2 Easy Steps to Increase User Engagement for Small Businesses

Posting to social media is a great but even better is creating conversation.

For years companies all over the world have been leveraging the power of social media to grow their business. But many fall short when it comes to the true intentions of content marketing.

When you post something that is intended to appeal to your ideal users, don't walk away. Those notifications letting you know how the post is performing are the important part.

Engagement refers to any time you and users following your business's profiles interact. That interaction can not only lead to high quality leads, it will lead to more followers who are genuinely interested in what you provide.

How did you grow your Instagram Followers? If you made the mistake if buying them then try our previous article: Why You Shouldn't Buy Instagram Followers

How can you optimize your content for user engagement?

Optimizing your content marketing to improve user engagement isn't easy. Many people limit their activities to a blog post and then hope for the best.

Many small business content marketing strategies consist of Googling a topic and rewriting one or more other pieces in a new voice. Video content is guilty of this with many videos borrowing, or outright copying a previously successful video.

In either case a user has already seen that content so why would they act on yours when it's the same thing?

Here's some more effective ways to create content specific for user engagement:

Step 1 - Create Something New

Your first step for your content is to accept that you will have to take some risks.

That risk pertains to the way you present your content, what you have to say, and how your brand brings something new to the subject.

This graph shows how this task is seen as the most difficult for businesses when trying to improve their online presence. While it's focused on B2B, anyone working in B2C should pay attention.

This initial step is crucial because it forces you to do more than dismiss your marketing as a task to set aside.

A lot of businesses leave their content marketing strategy entirely in the hands of an agency, which will not be effective. An agency or consultant is an expert on the how to distribute the content, but not an expert on your business, so you will have to work with them.

This means holding meetings where they can get input from you on what you do and why any user would want to buy from you or be your client.

A solid content marketing strategy for small businesses involves a consistently collaborative process.

Working together is your best route to creating great content.

Great content means the combination of your skill set with that of the person or people you're working with to make sure it gets seen by the most eyes and is structured to benefit your overall online presence.

Step Two - Engage, Adapt and Refine

The second step is to watch the reaction.

Look for peaks in Likes, Shares, or new Follows and notice the subject matter users have expressed more interest in. This can show you a lot about what value your users see you providing.

If you post regular blog posts or videos and see 5 -10 likes and no comments, and then you post something that receives double that or gets users commenting, you know to explore that subject in future content pieces.

If/when you do see comments, then you should absolutely respond.

The worst thing a brand can do is leave engagement hanging empty. Not only will the user who took the time to comment be put off, you'll be showing every other person who sees that comment without a response that your company isn't really paying attention.

Moreover, many users see social media as a great way to get a company's attention when they have a problem. So when they see that they can't even reach you on Twitter or Facebook they will naturally question your support in general and likely look for another vendor who does engage their users.

Look at the phenomenal traction Facebook groups have for people who are interested in a particular subject.

Your niche may not be large but if you capture an audience within it, your engagement will benefit your business by showing users your expertise, your willingness to interact with them (and potential clients), and your commitment those that buy from you or become clients.

To make it easier, it's best to have an internal set of canned responses ready for whoever monitors the accounts to reference. Chatbots are a new product that automate this process through Facebook messenger and they exist for a reason. Many business owners aren't able to juggle their responsibilities and learn and monitor their accounts.

For social media your best bet is to hire a professional and work with them. You can draft a set of responses to frequently asked questions and when in doubt they will email you so you can respond on your own time. This again speaks to the cooperative nature of having a professional help you with your social media.

Article Source: http://EzineArticles.com/10036457

Dan Fagella: Machine Learning for Restaurants and Food Services – Examples, Challenges, Trends

This year has been marked by an increase in artificial intelligence interest from new business sectors. Just one years ago, we hardly had any visitors or subscribers from the food services sector – and this year we have a strong interest from food processing and food services leaders, which has come as a surprise.

I was flown out this year to speak this year at the Ohio Restaurant Association’s annual conference, an event with over 4,000 attendees, mostly business leaders in the Midwest food services industry. This article is based on my preparation before that talk, and some ideas and details that I’ve added after speaking to a dozen-or-so business leaders in this sector.

Most of the audience was small business owners, but many of the perspectives in this article apply just as well to larger national food services firms.

All in all, AI has not penetrated the food services industry in general (compared to more active industries like healthcare or finance), and even less so the industry’s small- and medium-sized businesses (SMBs) – companies with revenues amounting to less than a billion dollars. Admittedly only a few trends are probably worth taking seriously in long-term strategic planning.

To help SMBs discern the right action around AI in food services, this article examines:

• The current state of machine learning in SMBs in food services
• The forward-looking trends of AI in food services
• AI technologies that may impact food services in the long term.

The State of AI Among SMBs in Food Services

Based on our research, we have found that for every 100 “AI companies”, only 1/3 of these companies have the academic or intellectual horsepower to leverage machine learning in any serious way while many are merely marketing hype. Of this number, a third has gone past pilot testing their product or service but have yet to show any real positive impact on their bottom line.

Any existing vendor applications are mostly exclusively made for large chains that have the data flow and the business model to merit the use of AI and machine learning. Whatever announcements companies make about investments and innovation for AI in food services is mostly unsupported by clear returns at this point.

At this early phase of AI’s life cycle, most AI companies are burning through venture money, trying to thresh out use cases and to their viability in the market. This seems to be true not only in food services but also in healthcare, finance, retail and other industries.

AI is clearly not for mom and pop operations at this point, and even if a business were fortunate enough to have money to invest in machine learning technologies, it probably wouldn’t have the data science talent and the R&D experience to really get value from artificial intelligence. Hence, even firms with the budget might be better off investing in security, customer service, marketing, or other areas.

For certain, there is a great deal of noise around AI but food services SMBs need to be able to discern the grain from the chaff. As it happens, the majority of AI applications should be ignored, with only a few worth considering in long-term strategic planning, not immediate implementation. We will cover some of these impactful long-term technologies later in this article.

Machine Learning Challenges for SMBs in Food Services

Among the barriers that face AI in food services, these are the most formidable:

• Most Innovation is for Bigger Firms: Vendor applications in food services (as with most other sectors) are costly and almost exclusively being developed for larger firms. These custom applications, such as video intelligence for assessing food quality, or food services robotics, are highly complex and require experienced machine learning experts on staff, plenty of resources, and/or huge volumes of data (neither of which are common for small or mid-sized food services businesses).
• AI is Resource Intensive, and Complex: The talent, budget, and time requirements to build AI and robotics applications is much more than most companies expect. SMBs are in no position to attract the best AI talent straight out of college and to compensate them with handsome salaries and clever perks like Google or Facebook. Even for restaurants with more than a hundred locations, data science talent is probably scarce or non-existent. AI not being a core or mission-critical part of operations makes it difficult to cultivate and develop from within a food service business.
• Food Services Isn’t an Machine Learning Innovation Hotbed, for Now: The food services industry receives little attention as a distinct niche for AI, unlike eCommerce, pharmaceuticals, banking, telecommunications, among others. Whatever scarce AI talent that exists is not beating a path toward food services – and the industry isn’t likely to see the same degree of innovation as many of the sectors listed above. This doesn’t mean that innovation will not exist in food services, but that we should expect a slower pace of innovation and adoption.

Use Cases of Machine Learning and AI in the Restaurant

Note that restaurant is not listed specifically, though customer service and marketing applications are relevant to this sector.

1 – Robotics

As far as we can see, robotics has no relevance for SMBs in the near term due to issues with scalability even for the biggest companies. However, SMBs may consider robotics as an alternative workforce if minimum wages for human talent continues to increase, and indeed raising minimum wages are likely to spur more investment in the robotics space in the years ahead. Some use cases in this area include convenience store chain 7-11 which has adopted streetbots and drones in its delivery services.

CaliBurgers’ Pasadena location started using Flippy, the artificial intelligence-driven robot developed by Miso Robotics, a robot that can grab and flip burger patties, handle buns and other ingredients to create hamburger sandwiches. As of March this year, Flippy has only started working in Cali Burger’s Pasadena outlet, and if successful, will be deployed in 50 restaurants by the end of 2019.

Another application is the Moley Robotic Kitchen, a pair of robotic hands that replicates human movement to prepare home-cooked meals, from a library of stored recipes in its system. The developers intend for the Moley robot to be for consumer kitchens when it is released this 2018, but enterprise-scale use has yet to be proven.  A check of the website showed that the product is still under development, and interested clients are directed to a pre-sales link that leads to a blank page.

2 – Smart Kiosk Technology

AI-integrated smart kiosk technology has potential, but is speculative and still in its pilot phase, even for the large food chains. For instance, McDonald’s self-service kiosks recommend products depending on a season, weather, and new or repeat customer’s preference, through a digital menu board. As well, this touchscreen menu board offers personalized options from which customers can choose their preferred amount of salt in fries, sauce in burgers and sugar in cold drinks. It also allows them to order and pay for their food, expediting process at point of sales process, reducing human error while giving customers control over their food choices.

Additionally, a mobile application will enable customers to place orders from their phone for pick up or synchronize with a kiosk to recognize their app profile, their favorite orders and preferred payment methods. McDonald’s had aimed to launch mobile ordering and payment in 20,000 restaurants by the end of 2017.

The technology hub at Wendy’s, meanwhile, was set up to create innovations in internal technologies for business intelligence and communications. The company is currently testing mobile-, voice- and beacon-based ordering; self-order kiosks; and a loyalty program.

In Asia, KFC in January 2017 had planned to deploy kiosks in China with facial-recognition software to predict customer orders based on age, gender and mood. However, this was met with some resistance in Hong Kong as customers continued to line up at the cashier.

Clearly, smart kiosks are not set to dominate the food service industry just yet. The big boys will need to first sort out the knots in innovation and customer behavior, and show that it can support the bottom line before AI technology makes its way to the little guys. Only then will more investment come in.

3 – Chatbot / Conversational Interfaces

Chatbots have received some attention recently recently, not only because these allow restaurants to enhance customer relationship, but also help change customer behavior, reduce order-taking and possibly garneri expanded market share among mobile-savvy users.

In January 2017, Starbucks announced voice ordering capabilities within the Starbucks mobile iOS app and the Amazon Alexa platform. This new tool extends adds a new layer to the barista and customer interaction, even before customer reaches the store.

For sure, it is a win for Starbucks in terms of technology innovation. But other than the press release, no numbers have been released to show this new interface has gained traction with customers.

Domino’s pizza chain rolled out its Facebook Messenger chatbot in Feb 2017, as an alternative to ordering via the phone or online. Prior to this, Domino had also introduced the pre-ordering via the AnyWhere platform, Twitter, Apple TV, Google Home, Amazon Echo, Ford Sync, SMS, Samsung Smart TVs, smart watches, an in-app voice assistant, and more. Likewise, Subway in April 2017 launched a chatbot at more than 26,500 domestic locations.

While high-ROI chatbot applications are the exception (our research seems to show that most chatbot initiatives are done as a PR stunt, or never produce meaningful returns), Domino’s seems to be among the few who are driving the technology forward. Domino’s also has the benefit of a limited menu size, and many repeat orders of simple favorites – making the programming and training of a chatbot more easily achievable.

How long before chatbots and other low-ticket applications become common for ordering food, delivery or otherwise, is anybody’ guess. What is clear is that large companies will mostly have the budget and data volume to build more effective bots and that chatbots are applications that are already in customers’ hands rather than developed by the food service businesses.

4 – Customer Recommendation Platforms

Consumer platforms will expectedly impact more restaurants in the next two to three years, but “adapting” to these apps probably won’t be much different than when restaurants learned to make “findable” listings on Yelp, Google Maps, and Halla.

For SMBs that want to be involved in customer recommendation technologies, it is imperative that they explore ways to achieve better reviews and ranking from customers to ensure they show up in the platform’s recommendations.

5 – Analytics Solutions and AI

There is almost no hype or current applications of analytics solutions in the food services space, but it might not be long until restaurants partner with technology companies to use predictive analytics to help forecast visitor traffic, food orders, inventory needs, as well as revenues and costs. Among the companies that provide analyics services:

Venga offers solutions that collect, analyze, and use data related to purchases, preferences, and habits to enrich the dining experience, personally engage customers, and ultimately increase repeat visits and sales.

• OpenTable connects diners with restaurnts by allowing to find a restaurant and reserve a table
• UpServe’s restaurant management platform is for easier payment processing, point of sale, reliable payments and in-depth analytics that can boost margins and save valuable resources
• PosIQ provides cloud-based Big Data solutions that collect customer data in real-time to help improve restauarnt businesses and customer experience.

Final Insights for Restaurant Business Leaders

In conclusion, AI is not expected to make its way into SMB food services in the near term. Some marketing automation tools will may integrate AI in the next two to three years, and some consumer recommendation apps will use AI to connect users to restaurants, but SMB food services leaders are unlikely to get their own hands on AI software (due in part to the challenging nature of these technologies, the cost of related AI talent, and the huge volumes of data required to make them work).

Meanwhile large firms will continue to explore viable AI applications that can leverage big company data, the results of which will eventually trickle down to SMBs. For now, AI applications in food services will use currently available consumer technologies, rather than in the hands of restaurants.

We don’t recommend that SMBs jump into the AI bandwagon just because it seems cool and “everybody is doing it”. This is an illusion, and a waste of time and resources. In truth, 95+% of SMB food services companies will be “late majority” adopters on the adoption curve, with only some in the “early majority”.



Here are some steps that food services leaders could take with respect to AI now:

• Consumer Apps May Be Relevant Near Term: Be mindful of and to carefully explore apps and consumer platforms that are becoming common or gaining popularity, and to allocate reasonable resources to those that might deliver value to your firm. Apps like Yelp (and it’s newer competitors) will likely become more effective recommendation channels.
• Most AI PR is Hype: Understand that most AI press releases are hype, and that most AI applications in food services are merely very expensive pilot programs, usually with little direct evidence of ROI. It behooves companies to seem “innovative”, and there is a felt social pressure to put on this appearance when their competitors and doing the same.
• Follow the Giants of Your Industry: Follow the big food services giants (McDonald’s, Subway, etc) to stay on top of where machine learning is becoming an important part of their business. Once an AI application begins consistently driving ROI to the biggest players in the industry, then we might expect the same technologies will become viable to the mid-size market. The best view to the future of AI in the food services sector will probably be looking at the successful AI applications of the industry giants. Until it works for them, there’s no good reason for small business owners to be concerned about them.

This article was written by Ayn Veronica De Jesus from an audio transcription of Dan Faggella’s presentation on “AI in Food Services” for the Ohio Restaurant Association.

Should Your Small Business Use ECommerce Chatbots?

Conversation is a core part of the buying process, whether you're asking the wine clerk what red she recommends or messaging a store owner on Facebook to see if they sell gift cards.

I'm sure you've visited a website and had a chat box pop up, asking if you needed help or offering to start a conversation. Chances are, the "person" with whom you're speaking isn't human.

A chatbot allows business owners to provide responses and solutions - and even generate leads and sales - 24/7 through automated customer service live chat. According to IBM, up to 80% of routine customer service questions could be answered by a chatbot.

There are two types of chatbots:

1. Those that rely on rules and can only accept/respond in limited ways.

2. Those that use artificial intelligence to employ sophisticated algorithms to accept/respond.

One of the areas we'll see this tech more and more in is healthcare. While nothing will replace a professional - especially in an emergency - it's convenient and fast to ask a bot a simple question about a cold or what medications interact with breastfeeding.

Here's an example: Florence is a popular online personal health assistant. "She" has become focused on two main features: health tracking and medication reminders. All users have to do is start a chat with her in Facebook Messenger, Kik or Skype and she'll jump in.

Now, let's take a look at three pros of conversational commerce:

1. Fast responses, multiple options. You're able to answer queries ultra-fast and this type of interaction is possible on a wide range of platforms including Facebook Messenger, SMS, Google Home, Amazon Alexa, Apple Business Chat and WeChat.

2. Money, money, money. You don't need to work late responding to questions and you don't need to pay employees to respond to every interaction. If you want to sell through your bot people can purchase your product or service anytime, anywhere.

3. You automatically gather data. eCommerce chatbots collect data so you can offer a more personalized experience each time someone communicates with your brand. And, you gain lots of valuable insight on your users' needs, pain points and buying habits.

Caption: Domino's uses a wide variety of tools to allow pizza lovers to rapidly order, pay and track their food.

Let's balance that out with three cons:

1. They can't replace humans. Especially in the medical and legal fields, there's a concern that patients may use these technologies instead of seeking professional help. Also, you shouldn't use conversational commerce as your only form of customer service. Your clients should be able to connect with a live person, at least during your regular business hours.

2. Misunderstandings can happen. The problem is with natural language understanding, which is the ability to determine intent. Bots aren't as skilled at understanding us as our fellow humans are - at least not yet. Customers will get frustrated with eCommerce chatbots that don't work well and will take their business to a competitor.

3. They're not right for every situation. It's true that many companies can benefit from this ever-changing technology, but don't try and force it. If your services are too complex to map out in AI chat or require thorough consultations, a chatbot probably won't help lead-qualifying efforts or sales.

Chatbots are far from perfect, and while they'll certainly advance going forward, you don't want to alienate customers now. By understanding the pros and cons, you can ensure you're making the best chat-choice for your business.





Article Source: http://EzineArticles.com/10075504