Hugues Rey's Blog Curation – Where Marcom meets Technology

 Infographics for Content Marketing: People who follow directions with text and illustrations do so 323% better than those using just text. And nearly 50% of the brain is focused on visual processing.

Source: [INFOGRAPHIC] How to Create Infographics for Content Marketing

Infographics can be powerful content marketing tools.

The format can help brands easily explain complicated subjects, processes, or data-driven insights. Research has found that people who follow directions with text and illustrations do so 323% better than those using just text. And nearly 50% of the brain is focused on visual processing.

Plus, social media networks prioritize visual content – and strong visuals catch peoples’ eyes. Tweets with images get 313% more engagement.

At NewsCred, we’ve found infographics to be valuable top-of-funnel content.

Emails that have “[INFOGRAPHIC]” in subject lines tend to get high open and click-through rates. And of all the content we produced in 2016, an infographic we created about Snapchat received the second-highest amount of pageviews and the most media pick-up. (Twenty publications ran it).

However, creating an infographic requires time and resources: In addition to deciding what the infographic will be about, gathering relevant data, and crafting a narrative, you also have to identify a goal for the infographic, create a distribution strategy, work with designers, and go through rounds of reviews and revisions.

Our friends at IBM THINK Marketing have created a helpful infographic and article that covers those steps. Read more in Ins and Out of Creating Awesome Infographics and see the infographic below.

IBM_Ins and Outs of Awesome Infographics.png

What’s the difference between CRM, marketing automation and DMPs? | Econsultancy

Source: What’s the difference between CRM, marketing automation and DMPs? | Econsultancy

To somebody like me who hasn’t worked at the sharp end of customer relationship management (CRM), it can be confusing to consider the theory.

I set out to discover where CRM, marketing automation, sales, and data management platforms cross paths, and how CRM is implemented.

CRM in layman’s terms

It is common for troubled CEOs to utter the immortal line, “we are putting the customer at the heart of everything we do”. Well, old-fashioned CRM did exactly that.

CRM programmes were designed to allow companies to foster customer feeling and loyalty, rather than simply transactional interactions. This focus on retention (keeping track of communication with the customer) delivered greater ROI than simply chasing more and more novel sales prospects.

CRM enables the analysis and management of customer interactions with sales, marketing and service departments, ultimately shaping the customer lifecycle but also, more broadly, organisational processes.

Isn’t that marketing automation?

Marketing automation systems often work closely with CRM software, indeed many CRM solutions have incorporated marketing automation functionality (e.g. Salesforce Pardot).

However automation is a fairly generic idea, reducing the repetitive labour of customer communications – for example, triggered emails or website messaging.

Characteristically, marketing automation has been used for lead nurturing, with resultant customers looked after by the CRM, but that distinction is perhaps no longer a helpful one, given marketing automation is more and more embedded with CRM systems.

How is CRM different from a data management platform?

Data management platforms (DMPs) are most associated with online advertising, where large amounts of cookie and campaign data can be managed (including third-party data), and are often managed entirely separately from CRM.

However, the integration of CRM and DMP platforms is a nascent but growing area. As Chris O’Hara points out, this can enable marketers to target non-openers of emails with display advertising (using anonymised email IDs), for example.

In Chris’s hypothetical example, a pizza company could deliver display ads during the week, up to Thursday (the day it knows certain customers are likely to order a pizza) when it could send an email coupon.

It makes sense that CRM and customer lifetime value data can be brought to bear on paid media, not just in an ad hoc fashion. It also makes sense that data management platforms should start to be used for more than simply display advertising, but to improve attribution of value to multiple marketing channels.

However, as many marketers make plain, increasing IT complexity can sometimes distract the organisation, particularly if there isn’t enough analyst capacity to dig into the newly expanded data.

Andrew Campbell, CRM expert, expands on this point:

“As the quantity of data increases, the need to identify quality data becomes a marketing imperative. Data management platforms have industrialised the processing and management of big data, but marketers need to find a way to drink from the hose!

“It is no longer practical (even in the cloud) to pull all available data (posts, tweets, app usage etc.) into a single Master Customer record.”

Where does CRM fit into small and large organisations?

CRM’s impact on sales, marketing and service makes it pretty central to any organisation. And CRM doesn’t just impact on these three departments, but should be viewed at the organisational level, often affecting supply chains and back-office processes.

Microsoft even uses the term XRM (‘anything’ relationship management) to define this idea of managing relationships across a business, not just the customer facing parts.

Customer data is often fragmented, stored in different parts of the organisation – bringing this together is one of the challenges of CRM. Some organisations have CRM departments that still sit separately to online marketing.

Using a slightly dry but useful typology model (see figure 1 below, taken from Econsultancy’s CRM in the Social Age report), we can look at the various development stages of CRM.

  1. Operational CRM: ‘reengineering the customer-facing business processes and systems to ensure the efficiency and accuracy of day-to-day operations across sales, marketing and customer service’.
  2. Analytical CRM: Storing, extracting, interpreting and reporting on customer data – to optimise business decisions and support customer-centricity.
  3. Collaborative CRM: ‘integration of the front- and back-office processes that combine to support customer interactions.’
  4. Social CRM: Deliver a consistent customer experience across social media, using analytics to support customer conversations and response handling. Integration of social data with broader CRM.


Is digital an effective mass market medium? – Marketing Week

Source: Is digital an effective mass market medium? – Marketing Week

Digital media are fast becoming dominant in brand marketing but can they deliver long-term effectiveness as well as short-term results, reaching both the masses and targeted audiences?

The amount of data available to marketers using digital channels is immense. But while digital is lauded for its ability to allow brands to reach the right consumer, at the right time, in the right environment, the question remains whether it is an effective medium for mass marketing.

Digital channels are often accused of making marketers think too narrowly in their activity, meaning they sacrifice reach and long-term growth for narrow, short-term metrics.

Brands have become more acutely aware of this trade-off since the publication of Byron Sharp’s book ‘How Brands Grow’, which suggests marketers should replace targeting with “sophisticated mass marketing”.

Last year, Procter & Gamble’s chief brand officer Marc Pritchard confirmed the company was moving away from targeted Facebook advertising after calling its approach too “targeted and narrow”. P&G, however, insisted it was not planning to cut its total investment in Facebook and would continue to use targeted ads for some products, such as selling nappies to expectant parents.

Meanwhile, Mars’s former global CMO Bruce McColl has asserted that he is “not a great believer in targeting”, adding that the company’s target is “about seven billion people [≈ population of the world] sitting on this planet”. Speaking at the Advertising Research Foundation’s conference last year, he said: “Our task is to reach as many people as we can; to get them to notice us and remember us; to nudge them; and, hopefully, get them to buy us once more this year.”

Both statements would indicate that the brands are following Sharp’s recommendation to “continuously reach all buyers of the category” and move away from standard segmentation and targeting if they want to grow their business. It is often not digital that springs to mind when looking to reach people at scale.

READ MORE: Mark Ritson – We should thank Byron Sharp, not attack him

Stay visible

Aviva’s brand communication and marketing director Pete Markey says there is a “sweet spot” of people the insurer wants to reach, but argues that if a business becomes too narrow, it will not be visible to customers.

Instead, he suggests the best segmentation is one the entire business can use that goes beyond targeting to focus on the experience the brand wants to give the audience.

“I see a role for broader segmentation, but I also see a role for smaller, microtargeting. It’s not one at the expense of another,” says Markey. “I’ve seen segmentation that works and doesn’t work. The danger is if it becomes the exclusive property of the marketing team, the rest of the business isn’t that interested. If you can’t use segmentation, it very quickly loses its credibility.”

IPA director of marketing strategy Janet Hull believes the natural progression is towards personalisation, driven by the rise in data generated by digital and social media.

“The insight you get from data you can then expand on, starting with the individual. That approach gives you more nuance, so you can serve creative work that works and improve the quality of advertising online and offline,” she adds.

Brands can use targeted marketing communications to deliver personalisation, which drives increased engagement and conversion rates, according to the IPA’s Social Works Personalisation guide published in March. However, the report acknowledges that strategies designed to increase brand relevance should always be balanced with campaigns that drive reach through mass marketing.

Targeting en masse

When The Economist wanted to increase brand awareness and drive subscriptions, it targeted a group of people labelled the “globally curious”, modelled on its database of existing subscribers. Despite being targeted, this was by no means a niche group, with The Economist estimating the number of globally curious people was close to 73 million.

“If a brand is to solve an awareness challenge, then reach is important, but we don’t conduct reach campaigns just for the sake of it. There will always be a longer-term goal and everything we do has a KPI to it,” explains Mark Beard, senior vice-president of digital media and content strategy at The Economist.

READ MORE: Tricks and tips for maximising digital effectiveness

“We need to make sure everything we do is driving some kind of interaction and [can] help us convert more people to subscribers,” says Beard

This was the approach taken by The Economist for its digital marketing campaign ‘Raising Eyebrows and Subscriptions’. The aim was to provoke the audience by serving them content they couldn’t ignore in order to demonstrate the relevance of The Economist’s journalism. The idea was to give people “their own epiphany” by offering content that compelled them to subscribe.

Part of this strategy involved placing ad units on articles the globally curious target group were already reading, which showed them a hopefully more interesting article authored by The Economist team.

The success of the campaign was highly measurable, generating 5.2 million clicks and 64,000 new subscribers worth £51.7m in lifetime revenue. The campaign, which won a Gold Cannes Lion last year, continues to run and has evolved to include an element of video.

The discussion is moving beyond reach to ‘attentive reach’, which takes into consideration more than just viewability.

Gerald Breatnach, Google

“We don’t really run campaigns in our digital department,” says Beard. “We don’t talk about campaigns, we talk about always-on activities and initiatives. With digital you can be short term, but it is possible to be long term too.”

Mondelez International’s digital and social media manager Pollyanna Ward is an advocate of operating an always-on digital strategy, which feeds into a wider campaign across TV, experiential and outdoor.

“For us in FMCG, we need to reach everyone. There’s no one specific audience. For us, the priority is always going to be reach. So you might have an always-on strategy where you’re planning things for an entire year and you might continuously pump out a brand message on Facebook, Twitter or YouTube.

“You can change that in real time and push out core brand messages at times when it’s relevant, meaning you don’t need to do continuous targeting all year round. Then shorter-term digital strategies are key with the big activations to get people buzzing and when they’re aligned with your PR, experiential and TV you get results a lot quicker.”

New measures of effectiveness

Concerns continue to persist over how effectiveness should be measured in the digital age. Facebook fuelled the fire last year after revealing a number of errors in the way it measures audiences, admitting it overstated for how long users watch videos on its site by up to 80%.

READ MORE: Mark Ritson – Facebook should hang its head in shame for measurement errors

This, in part, led P&G’s Pritchard to launch a searing assessment of the industry’s “murky” supply chain in January, blaming the “antiquated media buying and selling system” for its inability to cope with the technological revolution.

Last month, meanwhile, WPP CEO Sir Martin Sorrell urged Facebook and Google to step up and “take responsibility” for measurement errors and ad fraud, arguing the major digital players are “clearly not doing enough”.

Yet despite these concerns Facebook’s revenues continue to soar, rising 51% in the fourth quarter of 2016 to $8.8bn ≈ cost of 2011 Hurricane Irene

≈ net worth of Steve Jobs, founder of Apple, 2011
≈ Domestic box office gross, 2011
≈ cost of Spanish-American War
≈ Chernobyl costs, USD at the time

” data-evernote-id=”177″>[≈ Construction cost for Gerald R. Ford-class aircraft carrier] (£6.9bn). So as brands appear unlikely to move ad spend away from the platform, it is more important than ever to find new effectiveness metrics.

Dixons Carphone’s commercial marketing director Jonathan Earle suggests one method is to add bespoke codes to track customers who purchase as a result of seeing an advert on Facebook or YouTube.

The discussion is moving beyond reach to ‘attentive reach’, which takes into consideration more than just viewability.

Gerald Breatnach, Google

However, if marketers want to drive sales, he argues there are digital channels that deliver much stronger results than social media.

“If the metric is to get as many people thinking about us as possible, then social has a clear role to play, but if you want to drive sales, that’s where pay-per-click comes in,” he explains.

“If I had £100 to spend on a marketing campaign and my sole aim was to drive sales, I would just use social to drive awareness, consideration and conversation as there are better channels to drive the final sale.”

Earle argues that pay-per-click (PPC) is the most measurable marketing channel, serving content to customers that matches their organic online search terms. The team can then track that traffic from the click-throughs all the way to conversion and order value. The PPC activity takes between seven to 10 days to maximise, says Earle, who advises marketers to be clear upfront about what it is they want to deliver.

At Google and YouTube, the response to measurement issues is to offer free tools like AdWords and brand lift surveys, as well as third-party viewability verification.

“The discussion is moving beyond reach to ‘attentive reach’, which takes into consideration more than just viewability,” explains Google brand planning industry lead Gerald Breatnach.

“We tend to think of this in terms of ‘WAVE’ – measuring watch time, audibility, viewability and engagement. Marketers not only need to know how many views a campaign has generated, but the unique reach of the campaign and the average watch time. Ultimately, these campaign metrics need to link to sales results,” he says.

Defining success

Understanding that success looks different on each channel is crucial to accurately measuring effectiveness and in the digital landscape there is no one-size-fits-all approach.

“If I wanted to make an impact with the younger generation,  I would be looking to see how effective Snapchat is at delivering impressions, but when it comes to different channels I would be looking at the view-through rate to see if we have made an impact,” Ward explains.

“If the view-through rate shows they only watched 10 seconds of our 30-second ad, then did we land our product message in that time? Or have they watched a pretty piece of creative, but we’ve done nothing for brand linkage and brand awareness?”

Reach is important, but on its own it isn’t good enough.

Mark Beard, The Economist

Google’s Breatnach agrees it is a mistake to think all digital channels work in the same way or only equate to “tightly targeted” performance marketing.“Online video advertising on YouTube, for example, has the potential to offer attentive reach and deliver long-term brand results,” he explains.

“Search is naturally targeted to audiences further down the purchase funnel. Programmatic has the potential to deliver both mass reach and sophisticated targeting.”

Peugeot’s marketing director Mark Pickles appreciates that to measure digital effectiveness you need absolute clarity about what you want to achieve.

“For a new product, the primary focus is likely to be on reaching mass awareness within the identified customer segments. Whereas with an established product, often the place for digital is to convert existing awareness into some form of action – what I call a ‘nudge’ – which pushes the consumer a little closer to us,” he says.

“Clearly, the metrics for these two campaigns will be different and so will the media optimisation rules. Setting a campaign that is optimised to generate hard sales leads is likely to fail when it comes to reaching enough eyeballs, particularly as our programmatic algorithms quickly seek out targeted prospects.”

The Peugeot marketing team generally sets several secondary objectives and a primary objective, which Pickles emphasises it is important to be realistic about.

He says: “If I was to set the primary objective of a campaign action as ‘how many sales can I directly attribute?’, I’m likely to under-value the secondary deliverables. A campaign might sell a few cars, but generate sufficient awareness and action that translates to results outside the campaign metrics.”

Hull argues that it is fundamental for marketers to understand the difference between what they are doing for the long term and their brand activation objectives.

“You also need to make sure you’re getting the right investment for the KPIs that you’re setting, because there’s no point setting KPIs and then not putting the right amount of money behind them,” she explains.

“Some of the clients have been saying that they have been under spending relative to the promise they have made on what they’re going to deliver. We also need to get the languages to match up. New media brings a new vocabulary and new forms of measurement, but you have got to know how they relate to the other forms of measurement.

Holding your nerve

Armed with the ability to optimise 24 hours a day, seven days a week marketers now have the opportunity to tweak campaigns in real time.

Aviva uses econometrics and attribution to measure effectiveness, combined with its brand impact index, which investigates brand health. Markey argues that marketers cannot afford to ignore the opportunity to optimise.

“The idea that as a marketer you can be asleep at the wheel is so wrong. To push a campaign out and hope it works or do campaign evaluation at the end would be insane, lazy, suicidal,” he says.

“For me, when you start any activity you need to know how you’re going to measure it throughout and consistently go back, using neuroscience to refine activity.”

However, when exploring real-time optimisation marketers must resist the temptation to make knee-jerk decisions, which take them away from their original KPIs.

“When you’re spending money on a campaign that’s aimed at trading and selling, and you say ‘I want to sell 20 of this thing and I only sold three’, you can pull that money and put it somewhere else,” says Markey.

“When you’re trying something new, it’s much harder and you have to hold your nerve. You need the levers, but you need to know when to pull them. There are many examples of campaigns that are a slow burn. There is an expectation that when the new campaign has launched, sales will double tomorrow, and we all know that’s rarely the case.”

It is not what you track, but what you choose not to track that’s important.

Mark Beard, The Economist

The Economist’s Mark Beard agrees that it is very easy for marketers to fall into the trap of tracking everything on a digital campaign just because they can.

“It is not what you track, but what you choose not to track that’s important. We track two things predominantly – the number of prospects we’re bringing in and how many subscribers we’re generating as a result of those prospects,” he says.

“There are many other KPIs we could have reviewed as the campaign went along but the danger these days where much of marketing is done via machines, is if you give the machine the wrong KPIs to optimise you won’t get the results you want.”

This is where human input plays a vital role, says Beard, ensuring the right KPI is chosen at the outset and setting up the artificial intelligence to operate at its optimum.

In a world where optimisation can happen at the touch of button, it is more crucial than ever for marketers to set out with a clear appreciation of their KPIs and then design their digital activity to deliver as part of a holistic strategy.

Case study: Effectiveness on a shoestring

The objective: Australian swimming pool company Narellan Pools teamed up with agency Affinity on a targeted, data-driven digital campaign to increase its share of the crowded Australian market.

The research: The cross-referenced five years’ worth of first-party data, including sales, site analytics, leads and conversion rates, with five years’ of third-party data spanning the weather and consumer confidence. The research found sales spiked when there were two consecutive days with higher than average temperatures.

The campaign: Looking at the weather across 49 regions in Australia, the team fed real-time temperature data into the programmatic platform. When the right conditions were met, it activated the campaign across search, pre-roll video, banner and social, targeting people who had already shown an interest in buying a swimming pool.

The result: As Narellan Pools only advertised when the specific temperature conditions were met, the company was able to reduce its media spend by more than 30%. The campaign drove a 23% increase in sales and generated an incremental return of investment of $54 (£34) for every $1 (62p) spent. The campaign also went on to win the 2016 IPA Effectiveness Award special prize for best small budget campaign.

C’est quoi une Fintech ?

Source: C’est quoi une Fintech ?

Les acteurs se bousculent sur ce nouveau créneau de la Fintech, mariant finance et technologie : paiement, agrégateur de comptes, etc.

Les acteurs se bousculent sur ce nouveau créneau de la Fintech, mariant finance et technologie : paiement, agrégateur de comptes, etc. (Crédits : ACPR)
Vous connaissiez les biotech, vous avez peut-être entendu parler des Fintech: des startups qui réinventent la finance à l’aide des technologies. Parfois en bousculant les acteurs établis du secteur. Définition détaillée pour mieux comprendre les enjeux.

Contraction de finance et technologie, sur le modèle de l’expression “biotech” dans la santé, le terme “Fintech” serait apparu pour la première fois dans les années 1980-90 dans la presse anglo-saxonne spécialisée. Il s’est vraiment répandu après la crise financière de 2007 en dehors du monde de la finance pour décrire des entreprises innovantes, plutôt jeunes, utilisant les technologies du numérique, du mobile, de l’intelligence artificielle, etc., pour fournir des services financiers de façon plus efficace et moins chère. Il s’agit généralement de startups, même si des acteurs historiques du paiement ou du logiciel bancaire se présentent parfois sous ce terme plus tendance.

« 2015 est l’année où la Fintech est devenue grand public », estime le cabinet KPMG, avec une explosion des montants investis par les fonds de capital-risque dans les startups du secteur, suivis ensuite par les grands acteurs établis de la finance : 47 milliards de dollars avaient été investis cette année-là dans les jeunes pousses du secteur.

Pour autant, selon un sondage récent de Harris Interactive pour le cabinet Deloitte, 83% des Français ne connaissent pas le terme Fintech, et seuls 4% savent « à peu près » ce que c’est (ils sont autant à le confondre avec “fitness”…). Ils les utilisent parfois sans le savoir.

Le rachat de la success-story Compte Nickel, un compte sans banque distribué chez les buralistes, par BNP Paribas début avril, a mis en lumière ces nouveaux acteurs avec lesquels il va falloir compter.

Néobanques, crowdfunding, robo-advisors…

On distingue généralement plusieurs catégories de Fintech :

  • les Fintech BtoC (business-to-consumer) qui s’adressent au grand public, par exemple les « néobanques » 100% digitales, sans agence, qui proposent un compte et une carte de paiement à bas coûts (Compte Nickel, Morning), les cagnottes en ligne comme Leetchi ou LePotCommun, les applications de paiement comme Lydia ou de gestion des finances personnelles (Bankin, Linxo), ainsi que des outils de gestion de patrimoine (tableau de bord comme Grisbee) ou d’investissement automatisé (robo-advisors comme Marie Quantier) ;
  • les Fintech BtoB (business-to-business) qui proposent des services financiers aux entreprises, PME ou grands comptes, par exemple le transfert de devises en ligne (Kantox) ou l’affacturage dématérialisé (Finexkap) ;
  • les Fintech BtoBtoC (business-to-business-to-consumer), à l’image des plateformes de financement participatif, qui mettent en relation des porteurs de projets, créateurs, commerçants, PME, et des investisseurs, particuliers ou professionnels : crowdfunding en dons avec ou sans récompenses (KissKissBankBank, Ulule), crowdlending (prêts aux PME, comme Lendix ou Lendosphère) et crowdequity (financement en capital, comme Sowefund) ;
  • les Insurtech, dans l’assurance : du comparateur, comme Fluo, à l’assurance collaborative, comme Alan ;
  • les Regtech, des entreprises qui proposent des solutions technologiques pour répondre aux contraintes réglementaires et de conformité des acteurs bancaires principalement (notamment dans la connaissance client ou « KYC » dans le jargon) comme Fortia ou Neuroprofiler.

Ces entreprises ne sont généralement pas des banques ; certaines ont une licence bancaire, comme la néobanque allemande N26 ou la britannique Atom Bank. Elles peuvent avoir divers statuts, agréés par le régulateur financier (Autorité de contrôle prudentiel et de résolution ACPR-Banque de France), notamment établissements de paiement, conseillers en investissement participatif ou prestataires de services d’investissements (crowdfunding), conseillers en investissements financiers (courtage en ligne) ou sociétés de gestion de portefeuille (robo-advisors).

Les Fintech européennes bien placées

Dans l’édition 2016 du classement des 100 premières entreprises mondiales de ce secteur établi par KPMG, figurent trois françaises : Lendix, Leetchi et Fluo.

Dans le dernier « Pouls de la Fintech », qu’il publie de façon trimestrielle et annuelle, en partenariat avec la base de données du capital-risque CB Insights, le cabinet de conseil évalue à plus de 25 milliards de dollars les levées de fonds des startups du secteur sur l’année 2016, une brutale pause après deux années exceptionnelles et les doutes apparus à la suite des déboires de la plateforme Lending Club aux Etats-Unis.

En Europe, les investissements ont avoisiné 2,2 milliards de dollars, dont plus de 200 millions en France. Les plus gros acteurs sont chinois (Ant Financial, filiale d’Alibaba) ou américains (Stripe), cependant plusieurs fintech européennes se distinguent parmi les “licornes” valorisées plus de 1 milliard de dollars (la néerlandaise Adyen, la suédoise Klarna, la britannique TransferWise). Mais pas encore de françaises.

Licrones Fintech WSJ

[Les startups à 1 milliard de dollars dans les services financiers, Wall Street Journal, mars 2017]

How to Turn Twitter Followers into Repeat Customers [Infographic]

Twitter is a powerful tool which businesses can use to build meaningful connections with new and existing customers, brand advocates, influencers, as well as a larger, engaged audience. It’s also an increasingly important platform for providing quick, efficient customer service – of the small and medium-sized businesses that utilize Twitter, 85% agree that it’s a key medium for customer support.


AI Recommendations That Know You Better Than You Know Yourself

Source: AI Recommendations That Know You Better Than You Know Yourself

Apr 6, 2017
Daniel Surmacz  |

Every day, millions of people make buying decisions based on search – products to buy, restaurants in the neighborhood, and tons of other choices. According to the Nielsen Report “Global Trust in Advertising” however, while consumers rely on online opinions or price comparisons, more often than not, it’s word-of-mouth recommendations that are the most effective. The most credible advertising comes straight from the people we know and trust, and over 83% of respondents completely or somewhat trust the suggestions of friends and family.

So when we make a final decision to buy, it’s reasonable to assume that we ask a spouse, relatives or close friends for advice. After all, they are the ones who know us, our tastes, preferences, sense of fashion, etc.

But what if a computer was able to get to know you even better than your close ones?

What Computers Know About You Now

The digital era has made purchasing journeys more accessible, but an increasingly complex one. Choosing from hundreds or even millions of options make it harder to make a decision. Online recommendations systems change the way we browse and choose products – they narrow our decision-making process by bringing us closer to what we’re looking for, suggesting complementary or even alternative products.

This “knowledge” about your shopping persona usually comes from what you have purchased or viewed in the past, what shoppers with similar profiles have viewed or bought, as well as the date and time of viewing. Recommendation technologies listen to what you’re looking for and suggest products. They gather and analyze millions of datapoints about your preferences to serve ultra-precise suggestions.

It sounds simple, but these technologies require massive volumes of data to deliver accurate predictions. And, of course, the more information, the better. This is where deep learning comes into play – an innovative branch of artificial intelligence that solves problems by imitating the work of the human brain in processing data and creating patterns of decision making.

AI Will Predict What You Want

Most of us already have experience with data-based suggestions as shown above. We’ve purchased new products on Amazon recommended under the “Frequently Bought Together” section, or added new people on LinkedIn after seeing “People you may know.” Even watching a movie on Netflix exercises our familiarity with AI-based recommendations.

And now engines are only getting smarter. They employ deep learning tools that personalize a user’s experience by trying to figure out their habits even after just one or a few visits – sometimes during the first visit. Paired with real-time analytics, self-learning algorithms can enhance suggestions up to the point of prediction. Services like Spotify can predict the next song suggestion, while YouTube queues up recommended videos based on the current one you’re watching.

Ultra-precise deep learning is used in all sorts of digital industries, none more so than advertising. Self-learning algorithms help to achieve super-accurate recommendations that make advertising activities up to 50% more efficient. But how does it work in practice?

How Deep Learning Works With Recommendations

Let’s take the example of shopping for a new dress. When a shopper clicks on anything within the website, the recommendation mechanism captures every piece of information. It checks the color of the dress, details you were focused on, the price range, sizes and dozens of other actions points. It then connects as many interaction patterns as possible. By measuring and analyzing them (in real time) the system can understand the history, taste, interests or even mood – and then make accurate predictions of interesting products. Matching heels and jewelry selections, date-night outfits, or summer wear, could be recommended based on what is predicted as most effective. This all happens without any human input on the advertiser’s end. In the field of purchase prediction the self-learning algorithms have already obtained so much knowledge, that it has rendered manual intrusions unnecessary, if not straight up misleading.

Typical models of recommendations cannot do this. Most early recommenders simply gathered information and then selected products to show with rules predefined by a human, such as for example “Show jewelry only to those which visited female clothing, since they are most likely women”. Now, this can be substituted with “Our system knows having visited female clothing is some predictor for buying jewelry, but has also learnt to detect men who intend to buy jewelry for themselves or as a gift”.

Deep learning algorithms simulate our way of thinking, but learn by practicing outcomes without any human touch. A machine will analyze countless data sets relentlessly, without getting tired or bored, and will produce super logical, risk-proof decisions without stress, doubt or emotions. It will obey the general rules of the advertiser, but most importantly, it is able learn and write new rules with proactivity and performance unachievable by human work. This is the essence of the self-learning algorithms and why they are so effective for the ad industry.

Moving Towards AI-Personalized Experiences

According to Janrain & Harris Interactive, 74% of online consumers become frustrated by content that is irrelevant to their needs on a website.

What is more, Infosys found that 86% of consumers say personalization plays a role in their purchasing decisions.

Using ultra-precise recommendations strengthens the brand’s relationship with its consumers and in fact, accelerates retailers sales, improves conversion rates and increases revenue – no matter it is movie, music or ad industry. Better accuracy and more persuasive approach in turn make deeply targeted suggestions a must-have not only for e-commerce, but also banking, insurance, travel and even in everyday grocery shopping.

Steve Jobs is often quoted as saying that “People don’t know what they want until you show it to them.” The deep learning industry may make this feat an ordinary, automated experience for every user in the digital era.