Good for more than 20 hours of continuous portable playback, Marshall’s palm-size Emberton wireless speaker delivers punchy 360º sound like only a Marshall can, using an exclusive technology called True Stereophonic. All functions are controlled by a single multi-directional button and IPX-67 water resistance makes it party-proof. Quick charge capability gives 5 hours of playback on a 20-minute recharge.Reblogged 4 days ago from www.werd.com
With the rapid advancements in technology, the rise in Software as a Service (SaaS) companies, and a growing need for rich data-driven marketing campaigns, it’s no surprise that we are increasingly witnessing the integration of artificial intelligence in our strategies and operations.
Today, we’re showing you eight of the best ways to use AI in martech this year, so that you can make the most of your own marketing tech stack to grow and scale your business.
Martech has paved the way for what’s called predictive analytics, which can show marketers what may happen in future campaigns. These predictions are always based on existing data from previous campaigns and strategies, and with machine learning, your data and predictions only get richer over time.
You can use these predictions to help you create stronger campaigns that resonate with customers and prospects better, as well as to identify leads who are warm enough to proceed to the next sales stage in your business.
Another helpful way to use AI in martech is integrating chatbots into your customer service efforts.
There are several AI chatbot software and services you can add on your website or Facebook page, letting customers access instant information based on a pre-loaded set of options or questions or even with advanced keywords.
Here are a few use cases to apply AI chatbots in their marketing efforts:
Over 70% of consumers only interacted with highly personalized content and offers.
And with AI in martech, creating personalized content and offers becomes easier. Tools like Zyro, for example, have content generators that help businesses create more original content pieces without having to bear the brunt of manual search.
We’ve seen AI-based clustering systems with popular entertainment apps like Netflix or Spotify, who are known for their strong understanding of their consumers’ preferences and making targeted content recommendations.
And beyond entertainment, we’ve seen companies like Airbnb use this machine learning to offer users extremely personalized experiences and offers after gathering data like transaction history, preferences, and search history.
Social media management is perhaps one of the most useful ways a company can use AI in their marketing strategy. Marketers can now easily automate their content distribution and promotion efforts at scale, offloading tasks that were once manual by nature.
You can use popular social media management tools to create a content calendar, schedule posts, and manage comments and messages all from the app. Even if you have multiple accounts across different platforms, you’re able to manage all your content and data and review your analytics all in one dashboard.
Adjusting your prices based on the changing demands of the market is necessary in every business.
A seasoned business owner may know when to adjust prices based on quarterly data, historical trends, and other factors in the market. But this traditional method of determining prices is time consuming and prone to human error.
Using AI for dynamic pricing is definitely the smarter strategy. It uses algorithms to identify the best pricing for every possible situation based on data gleaned from customer behavior.
Targeting customers with customized discounts and offers becomes easier with AI. Price predictions are done continually and in real-time, so you can save valuable resources and avoid pricey mistakes.
Several industries ranging from airlines to travel companies now make use of AI for dynamic pricing to offer the most ideal prices to every customer.
As a business grows, it collects more digital assets or valuable electronic files such as images, PDFs, videos, and other documents related to your products and services.
These digital assets can be stored in servers or drives in the company, but they can be difficult to manage due to their increasing volume and the changing conventions of data storage.
Some important digital assets may even be lost due to inconsistent file names, migrating data, or even during a messy digital transformation project within the company.
Thankfully, integrating AI to digital asset management adds useful and well-organized metadata to the files. By using machine learning to tag assets, AI improves categorization, provides suggestions based on similarities, and offers advanced and refined keyword searches for users.
This is especially useful for e-commerce and the real estate industry which deal with a massive catalogue of images.
Using AI for retargeting campaigns essentially lets you follow potential customers even when they leave your online store or website, prompting them with reminders about your offers.
Possible use cases are for ecommerce stores sending cart abandonment emails. An observation by email marketing platform Moosend found that among all the users who click through after receiving a cart abandonment email, 50% make a final purchase.
AI retargeting is also used for email campaigns when a customer views your website or landing page but takes no action. You’re able to then retarget them through banner ads on other websites or social media ads, so you’re constantly top of mind.
A customer stays loyal to your business when they feel prioritized, and their experience and behavior can be measured using the net promoter score (NPS). The NPS identifies top customers through a simple question of how likely they would recommend the business to a friend over a scale of 1 to 10.
Customers are grouped accordingly:
Identifying a customer’s NPS becomes optimized through AI-powered surveys. They combine conversations with customers and actual metrics and automatically analyze textual data in order to improve the customer’s experience.
Thus, you can better increase the number of Promoters, know which Passives to appease, and lessen the number of Detractors.
Martech is a constantly evolving and exciting field due to the rapid developments in AI technology. Consumers are producing data at a massive scale every day, so using AI in martech is the logical way forward to grow your business.
Kevin Payne is a Content Marketer helping scale software companies. You can find him here.Reblogged 5 days ago from www.clickz.com
People are aligning themselves with brands that understand their customer needs, recognize them and connect with them on a human level.
In today’s COVID-19 environment, customer expectations are evolving at an accelerated pace – challenging brands to deliver and maintain a trusted relationship.
Now more than ever, brands need to make sure they are reaching their customers with the right content in the right channels at the moments that matter on an individual level.
This is crucial to drive and preserve customer loyalty, with Deloitte’s research showing that it can directly impact a business. Results revealed that thirty-nine percent of people surveyed switched brands after a bad experience, and 62 percent feel they are in a relationship with their favorite brands.
Connecting in this way may seem like no easy feat, but artificial intelligence (AI) and machine learning can help make it possible. These technologies help brands better anticipate customer needs and reach them in the moments that matter through real-time marketing.
Below are steps CMOs and marketers can follow to provide relevant customer experiences:
The needs of customers can change in an instant, impacting their customer journey. To offer value, brands need to be agile enough to engage with customers and provide them with a more personalized experience based on their real-time needs.
To do this, marketers should first define their ambition and develop a strategy with aligning priorities for the business and the customer. For instance, the goal could involve upselling or increasing loyalty – the list can go on.
Instead of trying to achieve it all at once, it’s recommended to establish specific use cases and the roadmaps to achieve them. Also, use AI and machine learning to run agile targeting and dynamic creative to meet the current needs.
It’s important to recognize that as customer needs, data sources, and the outside environment are in flux, the program execution and results could be as well. CMOs and marketers should follow a test-and-tune discipline and be prepared for an iterative process.
The people component is also key. Having the right talent in place, stakeholders and operating model is critical for real-time marketing success.
Brands will need to identify the specific data and insights needed to support their use cases, as each use case is unique. They should consider the following:
Brands have a lot of data within their own walls, but to meet and anticipate customer needs at the right moments, they need external data to provide a complete view of their customers and fill in the gaps. This can be environmental data such as location and season-based information, trend data, or contextual data.
For example, external social media data could tell marketers what a customer set is responding to – and the type of content they want to see on the platform. Also, given the changing data regulations and third-party cookies going away, brands need to lean into first-party data.
A customer data platform is valuable as it helps to create a single view of the customer that can be leveraged for the marketer’s needs. It combines a company’s internal data across all owned, paid, earned sources along with the external data.
With the right data and a better understanding of individual customers, marketers will have an important fundamental step completed to help create key personalized experiences.
AI and machine learning are vital for brands to better anticipate customer needs, while also accelerating speed to market experiences. When using complex data sets, machine learning helps with intelligent audience modeling, and AI helps to update a targeting strategy based on real-time insights.
With the ability to analyze more information and gain a deeper understanding, marketers are empowered to make informed and quick decisions to meet the changing needs of their customers across channels, messaging and experiences.
For example, when put into practice for a bank, AI could identify the individual customers who are currently in the market for a house using a set of data signals outside of standard demographic insights and target the individuals with the appropriate mortgage offer in the most optimal channel and moment.
Being able to orchestrate a personalized message deepens the connection and trust within the brand and customer relationship.
While these actions can support a certain use case or scenario, it’s difficult for brands to do this at scale and in real-time. One of the main hurdles many companies face is that customer experience is often thought of as a function of marketing or viewed in a silo.
Customer experience should be a true operational discipline with emotionally intelligent capabilities embedded into every area of a company’s operations. A key collaboration should be between the CMO and CIO.
This way, customer expectations and human insights, established through AI and machine learning can be used to influence the strategy and actions of brands in real-time that ultimately drive business results.
While AI can benefit the customer’s experience, it can also help CMOs and marketers with non-CX use cases. For example, AI can help to optimize marketing ROI, improve marketing performance and acquire new customers.
In a contact center, for instance, AI can work alongside customer service agents by informing optimal messaging for the caller. While CX may not be the core objective, personalized customer experiences can certainly help achieve the use case goals.
Marketers should trust their data and entrust AI to do more of the work by properly designing and deploying algorithms. Humans are still involved, but with more decisions happening autonomously and in real-time, they can focus on other strategic decisions and creative efforts for the customer.
By focusing on customers’ needs and providing the personalized experience they crave, brands can create resilient emotional bonds that lead to loyalty.
Kate Erickson is Managing Director at Deloitte Consulting LLP and Hux by Deloitte Digital.Reblogged 5 days ago from www.clickz.com
Designed to elaborately display five timezones, the Nixie Time Zone V2 by Adatte Design is a desk clock worthy of a discerning mad scientist. The clock features six rare original Nixie tubes inside of a body miled from solid aluminum with an anodized bronze grey finish. The wheel to the left of the …Reblogged 5 days ago from www.acquiremag.com
Utilizing vacuum insulation—similar to a double-walled thermos—the QOOL Box cooler requires no power source or ice yet can keep contents cool, even frozen to -15ºC for up to 10 days. A unique Phase Changing Material replaces ice as the source of cooling which emits a steady stream of cooling energy to keep perishables and cold drinks at exactly the temp you want them.Reblogged 5 days ago from www.werd.com
It should come as absolutely no surprise that training to become an astronaut is a decades long affair that involves a lot of unique experiences you’d never think about on Earth. One of those unique…Reblogged 5 days ago from coolmaterial.com
Retaining the iconic outline and design details that have made it a classic, Panerai’s latest Luminor variant, is the Marina 44 (PAM 01313). Within its large, high-spec and heavy lugged matte finish case, an automatic Swiss Made Cal. P.9010 movement powers the dial, date window & seconds sub-dial & delivers 3-days of power reserve. A blue satiné soleil face is matched with a Dark blue alligator strap for a refined yet sporty look.Reblogged 5 days ago from www.werd.com
The 500-lumen rechargeable Sitka Lantern from UCO offers bright LED light for after-dark ambiance, plus its extendable arm brings the tabletop lantern up to 26”—a level that positions the light perfectly for stuff like campsite dinners & card games. An Infinity Dial allows users to fine tune the light’s output from low to high, and features a Northern Lights mode that cycles through red, green, and blue lights.Reblogged 5 days ago from www.werd.com
As part of ClickZ’s AI in Marketing Summit 2020, Brian Solis, digital analyst, speaker, and author, gave a keynote where he shared some of his thoughts on the future of customer experience, marketing, and the role of the brand in a post-COVID-19 world.
Solis is a digital anthropologist who has recently taken the role of Global Innovation Evangelist at Salesforce. He discussed how AI is playing a pivotal role in what he dubs the “automated enterprise.”
Businesses are increasingly transforming into automated enterprises using various AI-based technologies (e.g., RPA, chatbots, real-time analytics, etc.) to better leverage data and provide superior marketing and customer-centric experiences.
“We’re moving into a predictive and intelligent enterprise that can help humans make better decisions,” Says Solis. “Eventually, we’ll be in a position where data and platforms will make decisions without checking in with human counterparts. Ultimately, in the next ten years or so, we’ll start to see the onset of a quantum enterprise.”
Solis views the new post-COVID-19 normal as a novel economy—this is not a new normal, but an interim normal which is permanently changing consumer behavior.
“The interim normal means we need to learn how to operate in these times as best we can, but also that we set the stage for what comes next,” explains Solis.
Solis emphasized that AI gives us the tools to track new consumer behaviors as they’re evolving, so that we can move forward in this interim normal.
“I want to find ways to deliver incredible experiences to customers and look for ways to enhance the types of experiences that make the best of this situation,” explains Solis. “This is an opportunity to reimagine.”
The novel economy is what Solis describes as the mission of moving forward in the most positive, productive, and human-centered light. It’s a reaction to the crisis of COVID-19, and is structured into three main segments, as follows:
This is the reactionary phase immediately surrounding the onset of the crisis and requires that companies stabilize operations and maintain business continuity (e.g., restaurants closed in-house dining and moved to curbside pickup).
The “Alive” phase focuses on building, securing, and operationalizing your organization for a state of transition. Per Solis, “You must master the new normal and start exploring opportunities for business model innovation.”
Thriving in a post-COVID-19 world means your organization has successfully transitioned to the “next normal,” achieving operational excellence, disruption proofing, and innovating forward.
Source: Brian Solis
“The novel economy is just a period of time. What comes afterwards is an opportunity to deliver greater experiences. Businesses must find ways to deliver good experiences to customers in the current environment than they did BC (Before COVID-19),” says Solis.
The BC economy was driven by immediacy and impatience and enabled by AI technology (e.g., Amazon). The BC consumer was accustomed to choice, immediacy, convenience, accuracy, and (overall) a personalized, integrated platform experience.
The AD economy (After Disruption), is a predominantly digital one. It’s forcing organizations to be much more responsive.
“Digital’s never been more important,” Says Solis, “But we also have to consider the emotional and human factors that are happening to an entire generation in the AD economy.”
Solis writes, “COVID-19 represents a deep somatic marker in our lives, an emotional bookmark that permanently links memories to visceral, emotional responses.”
New data is starting to show that:
The virus is changing the mindset of consumers, but also their behavior, with digital shoppers driving 20% revenue growth in Q1 2020 versus 12% in 2019.
Likewise, digital revenue increased 41% during the last 15 days of Q1 2020, while mobile e-commerce traffic grew by 25% across all industries. Finally, mobile phones represented 71% of total traffic share and 56% of total order share for Q1 2020.
“This device changes the game,” says Solis. “It’s about speed, simplicity, and personalization. Basically, any data that you have pre-March, should probably be thrown away and you should focus on real-time data now and moving forward.”
AI and machine learning are important in the new post-COVID-19 economy because they enable speed and convenience, delivering a better digital customer experience. Prior to COVID-19, nearly 60% of consumers had high levels of interaction with physical stores, but that’s dropped to about 24%.
This likely won’t change soon, with just 39% of consumers anticipating a high level of interaction with physical stores in the next 6 – 9 months.
“This is where AI and machine-learning can help provide the humanity that’s missing from those personal interactions, explains Solis. “People are making a conscious effort to buy less because our values are changing, making us rethink life. This is changing how we view brands, which means the brand itself—in terms of marketing—needs to evolve.”
Solis explained that for brands to thrive in the novel economy, they must elevate AI’s importance within the C-Suite by hiring AI futurists. He also stressed that it’s important for AI to serve a purpose—to create memorable experiences that serve to humanize the brand. Solis calls this, “data-driven empathy.”
“You have to rethink what it means to be a brand in the novel economy. As we think about a post-COVID-19 world, brands should focus on data-driven empathy, renewed vision, building trust, updating values, and safety. You must ensure you’re communicating that you have the customer’s health and well-being in mind.”Reblogged 6 days ago from www.clickz.com