To absolutely no one’s surprise, business owners’ appreciation for customer data, in one form or another, is as old as commerce itself. Keeping track of customer purchasing habits and preferences — even when done informally — has been central to the shopkeeper’s role since biblical times. But it wasn’t until the last century that the utility of customer data underwent a transition from a proprietor’s instinct to an operational necessity. And the ongoing digital revolution empowered organizations to go even further, extracting valuable, highly personalized insights and intelligence from massive collections of customer data with the help of artificial intelligence (AI).
Even before the advent of AI, there were companies that made thoughtful use of customer data — combining it with seasoned business judgment — and experienced remarkable success. In 2003, the CEO of Harrah’s Casinos wrote about his company’s collection and interpretation of data regarding patrons at its 13 gaming locations.
By studying customer engagement data and ignoring competitors’ traditional focus on high rollers and ersatz architectural replicas, Harrah’s discovered that the smaller slot machine players were the ones who largely drove the company’s revenue. That realization led to a series of focused reward and incentive programs that led to significant growth at a time when the gaming industry had largely stagnated.
Today, enterprises of just about every size and industry are struggling to embrace the power of data to improve their own production efficiency, business processes, planning and revenue. The tools for capturing and analyzing that data are becoming more affordable and improving all the time.
The use of customer data has been massively successful in many markets and industries, such as the airline industry, where big data implementations have made a convincing case for the technology’s business value. As a result, companies are making massive investments in acquiring, subscribing and otherwise implementing the digital tools needed to keep them ahead of the competition.
But artificial intelligence, which is often as powerful as science fiction fans imagine it to be, is by no means a simple technology. The algorithms that drive it are essentially opaque to all but the most advanced data experts, and they change as the system learns more.
As a technology, AI is still immature. It comes with limitations, and it can be expensive to implement. That means a thoughtful and strategic approach to adopting AI as an element of a company’s digital transition is essential. One strategy is to apply AI to projects where its ability to sift through mountains of data in search of useful insights can overcome the limitations on both human and conventional computer bandwidth to handle massive volumes of information.
For example, AI can be used to provide financial advice to customers. AI could periodically rebalance an investor’s portfolio to achieve a predetermined ratio of stocks to bonds or find a path to minimize the investor’s taxes. On the other hand, it would take an experienced financial advisor to do things that AI is ill-equipped to handle, such as understanding the client’s investment goals or providing retirement planning or coaching.
In the case of marketing, which has become a data-intensive industry, the marketer’s challenge is to use that data to predict the customer’s behaviors — the best times, locations and means to reach that individual with an appropriately crafted message or experience.
By extension, it can also peg other good prospects and high-value leads with similar behavioral patterns. Beyond that, AI can drill through piles of seemingly inconsequential data to unearth valuable patterns and business intelligence. But, alas, it is lousy at crafting creative assets and compelling messages. This is why AI is best applied as the interface between the data-driven and creative marketers and their voluminous datasets.
However, diving deep into the data — even with AI on your side — is not an amateur sport. Doing it successfully requires someone with an understanding of data science, and turning marketers into data scientists just isn’t practical. What’s needed instead is to bring the tool sets typically reserved for data scientists intrinsically closer to the marketing stack.
While in the past, data scientists may have lived in their own silos, data scientists and marketers now need to work together, using data in the same ways, to show the value of their efforts. AI technology, in combination with marketing analytics and intelligence tools, can be a massive enabler in this regard. But extracting the marketing value from AI takes more than just technology.
Successful AI implementation starts with executives and management teams, whether it’s the board or the CMO. It’s critical to set the organizational expectation that it’s not good enough to just do something; you need to be able to monitor, measure and interpret it to extract future value.
It’s also important to understand the performance of campaigns, channels and promotions over time and across customer segments. Additionally, you need to be able to generate visualizations that tell a story supported by facts. You need to build intelligence that can move an organization toward marketing success to accelerate sales, generate revenue and support mission-critical goals. In this regard, there can be no separation between AI-based solutions and the business leaders responsible for corporate strategies.
AI, machine learning and related cognitive systems are well equipped to perform tasks, but not jobs. And while they can perform those tasks impressively, they fall short of human creativity, savvy and instinct. Organizations that figure out how to combine the best of advanced AI technologies with the unique strengths of human intelligence will emerge as market winners.
Cast your mind back to the latest media article that inspired you. When considering who to share it with, it’s likely that you immediately knew who in your circle of friends would find it interesting. What if there was a simple way to create such a targeted approach to sharing information when it came to your marketing? Luckily, with the advent of artificial intelligence (AI), almost anything is possible.
YOUR MARKETING MUST SPEAK TO YOUR AUDIENCE
With evolving customer expectations, it is obvious that the days of blasting out the same marketing communications to every customer will no longer cut the mustard. One of the biggest challenges marketers face is getting relevant content in front of the right customers.
With the exploding volume and speed of data growth, it has become increasingly difficult for marketers to comprehend data and identify the right customers to talk to.
71% of senior marketers say they feel overwhelmed rather than empowered by the data available.
In response to these challenges, marketers are investing in AI to improve the status quo. It’s designed to mine large volumes of data, recognise patterns and insights to your business problems, and identify customers associated with them.
AI GOES BEYOND HUMAN PERCEPTION
There are answers in your data that can help you reach the right customers through better-curated insights. AI helps you surface these at scales and speeds that can’t be accomplished through manual analysis.
That’s why we rely on AI to uncover patterns that aren’t always intuitive to human perception. Digital Alchemy’s uDiscover provides marketers with a refined list of target customers that are highly likely to respond positively to marketing communications aimed at changing their behaviours. uDiscover will synthesize available data and identify relevant behavioural patterns and traits that marketers will be able to target.
TECHNOLOGY MEETS BEHAVIOURAL SCIENCE
Used in conjunction with uDiscover’s extensive library of industry-specific business problems, marketers now have access to cutting-edge customer targeting. For example, for a particular business opportunity such as moving to another bank after the conclusion of a fixed-rate loan period, uDiscover has the capacity to identify all available behavioural indicators and provide marketers insights on which behaviours or traits are more prominent than others. This is achieved through analysing the behaviour of the customers that have already performed similar actions, and applying the learning to the active customer base. In our banking example, some actions may include calling the bank to inquire about new home loan options or using an online home loan calculator.
AUTOMATED CUSTOMER TARGETING POWERED BY AI
With such rich customer information at your fingertips comes an opportunity to identify target groups based on their behaviour and establish relevant communications at every step of the customer journey. uDiscover does all the work to make this as seamless and hassle-free as possible.
For instance, when thinking about the home loan example above, uDiscover would work its magic and compile a complete list of customers that have the same trait as the target group. Armed with the list of customers who are most likely to close their loan and move elsewhere, marketers can proactively reach out to retain this particular group of customers. Every time new data is added uDiscover will adapt accordingly, making sure that the list of customers stays relevant and always up to date.
By knowing how a certain group is likely to behave and react, marketers have the power to influence choices before they are made.
Such an ability to influence behaviour will not only transform the way you work, but also the way you target your customers. As more businesses actively implement AI, marketing automation is inevitable. Within the next three years, all campaign targeting briefs are expected to become self-populated by AI. More accurate targeting will leads to better experiences, which will inevitably create more data to process.
This article was originally posted on – https://www.digitalalchemy.global/ai-for-customer-targeting
Today, the wide and diverse world of marketing seems unrecognisable from its various incarnations before the era of interconnectivity and super-fast data.
Where focus groups had to be assembled and used as predictive machines for marketers in days of yore, we now have a considerably more efficient set of tools to play with.
The arrival of Artificial Intelligence (AI) and big data has had a profound effect on a variety of industries worldwide. The wealth of computing power and detailed analysis capable of being provided through this modern form of technology makes it possible to transform a range of professions for the better – and marketing has to be regarded as a key beneficiary.
AI software market revenue is forecast to increase to $120 billion by 2025.
Just a matter of years ago, it could’ve felt a little left-field to explore the range of applications both AI and big data could have in improving your various marketing strategies. But now, it feels more tricky to find an area of the industry that doesn’t seem ripe for this kind of enhancement.
It’s fair to say that modern technology can save a significant amount of time and money for marketers looking to optimise their campaigns and deliver persuasive messages to the right audiences at the right time. But how exactly is this done through the use of AI and big data?
Here’s a deeper look at AI and big data applications within the world of marketing, and how the technologies can help you to forecast your campaigns:
Artificial Intelligence analysis
AI in marketing is continuing to grow exponentially. This form of technology is primarily used as a means of analysing the demographics of audiences alongside the analytics behind a business’ performance online. Predictive analysis has the power to highlight metrics like bounce rates, page visitors, the time spent on specific pages and click-through rates, while AI can help users to make smarter decisions based on such information.
Here, AI helps you to understand and concentrate on the specific areas in which your strategy can work best – or, alternatively, where it needs a little improving.
AI-driven predictive analysis can also interpret scores of data in order to build well-informed predictions for your future engagements too. This technology has the power to identify and investigate previous errors as a way of forming a prediction on how best to prevent the same problems arising in the future. This can help to direct more prospective users to your content and enhance their experience within your pages.
AI can also help you to anticipate how best to utilise your Call-To-Actions as a way of increasing your conversion rates. In fact, according to Ventana Research, as much as 68 per cent of entrepreneurs claimed to have developed a competitive edge with this technology. Furthermore, the consumer goods giant, Unilever, managed to reduce their forecasting errors by as much as 15 per cent while saving millions through the help of this form of analytics.
AI enables companies to assess how specific changes impact conversion rates. For instance, Walmart reported that a decrease in the load time of their website of just 1 second can lead to a 2 per cent increase in conversions – which is translated into millions of dollars.
Predictive analysis that offers concrete data can save companies a ton of energy and resources that would otherwise go into complex A/B testing.
Big data transforming predictive marketing
Big data marketing revenue is expected to hit $103 billion by 2027.
The art of forecasting marketing campaigns is one that seemed unimaginable in a more analogue age. With so many metrics and such difficulty associated with the anticipation of customer behaviour, early tools designed to provide guidance for businesses tended to have a high margin for error – or be wholly misleading in some cases.
MarTech relies on the efficiency of predictive technology today – and you’ll rarely come across a sale online that hasn’t arrived as a direct – or indirect – result of the optimisation of big data.
Predictive marketing is an essential tool for any ambitious eCommerce business, and the technology is driven by the implementation of data science as a means of predicting which marketing actions are more likely to succeed as opposed to the ones that look well-positioned to fall flat.
Although both predictive analysis and predictive marketing sound like similar concentrations within the world of modern marketing, there are some profound differences to keep on top of. In the domain of predictive marketing, the analytics behind a marketing campaign is taken a step further and contains a broader implication. Whereas predictive analysis typically relies on predictive models to provide a clear insight into the future. If you’re looking to really put your marketing strategy to the test and gain some insights into the decisions you’re planning on making – predictive marketing is just the tool for the job.
Based on this information, we can use the example of a scenario to further differentiate between these two analytical interpretations. Here, a predictive marketing expert – typically a data scientist or analyst – collates big data regarding a business from a range of sources and then analyses it alongside the company’s marketing and customer data. This information provides the analyst with a predictive model that’s capable of quantifying the success of a specific marketing campaign.
Changing tides of customer profiling
The marriage of AI and big data can carry plenty of benefits to marketers online. Specifically, in the way, that customer behaviour can be mapped out and even anticipated. Because data has the power to be so sophisticated, the marketing efforts built around this type of technology can potentially bring considerably higher conversion rates.
This is particularly pertinent in developing customer acquisition efforts. AI helps to interpret existing data in a way that can optimise campaigns to target exactly the right customers – and at the right time.
Predictive marketing models can enable marketers to customise their campaigns and acquisition strategized into different sections of an effectively segmented potential customer base, bringing better chances of enabling conversions.
In a similar manner, insights into the future behaviour of customers can help a business to map out its customer retention strategies. When the company knows when a segmented group of potential customers are likely to leave their site, or abandon their cart, the marketing team can design a bespoke retention plan that anticipates this departure and nips it in the bud – so to speak.
The ability that AI holds in effectively interpreting masses of data in order to effectively segment target audiences based on their behaviour can’t be underestimated by marketers. Having the ability to unleash bespoke retention strategies for certain behavioural groups as opposed to a straightforward blanket effort to retain visitors can ultimately pay dividends in successful campaigns.
Discovering better prospects
AI and big data can also optimise the efforts of B2B marketers in helping them to gain more quality leads thanks for the collaborative technical efforts of predictive marketing. The apply this, marketers apply their predictive models through a field of signifiers to interpret – with much better accuracy – which businesses can make for strong potential customers and clients.
Data pertaining to a prospective B2B customer’s company size, type of products, levels of revenue along with other metrics that can even include expansion efforts, management changes and thousands of other variables can be thrown efficiently into a mix to identify the best prospects to target.
Using predictive analysis models, marketers can generate long lists of businesses with suitable behaviours and build a quality database that’s rich in effective prospects.
These efforts can help AI and big data to optimise marketing campaigns for both customer-facing businesses and B2B endeavours in a way that not only saves time but one that can be effectively monitored, analysed and have progress forecasted for the desired duration of a campaign.
Economical applications
Some marketers cite the financial implications associated with this roll-out of modern technology as a drawback. But it’s important to note that the wealth of data available would cost fortunes in employee hours to interpret effectively.
Fundamentally, AI and big data-based solutions offer a level of marketing insight that would otherwise be impossible to gain manually, and with this in mind, the costs can be surprisingly competitive.
Is AI and big data essential to the efficiency of your marketing campaigns? It’s certainly possible for some established companies that are nestled in clear niche markets with a dedicated customer-base to feel that this level of analytics and forecasting is superfluous to their efforts. However, these technologies could be imperative in locating a wealth of new customers based on their interactions with said company online – and must be taken into consideration if there are plans for future growth.
Predictive analytical models can be an excellent tool for forecasting marketing performance based on previous campaigns and have the potential to uncover fresh opportunities for businesses to improve and better understand their customers’ behaviours.
With more adopters looking to technology as a tool for better insights, there’s an emerging risk that those who fail to adapt could be left behind. Fortunately, the more AI and big data solutions have developed, the easier they have become to utilise for marketers. In fact, there are many analytics agencies out there that now offer up scalable solutions for businesses that cater effectively for both large and small organisations.
In the wide world of marketing, time really does mean money, and with the power of AI and big data on hand to anticipate the quality of your campaigning and make accurate predictions towards the success of your marketing strategy – there promises to be plenty of time on the side of early adopters.
Today we start a new year and a new decade. The past decade has in many ways been about the dominance of mobile followed by the rise of voice assistants. The decade started with the launch of the Siri app in the iOS App Store and concluded with more than three billion voice assistants in use worldwide.
What is ahead of us for the first year of the second decade of voice assistants? Voicebot reached out to over 40 voice industry professionals to get their predictions for 2020. They range from the rise of sonic branding and new architectures for voice assistants to the increasing importance of hearables and voice in the car. There are also predictions about the first hundred million dollar voice apps.
There is a lot of depth in here with so many experts weighing in. So, in the spirit of TLDR, we have included a word cloud of the responses below.
In 2020 we’ll see more and more brands incorporating sonic branding into their overall marketing strategies, with Alexa Skills and Google Actions becoming more popular. It will be interesting to see how companies will build brand trust with voice technologies that aren’t quite fully trusted yet, and I predict that the use of the human voice will be a big factor in building that trust. Not only is there a preference for human voices in voice technology, but a human voice also increases information retention.
The love affair we have with hardware design will migrate to a love affair of Voice Interface Design. Although I doubt “a Jony Ive of Voice” will emerge within 2020, I predict that by the end of this decade, we will know the names of a few revered VUI designers. It will be those who can design the future by understanding both its current technical limitations and trajectory while harnessing anthropological, sociological, and humanity-first guiding principles.
There will be more surprising acquisitions in 2020 similar to Apple’s acquisition of Pullstring and as a handful of B2B enabling platforms breakout of the pack. Look to Amazon, Google, Salesforce, Apple, Adobe and others to compete for technology and talent. I think we’ll see a major retailer make a big play in voice in 2020 and I wouldn’t be surprised to see custom naming for devices hit one of the big two (you know who they are) in a future release. And, of course, VOICE Summit will be a blast. 🙂
In 2020, having a voice presence will start to be a strategic and business differentiator for companies. We are moving beyond voice as a side innovation project to it being a first-class citizen on the same level as social, mobile and web. Companies who have or will soon establish a voice presence will start to reap the business benefits over laggards, much like what happened with the web and mobile.
Related reading: How to Build a Samsung Bixby Capsule
JASON FIELDS, CHIEF STRATEGY OFFICER, VOICIFY
It feels like the voice groundswell is approaching land and executives can begin to see the shape of the conversational experience wave. This in concert with other developments like the maturity of voice assistants, the emergence of voice commerce as a real topic, and a growing ecosystem voice solutions and agencies leads me to believe that 2020 is going to see a noticeable increase of formal voice strategy and inclusion in customer journey maps.
A focus on voice search will dominate in 2020. Organizations will seek new opportunities to tap the power of virtual assistants and conversational AI – to help consumers to discover and engage more fully with their brands through next-generation SEO and conversational calls to action.
Discoverability is the key issue holding the ecosystem back from realizing the potential that voice experiences offer. Even with the encouraging market penetration of voice platforms, consumers are for the most part unaware of what voice can do. I do not believe voice platform vendors, voice experience developers, or businesses can solve this problem in isolation. I predict an independent third party will attack this issue with a platform that brings together consumers, vendors, developers, and businesses to provide shared value and incentives to cross the chasm.
Key areas of concern for voice apps on assistants are convenience, context, memory, personalization, monetization, retention, and discoverability. With millions of owners of smart speakers, 2020 will be the year that a significant advancement will be made in discoverability and these owners will start consuming these voice apps.
First, the main challenge to 3rd Party developers and also to brands is the challenge of discovery. How do consumers become aware of their Action, Skill, Capsule (or whatever other non-specific name other companies decide to call what is really a voice app)? Until this gets solved and solved consistently, we won’t see mass adoption of the medium by big players.
Matt Ware, First
As much as the onus does sit with these brands to self promote, Google, Amazon and Co need to pick a path and stick with it. Consumers have become used to having the right tool put in front of them at the right time. Implicit Invocation was the obvious path to continue this trend. However, that seems to be getting wound back in favor of “recommendation at signup” or fulfillment of the required task without the user knowing which Skill or Action did the heavy lifting. This year Discovery will be a focus for the ecosystem owners as they try to find a way to achieve the balance between wanting to own the whole user experience 1st Party and needing 3rd Party information and functions to actually deliver the service or item.
Second, Asia pulls away from the US and Europe with Voice. Asia is already seeing explosive growth in Smart Speaker shipments and development across the three main players (Xiaomi, Baidu and Alibaba). Population, funding, acceptance of digital payments and a friendly government environment will see this growth continue along with their dominance. The main battlegrounds will be South East Asia, India, Africa, and Australia. In all of these locations, there is less opposition to Asian technologies and large existing ex-pat Chinese populations happy to bring their preferred assistant of choice with them. While there is still the opportunity for growth in the U.S. and Europe, it’s Asia where there is money to be made.
TIM MCELREATH, DIRECTOR OF TECHNOLOGY FOR EMERGING PLATFORMS, DISCOVERY, INC.
The “de-app-ification” of Alexa skills and Assistant agents. This year there is going to be a blurring of boundaries between third-party development, content presented in first-party platform templates, cross-linking between smaller related features, and a move toward shared (but extensible) domain language models.
Assistants will move from intent classification and named-entity recognition, which is a manual and rigid process, and they will become more sophisticated, learning from examples and moving past the limitations imposed by mapping every message to one intent. The representation of state and context will be learned from the data itself, letting the users teach the assistants things that were not anticipated and making the assistants able to understand and respond to unexpected inputs.
The rise of a new domain-centric development model for third-parties. The initial wave of voice was based around an app-centric model. This made sense, as the analogies and onramps for developers coming from mobile and web were so easy to make. But domains make more sense for users. Domains are top-level intents with third-party fulfillment. It also means that users are defining functional boundaries, not developers or product designers. Finally, it means discovery is moot. Forget about tricks and gambits to make users memorize and chant invocation names. Instead, builders must discover users where they are, in users’ natural expressions and requests.
To effect this expeditiously, the platforms must provide a way to bring third-parties in on top-level intents fairly and transparently. And third-parties must take users as they come – with a myriad of queries and commands that may not fit neatly into their existing app-centric way of thinking.
In 2020, voice assistants will start to perfect the invocationless open of an app. This will happen because once Samsung fully releases Bixby, it will start to gain popularity and will spread to the other platforms. Voices will also start to sound more human. It is clear that people respond better to a less robotic a voice. It follows that the engineers at Samsung, Amazon and Google would focus energy on that.
2020 will be the year that the voice app ecosystem starts to make significant money. A combination of improved tools from the platforms and a greater focus on creating real value by developers will finally convince consumers to start parting with their hard-earned cash. We’re unlikely to see the first voice app unicorn in the next 12 months but perhaps we’ll spot a couple of multi-hundred hoof-prints pointing the way.
Competition will tighten among major tech companies for developer attention, leading to heightened investment and accelerated feature development over the course of 2020 for Alexa, Google Assistant, Bixby, and Siri.
The continued rise of voice commerce, specifically non-obvious ways voice removes pinch points in the customer journey. Voice commerce doesn’t always have to be at the last mile of the transaction but can have a very valuable part to play in the customer decision journey influencing the transaction.
As with any new channel, user acquisition, discovery, and monetization can be challenges. For Voice Assistants to continue to take off, and more users and enterprises to adopt them, I am hopeful the ecosystem continues to evolve and more opportunities for user acquisition, discovery and monetization come forward. As the ecosystem evolves, and enterprises see the value in Voice Assistants, hopefully, more initiatives move from innovation teams to business units in the coming year. We are still relatively early in the space and it is exciting to see the new use cases that emerge.
The rise of Domain Specific Voice Assistants. Products will start having natural language voice assistants on board, without privacy concerns or internet connections. Chip companies will announce a number of AI chips that support this at a cost that can be used in IoT, home appliances, and other consumer products.
Voice AI on the edge for low-resourced IoT devices will come to the fore, with many more devices avoiding the cloud for both privacy and performance reasons. In addition, biometric authentication and emotion recognition will transform how we use voice assistants. We can look forward to using any smart speaker in the world with reduced friction and more relevant responses.
Apple will also launch some kind of voice skills, but they will be over-regulated and limited in performance. Unfortunately, Apple will continue to lag behind the other platforms. Hearables and voice-enabled wearables will be the catalyst for much greater usage and a wider variety of use cases, as mobile is inherently hands-free. Phone-zombies may even start to disappear!
The availability of Alexa and Google Assistant tightly integrated into fully connected vehicles will begin to achieve critical mass in 2020 and will accelerate the use of voice assistants by the masses. The writing is on the wall and all stakeholders including car manufacturers, voice assistant platforms, radio broadcasters, streaming services, and brands will need a conversational AI strategy in order to win in this paradigm shift.
Despite some predictions of a slowdown, the average daily usages of voice assistants will grow considerably more in 2020 than any previous year. This will be largely driven by the use of devices in automobiles and wearables – mostly earbuds.
One of the breakout hits in the voice space for 2020 and beyond will be voice-activated rings, starting with the Echo Loop. Alexa users will appreciate the ability to use Alexa anytime, anywhere without having to have a smart speaker nearby, or headphones in their ears. I predict that in 2020, Apple will take notice of this new “category” (voice-activated rings), and begin developing a Siri-compatible ring, which, sometime after 2020, will become an even bigger hit than the Echo Loop.
The tipping point for smart displays has come to pass. By the end of 2020, the most highly-used voice apps (outside of sleep) will include robust, stimulating visual experiences.
2020 has to be the year that Siri opens up a voice marketplace! Many in the industry expected this to happen in 2019. This will help brands and third party developers have a presence on one of the major voice assistants and the leader in the ‘hearables’ space with AirPods. We may also see Apple announce a new product this year with voice-enabled AR glasses.
YANNICK OSWALD, PARTNER, MANGROVE CAPITAL PARTNERS
A lot more is to be expected from Apple in the coming year. The tech company is already releasing at an accelerated pace new voice commands these last months and I expect them to open up their voice ecosystem to a wider developer community allowing startups to build apps with cutting edge voice-first commands.
JOHN CAMPBELL, MANAGING DIRECTOR AND FOUNDER, RABBIT & PORK
Firstly, I think we’ll start to see Amazon and Google start to release features for Skills and Actions directly related to earbuds and in-car usage. For example, being able to use data from the accelerometer in the Echo Buds in your Skill. This will start to open-up new use cases for Skills.
Secondly, Apple will launch “voice” or “Siri voice apps.” The platform will not be as feature-full as we’ve seen with Alexa Skills and will be deeply integrated with the existing app store.
Apple will continue to open up Siri for 3rd party skill development, which will raise the prominence of voice as a channel that consumer-focused apps need to operate on.
I think that the biggest breakthroughs within the voice space will be driven by media-based companies that supply content in new formats conducive to voice assistants and their affiliated hardware. Food Network Kitchen will provide a blueprint for how media-companies like Discovery can adapt their content to multi-modal voice devices. Spotify will help to shape the way we think about how voice assistants can work in conjunction with music and podcasting and will blaze a trail of new ways to access and share said content. I also think we’ll see hearables continue to play a prominent role within the voice ecosystem, particularly as on-the-go applications are developed and take advantage of mobile data inputs, such as GPS.
The gimmick is over. Voice is now known, popular, and frequently used. In 2020, the industry will pick up its game and provide real value to customers. Users at this point are past the pleasing effect of the coolness of voice interaction with a machine and will demand useful functionality. In 2020, the focus will be on serving the day-to-day lives of users with the content updates they are searching for, the knowledge they would like to be informed about, and their daily tasks predicted and more easily completed. Any company not providing consumers with real value in a well-polished experience will be tossed to the side of the road and forgotten. The expectation of quality will simply be higher. That means the platforms, brands, media, and other content providers will need to step up their game simply to keep pace.
They key is removing friction. While it is easy to get a weather forecast, getting a podcast, for example, has been loaded with frustration. Apple Podcasts just did a deal with Amazon so the app works seamlessly with Alexa. Now, just to get people to use it. Awareness and learning are always a challenge for anything new. On to the car – the next big area. GM, Toyota, BMW, Ford and Audi head the list of companies putting voice compatibility into infotainment systems. Some retroactively, meaning earlier models will have functionality. Let the anarchy begin.
In 2020, we’ll begin to see an expansion of enterprise voice use, and across all consumer-facing industries. The voice story will begin to shift — appropriately — from platforms and technologies to enterprise value. Mind you, this will be a beginning with full flowering seen in 2022 or later.
I believe this is the year that we will see finally significant adoption of internal-business-focused voice solutions ala Alexa for Business. The ROI is extremely compelling, the use cases are numerous, and enterprises are finally familiar enough with voice to make investments in optimizing their internal processes with voice automation.
My prediction is that we’ll see the most growth going to the healthcare industry: developments such as synthetic voices, the ability to interpret emotional nuances, predictive behavior, medical robotics, devices, home monitoring, patient/caregiver interaction. All of these seem to be emerging the fastest.
This is because the rapid growth from smart speaker to machine learning, and adaptation of the technology, UI, UX, and the new capabilities which are evolving every day. We now have a large aging population with caregivers and healthcare providers who need more remote monitoring and wellness check-ins and interactions. The list goes on.
We at VOGO Voice predict that businesses will start to leverage voice assistants and smart speakers more in 2020. We feel that companies will establish more personalized services for their customers using their own customer data to enrich the voice experience. Businesses will also enable workforce efficiencies and enhance worker safety using a combination of voice interaction and realtime geospatial data for “hands-free” data collection. In the public sector, we feel we will see more civic “smart city” initiatives that allow citizens to interact with city, county, and state agencies through smart speakers.
2020 will result in a double movement of the voice market. First, an extension of the installed base. Europe, including France, from which I am speaking, is just beginning to consider voice. Through cars, software, devices such as STB, basic usage, voice acceptance will [increase]. More vertical uses, addressing specific business needs and contexts, will create the long-awaited #voicefirst killer app, but I think it will be in B2B. This is what we are beginning to see on construction sites, in factories, and through the vocal extension of software in wearables.
Concerning ethics and actors, the disappearance of Snips that I wrongly predicted to be a success last year, is an epiphenomenon, as other movements are already launching to offer an open-source NLP. Initiatives, such as Mozilla and for example in France the Voice Lab, bear witness to this.
Voice based advertising will become a reality (not just an one off science experiment). Engagement data will make voice advertising attractive to advertisers.
I predict that 2020 will be a continuation of 2019. Brands with some experiences will continue to improve and learn from their users. Most of the use-cases will be engagement with customers (FAQ, Store Hours, etc). More interesting interactions like transactions will likely come after 2020. Keep an eye out for competition outside the U.S. next year. Companies like Baidu are making amazing progress.
Global brands will become intelligent in 2020. Conversational systems, including voice, are going to have content be highly personalized, independent, and conversational. For example, they will be able to run independently. I think they will become more seamless and affect our global conversation on social media, and in our homes, in more seamless and autonomous ways. I also predict a hundred million dollar app.
There’s been a lot of positioning this year towards audio publishers (Pandora and Spotify, to name a couple) and big advertisers (like IKEA, HP and Infiniti) moving towards — and testing — voice dialogue ads. I think this sets up 2020 to be the year that voice-enabled advertising really makes its mark on audio content consumers. Heading into 2020, voice dialogue advertising is now capable of leveraging far more advanced voice AI technology to replace the passive, often irrelevant and unwelcome ads that listeners are accustomed to. This is a big deal, as these interactive experiences are, in early deployments, proving to earn greater engagement and conversion (i.e. good for audio publishers, advertisers, and listeners alike).
While the 2020 buzz will be the opportunity in hearables and voice-on-the-go. The success story of 2020 will be voice and accessibility as brands recognize the opportunity for business and social good driven by high user adoption rates across multiple niche audiences.
Voice assistants will take the next step to become real personal assistants and this will make people more likely to use their language assistants more often.
There will be a “broadcast to the world” event where a voice agent talks to everyone at the same time. It may be planned or it may be an accident–Google issues an emergency notification about a global threat; Jeff Bezos issues a personal message the day before the election; Siri gets hacked — who knows! And it may or may not contain an interactive element where the system is able to act effectively on the hundreds of millions of responses it will receive. But, it will illustrate voice’s power to touch everyone at an emotional level at the same moment — a bit like Orson Welles’ War of the Worlds and radio. I may be early — this may not occur in 2020, but later — but it is coming.
Voice assistants across all devices have become incredibly useful and are now well accepted. We see [several] trends developing in 2020 that will continue the voice AI revolution.
One is due to the natural maturing of voice as an interface. Now that voice is common on “what” a person is speaking, the next step is knowing “who” is speaking. If you know who is speaking, you can prevent the wrong person from using the device to order on an account, or you can personalize the response when someone says, “play my music.” Knowing the speaker’s identity is already possible with Google and Alexa but not broadly used nor easily integrated into a process flow. Third-party products will take better advantage of this capability, adding services and capabilities by including third-party software to know “who” is speaking while leaving it up to Amazon and Google to continue to determine “what” is being said.
Confusion is the word for the Voice industry 2020. The hype led and leads to inflated expectations that keep not being met. Although more people will use more Voice it will also get more confusing and therefore disappointing. Some examples from a user perspective:
The Alexa buds don’t work well on my phone’s Siri.
My car’s voice system is not aware of my Google Assistant preferences and requires I talk to it differently.
I keep forgetting that new invocation I found the other day.
My “voice” blinds from Ikea won’t close unless I pronounce the commands perfectly.
My Hue lights work on Alexa but not on Google Assistant. I can’t converse.
My voice assistant hears me talking about brand “X” and then my Instagram is flooded with ads for that brand.
The confusion will lead to a feeling of apathy and idleness with organizations. There will be two groups. One group that just uncovered the potential of Voice and is super excited and ready to try and explore. Then there is the second group that learned about the potential and also learned they can’t get it to work yet. The apathy group. This, in turn, leads to more confusion with what looks like promising initiatives being launched as well as initiatives being halted.
This article was originally posted on – https://voicebot.ai/2020/01/01/voice-ai-2020-predictions-from-46-voice-industry-pros/
You must have heard it several times that Artificial Intelligence is the future of growth. You must have read pieces that talked about how AI is helping businesses in increasing their sales, taking their customer experience to another level, improving their marketing, and reducing their operating costs.
You decided to implement Artificial Intelligence in your business, but the results are far from expected. This is what might have gone wrong and what you can do about it.
You Don’t Understand What AI Can Do
Everyone is talking about AI, but there are very few people who know what AI can actually do for them. Most people think that if they throw a problem at AI, it will solve it for them – like it’s a magic box. But that’s not the truth. The truth is, AI can’t solve a problem for you – unless you clearly define the problem. Once you understand and define the problem you have to solve – you have to map out some use cases of how AI can help.
So, take the time to become familiar with how AI works so that you know what it can do for you; otherwise you will fall miserably. As every problem require a different approach and datasets to generate meaningful results.
Getting results from Artificial Intelligence should be easy. Knowing what you want from AI is where you want to spend your time.
You Try To Use AI Everywhere
While it’s true that AI can help you in improving every aspect of your business, but that doesn’t mean you should use artificial intelligence everywhere. Why? Because it’s is expensive and difficult to use AI. If you implement it everywhere, there are high chances that you will fall.
So what should you do? How to boost the odds for success?
Well, the answer is simple – you must learn to crawl before you can walk. So, start small. Once you’ve been able to deploy it successfully in one aspect of your business, add to it.
If you want to make Artificial Intelligence work for your business use it wisely – not liberally.
You Think AI Will Work On Its Own
Most businesses believe that AI is brilliant once implemented in any aspect of the business; it doesn’t need human intervention. But, the question is AI smart enough to operate without human supervision or intervention? Not yet! If you take the human out of the equation, you will not benefit from AI.
Use AI to automate your routine tasks and synthesize the data. This way you can get deeper, actionable insights more quickly and it will give your team the time to focus on strategy and drive business results that were not achievable otherwise.
Though it’s true that artificial intelligence-driven technology will evolve with time and will get smarter over time, but it will not replace people anytime soon.
You should not use AI to overtake humans – you should use it to take over certain tasks.
Your Business Doesn’t Have An AI Strategy
Most businesses have already jumped on the AI train, and those who haven’t will soon hop on it. But there are very few who have an AI strategy in place. According to research done by MIT Sloan Management Review, large businesses with more than 100,000 employees are most likely to have an AI strategy – but only 50% of them currently have one.
If you don’t have one in place, you won’t be able to harness the power of artificial intelligence. That’s why your business needs an AI strategy. Now the question is how to create AI strategy and what to include in it?
Here is an excellent article by Bernard Marr on the topic:How to develop an Artificial Intelligence Strategy: 9 things every business must include
AI is making big splashes in every aspect of the business – be it marketing, sales, or customer service. That’s the reason businesses are investing more and more on artificial intelligence. According to Forrester, investment in AI will increase more than 300% over the next year. But if you want your investment to generate returns avoid making these mistakes.
This article was originally posted on – https://inc42.com/resources/is-artificial-intelligence-not-working-for-your-business-learn-how-to-fix-it/
