A recipe for the MarTech Layer Cake: DMP, CRM, AI, DM, CRM … and much more | source: Econsultancy

Source: A recipe for the MarTech Layer Cake | Econsultancy

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Today’s “marketing stack” really consists of three individual layers.

Data management contains all of the “pipes” used to connect people and identity together; orchestration ties all the execution systems together to reach customers at the right time and channel; and AI is the brains behind the stack.

This technology layer cake was presented at this year’s Industry Preview by Brian Anderson of LUMA Partners who talked about the future of marketing technology.

Anderson’s unifying marketechture drawings looked like an amalgamation of various whiteboarding sessions I have had recently with big enterprise marketers, many of whom are building the components of their marketing “stacks.”

Marketers are feverishly working to build a vision that can be summed up like the image below. Let’s discuss each layer in a little more detail..

tech layer cake

The Data Management Layer

The first layer, Data Management (DM), contains all of the “pipes” used to connect people and identity together. Every cloud needs to take data in from all kinds of sources, such as internet cookies, mobile IDs, hashed email identity keys, purchase data, CRM attribute data, ecommerce data, and the like.

Every signal we can collect results in a richer understanding of the customer, and the DM layer needs access to rich sets of first, second, and third-party data to paint the clearest picture.

The DM layer also needs to tie every single ID and attribute collected to an individual, so all the signals collected can be leveraged to understand their wants and desires. This identity infrastructure is critical for the enterprise; knowing that you are the same guy who saw the display ad for the family minivan, and visited the “March Madness Deals” page on the mobile app goes a long way to understanding marketing attribution.

But the data management layer cannot be constrained by anonymous data. Today’s marketing stacks must leverage data management platforms (DMPs) to understand pseudonymous identity, but must find trusted ways to leverage personally identifiable information (PII)-based data from email and CRM systems.

This latter notion has created a new category—the “Customer Data Platform” (CDP), and also resulted in the rush to build data lakes as a method of collecting a variety of differentiated data for analytics purposes.

Finally, the data management layer must be able to seamlessly connect the data out to all kinds of activation channels, whether they are email, digital advertising, social, mobile, OTT, or IoT-based.

Just as people have many different ID keys, people have different identity keys inside of Google, Facebook, Pinterest, and the Wall Street Journal. Connecting those partner IDs to an enterprises’ universal ID solves problems with frequency management, attribution, and offers the ability to sequence messages across various addressable channels.

You can’t have a marketing cloud without data management. This layer is the “who” of the marketing cloud—who are these people and what are they like?

The Orchestration Layer

The next thing marketers need to have is an orchestration layer, also thought of as journey management. This is the “When, Where, and How” of the stack.

Email systems can determine when to send that critical email; marketing automation software can decide whether to put someone in a “nurture” campaign, or have a salesperson call them right away; DSPs decide when to bid on a likely internet surfer; and social management can tell us the best time to Tweet or Snap.

Content management systems and site-side personalization orchestrate the perfect content experience on a webpage, and dynamic creative optimization systems have gotten pretty good at guessing which ad will perform better for certain segments.

The “when” layer is critical for building smart customer journeys. If you get enough systems connected, you start to realize the potential for executing on the “right person, right message, right time” dynamic that has been promised for many years, but never quite delivered at scale.

Adtech has been busy nailing the orchestration of display and mobile messages, and the big social platforms have been leveraging their rich people data to deliver relevant messages. However, with lots of marketing money and attention still focused on email and television, there is plenty of work to be done before marketers can build journeys that are fully connected and communicating across all touchpoints with a consumer.

Marketers today are looking to better connect various systems and gett them to talk to each other to figure out the “when, where, and how” of marketing.

The Artificial Intelligence Layer

When every single marketer and big media company uses a DMP, and has figured out how to get journey management working, it is clear the next big initiative to tackle is how to make it all smarter and efficient with the AI layer.

Artificial intelligence represents the “why” problem in marketing—why am I emailing this person instead of calling her? Should I be targeting this segment at all? Why does this guy score highly for a new car purchase, and this other guy who looks similar doesn’t? What is the lifetime value of this new business traveler I just acquired?

While the stacks have tons of identity data, advertising data, and sales data, they need a brain to analyze all of that data and decide how to use it most effectively. As marketing systems become more real-time and more connected to on-the-go customers than ever before, artificial intelligence must drive millions of decisions quickly, gleaned from billions of individual data points.

How does the soda company know when to deliver an ad for water instead of diet soda? It requires understanding location, the weather, the person, and what they are doing in the moment. AI systems are rapidly building their machine learning capabilities and connecting into journey management systems to help with decisioning.

All together now

The layer cake is a convenient way to look at what is happening today. The vision for tomorrow is to squish the layer cake together in such a way that enterprises get all of that functionality in a single cake.

Sooner than you think, the marketing technology stack will have some kind of built-in DMP. Journey management systems will all have built-in artificial intelligence as a means for differentiation. Look at email orchestration today. It is not sold on its ability to deliver messages to inboxes, but rather on its ability to provide that service in a smarter package to increase open-rates, conversion and provide richer analytics.

It will be fun to watch as these new components come together to form the marketing clouds of the future.

Chris O'Hara

Les médias propriétaires des marques impactent de plus en plus la perception des consommateurs | CB Expert

Source: Les médias propriétaires des marques impactent de plus en plus la perception des consommateurs | CB Expert

Paid, Owned, Earned Media : la publicité média continue de reculer et l’avis des internautes progresse au détriment des retombées éditoriales

Le Pôle Media d’Havas Group présente aujourd’hui les résultats de la 6ème édition de son baromètre de la perception des marques, sur les différents points de contact Owned, Shared, Earned, Paid (OSEP). La dimension Shared a été intégrée l’an dernier : elle est définie comme l’association de marques de secteurs différents pour proposer de nouveaux produits/offres ou promouvoir une cause. Elle est mesurée uniquement par secteur.

Dans la perception des marques par le consommateur, les médias payants ne comptent désormais plus que pour la moitié (50%), contre 53% en 2016 et 59% en 2011. Ils se maintiennent toutefois devant les médias propriétaires (36%), en progression de 3 points vs 2016, et devant les médias publics (14%), stables.


Au sein du Paid Media, les tendances observées il y a un an se confirment. La perception de la publicité continue de s’éroder (-1 point) au profit du sponsoring (+1 point), même si elle reste nettement majoritaire (74%) dans la perception (contre 11% pour le sponsoring). Le poids des prospectus et emailings reste stable à 15%.

 

Au sein du Owned Media (catégorie en hausse de 3 points), les points de vente passent sous la barre des 50% en perdant 1 point de perception et s’établissent désormais à 49%. C’est le CRM qui profite de ce léger retrait (+1 point à 6%). La perception des sites internet, applications et pages de la marque sur les réseaux sociaux est quant à elle maintenue à 45%.

 

La perception de la page de la marque sur les réseaux sociaux (9%) gagne un point au détriment de celle des sites web et applications de la marque (46%, -1 point).

 

Au sein du Earned Media, l’opinion des proches reste majoritaire à 49% (-1 point), devant les retombées éditoriales (30%, -2 points) et l’opinion des internautes qui continue de progresser (21%, +3 points).

Havas Media detail earned-min

 

La présentation des résultats est disponible ici : Havas Media OSEP 2017.

 

Le baromètre OSEP 2017 est réalisé par l’institut CSA Research, auprès de 5400 individus de 15 à 59 ans interrogés en novembre 2016 sur 270 marques et 22 secteurs d’activité. 

– Paid Media :Publicité dans les médias, Sponsoring et Mécénat, Prospectus, Mailing et E-mailing
– Owned Media : Points de vente, Sites Internet ou Applications mobiles des marques, Pages Facebook des marques, Catalogues et Magazines des marques
– Earned Media : Opinion des proches, Opinion des internautes, Articles de presse et reportages citant la marque.
– Shared Media : Association de marques de secteurs différents pour proposer de nouveaux produits/offres, promouvoir une cause (mesuré uniquement par secteur sur le baromètre OSEP).

Emmanuel Charonnat

Top 10 – ‘Meaningful Brands’ – Belgique (Source: Havas – Infographie: Mediafin)

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Meaningful Brands is a unique global study from Havas Group that links brand performance to our quality of life and wellbeing. This year, Meaningful Brands 2017 also reveals new data that tracks the relationship between a brand’s business performance, its meaningfulness and the content it produces.

The largest global study of its kind – spanning 33 countries, 300,000 people and 1500 brands – it is also the first study to analyse and measure content effectiveness at this scale.

Meaningful ?  Go beyond the product, exploring how brands tangibly improve peoples’ lives and the role they play in society !

More Meaningful News ? http://www.meaningful-brands.com/en/news

How to change the recruitment process using a Chatbot (by George Perry – Dialect.ai)

Source: How we changed our recruitment process using a Chatbot

I have been working in the marketing and business development team of my company for a little over a year now, and as a retail consultancy harnessing the power of big data, our growth in recent years has been phenomenal — closing on 30% client growth for this financial year. This growth, however, has precipitates a perpetual problem: we are always stretched to capacity. Like many companies in our sector, finding staff that are both experienced and able to adapt to the latest technology is difficult and frustrating. And within the incestuous world of retail, searching for new faces is even more tiresome.

It means, as a company, we invest heavily in graduates; hiring fresh-faced students who are eager to learn but lack decent industry experience. Although they are great investment, the vast number of applicants for the junior roles can often result in graduate recruitment days which yield candidates who are either not a good fit or when they learn more about the job, drop out of the process — a frustrating but unavoidable consequence of the recruitment process.

Or is it?

I knew we had a job to be done — a graduate recruitment process with better, more suitable candidates that are actually interested in applying for the role — but we had no clear way of improving the process. Well, until we looked at chatbots.

The Perspective

I had been building chatbots in the music and events industry for around 6 months before trying to apply their potential to our recruitment process. And the one key learning that I took with me was the lens through which companies and businesses need to look at chatbots. This was a revelation that one of my co-founders at Dialect, Cameron, can be credited for finding. He began looking at chatbots as automated digital employees, rather than a new marketing or sales channel — essentially, instead of hiring a person to do a job, you hire a chatbot.

So, with this new insight, began building our newest recruitment employee…

The Planning

The key focus when planning was around the job to be done — receive more suitable applicant — then building outwards. Essentially, we wanted to filter the massive pool of potential graduates into a smaller pool of desirable applicant:

The next thing I did was look at our current recruitment process and finding out which bits were not working, or where the costs of a hiring an employee to ease the problem were too high. It looked a bit like this:

As you will see, there are flaws in this process:

  1. The job spec is competing in an arena against loads of other jobs when posted on a job site
  2. It is not being shown to a targeted audience, only relying on keyword searches
  3. The application process is one way, meaning we only have their CVs as reference until we interview
  4. The traditional job spec is rigid and does not sell the job correctly, meaning we lose potential applicants who lose interest following the interview

With a tradition solution — an intern or recruitment employee — these problems can be overcome, but are expensive. I was sure that a chatbot created on Facebook Messenger and used in conjunction with tools within the Facebook platform would also suffice, even potentially producing a quicker, more responsive experience. This was how we would overcome the previous hurdles:

  1. The job spec would only be posted on our company website, with no other jobs to compete against
  2. Instead of relying on job site searches, we would create a Facebook advert and target it to the desired demographic — recent graduates
  3. A conversational interface would provide a two-way application process, providing the desired information that is not displayed in a CV
  4. A chatbot can explain the job role, the company and other key information, answering specific questions to filter through only the most suitable candidates

This is how the applicant journey would change:

Using this new approach, the obstacles that were prevalent previously can be overcome without hiring another physical employee. Applying a conversational interface to the entire application process enables fluidity and suits the wide range of variables which come into play. With this basis, I began building the chat flows into the bot.

The Building

The build process is based around 3 fundamental stages of the job we are trying to do — filtering candidates that are suitable:

1. Discovering if the candidate fits the basic job spec

2. Educating the candidate about the role

3. Finding out if the candidate is still interested

These 3 stages would represent the building blocks for the chat flows, and would funnel candidates like this:

The creation and implementation of the bot was performed using Chatfuel — in my experience the most feature-heavy bot building platform currently available. It enabled me to begin with a completely blank canvas that could be customised and moulded during testing to create the most effective experience possible.

Stage 1:

The first stage of building was about questioning; finding out basic information about each candidate, enabling us to proceed and weed out those that didn’t fit. This meant asking:

  • Are you at university?
  • Have you graduated?
  • When do you graduate?
  • What university did you attend?
  • Do you have any relevant industry experience?

These brief but simple questions allowed us to initially filter applicants and begin building a profile about their current position and availability. It looked something like this:

As you can see, Jacob is a student study at university; however he doesn’t graduate until 2018, which doesn’t match our spec. We were also able to find out whether we could contact him again in a year once he has graduated, allowing us to gather a pool of ‘potential’ candidates for the future. Matty was not at university, nor had he gained a degree or any industry experience, meaning he was not a fit. He was filtered out and as a result, it would be one less unsuitable application which would have been received from the traditional process.

The advantage to using the chatbot’s conversational interface is the unique ability to collect real-time data and provide customised and personalised responses based around user input — just like conversing with a physical person. Essentially, the chatbot’s machine learning could use the candidates’ inputs to generate varying outputs. The aspiration of many in the AI and machine learning community — and what many of the big players in the industry are on the brink of achieving –is that a bot will serve a unique user experience based on personal data. Chatfuel’s AI is far less sophisticated than that, but with enough time and testing, impressive results can be achieved.

The first personalisation was based around the question: ‘Which university did you attend?’ Because we were recruiting for the role of Graduate Client Manager (GCM), I was able to use our current GCM staff profiles to create varied and personal responses. This is how it worked:

The range of alumni in our Client Management team meant that we were able to serve 12 different answers to candidates depending on what university they inputted. You can see that Josh attended Goldsmiths, a university we have never recruited a GCM from before; whereas Doug attended Manchester, which was once attended by one of our GCMs, Hannah. I was able to serve a few key skills and qualities which are vital to a candidate in this role, then asking whether the applicant was similar — another opportunity to funnel out unsuitable graduates.

Stage 2:

Stage 2 required us to provide a brief description of the role and the company. The outcome that we are trying to achieve in this section is discovering if the candidate is still interested in applying, even after they have been introduced to the job in more depth. This meant receiving affirmation of intent, whilst providing a realistic and insightful description in a short space.

Unlike roles that required experience and skill, graduate positions rely much more on the personality and fundamental skills of a candidate. Companies understand and accept that young people straight out of university require investment through time and education; therefore the qualities and skills that graduates need to be successful are more closely related to their personalities. This is why we wanted to know if our candidates had a good attention to detail, could work under the pressure of tight deadlines, and enjoyed solving complex problems — they were skills that were innate, not taught.

Here is how Stage 2 looked for the applicants:

As you can see, this was an opportunity to upsell the role, conveying a tone of voice that cannot be communicated on traditional job sites. At this stage of the conversation, the candidate could really get a feel for the company’s mission and what environment they would be entering if they were successful. Finally, we once again confirm the candidates intent on applying, funnelling out those that have lost interest.

Stage 3:

By Stage 3, our cohort of potential applicants has been filtered to the point where any candidate who applies meets the criteria of our basic job spec, and without any more questioning, could apply for the role. However, in most cases, candidates have their own concerns and queries about jobs they apply for; concerns that can be the difference between applying and not applying. The next step then would require us to answer those potential questions through an autonomous piece of software, rather than through a human.

The help you understand the complexity and scope of this challenge, and all the potential questions and answers that can be cumulated in regards to a job application, I have produced a diagram to show the basic structure through which I attempted overcome this problem:

The help quantify the mountain that I had to climb to ensure each and every candidate was satisfied enough to apply for the role, I created 20 categories of Frequently Asked Questions, based around the most common questions that we receive from applicants. Within each category, there could be anywhere from 1 to 10 different questions, each with very different answers and on top of that, a multitude of different ways of asking that question. For example, if we take ‘Probation FAQs’ — a relatively narrow category — the different variances could be:

  • Will I have a probation period?
  • How long is my probation period?
  • Will I have to take a test at the end of my probation period?
  • What happens if I fail my probation?
  • What do I need to do to pass my probation successfully?

Now, think of how many ways you can ask these questions. Let’s take ‘how long is my probation period?’ and break it down into different possibilities:

  • How long is my probation period?
  • What length is my probation period?
  • What’s the probation period?
  • Is there a long probation period?
  • How many months is my probation period?
  • When will my probation period end?

This is just a few of the top level variations, discounting the regional accents and dialects which shape the various linguistic styles in the UK. Even variances between the word ‘my’ and ‘the’ would instantly multiply the possible combinations just around one category — that’s the versatility of language!

However, when it comes to teaching a machine how to make sense of all these variations, the task is monumental. It meant that no matter how much time and effort I put into teaching the bot all the different variations, I could never completely cover every angle. There needed to be a threshold for how detailed a question could be, a point where we say ’no more’ — just like when you ask a person a question and they say ‘I don’t know’.

In order to reach this threshold, instead of starting with the questions, I began with the answer. The answer, unlike the questions, has limits. It is the only reference point we can tangibly use to create a workable and realistic automated response to all of these questions, without spending hours and hours actually inputting the variations. So, instead of building outwards, I built inwards. This is how it looked:

When we overlay the probation example into this process, we really see how this helped:

Because we have settled on a top-level answer that covers the 4 ‘sub-categories’ of question, I was able to say ‘this is what the candidates need to know about probation, anything else can wait’ — i.e. this amount of information is enough for them to apply for the role. Of course, the answer is variable, but with the help of our in-house recruiter, we were able to build coverage of all the relevant points connected to the role based in one answer.

This is quite an abstract process, and from here, I still had to spend time building out all the variations of question that covered the probation topic. As the standard AI in platforms like Chatfuel develops, the creative input will diminish. I assume that one day I will only need to input basic categories to achieve the same results, with the AI harnessing data from the internet to fill in all the gaps.

To help you see how this worked in reality, and the wide range of questions I was able to cover using this process, here are some screen examples:

In addition to prompting completely ad-hoc questioning, I saw an opportunity to alleviate many of the potential friction points the bot could encounter by building guides at the end of the chat flow. Utilising the ‘quick reply’ feature on Chatfuel, I was able to build in FAQs buttons with the most popular categories as headings. The aim was to reduce organic questions and display to the candidate that other information was available at the touch of a button. Here is what it looked like to use:

The Results:

I’ve spoken a lot about how and why I implemented a chatbot into our recruitment process, and I’ve shown how recruiting a chatbot should, in theory, solve our job to be done. However, the important aspect is the end result and how it actually changed our ability to recruit great grads.

Because we wanted to test the uplift of the chatbot on a quantitative level, we ran a control campaign alongside the bot campaign whereby candidates interacted with the ad and were prompted to send their CV and covering letter to our recruitment email address. The control candidates were then judged suitable for interview based on their CV alone — I wanted to compare the two funnels to see which one produced the most suitable candidates.

In terms of tangible results after the first two weeks, we had 14 applications from the control group, with 8 worthy candidates. That’s 57% of those who engaged with the ad who we invited to attend our next graduate recruitment day. To put this in perspective, 57% is a good number for us. Our average graduate recruitment day will be made up of 12–14 potential candidates from job sites, with typically only 1 or 2 reaching the next stage — to receive 8 worthy candidates from the control group was awesome!

The bot ads yielded a total of 15 conversions — candidates that engaged with the ad and then began a conversation with the bot. Of those 15, 10 completed their conversations (they didn’t just disengage midway through). Of the 10 that completed chat flows, 6 were judged as worthy candidates and were given the chance to apply. We received 5 applications, all of which were from worthy candidates.

The significance here is quality. The job we wanted to complete was improving the quality of candidates we received CVs and applications from, which, as you can see, was achieved. Although we saw candidates disengage with the bot and not complete a conversation — a 1/3rd of candidates — we only received applications from those who we deemed suitable.

The bot has now been live just over 1 month (we have paused the ad campaigns but left the bot running on our Facebook page) and we have seen 74 unique users engage in conversation with our new digital employee.

I’m sure you’re dying to know whether or not we have a new Graduate Client Manager? And yes, we do! Unfortunately, I cannot say that all three came through the bot — we only had one from the bot, another from the control group, and one from our traditional recruitment process — so any recruiters, don’t worry, your jobs are safe (for the moment!)

The Conclusion:

The biggest piece of advice and learning that you and your company can take away from this project is in relation to gap I managed to fill by employing a chatbot: bots can fill certain gaps just as well as human, improving process along the way — we made genuine progress. We could have allocated expensive human resources to the problem, which would have likely solved the issue, however, we utilised the potential of a bot to solve the problem in a new way.

Of course, not every company has the luck of having a bot developer working for them, but as many of the bot platforms reduce the level of expertise needed to develop a bot, the more accessible they will become. You can see from my experience that the inclusion of a conversational interface was the real difference between quality and quantity. I believe that as businesses begin to get the hang of harnessing big data to create more personalised, individual interactions with their customers, bots will play a massive role in enhancing this quality. In addition, what I’ve demonstrated here is the application of a bot on top of Facebook’s vast data reserve, which we also utilised as a recruitment tool. It is these two resources working in tandem — bots and data — that provided a real change in our process.

So, next time you’re thinking about a problem, ask yourself: could a bot do this? Chances are, it probably could!

If you want to check out the chatbot from this project, here is a link: m.me/more2careers

Quelles évolutions pour les médias sociaux en 2017 ? (Hub Institute / Sciècle Digital)

Des chatbots, des fonctionnalités à venir sur Facebook et les autres réseaux sociaux, des marchés dévorés, du social CRM, de la détox, du live, des vidéos verticales, les influenceurs, de l’intelligence artificielle… Tout ce qui va émerger, exploser, ou encore se perfectionner en 2017 se trouve dans le report.

Aesthetics : cette IA peut prédire si votre photo sera appréciée ou non – Test 

Source: Aesthetics : cette IA peut prédire si votre photo sera appréciée ou non.

J’ai fait le test, c’est déroutant …

Tout d’abord l’explication du Sciècle Digital: ” Everypixel Aesthetics est un outil en ligne qui permet d’analyser votre photographie pour vous donner un score (en pourcentage) sur sa qualité.

Si vous hésitez entre deux photos qui vous plaisent de façon subjective, mais que vous avez besoin d’un avis objectif, les intelligences artificielles sauront toujours vous guider. Par exemple, des chercheurs du MIT ont créé un algorithme permettant de calculer la mémorabilité d’une image. Ici, Everypixel Aesthetics utilise un algorithme pour à la fois marquer votre photos avec des tags (computer, place of work, office, no people, etc.) mais aussi pour donner une note à votre photo.

Derrière cet outil se cache la startup Everypixel. Une société qui veut connecter toutes les bibliothèques de photographies en une seule et ainsi donner leurs chances à tous les photographes. En regroupant ces bases de données sous des critères communs, Everypixel sera capable de vous trouver le cliché qui correspondra le mieux à vos besoins et qui aura le plus d’effet.

Everypixel Aesthetics IA

C’est pour cela que Aesthetic est là. En uploadant une de vos photos ou bien en donnant une url, l’outil vous dira qu’il y a x% de chance que votre image soit ‘super’. « Surpris par le résultat ? Ce service ne mesure par la coolitude ou la beauté d’une personne ou bien d’un objet sur une photo. Il prête uniquement attention aux parties techniques comme la luminosité, le contraste, le bruit, etc. » Voilà donc les critères rationnels qui sont examinés.

Everypixel Aesthetics IA

Pour rentre l’avis de ce réseau neuronal artificiel proche de celui de l’Homme, les paramètres d’entraînement ont été définis par des designers, des éditeurs, ainsi que des photographes. Ensuite, 956 794 clichés (très exactement) ont été examinés pour créer un algorithme fidèle à nos gouts, mais dénués de sentiments.

Encore en version beta, cet outil est en accès libre pour que vous puissiez tester vos photos, en attendant qu’il soit intégré dans la recherche d’images sur Everypixel.”

Source

Quelques exemples sur ma personne:

15 Reasons Why Chatbots Are The Holy Grail For Digital Marketers And Brands

Source: 15 Reasons Why Chatbots Are The Holy Grail For Digital Marketers And Brands

15 Reasons Why Chatbots Are The Holy Grail For Digital Marketers And Brands

At Heyday, we’ve been evangelizing brands on the virtues of bots for nearly a year now. 2016 was a year of experimentation and education. Marketers would chat with us out of sheer curiosity, but most of them were not ready to take the leap and invest in the space just yet. In 2017, the vibe of the market is completely different. Marketers are now grasping the opportunity to build a one-on-one communication channel with their customers and they’re starting to embrace fully this new medium. Big things are about to happen and we can’t wait to share with you what we’ve been cooking later this year.

In the meantime, here’s a homemade infographic we created as supporting data when chatting with potential clients who are unfamiliar with the chatbot space.

Feel free to share your own interesting stats in the comments section 🙂