westerners, tend to miss is how convenient these interfaces are to people using other forms of alphabets. China is the most striking example, with adoption up 1,500% this year, and forecasted to grow at a 220% CAGR17-23.

Competition will drive technological enhancements

All the statements I’ve made in the previous section rely on the assumption that voice recognition technology will get better over time and expand to new use cases. I am confident in this assumption. More precisely, I believe that enhancements will mainly be driven by competitive pressure, and that new use cases will be generated thanks to the openness of voice ecosystems.

Competition is driven by three factors:

  • The intrinsic value of the voice data and ownership of voice-activated channels
  • The strategic value of these same items for companies that rely on them to capture future growth (e.g., Amazon’s plans on using Alexa as a way to ramp-up its advertising business)
  • The winner-take-all characteristic of the voice recognition technology market.

The best way to grasp the intrinsic value of the voice data and the importance of owning voice-activated communication channels, is to think of this as a new opportunity to seize the search market. Voice data carries similar value in that it can be used for the exact same purpose – better understand customers’ needs to tailor their experience and push them targeted ads. Similarly, ownership of the channel means having direct access to consumers, which can be both used by the owner and resold in the form of advertisements.

This has strategic value for each of the Big Tech players. For Google, succeeding is key to expand and protect its search empire. For Amazon, victory is all about unlocking new growth in the advertising business and securing a key e-commerce channel. For Apple, it’s a probably more tied to building additional software differentiation factors that could drive hardware purchases and drive advertising revenues at the same time.3

Finally, competitive intensity is heightened by the fact that voice recognition technology has the typical profile of a winner-take-all market. That is, the most used provider can reap benefits that will drive its differentiation further. In that case the main benefit is the access to more voice input than the others, which refines the accuracy and relevance of the voice recognition technology.

There in an importance nuance though: each language is a new market. Just like it was the case with social media, winners in China might be different from winners in France or in the US.

Open platforms will drive use case expansion

We’ve just described a common scenario, where competition drives technological enhancement. A less common scenario, is also at play when it comes to voice recognition technology: collaboration-driven technological enhancement, where external developers contribute to differentiating a provider from the rest (think 3rd party apps on the Apple Store).

All major providers that I am aware of have released SDK of sorts, and hope to on-board as many developers as possible to build exciting, differentiating, apps for their platform. Amazon currently leads the way, with over 25,000 skills (i.e., apps) available – way ahead of Google Assistant (c. 2000) and Cortana (c. 250). Volume is not all that matters though. Quality and relevance also do.

 

Voice Apps Per Voice Assistant - Q1 2017 - Amazon Alexa, Google Assistant, Microsoft Cortana

This bit will be very exciting to watch as this is where the new use cases will emerge.

A few recent examples include predictive shopping lists by Monoprix (French retailer, supported by a consultancy called Artefact)4, voice-activated thermostats by Johnson Controls, Honeywell and Schneider Electric as well as real-time translation devices by Waverly Labs.

 


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  1. More on this here.

  2. WAP browsers were the very first mobile browsers that looked like this.

  3. This also applies to non-US providers (e.g., Baidu, Alibaba, iFlytek, etc.)

  4. Full disclosure: I am an investor in Artefact.

Fire Your Social Media Agency

Last week, Facebook started testing a new model in which publishers’ content no longer appears in users’ newsfeed, unless they pay to “promote” it. The new model has been deployed in six geographies as a test. Facebook have since announced that they have “no plan to roll this out beyond these test countries” – unless of course these very tests reveal that “it’s an idea worth pursuing any further”.

I have always been quite skeptical about the potential benefits that social media can have for brands, especially when it comes to Facebook. It seems to me that the costs involved with owning or outsourcing a dedicated social media department typically outweigh the potential benefits. One of the very first articles that I ever wrote online was making that very point. That was in 2013.

A lot of things changed since then, and I have gone from skeptical to convinced – for most brands, organic social media marketing is a complete waste of resources1

Looking back at the social media craze

Most brands initiated their social media operations somewhere between 2007 and 2013. Looking back at what the mindset was at this time makes it very clear – it was heck of a gold rush. Most brand managers were running around and ramping up their social media presence (and spending), in the hope of stumbling upon the great sought-after treasure – aka the buzz.

The general consensus at the time was that it was possible to hit the marketing ROI jackpot thanks to social media. With very little investment, one could reach billions, thanks to word of mouth and network effects. Here’s a quote from a Business Insider article from 2011 that sums it up well:

The promise of social media marketing–messages spreading from your targets to their friends through their social graph–is real, at least for these brands on Facebook.

Everybody rode the trend. Academics published papers suggesting ways to systematize virality, “viral marketing agencies” became a thing, and of course marketing professionals went on a shopping spree, while bragging in specialized media about their flashy campaigns and social monitoring centers.

And I have to say, it looked appealing. Especially since there were three other strong supportive arguments in favor of investing in social media once you were hooked by the virality bit:

  • Opportunity cost: Doing social media marketing was said to be cheap anyway, so why not take a chance since there is not much to lose.
  • Game theory: Consumers were said to be willing to interact with brands online. Not being on social media to interact with them would give them a chance to flock away to the competition.2
  • Owned Channel: Just like a mailing list, social media fans and followers were said to be an audience brands would own and be able to reach directly, thus we are investing in social media meant investing in something tangible.

.

What’s left today?

Not much, really. Turns out, all of the points above have now been proven wrong.

For your typical Fortune 1000 company, running social media operations now accounts for about 15% of the total marketing budget – and it is expected to grow by 90% within three to five years. In most categories, consumers don’t even care about talking to you (wink, wink, Charmin Toilet Paper’s 0.001% engagement rate). Finally, it became impossible to reach more than 0.5% of these audiences brands thought they would own, without paying a hefty “sponsoring” premium. It sucks.

The graph below shows how organic activity on Facebook went from being attractive-ish, to becoming completely useless.

Evolution of Facebook's organic reach, Graph

It also gives you the rationale behind why Facebook organic marketing is now everything but cheap. Do the math: is it really worth it to hire hordes of social media strategists and community managers to craft neat editorial lines and social strategies when your posts will end up reaching just 0.53% of your audience?3

To me, the answer is a massive no. Now that doesn’t mean that I am advocating for a total social media runaway. I am actually willing to recommend the opposite: stay there. Facebook – you already know it – offers unique targeting capabilities that allow you to chase down relevant consumers and leads in a really efficient way. The path to achieve that though, is not paying social strategists to do organic marketing. It’s paying Facebook for targeted ads instead.

The fix: move money around

So, to the title of this post, I would suggest that you consider reallocating a good chunk of you are spending on agency fees or internal strategists, and handle that money to Facebook instead. The good news is you don’t even have to do it for real to get a precise, quantified idea of the potential results. Just do the following calculation:

  • Sum up all the money you’re spending on social media content creation. That goes from social media strategists, to campaign management fees and community management.
  • Substract social media expenses that seem essential to your business (e.g., if you’re a Telco, you might want to keep a solid community management budget to make sure you handle customer complaints and requests properly).
  • Take your total and use Facebook Ads’ simulation tool to see how many people you could be reaching thanks to that money.

If you’re like most businesses, the difference should stand out, to the point that you might now be willing to fire everybody and blow it all on ads. Don’t. Years of fighting with Facebook’s shrinking organic reach have taught social media marketers a lot about what kind of content works on Social Media and what does not. This expertise will do wonders when unlocked by the power of ad dollars.

You still need some brains to create paid campaigns that are good enough for people to engage with them. You just need less of them.


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  1. Organic is the keyword here. Regular paid marketing campaigns are still super relevant. Here, I will focus on Facebook only to make my point. Yet all claims made in this piece are applicable to other major social media platforms

  2. Here’s a quote from Forbes that illustrates that alarmist mindset very well: “If you’re not using social media to drive revenue, not only are you missing out on money, you’re risking the entire future of your business. Before you know what’s happened, your competition can swoop in and, in a very short time, take what you’ve spent years building.”

  3. Yes, it might be slightly more with really good engaging content thanks to the network effects we mentioned above, but the increase is both uncertain and nowhere close what you would get by simply handling that money to Facebook rather than to your strategists.

AI-powered marketing 101

“How is AI going to change marketing?”, “What is AI-powered marketing?”, “Should we use AI in marketing?”… All these are questions that I get asked a lot. By clients at the office, by strangers on forum boards, and even by my friends at restaurants and house parties.

The reason why this question is so frequent, is because there is still a lot of confusion around it. Academics have their own definition, which is somewhat restrictive; while vendors seem to be labelling anything they sell with “AI” to sell more of it.

The truth is, it doesn’t have to be complicated at all! Months of answering these questions allowed me to build a comprehensive approach to guiding people through what AI-powered marketing really means, how it is being used, and why it matters.

First, let’s start with the definition I typically give:

AI-powered marketing is a set of emerging marketing practices that rely on artificial intelligence techniques to improve the understanding of customers’ behavior. Thanks to it, businesses can create better customer experiences that drive revenues up (i) and optimize marketing spend (ii).

In other words, AI-powered marketing is the solution to the #1 problem that marketers have been facing ever since marketing emerged: how to get a precise understanding of customers.

This precise understanding is the most critical component of any successful marketing plan1 Marketers previously had essentially two ways of finding answers: market research, and gut instinct.

Both are very imperfect. Market research takes a lot of time and money, and can potentially be biased. Gut instinct is unreliable and virtually impossible to scale.

True, marketers also had massive amounts of data collected over the years (e.g. loyalty program history for each customer), but the data was too hard to be leveraged due to how large, dispersed and dirty the data sets were.

Artificial intelligence brought means of analyzing such data sets, thus giving marketing departments the opportunity to use this data to serve the purpose of understanding customers better.

AI-powered marketing was born.

The reason why it’s a game changer is because it dramatically changes how marketing approaches the matter of understanding with customers. AI-powered marketing allows to shift from an understanding based on identity (i.e. age, gender, zip code…), to a more precise understanding based on behavior (i.e. purchases, online activity, returns…).

To give you a more practical idea, here is non-exhaustive list of various AI-powered marketing techniques that are currently in use:

  • Recommendation engines that can tailor product recommendation at the individual-level. Amazon and Netflix are the most famous examples, with an estimated 35% of Amazon’s sales coming solely from recommendations shown to shoppers2
  • Dynamic pricing models that make prices fluctuate in real-time depending on the state of both demand and supply. Uber’s price surge is the most famous example. Yet, it could soon expand to other industries, like gas stations3
  • Propensity models that can predict individual consumer’s behavior. MAIF (French insurance company) is using one to predict the likelihood of a customer leaving to the competition in order to take action before it happens4
  • Sentiment analysis models that scan what people say about any given product to identify potential shortcomings and opportunities. Arby’s (US fast food chain) realized that its consumers were in love with their sauces thanks to it. Consequently, they launched the sauces as standalone products and leveraged the insight to fuel their marketing campaigns5
  • Programmatic advertising models that automatically bids in real time for online ad spaces based on how likely it is that the viewer will end up buying whatever is being displayed.

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  1. To illustrate how important it is, please refer to the following case studies I wrote: How market research revealed consumer insights that saved Pampers’ from going bankrupt in China, How Cialis has beaten Viagra by leveraging a better understanding of consumers’ purchase drivers.

  2. More here.

  3. PriceCast Fuel is a technology built by a2i, which enables gas stations to adjust their prices in real-time based on the current state of demand and supply, more about their technology here.

  4. This project has been conducted by Artefact, a French startup agency that specializes in predictive marketing. Check them out here. Or Target (US retailer) that predicts the pregnancy of its consumers[footnote]This certainly is the most famous business case in predictive marketing. I wrote about it here.

  5. More here

Will Cryptocurrencies Kill Online Ads?

10/17/17 – UPDATE: The practice has now spread to mainstream players, with CBS’ Showtime reportedly using the very mining tool discussed below. I believe many more websites will soon follow, as alternatives to CoinHive start coming up and offer much more attractive commission rates.

A few weeks ago, The Pirate Bay made the news again in the deep web community. Some users noticed that TPB was running a hidden script that hijacked visitors’ CPU to mine a cryptocurrency called Monero. 1

The Pirate Bay responded to users and declared that they were experimenting the script for some time as a potential alternative to ads.2 Here are some figures to give you some perspective on the amount of money we are talking about here: The Pirate Bay gets about 250M to 300M visits per month, pocketing an estimated $227M in ads revenues every year.

That’s about how much The Pirate Bay think they can get from that script, assuming of course that their actual plan is not to simply add an extra revenue source while keeping ads online (which seems to be the most likely scenario to me though, more on that below).

It starts to get interesting when one figures out that the guys behind the script have nothing to do with The Pirate Bay. It’s a separate company called CoinHive. They will let anyone use their script for free, in exchange for an eye-popping 30% commission on all earnings generated by the script.

This is really exciting! Think about it: their solution effectively creates a completely new revenue source that online services can leverage.

Consequently, many argue that online ads are soon to be replaced by mining scripts. Here, I’ll be making the opposite point. I believe that online ads are here to stay, due to simple mechanics of supply and demand. Yet, mining scripts could indeed bring interesting changes to the digital space, mainly by allowing players who failed to monetize so far, despite being popular, to finally turn a profit.

Replotting online traffic monetization

So far, online traffic monetization typically came in three flavours: selling products/services, selling ad space and selling recommendations (i.e., affiliation).

Content publishers and curators though, were mainly restricted to the selling ads bit. Yes, a few of them managed to monetize their audiences by selling products (e.g., The Next Web’s Deals section) or services (e.g., The New York Times earns 6x more with subscriptions than it does with online ads). However, this clearly isn’t the majority.

Consequently, their revenues typically depend on how many people visit their website, and how many ads are shown and/or clicked. Buzzfeed, Slate, Business Insider and the like succeeded online because they focused on doing just that: creating clickbait-ish articles that get many people to click and watch whatever ads they show.

Mining scripts imply a different logic. They earn publishers money based on the amount of time spent by each user on the website, not on the amount of people that visited the website and were shown ads to and/or clicked those ads. What it means, is that a website that gets 1,000 visitors spending 40mn each, could potentially earn more than a website with 10,000 visitors spending 15 seconds each.

This creates an interesting plot: businesses that failed to monetize through advertising, but that nonetheless get decent traffic could finally find relief (I’m looking at you, Twitter and newspapers of the world).

For now, it’s especially relevant for websites offering content that is typically browsed for somewhat extensive period of time, and ideally from desktop3 (e.g., music and movie streaming, forum boards, “free” online tools such as SmallPDF, etc.).

Yet, as CPUs becoming more powerful and cryptocurrencies gain broader acceptance, it will become increasingly likely that these scripts will spread online, and potentially go beyond the services mentioned above.

Online Ads Are Here to Stay

Now, you might have noticed that mining scripts and ads share the same fundamentals. Both are about exchanging an attention window for money. This might have to do with why so many people are excited about mining scripts replacing ads.

From RSA: “[Mining scripts are] a legitimate capability that we expect to see gain significant traction (over ads in many cases) across all types of verticals that rely on web-presence.”

To my mind, those making the point that mining scripts have the potential to kill ads are missing the following points though:

  • Online ads work. The reason why they’re around is because they typically have a positive ROI. The demand for ad space can consequently either grow or remain constant in the near future. Simple economics dictate that if the supply of ad space decreases due to many websites dropping ads, the price of ad space will rise and attract new (or former) sellers in that business.
  • Site owners have no incentive to remove ads altogether. It makes much more sense for them to diversify their revenue sources, and simply add mining scripts as an additional revenue generator rather than a full replacement. Who would turn down extra money?
  • Mining scripts are as easy to block as ads. There are already a few plugins online that block CoinHive, like NoCoin. Looking forward, it seems very likely that web browser developers and app stores gatekeepers will consider blocking content that boasts mining scripts which can be very damageable to the user experience if used improperly.

All that doesn’t mean that many websites are not going to start using these scripts. Rather, it means that they will start using them not as an alternative to ads, but as a complementary revenue source. And it’s a good thing! If properly configured, these scripts no noticeable impact on the user experience and create a fair, painless, way of paying for whatever content is being provided: earnings are correlated to time spent using a service.


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  1. Monero (XMR) is a super cool Bitcoin-like project that aims to provide enhanced privacy to its users. Unlike Bitcoin though, Monero can be mined using solely CPU power, which makes it a good candidate for such a script. For a quick 101 on blockchain, cryptocurrencies and mining, read this.

  2. 10/17/17 – UPDATE: They reportedly discontinued the experiment due to user complaints

  3. The desktop part is just temporary though, soon mobile devices are likely to boast CPUs that are powerful enough to deliver on crypto-mining.

Dynamic pricing is eating the world

Since all men are equal before God, all men should pay the same price for the same goods. That was essentially the idea that the Quakers had in mind when they started selling their products at fixed prices. The price tag concept was born, it was mid-18th century.

Since then, setting and paying fixed prices have become the normal way of doing in business. True, some industries got rid of the practice along the way (eg. airlines, hospitality, rentals, insurance, online display ads…), but most economies have remained built around the very concept of price tag.

A shift might be just around the corner though. Lately, dynamic pricing seems to have extended way beyond the usual yield-management suspects. Uber is using it for cabs, Disney, for theme parks and Marks&Spencer for retail.

The list goes on and on, and expands beyond these somewhat famous examples. However, the fact that dynamic pricing has been under the spotlight lately, doesn’t mean that it’s a new concept or idea. The truth is, most industries have been using some form of dynamic pricing way before it became the trendy buzzword it is today:

  • Coke has been priced at different price points in each distribution channel for years, ranging from a few cents a bottle in supermarkets, to over $10 in luxury hotels.
  • Aspirin is notoriously more expensive in gas stations, than at a chemist because if you are considering getting some in a gas station, you must be facing an emergency of some kind, and willing to spend more.
  • Even markets’ fruits sellers typically give deep discounts when closing time approaches in order to stimulate demand and clear their inventory that would otherwise perish.

What is even more noteworthy, is that at their very core, these examples are very similar to the modern applications of dynamic pricing. All of them attempt to get a sense of consumers’ willingness to pay at a given moment, based on several data points such as competitive intensity, inventory, location, consumer segment, urgency, etc.

Yet, the modern landscape of dynamic pricing is different in two important regards, which are useful to understand what made possible today’s widespread adoption of dynamic pricing. First, dynamic pricing implementation have become much more accessible than it was before. Second, dynamic pricing models have become much more sophisticated than they were, giving marketers great hopes about its potential.

Dynamic pricing for all

Three barriers used to block the way for most businesses to start using dynamic pricing: complexity, perceived risk of backlash and lack of relevant data. For most industries, these difficulties have been overcome during the past decade though:

  • It became cheap and easy to experiment with dynamic pricing: When American Airlines started their yield management program in 1985, they had to build everything from scratch. Now, one can simply give a call to any SaaS company and start experimenting on the spot for a few thousand dollars or less.
  • Consumer acceptance increased – risk of PR backlash decreased: The first-movers took the risk of PR backlash (and sometimes got burned, like Uber). Consumers have now come to understand that dynamic pricing happens, which lessens the risk of major PR backlash for newcomers.
  • Marketers are trying to find ways to leverage the data they have at hand: Marketing and IT departments have collected huge sets of data over the year, without necessarily knowing precisely what to do with it. The recent breakthrough of AI made it possible to analyze these super large data sets, thus enabling the creation of efficient dynamic pricing models.

These changes are what allowed industries that would have stuck with their former fixed price models otherwise. Consider cinemas, an industry that is far from being cutting-edge in MarTech. Many of them are now adjusting prices in real-time based on pre-sales, time of booking, weather and various other demand drivers. None of them built a dynamic pricing model from scratch though. They simply phoned Smart Pricer, a software company that does that for their niche.

The truth is, roughly anyone can do somewhat advanced dynamic pricing today. Even the most non-techy WordPress site owner can do it: there’s a $129 plug-in that unlocks the feature!

From guesses to measurements

The second interesting change, lies in the capabilities and promises of dynamic pricing models. More precisely, in the fact that we shifted from extrapolation-based techniques, to measurement-based techniques.

Extrapolation techniques rely on extrapolating an averaged observation about the behavior of a few consumers to the whole consumer base. For example, the fruit seller knows that most people will be willing to buy more fruits if the price drops. So, he drops the price for everyone, by a number that seems reasonable to him.

Measurement techniques are different. They rely on accurately measuring consumers’ willingness to pay based on the analysis of several impactful data points. For example, Amazon’s dynamic pricing algorithms can precisely measure the traffic volume it gets on individual product pages, and what the conversion rates are for each product. If it detects a surge or a decline, it can adjust the price and A/B test different price levels until it finds the sweet spot.

Below is a graph that maps the price evolution of a footwear deodorizer. The price spiked seven times over six months, up to x2 the normal retail price. It turns out that each of these spikes happened following the publication of articles about that very footwear deodorizer in major press titles. These articles most likely resulted in traffic spikes on Amazon, who raised the price more or less depending on how big the traffic increase was, from an extra $5 to an extra $9.

IMG - Amazon Dynamic Pricing Case Study Example

The ability to personalize price points for each individual consumer is the other big innovation brought by measurement-based techniques. Just like regular dynamic pricing, the idea of personalized pricing is not new per se, insurance companies have been doing it for ages when charging smaller premiums from “good drivers” for example. Also, it is not yet as widespread as regular dynamic pricing due to higher technical requirements. Yet, the few cases I heard about make it sound very promising!

Consider the story of Mavi. They are a premium jeans brand retailer that increased their orders by 68% thanks to similar techniques. More precisely, they are personalizing the discounts they give to their customers, based on each customer’s profile. The top predictor of the discount level most likely is the average order value. What is really interesting though, is that they also included the predicted customer lifetime value as a predictor of the discount level.

I find this addition really exciting as I believe it has the power to change what pricing is used for. Pricing has traditionally been about three things: positioning your offering, avoid leaving money on the table, and clearing out your inventory. By taking customer lifetime value into account when it comes to setting prices, one adds an extra use case for pricing. That is, using pricing as a customer retention tool.


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Professor Tournesol vs. Steve Jobs

When I was a kid, I was a fan of a Belgian comic series called Tintin. One of the characters, called Professor Tournesol, excelled at creating pointless inventions, like these engine-powered roller skates. A few years after that, I was gifted my first iPod (by the amazing Taylor family!) which, unlike Tournesol’s roller skates, felt like an amazing innovation.

All these memories came back to me recently, as I was talking with a friend about Tesla. He made the following point: “electric cars have been invented a long time ago, so why is everybody calling Tesla an innovative company?”

This point is legitimate. So legitimate that some variant of it keeps coming back every time innovation happens, like it just did this week with The Verge listing what the iPhone X borrowed from the Palm Pre.

When BlackBerry released their first email phone, people said that sending emails on your phone was doable 10 years back. When the iPhone came out, people said that touch-screen phones were not new. And now that Tesla is releasing electric cars, people say that electric cars have been around for a while.

Such statements are true, in the sense that none of these companies invented the technology it is famous for. Yet, they fail to recognize the key point: merely inventing something is not being innovative. Innovations have an extra component that inventions lack: they are popular. In other words, an innovation is an invention that has spread enough to affect the behavior of many. To convince yourself, just head to the closest fair. You will see tons of pointless Tournesol-minded inventions – AC-shoes, shoe-umbrellas, hands-free potato peeler… whatever, you name it. It will be obvious that none of them is an innovation.

So innovative companies are not necessarily the ones that invent stuff. They are the ones that bring inventions’ characteristics closer to the mainstream demand’s expectations. And when that happens, the invention goes from the innovators public, to the early adopters public. That’s the genuine “innovation moment“.

When you look back at each of the examples mentioned above, it becomes obvious that innovators succeed thanks to their ability to extend the customer demand beyond the innovators bit by making existing technologies convenient enough to be suitable for the general public:

  • Yes, it was possible to send emails using your phone before BlackBerry. Yet, the process was so painful that only a tiny niche of users bothered doing so. BlackBerry made that process way easier, and got a wide audience of people to start doing it.
  • Yes, it was possible to download, burn, store and listen to MP3 files on mobile players before the iPod. But it was too complicated for most people to give up their old habit of using CDs. Apple’s iTunes, coupled with the iPod gave people a one-stop shop for doing it all, enabling the general public to switch.
  • Similarly, yes, electric cars existed before Tesla. But unless you were a die-hard ecologist, bad design coupled with uncertainty about convenience (eg. charging) made most people look the other way. Tesla brought good-looking cars and made people feel confident about the charging part, which is what was needed for early adopters to do the big leap.

In all of these cases, it’s not raw engineering power that made these companies successful. They did spend a lot on R&D (though not necessarily so much compared to their competitors), but it’s their razor thin understanding of the demand characteristics that allowed them to succeed. They knew how to tweak the offering in a way that would make it more convenient and appealing. And that’s what the innovation process is all about: understanding what holds early adopters back and fixing it. More of a marketing function than anything else if you ask me.


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Chewing-gum’s digital problems

This article has been quoted by Campaign Asia. You can read their article as well for additional insights on the topic.

Chewing-gum manufacturers are struggling. Their sales volumes and their revenues have been on the decline for the past 10 years or so. To give you a more quantified perspective, Rabobank estimated the drop in volume to be about 20% during the 2008-2013 half decade1.

I find the way that they managed this issue so far to be really interesting as it illustrates really well how so many categories are still using analog fixes for what really are, digital problems.

In the analog setting, most consumers bought chewing-gums not because they planned to do so, but because it was placed near the check-out counters in supermarkets. It is a pure impulse-driven product. Digital changed the rules.  Now, fewer people visit supermarkets because of drive services and online retailers, and those who still come are less likely to pay attention to chewing-gums while queuing to check-out as smartphone screens are far more interesting than supermarket shelves.

As a result people buy less chewing-gums, and Wrigley’s, Mondelez & co are bleeding money. So far, they mainly tried three options to solve the issue, all of which are somewhat artificial:

  • They started selling bigger packs to boost overall volumes. In supermarkets, most chewing-gums are sold as either big boxes or as a set of 5 smaller packs. This allows manufacturers to increase the average amount spent per purchase.
  • They slashed their prices. Chewing-gums used to be some of the highest margin products one could find in a supermarket. Brands recently pushed back on their prices in many markets, which triggered boosts in volume. In France, the prices have been lowered by an average 15%, which led to a boost in volume of about 0.5%.
  • Launch new products at a faster pace to stimulate growth. We now have chewing-gums with every possible shape, taste and packaging. A small-size supermarket in France will have on average about 30 different kinds of chewing-gum.

All these solutions can limit the damages in the short run, but fail to address the root cause of the issue, which is frequency.

What is really important, is not how much do people spend on chewing-gums, but rather how often do they buy them. On average in France, 4 people out of 100 buy chewing gums when they visit their supermarket. Take that figure to 5, and you increase your revenue by about $70M.

Relevant solutions will focus on creating new ways to trigger this purchase, and restore frequency at its previous, analog, level. Potential options could include:

  • Automating the purchase by selling chewing-gums as a subscription, like others have done with comparable low-engagement categories like socks.
  • Making the chewing-gums more visible in supermarkets. Wrigley’s tried installing LED-equipped merchandising in 40 shops in France, which led to a 15% boost in sales.
  • Thinking about new ways to trigger impulse purchase in a digital world. For instance, we know that one of the reasons why frequency decreased, is because people now browse their smartphones while queuing for check-out instead of looking at what is around them. It creates an opportunity to push location-based targeted advertisements and/or to piggyback the retailers’ apps and push chewing-gums through it. Similarly, Amazon’s Dash buttons could be a great tool for the category.

As a final note, considering rationalizing product portfolios might be a clever idea. The focus on innovation of the past two decades made it really hard for consumers to navigate through the category, plus it most likely increased logistic and production costs. Savings could be made at that level, in order to increase expenditures on frequency-first projects and experiments.


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