How can Machine Learning improve your B2B online sales?

Machine Learning is becoming more integrated into our daily activities. It is quite normal to think, “How will Machine Learning enhance eCommerce?”. Many things have affected the eCommerce industry over the last few decades, and Machine Learning promises to provide advancement even more.

According to Stats, 2017, retail eCommerce sales worldwide reached $2.29 trillion and grew to $2.774 trillion by the end of 2018. This proves that eCommerce will continue to get an enhancement of 20% every year.

There are some assumptions that by the year 2020, real-time ads personalization will play an important role; and also new Machine Learning-powered platforms will provide lots of advantages to sales and marketing.

According to eCommerce professionals, U.S. B2B eCommerce sales will touch $1.9 trillion by 2020, as worldwide B2B online sales will touch $6.7 trillion.

Machine Learning has enhanced the processes, and businesses interact with one another and with their consumers about items and services. Many eCommerce store owners have moved from merely appreciating the advantages of Machine Learning to actively looking at methods in which Machine Learning can be advantageous to enhancing the brand.

Up till now, Machine Learning has been utilized exclusively by worldwide organizations due to their not so accessible cost. 

According to experts, by 2020, over 80% of all consumer interactions will be handled by Machine Learning. Percentage of vendors planning to invest in Machine Learning and IOT technologies:

• Big Data Solutions – 77%
• Beacons, Location-Based marketing – 70%
• Asset Tracking Sensors – 75%
• Inventory and Supply Chain Management – 65%
• Cognitive computing/ Machine Learning – 72%
• Sensors for Tracking Consumer Traffic – 71%

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Machine Learning for content creation

B2B eCommerce vendors can utilize Machine Learning to develop content that most of the consumers look out for. For example, Machine Learning tool sets can help the fast growing brands to search the most frequently asked consumer queries and responses.

These questions can be combined with the product detail pages to make it full of information and decrease the working weight on support teams. 

It is very difficult for organizations and businesses to find the best content writers as it is a challenging task. So if an organization does not want to hire content writers right now, here are some tools that are quite cheap and the writing job becomes very easy.

• Quill

Quill is one of the best options to find the hidden potential of the information  This makes it simple for the employees to understand the exact concept of it. And when they got the concept, they will be able to make good communication with the insights in the best way.

• Word AI

Word AI acts exactly like a human. It does not take the sentences as the group of the words. Rather it converts the collection of the words into meaningful sentences that will build the humans interact with each other.

• Article Forge

Article Forge is used to automatically rewrite the content just the way a human being does. This tool automatically researches any specific topic, reads an infinite number of blogs, content and then writes the complete content in its own words.

• Articoolo

Articoolo is a machine which works completely like a human being. It is a content creation tool that writes the whole content just like a human. Firstly, it focuses on the concept of the given topic. 

• Wordsmith

Wordsmith is used to convert the data into eye catchy narratives. It produces written analytics by transforming the given information. It works on those insights that are related to the humans, company structure and the overall objectives of the enterprise.

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Machine Learning for customer segmentation

The importance of B2B lead generation depends very much on focusing on the right aspects. Customer segmentation is very essential to anticipate the requirements of B2B buyers. 

Only with the help of segmentation, B2B vendors can enhance personalization contributing to better online sales.

In the USA, 70% of store owners see personalization of the consumer experience as their first task.

While doing customer segmentation, you get some similar characteristics in each customer’s behaviour and requirements. Then, those are mentioned into groups to fulfill the requirements with different strategies. Moreover, those strategies can be an input of the

• Targeted marketing tasks to particular groups
• Development of the product roadmap
• Launch of characteristics aligning with the client requirements

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Machine Learning and the customer experience

Machine Learning provides a more personalized consumer experience to eCommerce brands. Today, consumers want excellent communication with their favorite brands in a personal way.

In fact, according to a study, 73% of users got irritated by being presented with non-informative content. 

Machine Learning provides store owners the ability to personalize each interaction with their consumers, thus serving them with an enhanced experience.

Machine Learning reduces customer service problems before they even occur. As a result, there are lower rates of cart abandonment, and sales are higher. And customer service bots are used to have unbiased solutions around the clock. 

There are four ways Machine Learning enhances the customer experience

• Personalisation

Machine Learning makes a highly relevant and much needed communication. According to experts, over 90% of online users in the U.S. and Europe experience that advertising is more intrusive in today’s era compared to two years ago.

• Computer vision

This technology can determine every object, user, complicated scenes within both images and videos. 

For example, Twitter’s Magic Pony technology creates pixelated images efficiently and improves the quality of video captured on cell phones in worst lighting conditions. 

• Natural language

Speech recognition processes provide lower error rates than humans, and Google’s Cloud Speech API identifies more than 80 languages and variants, developing a worldwide user base. 

For example, Relative Insight, U.K. startup, converts language into data and helps store owners predict the words that will be best suited for targeted users to enhance short-term engagement and develop lasting, long-term relationships.

• Decision support

Machine Learning provides the best prediction of a user’s approach. Digital tools and services have the collection of best features that serve advanced recommendations and help consumers make decisions quicker.

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Providing intelligent marketing

Because Machine Learning can exceptionally compress and analyze important information from various platforms and make precise predictions within a given period of time, something which humans with their limited resources are not able to achieve. 

B2B vendors are now using Machine Learning capabilities to improve business intelligence, forecasting, and research.

• PPC advertising

Most eCommerce store owners allocate their pay-per-click budgets to Facebook and AdWords. According to experts, Google controls around 40.7% of the U.S. digital ad market, and Facebook got 19.7%.

• Personalized website experience

According to the 2017 Real-Time Personalization Survey, 33% of merchants use Machine Learning to serve personalized web experiences. 

While discussing the advantages of Machine Learning-powered personalization, 63% of store owners mentioned enhanced conversion rates and 61% experienced exceptional consumer experiences.

• Intelligent email content curation

Your experts usually spend so much time in compiling and scheduling weekly emails to multiple consumer segments. Even with an advanced segmentation, you can’t deliver a personalized email to every consumer. 

According to a study by Demand Metric, 80% of store owners say personalized content is much more efficient and effective than unpersonalized content.

• Churn prediction and smart consumer engagement

Machine Learning-powered churn prediction provides the best engagement of consumers, leading to higher lifetime sales, value and profits. 

Churn prediction varies to every product and organization, so the machine-learning algorithms should be built according to your company or built from the roots. With that information, you can develop better and efficient content.

• Automated image recognition

If you’ve recently visited Google Photos, you may have experienced the enhancement of the system of recognizing consumers and images. In recent years, software has become the best performer in recognizing consumers, with accuracy exceeding 99%.

Big names like Amazon, Pinterest, and  Facebook are utilizing AI-powered image recognition to identify users and objects from images and videos.

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Better Ads targeting

Machine Learning can collect lots of historical marketing information and analyze them to find out which Ads are the best option for most people, and at which phase of the shopping process did they attach these Ads. 

This feature can help store owners to accomplish more through the creation of eye-catchy content and proper Ad placement.

• Better correlations

When you feed the system a lot of data, you can build interesting correlations that would be quite difficult for the human brain to generate. For example, If the objective of your campaign is app downloads, the system will make sure that your targeted consumers will be able to see your ads, which will be achieved through optimizations a shopper may never think of doing.

• Create better reports

Media shoppers can automatically build detailed reports that are very efficient and shareable. The reports are very precise and accurate, and there is no requirement of advanced calculations to understand the complex stories. Media buyers will save the precious time they would spend digging through numbers, instead focusing on plans and outcomes.

• Make smarter media-buying teams

Machine Learning is very useful in scaling a team. But it doesn’t mean that a robot will take the tasks of humans. Machine Learning simplifies the activities of media shoppers, freeing them to work on high thinking.

Even having all the information provided by a machine, you might have to build some strong decisions at times, such as deciding how to utilize a specific budget to provide the exceptional results in a campaign.

That is to say, sometimes a machine can do what human beings can do. On the other hand, with the features of Machine Learning, even a newcomer will become an expert.

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Machine Learning can eliminate fraud

The more information you have, the easier it is to spot discrepancies. Therefore, Machine Learning is the best way to identify patterns in data, learn everything is right or not and get notified when some fraud is happening.

The best application for this would be fraud detection. Store owners are usually faced with consumers who purchase so many products using stolen cards or retract their payments after the products have already been delivered.

You may also love to read – eCommerce fraud – Everything you need to know

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Brand building

With the help of data analysis, Machine Learning can provide eCommerce site owners with insights on brand positioning, enabling them to build their marketing campaigns to reinforce their business. Marketers also prefer Machine Learning tools to generate brand-specific content that attracts more consumers.

The most important thing is that all the organizations own the consumer engagement layer. In other words, they shape the interaction of consumers with technology and brands. So, as an eCommerce store owner, we have to evolve with them and adapt to these enhancing behaviors.

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Machine Learning and price optimization

Machine Learning algorithms are very useful in collecting data regarding pricing trends, market prices, and demand for various products. 

Still, it can combine the whole data with consumer behavior to check the best price for each of your items. Price optimizing will satisfy your consumers as well as increase your sales.

• Importance of Machine Learning for retail price optimization

With the help of Machine Learning, you can build more complicated strategies that are best to achieve their KPIs. Machine Learning techniques can be used efficiently to optimize prices.

• Define goals and constraints

Vendors can follow a different, clear objective of profit maximization. However, they can also focus on consumer loyalty (e.g. enhancing the conversion rate) or in getting a new segment.

Each particular scenario will affect the way the problem is structured. It is the best and very interesting way to test different scenarios for the same vendor, which implies utilizing various structures.

• Execute and adjust prices

Once the structure is ready, prices can be discussed for the new items and tested. Depending on the models, the estimate may be the real price or an approximation. The prices obtained by the structure can be subsequently adjusted manually by the store owners and optimized continuously.

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Leads from social media campaigns

Social media platforms like Twitter, LinkedIn, Facebook, and Instagram are good options to collect analytical information on advert campaigns. Still, most times, these are not the best techniques to generate profits.

However, Machine Learning provides more analytical data on campaigns. Machine Learning tools can assess and tell which particular portions of an advert are more effective as well as provide the best suggestions on the way to connect with potential consumers.

• Quickly identify important conversations

Machine Learning systems are fully functional with example posts to obtain patterns in texts or images. They are an efficient option to interpret tiny nuances and can provide the most relevant outcomes to your queries with highest accuracy.

• Detection of emerging topics and trends

Machine Learning is very convenient to recognize patterns in the language, images, videos or in metadata. And we can easily use these patterns to sort posts into predefined types.

• Analyze text in any language

Machine Learning depends on examples to identify patterns, it can utilize these posts in any language to learn to define additional posts as long as these posts are correctly annotated with the exact predictions.

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Key takeaways for B2B businesses

As B2B shoppers have moved towards online shopping, data generated through every consumer interaction is growing very fast. 

Hence, it is clear that any brand that wants to grow with enhancing demands and expectations of consumers; and also wants to rank up its B2B sales, as a matter of urgency, adopt the practical application of Machine Learning in its processes.

Also, B2B store owners can contact MagnoStack experts and adopt Machine Learning at a reasonably low cost to derive excellence from consumer interactions. This will act as the fulcrum of how your brand operates, adopting a customer-centric approach.

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Why choose MagnoStack to increase B2B sales?

MagnoStack has an expert team who always utilizes the latest tools and technologies to provide the best, exceptional, fully efficient and flexible development services.

We have highly experienced developers and engineers who have the expertise in developing the latest technologies and development plans to execute the services of Machine Learning and Artificial Intelligence perfectly.