Improve your B2B marketing with AI

If global lockdown has taught us anything, it’s that people really cherish their connections to the outside world. We’re being reminded of what is important – and connectivity is high on the list.

The workplace, meetings, tradeshows, festivals – all of these have had to be conducted online, meaning B2B businesses have an opportunity to really cement their standing with prospects, and existing customers, via empathetic, relevant personalised digital experiences.

Because of this, organisations need to be more agile. As lockdowns continue to be turned on and off, they need to respond quicker, with ways to engage that fit the moment. The pandemic has forced marketers to think of new, innovative ways to generate revenue, which has in turn pushed them to evaluate the technologies they are using in order to do so.

For organisations that have already baked Artificial Intelligence (AI) into their technology systems, this is easier than for others. And, in a world where shifting sands might be all we have to base strategy on for the foreseeable future, marketing automation will increasingly become a must.

The popularity of AI is growing, with the global AI software market expected to reach $23 billion by 2025, according to data from Statista. But with it being such a vast ocean of a topic, where do organisations begin?

Here are four key tenets that those in the B2B marketing space should understand before dipping their toes in.

1. Define the problem before seeking the solution

The challenge is not to find the technical solution but rather to define the problem precisely enough to let AI address it. This is a crucial mind shift from thinking ‘AI is the answer’ (how) to considering, ‘what is the problem that we are trying to solve’ (what).

The better the question, the more useful the answer. It may well be that the current situation will catalyse this kind of thinking, while many industries face some new and very real challenges.

Many B2B brands have found that website and Facebook messenger chatbots can help capture leads and provide visitors with more information. Printer manufacturer Epson has also harnessed the power of AI to send out fully automated emails that seem like they’re from a real person.

While these are still fairly practical and simple applications of machine learning, businesses can apply AI in the analysis of hundreds of factors that can speed up decision making when those decisions need to be made and acted upon quickly.

2. From blank slate to cunning machine

Any AI system starts as a tabula rasa, a blank slate. Its efficiency depends on what we teach it to do. AI analyses any data set far more deeply and effectively than we can, but we cannot expect more from it than we give.

If we teach a network to predict marketing trends based on a set of data, it will be extremely useful but only in that particular dimension. AI is only ever as good as its teacher.

AI has come under fire in the past when it comes to issues around, for example, bias in recruitment processes. While the initial system is free from any kind of biases, assumptions or instincts, as data is loaded in, some biases may become learned behaviour. So, it’s incredibly important to test a system periodically (as it learns constantly) against any unwanted outcomes that may result in unintended exclusion or discrimination.

3. Data is everything

Efficient AI learning hinges around human understanding of what constitutes a proper data set. The general perception tends to be that the bigger the data set, the higher performance expectations can be. However, it takes both quantity and quality to determine the efficiency of the machine.

For example, if we want an AI application to predict future clothing trends, we need to make sure we combine various dimensions of the historical reference trends (color, material, pattern, length, etc.). If we want to teach AI to identify trees, but we only present birch trees as learning examples, then we shouldn’t be surprised when it doesn’t recognize an oak tree.

4. Neither man nor machine are infallible

One key thing to remember is that AI systems make mistakes. There is no system free from them. And there is a reason for it. Systems imitate human-like reasoning and the ability to generalize problems is their biggest advantage over classic software programming. However, they also inherit our fallibility (although it is less marked than in humans). So, any application will always require some form of human audit. No action should be left solely to machines (at least for now).

Still, AI is a powerful companion that B2B brands can benefit from in many ways. For example, it can help brands in heavily regulated industries avoid compliance problems when posting to social media. CRM tools are now starting to leverage AI features, like predictive analytics and machine learning, to identify patterns and trends in customer behavior that can help identify marketing leads and alerts the human user so that they can act on them.

We can apply all the best practices to artificial intelligence applications, but it is ultimately humanity that dictates success or failure. B2B customers may be making rational decisions on behalf of their organisations but they still expect engaging, personalized and human experiences. So remember, when baking AI into your B2B marketing toolset, that man and machine must form a perfect team to build success in any AI driven project or application.


This article is written by Jedrzej Osinski and originally published here

AI in Marketing: Myths vs. Reality

The true promise of AI in marketing is greater speed, writes Jonny Bentwood, Global Head Of Data & Analytics at UK PR agency Golin

There is a great deal of hype around AI within a marketing framework. Whilst the technology could be incredibly useful and adoption could drive efficiencies and competitive advantage on a global stage, there is currently a great delta between what it can do, and what it currently is being used for.

To start with, let’s distinguish between AI and machine learning – often used interchangeably and incorrectly. In summary, while machine learning is about understanding the past, AI is about working on the future. However, even with that understanding, there are far more misunderstandings around AI and what it can and can’t do.

AI taking over the world

There is a huge amount of incorrect fear about AI. This isn’t that the fear isn’t justified but rather that it is misplaced. AI does have the potential to be destructive as Stephen Hawking and Elon Musk have said- but most people when thinking about the dangers of AI think of Terminator.

In reality, we should think of AI more like the sorcerer’s apprentice in Fantasia. The danger of AI is not self-awareness, but our own inability to correctly frame the limitations of the question that AI supports. In Fantasia, Mickey asked the sorcerer to help fill the cauldron. The problem is that he didn’t tell him when to stop. Asking an AI to identify the best way to cure cancer (without proper limitations) could result in a diagnosis of human extinction.

Fortunately, AI is far away from this paradigm-shift in the agency-world, but the concept of AI being a quick way to answer questions, provided the framework is watertight, is crucial. We should be careful of our AI overlord’s support in case the person asking the question didn’t ask it correctly – without these checks and measures, our love of speed in an agency could result in poor advice to clients.

AI delivering little more than sentiment and relevance ‘currently’

The hype of AI is that it can solve problems in creative ways not imagined by ‘inferior human brains’ at a quick speed. Imagine the possibilities of coming up with a creative idea that is so radical that Cannes Lions will be up for the taking. This is the promise. Data and creativity without being limited to previous boundaries regarding what is feasible.

However, if this is the dream. The reality is far more mundane. AI is strong when it comes to:

  1. Sentiment analysis. Whereas sentiment coding has been problematic at best, current AI can understand attitude on an entity level and learn and improve all the time. This is available now and PR agencies are using this tech on a daily basis.
  2. Relevance. Consider Apple. Imagine how difficult it is to understand what is happening in their world when their name could easily be confused with a fruit, a cake or indeed a record company. AI has found a solid use case in understanding context and removing the need for humans to spend unnecessary countless hours categorising and coding.

The future: Value-based Pricing due to Scenario Testing

In 2021, I anticipate AI helping more when it comes to scenario testing. Why should an agency spend $$$ of our client’s money without knowing their idea will work. Combining the beauty of big data with the capabilities of AI-powered tech such as Cortana or Watson will enable firms to value price their work, as they will know the likely impact of their work before commencing. Work will be done quicker but with higher investment in tech – clients will have to pay for this helping hand to turn the model from hour based work to value-based.

Golin’s work on customer experience could be further enhanced as the AI understands the content that is being directed at the company. Instead of relying on complex workflow and rule-based process, the AI could deliver on-trend, identify opportunities and deliver on micro-changes that can result in huge impact

The Far Future

Already “AIs” are composing music and writing novels that the best Turing Test finds difficult to distinguish from human creations. Will this lead to fewer humans in the workforce if the AI can plan, create copy and implement the campaign. I doubt it. What will change is speed – for that AI will be needed. But for the rest – we will still need us carbon-based workhorses. If for no other reason, than I hope to keep my job!


This article is written by Jonny Bentwood and originally published here

Marketing In the ‘New-Normal’ Era with AI and CX Tactics

The global coronavirus pandemic is dramatically changing our world, including the landscape of customer experience (CX) much faster than the marketing and media industries could have anticipated.

With people at home, brick-and-mortar businesses have to quickly adopt new digital strategies to provide their customers with what they need right now.  In order to deliver on customer expectations, the best brands have strategies that continuously develop relationships through a series of thoughtful interactions, resulting in an increasingly hyper-personalized experience across the customer journey, which is usually backed by artificial intelligence (AI).

Companies who are already using AI with their CX efforts need to adjust their strategies to our world’s collective “new normal.” Customers’ experiences are underscored by anxiety, concern, stress, and confusion, and today’s AI must be emotionally intelligent. With this new and ever-changing landscape in mind, the following areas are what marketing and customer experience leaders must do to shift accordingly.

Hyper-Personalization with AI and CX: More Than A Name

Hyper-personalization is the CX term for it, but the root value is actually empathy. Human beings want to feel known — it’s about trust and comfort (especially at a time like this). Businesses can (and should) make their customers feel known and valued with digital experiences. AI makes this possible across huge swaths of customers in a digital landscape.

Personalization tactics have grown well beyond simply using someone’s name or location in an email campaign.

By continuously developing a healthy mix of both profile data (name, age, preferences, etc.) and behavioral data (what the customer does at your various touchpoints), companies can send timely, personalized communication or create unique experiences that are specific and helpful to each customer.

A great example of a company collecting data to empower hyper-personalization is Spotify. The streaming music app used by millions regularly looks at data to automate song suggestions and create daily or weekly playlists. While other streaming services pair song suggestions based on your listening preferences, few are actually predicting that you will or won’t like a new album (at least not with the success rate I find on Spotify).

Spotify also suggests playlists based on world events and situations that users are likely facing — humanizing the experience. For example, the company released a COVID-19 quarantine playlist for those needing some upbeat music (or meditation, study music, etc.) in their lives. Spotify’s ability to deliver on that experience and then to continually nurture a relationship with their customers is based entirely on their progressive use of data and AI.

Data Across A Digital Ecosystem

Being able to collect, decode and leverage complex data sets is essential for meeting CX demands during this quarantine period.

Since personalization is core to a dynamic CX, companies need to consider new and interesting ways to connect the data they have and to continually refine CX profiles for accuracy. Customers’ data should be drawn from and influenced across the customer journey: from marketing and sales and customer retention to product management and customer support. The entire digital ecosystem of data should be a collaborative touchpoint between product development, marketing, and support.

Customer’s Privacy And Personal Preference

Trust is an essential component of CX, particularly right now during this time of uncertainty. While customers are no doubt becoming more and more comfortable with the benefits of personalization, they get turned off if they think a company isn’t being responsible for their data. Building AI solutions that allow users to progressively provide information in exchange for real value is paramount.

While the promise of AI around automation and personalization is exciting, the narrative a company builds around AI and CX strategies need to align closely with customer needs and expectations. Customers want and expect hyper-personalization already; they just don’t want to think about what it took for a company to get there. Given our reliance on the digital world in our new reality, it’s more important than ever that companies are transparent and good stewards of your data.

AI also needs to be able to adapt to unprecedented circumstances and override some personalization settings in case of a crisis. Specifically, CX needs to include awareness of potential news events so that customers aren’t being served with distressing or inappropriate ads.

For example, takeout and delivery apps like GrubHub and Postmates have pop-up notifications about COVID-19, which also remind users about the impact this pandemic has on the entire restaurant industry (i.e., your order might take longer than usual due to staff shortages or certain restaurants that are not open, might not be accurately reflected in the app).

The old fashioned face-to-face, human-to-human customer service experience can’t be replicated across millions of online customers. But in times like this, if companies want to grow and set themselves apart from others, AI needs to be used primarily as a tool for automating and analyzing customer data collection so the CX can be relevant and emotionally aware to today’s ever-changing landscape.

This marriage of AI and CX will help companies develop a strategy for leveraging hyper-personalized data to give their customers what they truly want and need.


This article is written by  and originally published here

Conversational AI Is Accelerating in the New Normal

The most natural interface to interact with is voice. Even before the COVID-19 outbreak, voice experiences were well on their way to becoming the next big customer engagement platform.

In 2016, seven million people in the US owned a smart speaker. That number jumped to 33 million in 2017 and doubled to hit over 66 million at the end of 2018. Today, nearly 90 million US adults own smart speakers. These numbers will only continue to rise as the world deals with the fallout from COVID-19 and its impact on customer experiences.

Since the start of the pandemic, 35% of US smart speaker owners say they listen to more news and information through their devices. Another 36% say they have increased their consumption of music and entertainment through their speakers. Couple that with the reported 65% increase in Alexa Skills usage in April and May, and it’s clear that people are relying more on voice technology as they spend more time in their homes. While the increase in smart speaker usage is a positive sign for the staying power of voice, newer use cases maybe even better indicators of just how far the market for voice can go.

Voice Is Evolving

Since its inception, voice has been a consumer-first platform oriented around Google Actions, Alexa Skills, and all the devices that supported these functions. As the smart speaker usage statistics mentioned earlier indicate, voice to this point has primarily been used for the purposes of media and entertainment, smart home control, news, and information, hands-free culinary help, and small retail purchases. But voice has recently started to evolve into different areas.

Voice capabilities are now being added to existing digital properties — such as mobile and web apps or IVR (Interactive Voice Response) systems — and voice navigation that previously progressed consumers through a control tree is now turning into assisted functionality. We’re also seeing more and more brands launch their own voice assistants, such as the BBC’s recently announced Beeb, to meet the specific needs of their customers.

The biggest development, however, maybe the new use cases we’re seeing as a result of the precautions now being introduced under COVID-19. China has introduced voice-controlled elevators, where users simply speak the floor they are looking to travel to instead of touching the elevator buttons, to help stop the spread of the virus. There is similar potential for retailers, where voice can be a means to get product information, navigate the store layout, or checkout with contactless payment.

While voice has taken center stage in many of these conversations, it is not the only conversational AI technology that can help mitigate the risk of infection. COVID-19-specific chatbots are also being launched by healthcare organizations to educate people on the virus, answer questions, and more.

Things Aren’t Changing … They’re Accelerating

While the COVID-19 pandemic certainly played a role in these new experiences and use cases making their way into the mainstream, they were areas that would have been explored in the future — though likely not for a few years.

Scott Galloway, a clinical marketing professor at New York University, recently stated in relation to the pace of change and the pandemic, “Things won’t change as much as they will accelerate. While other crises reshaped the future, COVID-19 is just making the future happen faster.”

This quote applies perfectly to conversational AI in 2020. Although it was already well on its way to becoming a key customer engagement platform, the pandemic and ensuing lockdown forced the pace of innovation to increase quickly and urgently. The key for businesses will be ensuring that the customer experience remains of the highest quality when bringing conversational AI features to market quickly in order to meet the market demands of our new normal.


This article is written by Inge De Bleecker and originally published here