Is your company ready for data?

Take the 5-minutes survey and share your views.
If you participate, you will get a free access to the results.
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The BMMA is conducting a survey about the Data Readiness of Belgian companies. Since data is at the core of our challenges and concerns as management and marketing professionals, we thought it was critical that we address this burning topic by collecting the insights from the market.

How do you perceive the role of data within your company in various areas such as leadership & culture, operations, customers and products?
Thank you in advance for your participation.
The BMMA team.

Modern marketing: What it is, what it isn’t, and how to do it (Source: McKinsey)

 

About the author(s)

Sarah Armstrong is an alumna of McKinsey’s Atlanta office, Dianne Esber is a partner in the San Francisco office, Jason Heller is an alumnus of the New York office, and Björn Timelin is a senior partner in the London office.

Source: https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/modern-marketing-what-it-is-what-it-isnt-and-how-to-do-it?cid=other-eml-alt-mip-mck&hlkid=f099e9e5ddab4005b16a034b789bc5d2&hctky=9681456&hdpid=f805747a-735d-4f59-bb94-0e5360ea5d0b

What does “modern marketing” mean to you? We can all probably think of a clever digital campaign, an innovative app, or some inspired creative work shared across multiple channels.

While these examples contain some of the hallmarks of modern marketing, in our view it is much bigger than that. Modern marketing is the ability to harness the full capabilities of the business to provide the best experience for the customer and thereby drive growth. In a recent McKinsey survey, 83 percent of global CEOs said they look to marketing to be a major driver for most or all of a company’s growth agenda.

Delivering on this promise requires a whole new way of operating. Marketing departments need to be rewired for speed, collaboration, and customer focus. It’s less about changing what marketing does and more about transforming how the work is done. Based on successful cases we’ve seen, we estimate that making this change can unlock 5 to 15 percent of additional growth and trim 10 to 30 percent of marketing costs.

Where to start

In our experience, most senior leaders understand that marketing has to modernize, but they are less sure what specifically that means. Too often, they focus on a handful of initiatives or capabilities and then grow frustrated when the promised value doesn’t appear.

For this reason, it’s crucial to have a clear view of what constitutes a model for modern marketing (Exhibit 1). While each of these components is familiar, we have found that the clarity of seeing them organized into a cohesive model gives leaders a better sense of how to track all the elements and how they should work together.

That clarity is crucial as leaders develop plans and programs to modernize each of the capabilities and enablers (Exhibit 2). The traditional way to create content, for instance, is to roll out periodic, one-size-fits-all campaigns that can be modified only to a limited extent. On the other hand, a modern marketing organization has systems that allow for large volumes of messages and content to be constantly created, monitored by performance analytics, and then adjusted as needed. Take personalization. It used to mean broad offerings and experiences across large consumer segments. Today, the goal is to leverage data from all consumer interactions to creatively deliver as much relevant one-to-one marketing as possible.

While most CMOs we know have made progress toward developing modern marketing organizations, many are discouraged by a lack of progress. We have found that the core issues are the absence of a commitment to the full suite of changes necessary and a lack of clarity about dependencies. Without that understanding, we find that teams tend to naturally gravitate to working on things they know best or are most excited about, ignoring other elements. This creates blind spots in the transformation process that lead to delays, frustration, and, ultimately, a loss of value. Modernizing marketing capabilities, for example, requires an upgrade of four key operational enablers. But a successful transformation won’t succeed without three mindset shifts that provide a foundation for change.

Before embarking on a modern marketing transformation, there are three mindset shifts that are necessary to enable change.

1. Unifier mindset

To drive growth, marketing leaders must work collaboratively with diverse areas of the company, from sales and product innovation to finance, technology, and HR. In fact, our research has shown that CMOs (or those in a similar role, such as chief growth officer or chief customer officer) who function as “unifiers,” leaders who work with C-suite peers as an equal partner, drive greater growth than those who don’t. Unifier CMOs adopt the language and mindset of other C-suite executives, articulate how marketing can help meet their needs, and ensure that they understand marketing’s clearly defined role. Moreover, this creation of productive, collaborative relationships doesn’t end at the C-suite. Marketing leaders should role model—and set expectations for—how each member of the marketing team should collaborate seamlessly with colleagues in other functions.

2. Customer-centric mindset

Putting customers first is not a new idea, of course. What’s different today is that marketers have unequivocal evidence that meeting customers’ needs creates value and delivers competitive advantage. Modern marketers must also be aware of the challenges of complexity and scale they must meet to achieve customer-centricity. They involve commitments to several elements: a design-thinking approach to solving customer pain points and unmet needs; a centralized data platform with a unified view of customers, culled from every possible touchpoint; the continuous generation of insights from customer-journey analytics; the measurement of everything consumers see and engage with; and the hiring and development of talented people who know how to translate insights about customers into experiences that resonate with customers.

The first step is to realize that customer segmentation goes deeper than you think. The best marketers are developing capabilities for efficient engagement across numerous microsegments. By doing this, marketing organizations can better understand the motivations and behaviors of their most valuable customers. They can also organize their efforts around acquiring more of them and creating greater loyalty.

3. Return on investment (ROI) mindset

Digital channels and improvements in analytics and data science now make it both possible and necessary for marketers to be accountable for delivering value across all channels. To operate with an ROI mindset, everyone needs to operate as if the money they are spending is their own. This means closely monitoring investments, putting in place standards to identify those not generating value, and creating a culture of accountability in which underperforming investments are scrapped. Such financial rigor will not only help marketing fulfill its mandate as a growth driver; it will also build credibility with the CFO, unlock additional investment, and demonstrate marketing’s value to the entire company. One streaming company, for example, has built into the core of its culture continuous A/B testing of hundreds of variants of its website and apps and measuring their impact on viewing hours and retention. To support this, each product team has its own embedded analytics talent.

Enablers: Operating like a modern marketer

1. Organizational design and culture: Turning mindsets into behavior

To support modern marketing behavior, companies can take a number of practical actions, including the following:

  • Incentivize group success. Since delivering value to the company is a cross-functional team sport, marketing organizations need a culture focused not just on individual achievement but on shared goals, team performance, and accountability. This means changing how marketing organizations reward, acknowledge, and evaluate talent, such as the inclusion of cross-functional team key performance indicators (KPIs) tied to individual compensation. Top talent should also feel a sense of purpose and motivation, derived from an environment that delivers energy and enthusiasm. None of this happens by chance.
  • Elevate consumer insights and analytics. Because customer-centricity and ROI mindsets are critical for modern marketers, customer insights and analytics can’t be support functions within marketing. In a modern marketing organization, they will have a prominent and visible role and a leader who reports directly to the CMO. This serves as a reminder that the voice and behavior of the customer must be at the center of everything and that no marketing activities should be executed without the backing of relevant insights and the ability to measure performance.
  • Turbocharge marketing operations. Marketing operations is a backbone function, essential for a modern marketing organization to move with speed and flexibility. To make sure that marketing spending, technology, and processes are all managed to deliver maximum impact and efficiency, the best companies have installed a marketing operations lead, also reporting to the CMO. In some cases, marketing operations will exist as a shared service or central function across marketing. In other cases, it will be distributed across numerous operating units to provide autonomous execution capabilities. We’ve seen marketing operations provide a 15 to 25 percent improvement in marketing effectiveness, as measured by return on investment and customer-engagement metrics. One global financial-services company, for example, figured out that by accelerating the delivery of IT-dependent functions to marketing, it was able to generate an extra 25 percent of revenue. That was worth $100 million per year.

2. Agile marketing at scale: Getting serious about moving beyond pilots

By far the biggest change to marketing’s organizational design is the shift to agile.

As a decentralized, cross-functional model, agile is critical for operating with speed. Even the most digitally savvy marketing organizations have experienced revenue uplift of 20 to 40 percent by shifting to agile marketing. Small teams of people, called squads, work in the same place and have decision-making authority to execute highly focused tasks. Organizing squads around specific customer objectives ensures that everyone on the team is connected to the customer. Giving squads clear KPIs, such as a volume of new customers or specific revenue goals, ensures that everything is measured and evaluated. Marketing organizations that adopt agile have moved anywhere between 50 and 70 percent of their work to this more streamlined and accountable approach, quickly cutting loose anything that isn’t creating value.

Scaling agile marketing, however, entails more than flattening out an organization chart or establishing cross-functional collaboration. Squads need to have supportive participation from departments such as legal, IT, finance, and often agency partners as well. Without this broader organizational support, agile teams are confined to small pilots with limited impact. At one bank, for instance, the legal department and controller’s office were resistant to providing staff to agile marketing teams because of competing priorities. Marketing leadership knew their agile approach wouldn’t work without the other functions, so they invested sufficient time with each function’s leader to articulate how the agile team would work, what value would be generated, and how it would support the business’s overall goals. This effort gave functional leaders enough confidence in the process that they agreed to provide people to the agile squads.

3. Talent and agency management: A constant balancing act

Given the complexity of marketing today and the range of capabilities needed, marketers need a new talent strategy built around three elements:

  • Insource mission-critical roles. While there is no single model for the functions a marketing organization should handle itself, insourcing usually makes sense when there is a desire for ownership of data and technology; when companies seek strong capabilities in a certain area; or when insourcing will greatly accelerate the speed to market and allow for the constant creation, testing, and revision of campaigns.
  • Hire “whole-brained” talent. Today’s in-house roles require a broader skill set, with a balanced mix of left- and right-brain skills. This means, for instance, content producers and experience designers who are comfortable using data, and data-driven marketers who are willing to think outside the box and move closer to consumers. McKinsey research shows that companies able to successfully integrate data and creativity grow their revenues at twice the average rate of S&P 500 companies. Most importantly, modern marketing organizations don’t need managers to manage people; they need people to manage output and track performance.
  • Foster an ROI-focused management style. In an environment where autonomous teams are given the ball and asked to run with it, managers need to be comfortable setting KPIs, overseeing output, and tracking the performance of agile teams.

4. Data and technology: An obsession for looking ahead

Marketing metrics have traditionally looked backward to unearth insights about past behavior and measure the effectiveness of current campaigns. Modern marketing organizations use data analytics to look ahead. They anticipate unmet consumer needs, identify opportunities they didn’t know existed, and reveal subtle and addressable customer pain points. Data analytics can also predict the next best actions to take, including the right mix of commercial messages (for cross-selling, upselling, or retention) and engagement actions (content, education, or relationship deepening).

To do this, data must be centralized and easily accessible so that activity in one channel can immediately support real time, or near-real-time, engagement in another. Instead of the traditional approach, where IT takes the lead in data management, marketing leaders should work with IT leaders to develop a shared vision for how data will be accessed and used. This starts with the CMO and CTO/CIO collaborating closely on a business case and road map and then rallying the needed support from across the organization.


Because the pace of change in the marketplace continues to accelerate, becoming a modern marketing organization must be a “now” priority. Leaders unsure about the need to move aggressively toward this new model might bear in mind a character in Ernest Hemingway’s novel The Sun Also Rises, who is asked how he went bankrupt. “Two ways,” he answers. “Gradually, then suddenly.”

 

Reality Check: « On est en 2020 » : le 3 décembre 2015, Wout Dockx évoquait ses prédictions médiatiques pour 2020. Bien vu !

« On est en 2020. La révolution numérique est encore en plein essor. Mais beaucoup de choses ont changé. Presque tout le monde a un ordinateur en poche qui connecte tout et tout le monde dans un blitz ».

Les propos de Wout Dockx de 2015 font mouche aujourd’hui. Sa boule de cristal lui a donné de nombreuses autres perspectives il y a cinq ans avec, il se trouve, un degré élevé de vérité. Ainsi, Wout mérite le surnom de Mediaman qui lui est souvent donné.

Cette voyance n’était pas un non-sens de la part d’un charlatan :

  • En 2020, les résultats augmenteront mais iront laborieusement de pair avec « la poursuite d’un ciblage parfait. Le rêve de n’apporter que des messages pertinents s’est avéré vain ».
  • Aujourd’hui, les gens sont « rapidement fatigués d’être submergés de messages, avec un appel à plus d’intimité ».
  • Et faire don de vos données gratuitement et pour rien « est de moins en moins cool. Bloquer les messages intrusifs est un sport populaire ».

Et, c’est bien vrai !

À l’époque, Wout a également vu la technologie OLED comme une bouée de sauvetage . C’est ce qu’il a lu dans la paume de nos mains : « La technologie OLED s’est finalement avérée être la bonne, de sorte qu’aujourd’hui, une très mince et pliable double A3 tient dans votre poche. Alors que l’art de la large pagination lay-out pourrait être utilisée à nouveau sous forme numérique ». Cinq ans plus tard, nous regardons notre chemin et admettons que nous avons pris une direction différente.

Quelles sont les prévisions des médias pour 2025, Wout ?

source: https://www.mediaspecs.be/fr/on-est-en-2020-le-3-decembre-2015-wout-dockx-evoquait-ses-predictions-mediatiques-pour-2020-realistes-ou-pas/

A.I. & Retail: Carrefour propose,  en partenariat avec Google,  un sommelier virtuel pour aider ses clients  à choisir leurs vins (Source: Le Soir)

Google a aussi le nez fin

Carrefour propose,  en partenariat avec  le géant du web,  un sommelier virtuel pour aider ses clients  à choisir leurs vins.  De quoi inaugurer l’arrivée de l’intelligence artificielle  dans vos supermarchés.

 

Vous en avez déjà rêvé : mettre les pieds sous la table exclusive d’un restaurant 3 étoiles, vous faire bichonner par les équipes raffinées de l’établissement. De Karmeliet faisait encore récemment partie des quelques rares maisons belges à avoir reçu la haute distinction du Michelin. Benoît Couderé y a apporté son expertise de sommelier du pays durant plusieurs saisons. Avant de rejoindre le « retailer » Carrefour il y a deux ans.
L’idée ? Remodeler la cave des hypermarchés et la rendre plus accessible au commun des mortels que nous sommes. Alors quand les équipes digitales de l’entreprise lui ont proposé de se faire « disrupter », le maître aurait, paraît-il, accepter sans broncher.
Son savoir-faire est désormais intégré – par l’implémentation, d’abord, d’un riche fichier excell qui accorde mets et vins – à l’intelligence artificielle du géant de la Silicon Valley, l’Assistant Google. « L’idée est d’apporter l’élite à la table de chacun. Vous demandez : OK Google, je mange du lièvre ce soir et l’assistant vous propose en réponse un panel de vins (de la cave Carrefour, bien entendu, NDLR) qui s’accordent parfaitement avec votre dîner », explique Jean-Philippe Blerot, à la tête des projets digitaux et de l’e-commerce chez Carrefour Belgique.
En pratique, le « Sommelier Benoît » est aujourd’hui accessible en test dans trois magasins du groupe (Evere, Herstal et Zemst). À terme, une fois l’intelligence artificielle enrichie – « nous allons récolter les infos que les clients nous donnent à ce niveau, il s’agit d’une version bêta, loin d’être parfaite » –, le but est bien de l’intégrer aux applications de Carrefour. Et de décupler le principe à d’autres types de produits.
Offre plus personnalisée
Ce gadget sympathique peut sembler à première vue anecdotique. Il ne l’est pas. Google et Carrefour, c’est une histoire qui a débuté en juin 2018 et qui est faite pour durer, scellée par un partenariat à l’échelle mondiale. Chaque caddy rempli de produits par vos soins contient également une quantité de données personnelles impressionnantes. Or les grands « retailers » sont moins habiles que les Gaffa pour les exploiter et font face désormais à une concurrence féroce sur leur segment : Alibaba en Chine, Amazon ailleurs (notamment avec son service Pantry). « Il y avait une nécessité au niveau du groupe de s’allier à un géant du numérique », acquiesce Jean-Philippe Blerot. Preuve que le secteur de la distribution, sous pression, s’apprête à changer.
Intelligence artificielle (IA) et « machine learning » (soit les services « Cloud » de Google) permettent une multitude d’optimisations d’un business model donné : de la gestion des stocks en magasin à l’analyse de l’effet de la présence d’un concurrent, en passant par la traçabilité des produits jusqu’à, but ultime, la hausse du chiffre d’affaires.
Faire entrer l’IA en magasin et sur les applications du groupe, c’est la garantie d’une offre plus personnalisée pour (re)fidéliser un client qui éparpille désormais son pouvoir d’achat et se rend moins souvent dans des supermarchés géants. Influencer son parcours de courses aussi, s’il a tout de même fait le déplacement. « L’Assistant pourrait aider le client à mieux manger. En lui proposant de remplacer certains produits par des options plus saines sur base de son panier de courses habituel. L’idée générale est bien de personnaliser l’offre mais aussi d’encourager à la découverte », poursuit le responsable.
Des données « exclusives »
La question du respect de la vie privée est, bien sûr, ici, centrale. L’Assistant Google, qui touche 2 milliards de personnes dans le monde, est un écosystème à visée commerciale. La plateforme loue ses services à des tiers qui peuvent y développer des applications adaptées à leurs besoins.
Quid alors de la circulation, de l’utilisation et de la monétisation de vos comportements, « vinicoles » dans le cas présent. « Le « Sommelier Benoît » a été développé dans un environnement isolé, réservé à Carrefour. Aucune donnée n’est conservée par Google, nous ne gardons que le dialogue avec la machine », assure le responsable de projets. Pas question donc de retrouver sur le moteur de recherche des publicités connexes à vos demandes faites au sommelier (par définition, une telle extension serait contre-productive pour le partenaire). Chez Carrefour, on précise d’ailleurs « que si l’Assistant est intégré aux applications du groupe, une autorisation préalable sera toujours demandée aux clients avant d’utiliser leurs données. »
De quoi vous garantir un repas de Noël rehaussé de quelques grands crus du meilleur effet. Pour le plus grand bonheur de vos proches. Et également de votre « retailer » préféré…

Goodbye Funnel, Hello Flywheel: How to Build the New Customer Experience (CX) (Source: MarketingProfs)

Christi Olson is head of evangelism at Bing/Microsoft.

LinkedIn: Christi Olson

Twitter: @ ChristiJOlson

 

From mobile devices to chatbots and other intelligent agents, new digital experiences continue to disrupt customer journeys. Leading brands are not just enduring the changes but embracing the disruption—transforming the customer experience (CX) into a more human experience with meaningful customer touchpoints that drive higher engagement and deeper loyalty.

source: https://www.marketingprofs.com/articles/2019/42175/goodbye-funnel-hello-flywheel-how-to-build-the-new-customer-experience-cx

What is powering this new CX? The CX flywheel.

The concept of the flywheel in this context is simple: It’s essentially a virtuous cycle, whereing the more customer touchpoints that you create, the more data you acquire, and more data leads to greater personalization and ROI for more productive and plentiful customer touchpoints.

Like its mechanical counterpart, which stores rotational energy so that it can then be expended—to drive a train, for example—the CX flywheel fuels momentum for marketers by removing friction and blending the physical and digital worlds to propel growth in the digital era.

Businesses like Uber, Airbnb, and Netflix are already harnessing the flywheel, inviting customers further into the marketing process as customers rate and share their experiences, feeding the growth of the company.

In this customer-centric landscape, marketers are rethinking the funnel. They are looking to identify, target, and start dialogues with unique audience segments across all stages of the customer decision journey (CDJ), especially post-purchase stages such as retention and advocacy.

From funnel to flywheel, a new CDJ is taking shape, creating more meaningful touchpoints, and leading to increased revenue and ROI.

Goodbye Funnel

No doubt, future advertising will be completely different from what we know today. Marketers will use AI technologies like digital assistants, intelligent agents, and cognitive services like visual search and natural language processing to engage customers. It will be more natural, more customer-centric and friction-free. And it breaks with the traditional notion that marketers can reach their customers only in linear stages.

The funnel—a cornerstone of sales and marketing teams for over 100 years—is quickly becoming obsolete. Originally created in 1898, the AIDA funnel model (Awareness/Interest/Desire/Action) moved consumers in a linear model from a state of general awareness to a final purchase.

But that was then, and this is the data-driven now.

Hello Flywheel

Today’s consumers, especially digital Millennials, are shopping in new ways. They abandon carts in large numbers. They check reviews on mobile devices from the aisles of brick-and-mortars. They post their purchases on social. They may enter the CDJ at any stage.

In today’s world, a purchase can mark just the beginning of the CDJ as marketers turn their attention to new activity such as retention, expansion, and advocacy.

Powered by data science and customer momentum, a new customer experience flywheel is replacing the funnel as customers increasingly engage across multiple physical and digital channels. More touchpoints yield more data, which in turn yields more touchpoints—thus causing the flywheel to not only take shape but also spin faster and faster.

Microsoft Advertising’s Data-driven marketer’s blueprint for success study links the CX flywheel with more meaningful touchpoints and, in turn, higher ROI. We found that a better understanding of your CDJ leads to more customer engagement opportunities and up to a 45% incremental lift in ROI/ROAS.

In fact, today’s high performing marketers report that being able to create more customer engagement opportunities is the most important benefit of understanding your CDJ (see below).

How to Build the CX Flywheel

All businesses today should be moving toward a flywheel model that focuses on outstanding customer experiences to engage and empower customers. Here are some tips for success.

Start with the Cloud

The Cloud is the ultimate tool for flywheel success. It helps marketers unlock the potential of growing mountains of data: 2.5 quintillion bytes of data are created every day—with 90% of the world’s data created in the last couple of years.

From the Web to customer service to POS systems, data should be collected, managed, and unified in the Cloud.

First-party data such as website visits, time on page, purchase history, geography, etc., should then be combined with second- and third-party data to create richer touchpoints. In fact, 78% of high performers say combining first- through third-party data is a top priority. [Microsoft and Advertiser Perceptions study of 213 marketers and agencies in the US & UK, December 2018—January 2019.] For example, combining real-time search data with second-party LinkedIn data can help advertisers reach a targeted professional performing a specific search.

When collecting, managing, or combining data, high performing marketers are concerned about privacy and data security issues. As more brands move to CX flywheels, they should be very clear about how they are using data and put appropriate safeguards in place to secure customer data. In addition, brands need to be transparent with their customers about how they are using customer data to fuel both experience touchpoints and audience targeting.

Create meaningful touchpoints that drive insights

Think of modern touchpoints as consumer “action points” that help to power your flywheel. They work to spur engagement throughout the CDJ.

For instance, local inventory ads enable customers to shop and buy directly from the SERP. Instagram also lets consumers buy products directly from the app. Skills, digital assistants, and chatbots all work to reduce friction and quickly move consumers to a state of action.

At the same time, these touchpoints should work to collect data and help you build out fuller customer profiles. High-quality, first-party data enables you to create more personalized touchpoints, which further powers your flywheel.

Establish trust

Internal studies at Microsoft Advertising have shown that the majority of today’s busy consumers are willing to share their information, especially when it brings them value and saves them time. That said, today’s consumers also expect brands to respect their privacy and safeguard their data. This needs to be done in a transparent and respectful way.

The key for successful data management is transparency along with safeguards in place to ensure protection. Consumers should have control of their data with opt-in/opt-out capabilities and full disclosure of how you are using their data.

Based on Microsoft Advertising research, other best-practices include shifting away from cookies to first-party data, creating new internal standards for data collection and usage, and reducing the amount of consumer tracking. [Microsoft and Advertiser Perceptions study of 213 marketers and agencies in the US & UK, December 2018—January 2019.]

Future Flywheels

For years, marketers have been talking about reaching the right person with the right message at the right time. But flywheel success depends on a newer mantra: The right strategy with the right data and the right technology.

How can businesses get their CX flywheels to spin faster? The answer lies in more data, AI, and machine-learning (ML). With the right strategy, the right data, and the right technology, businesses will further transform the customer experience, continuing to explore new ways to enhance the customer relationship.

From personalization to lead scoring to predicting consumer behavior, A/B-testing, website optimization, and more, AI is illuminating the way.


ABOUT THE AUTHOR
image of Christi Olson

Christi Olson is head of evangelism at Bing/Microsoft.

LinkedIn: Christi Olson

Twitter: @ ChristiJOlson

Marketing Technology Trends for 2019 and 2020: more than 75% of the customers expect that companies understand their needs and expectations

Author: Avi Ben Ezra is the Chief Technology Officer (CTO) and Cofounder of SnatchBot and SnatchApp (Snatch Group Limited)

Source: https://www.newstrail.com/marketing-technology-trends-for-2019-and-2020/

 

Marketers, today have a variety of tools available to them to offer consumers and business buyers the convenience, relevance and responsive engagement expected. Customers are now better connected than ever before, they have presence and interact across a broad range of media. The fast pace with which marketing continues to move forward means that it is extremely easy to be left behind and the fast developments in marketing technologies are not something to be ignored. They not only make predictions easier, but also take a load of work off the shoulders of the employees in the marketing department but also increase the way that the rest of the company interacts with its customers, giving them the personalization and interaction that they demand. A good marketing program necessitates that marketers make use of these modern technology trends, since they understand the needs and behaviors of their consumers better than everyone else, presenting initiatives that will continue transforming their company campaigns to meet the challenges ahead.

With the second quarter of 2019 already gone, what are those technology trends that will continue to shape the way marketers plan ahead?

Taking the statistics from previous years and comparing then with the present, there is a significant rise in marketing tech users and companies are seeing noteworthy results in their competitive advantage.

It is estimated that the sharing of marketing metrics with sales teams has grown at a rate of 21% in the last year and a half and similarly, so have the number of data sources used by marketers. The adoption of AI is increasing at an even faster rate and marketers are making more use of coordinated channels too.

What is driving these increases?

The driving force behind the increases is the connected customer, who expects convenience, relevance and responsive engagement. Consumers judge a company from their overall experience and never separate interactions with various departments. Therefore, it is important that they see the company that they buy from as one with the company that they interact with and that they get the same level of service across all departments.

According to consumer surveys, today’s consumers don’t only have high standards for product quality, but also expect these to be matched in other interactions with other services that they might need. Competition is stiff and consumers have all the information about competitors available in seconds through their Smartphones and laptops.

Gone are the days when each department worried about its own performance. Today, sales, customer service, commerce and marketing are responsible for ensuring the entire consumer experience – no department can afford to act independently from the other. The initiatives for these efforts always fall onto the marketing department since it completely understands customer needs and behavior.

How can marketers lead their companies to success?

These three essential technology trends will enable marketers to continue to direct businesses in a successful direction.

  1. Personalization is the top priority

The majority of customers, more than 75%, expect that companies understand their needs and expectations. At the same time, over 50% expect that any offers they receive are personalized. Therefore, personalization is a crucial element and comes with important benefits which include: brand building, lead generation, customer acquisition, up-selling; customer retention and customer advocacy.

Artificial intelligence (AI) is one of the most important marketing tech tools for personalization because it aids marketers to unlock the data needed.

The capabilities of AI are expand continuously and so do the ways that marketers use it: predictive journeys, real-time offers, improved customer segmentation,  personalized channel experience, automated social and messenger app interactions, dynamic landing pages and websites, media buying, and offline/online data experience facilitation and programmatic advertising.

Personalization is important to customers, but so is transparency about how that data is used. Regulators also worry about transparency and recent data breaches have also shaken consumer confidence.

High percentages (over 75%) of customers do trust companies with personal information if it is to be used to fully personalize their experience, but they demand clarity on how the information will be used.

In the past two years, marketers are more mindful about how privacy and personalization can be balanced in order to fully satisfy their customer needs.

  1. Making sense of customer data

Data sources for marketers abound as they go about tallying email open rates, ad click rates and more. This information allows them to engage the right individual with the right information and also at the right time. In 2017, the median data sources available to marketers were 10 and the number has shot up by 15% already in a period of just 2 years.

More data does not necessarily mean a more unified view, as many marketers struggle to make sense of all the data that becomes available. A mix of various solutions is often the preferred set up for some marketers, where they unite data from marketing databases to email service providers. Another one of the fast growing areas in marketing tech is data management platforms (DMP) which are offering a wide range of solutions for this problem and for many others too.

Originally, DMP was mostly used for monitoring ad performance and optimizing media campaigns. Organizations have evolved its uses and now often include the management of customer identity and other solutions. DMP use is on the rise and it will continue to offer marketers a unified solution to their customer identity challenges and vital opportunities in marketing management.

  1. Cross-channel marketing

The top challenge and priority for marketers is to have the ability to engage customers in cross-channel, real-time conversations. This is where proper use of RPA and omni-channel chatbot solutions are valuable.

Even though cross-channel marketing is not a new concept, it is not always easy to achieve since most customers now use an average of 10 channels in their communications with companies. Standard expectations are to have two-way communication with customers. Most messages across channels are duplicates, and there is no coordination between them. The ideal communication, something which most companies are aiming to achieve, is to engage customers dynamically across channels through messages that evolve across each communication channel.

The channels of communication are mostly: website, mobile app, social advertising, video advertising, social publishing, Email, mobile messaging, banner ads, paid search (SEM) and voice activated personal assistants.

Marketers need to start meeting the expectations that customers have for cross-channel engagement. Marketing technology allows for such collaboration between marketing and the other teams within the business, allowing it to be more competitive.

The integration of new technologies will be the way forward for businesses in order for them to be able to better enhance their customer experience. Of the marketing technologies that are expected to have the biggest impact on interactions, data collection, and personalization are the technologies of artificial intelligence and augmented reality.

Conclusion:

Marketers need to take time to start understanding how the digital landscape and the technology it offers can improve the performance of their campaigns. Perhaps, starting with the implementation of one trend to may be enough, but incorporating them cohesively can lead to even greater results. Suggested reading: Avi Ben Ezra on Chatbot deployment in Europe. 

“Amazon va favoriser le rapprochement du retail et de la publicité”

Guillaume Planet, VP media & digital marketing du Groupe SEB, et ancien directeur d’agences médias (Havas, Fullsix, Dentsu Aegis…), livre son analyse sur les bouleversements du secteur publicitaire causés par le déploiement des activités publicitaires d’Amazon.

 

En dévoilant, lors de ses différentes communications financières depuis début 2018, des chiffres de ventes publicitaires en très forte progression, pour ne pas dire bluffants – plus ou moins 4,2 milliards de dollars sur les six premiers mois de l’année – Amazon a officialisé son arrivée parmi les grands acteurs du marché publicitaire. Le cabinet eMarketer prévoit même que le groupe devienne dès 2018 le troisième acteur de la publicité en ligne aux Etats-Unis, devant Microsoft et Oath.

source: https://www.mindnews.fr/article/13318/amazon-va-favoriser-le-rapprochement-du-retail-et-de-la-publicite/

Et ce n’est qu’un début. Car les passerelles entre ses activités de distributeur de produits en ligne et ses activités publicitaires lui offrent des perspectives énormes.

Si on s’arrête sur le secteur du retail, on peut supposer que les développements d’Amazon vont créer des vocations chez les autres acteurs tant cette évolution du modèle est intelligente. Elle s’appuie sur plusieurs leviers :

1 – Un avantage concurrentiel

Amazon a un double avantage concurrentiel avec les autres vendeurs d’espaces publicitaires : une possession massive de data transactionnelles, associée à une position de clients et non de fournisseurs vis-à-vis des marques qui achètent ces espaces publicitaires.

2 – Un cercle vertueux achat – data – publicité

Amazon jouit d’un cercle vertueux d’investissements publicitaires financés par les marques qui drivent un trafic très qualifié grâce à la data, nourrit le core business de vente des retailers, et alimente encore plus en data qui vont elles même nourrir le volet publicitaire.

3 – Un levier de marge

L’activité publicitaire, surtout avec ses actifs présentés plus haut, offre surtout à Amazon des perspectives de profitabilité élevée, alors que l’activité de négoce l’est peu par nature.

 

Quel impact sur le marché publicitaire ?

 

L’impact du développement d’Amazon sur la publicité est triple concernant le secteur :

1 – Les plateformes suivent le même sillon

Les autres acteurs en devenir vont devoir se rapprocher du monde du retail. Google en fait une priorité comme le montre les récents partenariats avec Wallmart et Carrefour et l’investissement dans JD.com. Tencent en fait de même, et Facebook s’y intéresse aussi très probablement, comme le montre la place de marché actuellement en test sur la plateforme.

En effet, les opportunités sont grandes pour ces acteurs en termes de data très pertinentes pour nourrir l’efficacité des solutions proposées. Le retail ouvre aussi accès à d’autres types de budgets marketing des marques : les fameux budgets “BTL”, dédiés aux points de ventes, souvent supérieurs aux budgets publicitaires.

2 – Des opportunités pour de nouveaux acteurs

Ces développements du marché derrière Amazon créent des opportunités pour de nouveaux types d’acteurs pure players de la publicité retail, par exemple Criteo.

3 – Les médias encore plus marginalisés

Mais Amazon pousse surtout un peu plus les acteurs historiques de la vente d’espace publicitaire – je parle ici des médias traditionnels – vers un rôle plus marginal sur ce modèle économique. Ces derniers, déjà chahutés par Google et Facebook, font face à un nombre croissant de concurrents mieux armés pour profiter des transformations du secteur de la publicité : ils sont riches en data ultra-pertinentes, matures en expertises digitales et data, possesseurs d’infrastructures techs sophistiquées, et hyper-puissants financièrement.

Face à cette nouvelle donne, les médias prennent de plus en plus le sujet dans le bon sens. Après une période de déni et de diabolisation des GAFA, ils cherchent maintenant de plus en plus à investiguer de nouveaux modèles économiques et revoient leur relation avec Google et Facebook, qui doivent être assimilés à des partenaires pour contribuer à engager au mieux leurs audiences.

 

Quelle réaction pour les grands distributeurs ?

 

Les retailers réagissent différemment. Ils ont étonnamment tendance à se rapprocher immédiatement de leurs nouveaux concurrents : Wallmart et Carrefour pactisent avec Google, Carrefour avec Tencent, Auchan avec Alibaba, Monoprix avec Amazon… Leur objectif est d’apprendre à travers ces partenariats, mais les risques sont évidemment importants.

Quels sont-ils ? Que Monoprix perde l’accès à la data, moteur du nouveau modèle vertueux du retail en s’associant à la market place d’Amazon. Que Carrefour et Wallmart offrent potentiellement à un futur concurrent – au minimum sur le volet publicitaire -, Google, l’opportunité de développer sa courbe d’expérience dans l’univers du retail. Enfin qu’Auchan prend le risque de donner les clés de compréhension de nouveaux marchés cible pour Alibaba.

Le rapprochement avec des acteurs certes matures en digital et data, mais moins menaçants (de type Criteo, par exemple sur le volet publicitaire) serait probablement une démarche moins risquée pour apprendre les nouveaux codes de ce secteur.

Havas Media Belgium se renforce – 13 nouveaux Talents en 6 mois : expérience et jeunesse, une formule gagnante !

NewTalents_Août2018.jpg

En plus de la mise-sur-pied d’un programme d’éducation intensif, Havas Media a également recruté de nombreux talents, qui renforcent aujourd’hui l’ensemble des entités de l’agence.

En début d’année, on enregistrait déjà l’arrivée d’éléments expérimentés tels que Marc Dewulf (ex-Dentsu Aegis) COO assurant la direction de l’ensemble des expertises ; Ruben Ceuppens (ex Social Lab) Head of Socialyse ; et Arnaud Destrée (ex-GroupM) Head of Programmatic.

Depuis mai, pas moins de 10 nouveaux talents ont rejoint l’agence !
Patricia Lo Presti (ex-UM) à l’équipe Commerciale (Conseil) en tant qu’Account Director, tout comme Maurine Piette (ex-MediaCom) comme Account Manager, et Josiane Uwimana en tant qu’Account Executive.

 

Caroline Grangé (ex-IPM) a pris la tête du Publishing (Presse et Digital), accompagnée par Aurélie Renquet (ex-IPM) et de Séfana Zoufir, respectivement Publishing Account Manager et Publishing Account Executive.

 

Sandra Ruiz-Pelaez (ex-Havas Media Barcelona, ex-OMD España) est venue apporter son expérience internationale en tant que Performance Expert.
Gaetan Ickx – PhD en sciences biomédicales à l’UCL (CSA, Data Analyst), Céline Denoiseux (Broadcast, Account Executive) et Julien Droulans (Operations Coordinator) sont venus finaliser les recrutements de ces huit premiers mois.
Hugues Rey, CEO Havas Media Group: “Nous évoluons dans un environnement serein depuis de nombreux mois, qui se traduit par des recrutements optimisés; nous investissons davantage dans la recherche et la formation des profils adéquats, qui apportent une réelle plus-value à l’organisation. Nous sommes heureux de pouvoir accueillir des talents d’horizons divers qui viennent enrichir les équipes de leur expérience nos équipes”

Artificial intelligence and machine learning: What are the opportunities for search marketers? (Author: Albert Gouyet)

Did you know that by 2020 the digital universe will consist of 44 zettabytes of data (source: IDC), but that the human brain can only process the equivalent of 1 million gigabytes of memory?

Source: https://searchenginewatch.com/2018/01/02/artificial-intelligence-and-machine-learning-what-are-the-opportunities-for-search-marketers/

The explosion of big data has meant that humans simply have too much data to understand and handle daily.

For search, content and digital marketers to make the most out the valuable insights that data can provide, it is essential to utilize artificial intelligence (AI) applications, machine learning algorithms and deep learning to move the needle of marketing performance in 2018.

In this article, I will explain the advancements and differences between artificial intelligence (AI), machine learning and deep learning while sharing some tips on how SEO, content and digital marketers can make the most of the insights – especially from deep learning – that these technologies bring to the search marketing table.

I studied artificial intelligence in college and after graduating took a job in the field. It was an exciting time, but our programming capabilities, when looking back now, were rudimentary. More than intelligence, it was algorithms and rules that did their best to mimic how intelligence solves problems with best-guess recommendations.

Fast forward to today and things have evolved significantly.

The Big Bang: The big data explosion and the birth of AI

Since 1956, AI pioneers have been dreaming of a world where complex machines possess the same characteristics as human intelligence.

In 1996, the industry reached a major milestone when the IBM’s Deep Blue computer defeated a chess grandmaster by considering 200,000,000 chessboard patterns a second to make optimal moves.

Between 2000 and 2017, there were many developments that enabled great leaps forward. Most important were the geometric increases in the amount data collected, stored, and made retrievable. That mountain of data, which came to be known as big data, ushered in the advent of AI.

And it keeps growing exponentially: in 2016 IBM estimated that 90% of the world’s data had been generated over the last few years.

When thinking about AI, machine learning and deep learning, I find it helps to simplify and visualize how the 3 categories work and relate to each other –  this framework also works from a chronological, sub-set development and size perspective.

Artificial intelligence is the science of making machines do things requiring human intelligence. It is human intelligence in machine format where computer programs develop data-based decisions and perform tasks normally performed by  humans.

Machine learning takes artificial intelligence a step further in the sense that algorithms are programmed to learn and improve without the need for human data input and reprogramming.

Machine learning can be applied to many different problems and data sets. Google’s RankBrain algorithm is a great example of machine learning that evaluates the intent and context of each search query, rather than just delivering results based on programmed rules about keyword matching and other factors.

Deep learning is a more detailed algorithmic approach, taken from machine learning, that uses techniques based on logic and exposing data to neural networks (think human brain) so that the technology trains itself to perform tasks such as speech and image recognition.

Massive data sets are combined with pattern recognition capabilities to automatically make decisions, find patterns, emulate previous decisions, etc. Self-learning comes from here as the machine gets better from the more data that it is supplied.

Driverless cars, Netflix movie recommendations and IBMs Watson are all great examples of deep learning applications that break down tasks to make machine actions and assists possible.

Organic search, content and digital performance: Challenge and opportunity

Organic search (SEO) drives 51% of all website traffic and hence in this section it is only natural to explain the key benefits that deep-learning brings to SEO and digital marketers.

Organic search is a data-intensive business. Companies value and want their content to be visible on thousands or even millions of keywords in one to dozens of languages. Search best practices involve about 20 elements of on-page and off-page tactics. The SERPs themselves now come in more than 15 layout varieties.

Organic search is your market-wide voice of the customer, telling you what customers want at scale. However, marketers are faced with the challenge of making sense of so much data, having limited resources to mine insights and then actually act on the right and relevant insight for their business.

To succeed in highly demanding markets against your competitors’ many brands now requires the expertise of an experienced data analyst, and this is where machine learning and deep learning layershelp recommend optimizations to content.

Connecting the dots with deep learning: Data and machine learning

The size of the organic data and the number of potential patterns that exist on that data make it a perfect candidate for deep learning applications. Unlike simple machine learning, deep-learning works better when it can analyse a massive amount of relevant data over long periods of time.

Deep learning and its ability to identify or prioritize material changes in interests and consumption behavior allows organic search marketers to gain a competitive advantage, be at the forefront of their industry, and produce the material that people need before their competitors, boosting their reputation.

In this way, marketers can begin to understand the strategies put forth by their competitors. They will see how well they perform compared to others in their industry and can then adjust their strategies to address the strengths or weaknesses that they find.

  • The insights derived from deep learning technologies blend the best of search marketing and content marketing practices to power the development, activation, and automated optimization of smart content, content that is self-aware and self-adjusting, improving content discovery and engagement across all digital marketing channels.
  • Intent data offers in-the-moment context on where customers want to go and what they want to know, do, or buy. Organic search data is the critical raw material that helps you discover consumer patterns, new market opportunities, and competitive threats.
  • Deep learning is particularly important in search, where data is copious and incredibly dynamic. Identifying patterns in data in real-time makes deep learning your best first defense in understanding customer, competitor, or market changes – so that you can immediately turn these insights into a plan to win.

To propel content and organic search success in 2018 marketers should let the machines does more of the leg work to provide the insights and recommendations that allow marketers to focus on the creation of smart content.

Below are a just a few examples of the benefits for the organic search marketer:

Site analysis

Pinpoint and fix critical site errors that drive the greatest benefits to a brand’s bottom line. Deep learning technology can be used to incorporate website data, detect anomalies tying site errors to estimated marketing impact so that marketers can prioritize fixes for maximum results.

Without a deep learning application to help you, you might be staring at a long list of potential fixes which typically get postponed to later.

Competitive strategy

Identifying patterns in real-time makes deep learning a brands’ best first defense in understanding customer, competitor, or market changes– so that marketers can immediately turn these insights into a plan to win.

Content discovery

Surface high-value topics that target different content strategies, such as stopping competitive threats or capitalizing on local demand.

Deep learning technology can be used to assess the ROI of new content items and prioritize their development by unveiling insights such as topic opportunity, consumer intent, characteristics of top competing content, and recommendations for improving content performance

Content development

Score the quality and relevance of each piece of content produced. Deep learning technology can help save time with automated tasks of content production, such as header tags, cross-linking, copy optimization, image editing, highly optimized CTAs that drive performance, and embedded performance tracking of website traffic and conversion.

Content activation

Deep learning technology can help ensure that each piece of content is optimized for organic performance and customer experience—such as schema for structure, AMP for better mobile experiences, and Open Graph for Facebook. Technology can help marketers can amplify their content in social networks for greater visibility.

Automation

Automation helps marketers do more with less and execute more quickly. It allows marketers to manage routine tasks with little effort, so that they can focus on high-impact activities and accomplish organic business goals at scale.

Note: To make the most of the insights and recommendations from deep learning marketers need to take action and make the relevant changes to web page content to keep website visitors engaged and ultimately converting.

Additionally, because the search landscape changes so frequently, deep learning fuels the development of smart content and can be used to automatically adjust to changes in content formats and standards.

Deep learning in action

An example of deep learning in organic search is DataMind. BrightEdge (disclosure, my employer) Data Mind is like a virtual team of data scientists built into the platform, that combines massive volumes of data with immediate, actionable insights to inform marketing decisions.

In this case the deep learning engine analyzes huge, complex, and dynamic data sets (from multiple sources that include 1st and 3rd party data) to determine patterns and derive the insights marketers need. Deep learning is used to detect anomalies in a site’s performance and interpret the reasons, such as industry trends, while making recommendations about how to proceed.

Conclusion

Think of deep learning applications as your own personal data scientist – here to help and assist and not to replace. The adoption of AI, machine learning and now deep learning technologies allows faster decisions, more accurate and smarter insights.

Brands compete in the content battleground to ensure their content is optimized and found, engages audiences and ultimately drives conversions and digital revenue. When armed with these insights from deep learning, marketers get a new competitive weapon and a massive competitive edge.

The Future of Data Analytics for Retail

Source: The Future of Data Analytics for Retail

Late last year, Amazon premiered a system that may well be the future of shopping. Nicknamed Amazon Go, it looks just like a regular brick and mortar store, except there are no lines, no self-checking machines, and no cashiers. The items you buy are checked by sensors, your account is charged through your mobile Amazon Go app, and you can just walk out of the store whenever you please.

Amazon Go is a revolutionary spin on retail, commerce, and the experience of going to a store. What’s really special about Amazon Go, however, is what it represents in terms of data.

All across the retail universe, the rapidly widening Internet of Things is becoming equipped for high-frequency event analytics. Across the board, that means faster decision-making, more helpful data, and smarter, more cost-efficient businesses.

The Future of Data Analytics for RetailCLICK TO TWEET

Retail and event-driven analytics

The “event-driven” company, according to VC Tom Tunguz, is one that consumes events as they occur, in real-time, from whatever data sources are available.

Rather than record data manually—making all your data liable to corruption—event-driven companies have set up the pipelines they need to always be collecting up-to-date, quality information.
event-driven saas

(Source: Tom Tunguz)

The first stage in this process—“events occur”—is the most important one to consider in the retail context.

On a website, those events are fairly easy to understand. They might be clicks, button-presses, or scrolling behavior. We’ve been trained to think about the web in terms of events—not so with brick and mortar. And yet, the amount of events that could conceivably be collected as data from a single retail experience is tremendous.

When people enter the store, what items they pick up, which they take with them and which they put down, what order they shop in, even how they navigate the store down to the most infinitesimal of details—all of this is information that could help companies increase revenues, lower costs, and build more efficient businesses. That’s also just the front-end of the retail experience.

The new retail Nervous System

The Internet of Things has spread rapidly up and down the production supply chain, laying the foundation for the future of retail.

RFID chips on products allow companies to track their inventory with an unprecedented degree of precision, even as their shipments rattle around in shipping containers, cargo ships move in and out of port, and trucks travel across the country.

Companies like Flexport make it possible to manage and visualize those complex supply chains, many of which were barely even digitized years ago. Others help optimize last-mile delivery, manage the capacities of warehouses, and plan out routes for truck drivers bringing goods to market.

In stores, the same tags that help track goods as they move around the world can be used to optimize pricing given alterations in local conditions or sudden surges of demand.

This network of physical/digital infrastructure is just the substratum, however, of the true analytics-enabled future of retail.

When data analytics meets retail

Event data is the foundation of all behavioral analytics.

When you’re tracking every discrete click, scroll, or other web action, you can start to look for patterns in the data that you’re collecting. You can see which pieces of content on your blog engage the most users, which version of your checkout flow is the best for conversions, and so on.

There’s already technology out there to help investors like those at CircleUp analyze data around small businesses and predict those that will succeed based on a large corpus of historical data.

With the infrastructure of the Internet of Things in place, the same kind of analysis becomes possible on a physical scale. You can start to find patterns in what people buy, when people order, and how to build a more efficient goods-delivery system.

The possibilities are extensive and powerful. In Amazon’s concept store, you can easily imagine sensors that take notice whenever your gaze rests on a particular item for longer than usual, or when you pick something up only to put it back down afterwards.

The decision to not purchase an item would be just as important for Amazon’s recommendation engine as a confirmed sale—that data could even be fed back to the supplier for their marketing team to analyze the lost sale. Visual recognition systems could be used to show you an ad in the evening for that dress you were eyeing at the store in the afternoon.

That’s just scratching the surface of an extensive universe of possibilities. Already today, IoT-enabled retail is allowing companies to:

  • identify fraud before anyone from Loss Prevention even notices it’s happening
  • systematically reduce shrinkage by analyzing exactly where it’s coming from
  • give estimated delivery times in as small as 10-minute windows

A few years ago, Amazon patented the idea of “anticipatory shipping”—moving goods around based on their predictive analysis of likely consumer behavior. Because of your history, in other words, Amazon could predict that you were about to order a pack of toilet paper—and make sure it was in stock at the closest distributor well before you even clicked on the order button.

In the retail world of the future, innovations like these won’t be cutting-edge. In the age of data analytics, they’ll be little more than table stakes.

The data analytics long tail

The free flow of event data in retail depends on the proliferation of data sources. The more sources of data that can be cross-referenced, the more patterns that can be found and the more intelligence that can be produced.

Fortunately, the retail space is in a great position for data sources. There are not only a massive number of in-store sources of data, from sensors to registers to RFID tags, but there are complementary online sources as well.

big data sources

(Source: IBM)

For businesses that exist only as brick and mortar, the proliferation of IoT components and data analysis will mean a massive step forward in terms of business intelligence.

For those that are both brick and mortar stores and online, the confluence of the IoT and traditional behavioral analytics will mean an unprecedented wealth of data and an unprecedented set of options for customer engagement.

For those of us who have thrown in our lot with data, it is an exciting and fascinating time to be around.