Magnus Unemyr

Setting the right price is a key factor in business success. Still, many companies use gut feeling rather than facts when determining the cost of their products. With predictive pricing, we can apply machine learning to the problem of setting the best price and improve revenue optimization.

We can change the price dynamically too.

Many companies use gut feeling rather than facts when determining the cost of their products

Dynamic pricing is often divided into two categories: revenue management, which is driven by supply like hotel rooms or seats on a flight, and supply driven, which is propelled by competitor price-matching or out-of-stocks.

Customer acceptance for dynamic pricing is generally quite high for products with fluctuating availability and demand, where the product by nature vanishes after a certain time.

Take a global car rental company with thousands of locations as an example. They may change millions of prices each day to optimize their revenue based on a huge number of factors, from the weather, to local events, to the prices of their competitors.

Customer acceptance for dynamic pricing is generally quite high for products with fluctuating availability and demand

This can clearly not be done manually, and prediction algorithms can help find the optimal pricing for each car, in each location, at every hour.

Luckily, readymade solutions for revenue optimization exist, and they can predict the best price of most products—provided you have enough data to crunch.

One example is Perfect Price, which offers a product that covers both revenue management and supply-driven situations. I had a conversation with Alexander Shartsis, co-founder and CEO of Perfect Price.

He says that “Today, consumers accept and even expect price fluctuations. Frequently, they are able to time them better than the companies setting the prices—costing companies billions annually. It seemed clear that there should be a simple service to automate the complex data science required to make dynamic pricing possible for any business.”

Perfect Price leverages a core platform based on a number of AI technologies, including supervised machine learning, reinforcement learning, clustering, and more.

This platform generates a demand function that is accurate down to the micro-segment level, meaning it is specific for very small groups of customers. It then can calculate an optimal price based on business rules and other configurable parameters in order to optimize the customer’s objective—profit, revenue, etc.

It is obvious this type of technology can benefit many companies, in particular those where the pricing is changing often, as is the case with flight tickets, rental cars, and hotel rooms.

Prediction algorithms can help find the optimal pricing

Additionally, AI can be used to predict the most likely selling price for certain goods that are never exactly the same, like the price of a used car or a house on the real-estate market.

In such cases, data about the sale of similar products are gathered over time, for example the selling price of used cars, along with data attributes like the type of engine, stereo, color, mileage, equipment, the owner’s neighborhood, and more.

Given this data, a price prediction algorithm can guess the most likely selling price for the next used car to be sold, even though it has never seen exactly the same combination of attributes before.

A price prediction algorithm can guess the most likely selling price

This works similarly for real estate properties and other categories of products where there is only one unit of the same configuration available. Regression algorithms are suitable for this kind of functionality.

An unexpected example of a solution like this comes from GoDaddy, the web hosting and domain name supplier.

Their domain value and appraisal tool uses natural language processing to understand the word combinations used in a domain name. It then predicts the price of the domains using those words on the domain name aftermarket.

That’s why when you go to buy a domain, the one you want may be expensive, while similar ones without the same pull can be quite cheap.

Getting market insights and an understanding of what your competitors are doing can be a major advantage. Companies have performed market analysis and competitive intelligence for decades, trying to make sense of their external environment, but the landscape is changing dramatically now.

The reason, of course, is that the Internet—combined with advanced data analytics and AI—can provide so much more information on what others in your field are doing.

With more and more business activities moving online, with most of their activities in plain sight, it has never been easier to gather competitor intelligence.

Every company that is reasonably active online will leave digital footprints that are open for anyone to watch and interpret.

Every company that is reasonably active online will leave digital footprints that are open for anyone to watch and interpret.

If they don’t, their customers, employees, and partners do. At the same time, this creates vast amounts of information, making it hard to digest manually.

In practice, it is almost impossible to keep up.

The process of gathering and analyzing market and competitive intelligence clearly benefits from having the right tools, and artificial intelligence is well positioned to help.

What can AI tools for competitive intelligence do?

In short, they help gather data and turn them into strategic insights you can use as part of your strategies.

Gather data and turn them into strategic insights you can use as part of your strategies.

You can spy on competitors and their products, obviously, but also partners, industry influencers, customers, review or career sites, or other organizations related to your market.

Any information about your industry and those involved in it can be gathered and analyzed at unprecedented scales.

Competitor websites are a prime location to study, including product description pages or press releases on websites and in the news.

Companies often publish information on new customers or new partnerships, events they will attend, financial updates, news on acquisitions, and more.

Monitoring this can be rewarding.

Any information about your industry and those involved in it can be gathered and analyzed at unprecedented scales.

Any publicly advertised or announced hiring, downsizing, or management team changes can provide valuable insights as well. If they hire, what positions are they filling and in which local office? Are they opening or closing any offices? Have they replaced their CFO or a business unit manager?

Data like this may signal areas where they are investing or are dealing with problems.

The most obvious thing to monitor may be the competitors’ product pages. There is often a lot to learn about product improvements, positioning, and market message, as well as pricing and insights into the general business model.

You may also benefit from collecting customer reviews from competing products, product review sites, or product forums. Many companies publish customer success stories and testimonials, or highlight new partnerships.

Finally, social media channels, blogs, and scheduled webinars may provide a wealth of information on what products or marketing resources your competitors promote, what customers say about them, and more.

It’s nearly impossible to monitor this type of information manually for all your competitors and other organizations relevant to your industry, and even harder to generate meaningful insights from it.

Tool support is clearly needed, and there are many options to choose from. Some of these tools can provide near real-time insights as well.

To make sense of the information gathered online, most of these tools use natural language processing (NLP) and natural language understanding (NLU) to harvest business insights from text.

Crayon, for example, claims to follow over a hundred different types of signals for each of your competitors. Their system collects information from millions of data sources and uses machine learning to improve the results.

Their tool tracks your competitor’s digital footprint both on and off their websites, thus detecting their activities and what is said about them online. This may range from product and price changes to customer and employee reviews or marketing campaigns.

In essence, it is a digital analyst tracking a competitor’s moves, automatically and at scale.

For example, if a competitor launches a product that is immediately met by criticisms about its suitability or functionality, these systems can provide the necessary data to create your own product free of those drawbacks, thus offering consumers a more attractive alternative.

I discussed their tool with Ellie Mirman, CMO at Crayon. She mentions that often, the most critical updates are those that aren’t announced in press releases, but rather are hidden in subtle website changes or a new customer review.

She is probably right, but sifting through so much detailed data on a regular basis is just not possible to do manually. This needs to be addressed by software automation.

Mirman notes, “With the incredible amount of intelligence data available, it’s critical to surface the highest priority updates to be able to analyze and act on them while they are still fresh. Crayon uses a combination of machine learning and analyst curation to separate the signal from the noise in competitive intelligence.”

Given the volumes of data, the latter may be both important and difficult to solve using traditional software technologies.

By leveraging AI, the system is able to tell the difference between the correction of a typo on a homepage (not meaningful) versus the change of one number on a pricing page (meaningful).

Harvesting valuable insights from vast amounts of data is indeed one of the best use-cases of artificial intelligence, and is becoming one of the most common.

Another vendor in this space is Quid, which can analyze markets and brand perception. Quid discovers patterns and trends, and monitors what customers say in conversations about a company, their products, or the wider market.

Artificial intelligence tools are able to understand the meaning and content of the information they find

While some of the information these systems use could have been gathered by pre-existing content scraping tools, artificial intelligence tools are able to understand the meaning and content of the information they find, turning data into actionable insights.

This is a big difference.

With AI, we not only get help in gathering the data from a huge number of sources, it can also understand what is written and condense the information into the most important insights.

Chatbots are predicted to be the future of customer relationships, as well as digital marketing. But why should you develop a chatbot for your business, and how do you do it?

In this blog post, I interview Larry Kim, the founder of MobileMonkey, a chatbot development platform.

Larry Kim started MobileMonkey in 2017, and today it’s a fast growing official Facebook Messenger Marketing solution provider platform that helps marketers and businesses automate customer communication and lead generation on Facebook Messenger.

Before Larry started MobileMonkey, he founded WordStream, a provider of AdWords, Facebook and keyword tools used by more than a million marketers around the world.

Larry is also a contributor to CNBC and Inc. Magazine. Key interests include: Chatbot Marketing, Google Ads, Facebook Ads, entrepreneurship and startups. He received Marketer of the Year awards from Search Engine Land, US Search Awards, and PPC Hero.

Now, over to the interview!

What is MobileMonkey’s Background?

MobileMonkey’s mission is to give marketers the tools to build chatbots without coding, on the chat applications customers are already using right now.

The problem: Marketing engagement is dropping while costs are increasing.

Email open rate = 20%. Email marketing CTR = 1%.

Facebook News Feed = 1% engagement of page fans.

Facebook ads = cost per click went up 122% in 1 year.

The solution: Marketers who reach customers in chat will be reaching them where they already are and seeing higher engagement and lower cost as a result.

Open rate in chat is 80%. CTR is 20%.

Facebook click-to-Messenger ads with a chatbot autoresponder after the click has a cost per lead that’s 97% less than traditional Facebook ads to website landing pages.

People use messaging apps more than social media. (Source)

MobileMonkey simplifies chatbot creation and seamlessly integrates marketing bots with your website, Facebook Messenger and Facebook ads.

What Problem Does MobileMonkey Solve?

MobileMonkey is an easy to use chatbot builder platform for marketers to design bots for Facebook Messenger, which has 1.4 billion users around the world.

What DoesMobileMonkey Do, and Why Is It Important to Your Customers?

Half of consumers say that they would rather chat with a business than any other communication channel. Yet less than 1% of businesses are equipped to automatically engage with customers in chat.

The heyday of the traditional Facebook ad is over, and the news feed is becoming less and less relevant each day

Facebook Messenger marketing automation is possible for businesses of all sizes with MobileMonkey. It’s possible figure out how to build a chatbot because the platform guides you through the entire process.

With MobileMonkey, marketers can:

• Answer FAQs
• Provide 24/7 real-time customer support
• Grow lead and contacts lists
• Create customer audience segments using system variables and custom attributes
• Send targeted messages to specific audiences
• Launch drip campaigns
• Nurture and educate leads
• Ad an interactive chatbot experience to Facebook ads
• Manage event sign-ups and reminders
• Handle customer surveys
• Run contests
• Make Facebook posts interactive

MobileMonkey is filled with chatbot tools that can allow any business to customize the customer experience based on their niche and goals. There are many out-of-the-box customizable chatbot templates streamlining the creation of bots for frequently-used, high-impact industries.

We also hear from businesses that installing a bot on a website can be a sticking point to progress, so MobileMonkey has a WordPress chatbot plugin that makes adding chat to your WordPress site as easy as a plugin install.

Can You Give a Few Examples of How MobileMonkey Can Be Used?

I built MobileMonkey as a flexible Messenger marketing solution. While the most obvious applications are for delivering answers to FAQs and providing customer support, it can do so much more. There are tons of great chatbot examples available through the platform, too, making it easy for anyone to get started.

Today, and into the future, it’s all about messaging and chat marketing.

One of my favorite features is the automation of webinar signups with a chatbot and event reminders through Messenger. Since it uses Messenger, the process is 100% mobile-friendly.

The process is also interactive and conversational, enhancing engagement, and can connect directly to webinar hosting platforms. Plus, as the user provides data, you can create audience segments for targeted follow-up.

Customer surveys with a Facebook bot are another area where MobileMonkey shines. Let’s face it; most surveys are pretty boring. That’s why people don’t fill them out. With MobileMonkey chatbots, it’s all about engagement.

After the massive Facebook data breach, social media platforms took a credibility hit.

The survey ends up quick and easy and completely mobile-friendly. The answers get saved in the customer’s profile – which is marketing gold – giving marketers the ability to follow-up based on responses.

That’s something you just can’t do with anonymous survey data.

MobileMonkey also gives marketers the ability to get hot lead alerts when a high-value prospect enters the funnel.

When the user hits the right part of the journey and has a question, MobileMonkey sends an email to your designated contact address. Then, your sales team can swoop in and keep the conversation going, ensuring that lead doesn’t slip away.

A ton of Ad Targeting options are just gone

Most marketers know that drip campaigns are marketing magic. Nothing beats being able to nurture a lead during their journey, turning them from a new contact into a huge fan.

With MobileMonkey, you can set up chatbot drip campaigns using one of the most engaging platforms in the world: Facebook Messenger.

As the user engages, the chatbot identifies the audiences that fit and adjusts the journey accordingly. This personalizes the experience automatically, increasing the odds that they’ll stick around and become a paying customer.

In Your Mind, What is the Future of Digital Marketing and Marketing Technology?

The heyday of the traditional Facebook ad is over, and the news feed is becoming less and less relevant each day. Today, and into the future, it’s all about messaging and chat marketing.

After the massive Facebook data breach, social media platforms took a credibility hit. Users felt that their trust was violated, and that mental shift hurt businesses that relied on traditional ads and news feed to make money through these platforms.

When people think of AI-powered chatbots, the first thing that usually comes to mind is virtual assistants, like Alexa or Siri.

The amount of time people in the US spend on Facebook fell. User growth stalled. Then, ad prices shot up, due to the reduction in ad inventory.

Plus, a ton of Ad Targeting options are just gone. Even Facebook Analytics was scaled back. This means the traditional approach just isn’t valuable anymore.

How Do You See AI Changing Marketing Onwards?

When people think of AI-powered chatbots, the first thing that usually comes to mind is virtual assistants, like Alexa or Siri. But, when I think of AI for marketing, I think the customer service use case is just scratching the surface of what’s possible.

What I’m really excited about is using chatbots for extremely engaging, personalized marketing. With bots, you can send surveys, registration forms, and so much more in an interactive way.

With bots, you can send surveys, registration forms, and so much more in an interactive way.

Then, I can send push notifications to keep the user in the funnel and nurture the connection. For example, an hour before an event, I can send a reminder to make sure they will attend.

AI is also giving us the ability to segment the audience. Highly specific drip campaigns can be delivered to a subset of users based on a range of criteria, all without me having to examine each user to see which groups are a fit.

This personalizes the experience to the individual.

If you marry one marketing channel, you aren’t going to succeed over the long-term.

All of these things are very easy to do and incredibly powerful, and they can all be done with a combination of AI and Messenger. It is beyond what email can offer, making it a trend that is going to change the future of marketing forever.

Do You Have Any Other Thoughts on Marketing Technology and its use in Marketing Onwards?

The most important thing to remember is that marketing technology is always changing.

If you marry one marketing channel, you aren’t going to succeed over the long-term. Every channel has a lifespan, and you should expect to use all of the ones you rely on today five years down the road.

Every channel has a lifespan, and you should expect to use all of the ones you rely on today five years down the road.

Today, right at this moment, we are in the middle of a once-in-a-generation shift in communication. Private messaging apps are the primary communication method for so many people, and that trend is going to continue.

We are in the middle of a once-in-a-generation shift in communication

In the next two years, messaging automation will be a norm. Businesses that embrace it today get the early-adopter advantage. And those that don’t are just going to find themselves playing catch-up when they inevitably realize that they’re already behind.

In this interesting interview, Larry Kim outlined the future of chatbot-powered Internet marketing approaches. Perhaps it is time for your company to develop a chatbot?

Marketing is becoming increasingly autonomous, meaning more and more of the touchpoints with consumers are created or managed by an automated system without direct human intervention.

On its own, this isn’t new. We’ve had it for a while with marketing automation systems that could nurture leads automatically and personalize content at basic levels.

However, we are entering a world of fully autonomous marketing, and that is going to change everything.

We are entering a world of fully autonomous marketing, and that is going to change everything.

Many marketing tasks like purchasing ads, optimizing campaigns, managing the customer journey, or redesigning a landing page as part of conversion ratio optimization (CRO) efforts are all human tasks that are now being replaced by AI-bots that do this faster and more effectively.

The same goes for content strategy and SEO optimization, CRM data enrichment, customer service engagements using chatbots, social media moderation, and much more.

Right now, we still need marketing teams, but they are increasingly being relieved of the grunt work of analytics to focus on the creative and strategic side of the industry instead.

It wasn’t long ago that everyone was talking about having a “mobile-first” strategy, meaning that businesses focused on customers accessing their online assets and services from mobile devices more often than from desktop computers.

Today, mobile-first strategies are for laggards. We are now entering the “AI-first” world, where companies increasingly focus on introducing AI to nearly every level of their business.

In fact, the AI-first mindset will transform many industries.

We are now entering the “AI-first” world

We are racing towards a world of hyper-personalization, known in marketing as the segment of one (or audience of one). Spammy mass marketing is no longer acceptable, and such strategies will not be efficient going forward.

With inbound content marketing becoming the norm, so are highly personalized customer experiences, with individualized marketing touch-points, where the right person gets the right message, in the right channel, at the right time.

One can argue that AI will make marketing relevant again.

AI will make marketing relevant again.

For decades, the friendly and familiar corner shop of the past was replaced by mass media that lacked personalization or an understanding of each person’s context and needs.

AI can enable us to place the customer in the center again and give personalized experiences throughout the customer journey. We can turn mass-marketing into a personal conversation, like the corner store of old.

The difference today is that it is automated and done at unprecedented scale.

AI will also make marketing more efficient and cost effective. Using machine learning algorithms, we can deploy self-learning systems that know how to optimize themselves to become continually better.

Optimize what?

Almost everything. Here are just a few examples:

• Email send times, cadence, and subject lines that give the highest open-rates
• Blog content that gives the highest engagement and SEO performance
• Predictions about which leads are more likely to convert into customers
• The most relevant product search results and product recommendations
• Social media analytics, posting, or moderation
• Business and competitor intelligence
• Ad purchase and campaign optimization
• The entire customer journey

There are even AI-tools that generate website designs automatically. Other tools can perform conversion ratio optimization, complete with automated design changes proposed, implemented, and tested by genetic algorithms.

Unless you have studied the field of AI-driven marketing before, you will likely be shocked to learn what can be automated using machine learning today. 

We are entering a world where marketing decisions are based on billions or trillions of data points, easily outperforming the gut feeling of any skilled marketer.

Marketing will not only be data-driven and fact-based, it will learn over time and optimize its own behavior. In other words, marketing systems will adjust automatically to changing customer behavior or other external factors.

This raises an interesting question: Will marketers have a job in five years? If so, what will that job look like?