Machine Learning in Sales and marketting

MACHINE LEARNING IN MARKETING AND SALES

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INTRODUCTION

Machine learning is a branch of AI, and it automates model building for data analysis.

Sales and marketing are better able to define a price optimization strategy using all available data analyzing using AI and machine learning algorithms. Pricing continues to be an area the majority of sales and marketing teams learn to do through trial and error.

In recent years, the marketing and sales domain have seen major transformations as disruptive technologies like Artificial Intelligence and Machine Learning continue to advance. These advanced analytics tools portray a vital role in assisting the marketing and sales teams that they require. Between 2011 and 2018, We Saw Tremendous Growth in the Number of ML Technologies and Their Integration into Marketing, Sales and Customer Service

WHY THERE IS A NEED FOR ML-BASED MARKETING AND SALES?

Presently, deploying ML is considered essential for gleaning data and reaping its full potential to enhance the bottom line.

In the last 10 years, there’s no field where ML has been more consistently applied than in digital marketing. That’s because, compared to other industries, internet companies:

Collected bigger, more structured, data sets.

Employed more data engineers.

Have a more tech-focused culture.

There are several benefits ML-based marketing and sales strategies, providing companies a significant boost into their businesses, include:

Improved marketing qualified leads (MQLs*); More sales qualified leads (SQLs**); Better insights to position marketing strategies; Boost in competitive advantages; Relevant target audience; Smart Point-of-Sale system; Highly precise marketing campaigns; Improved profits and sales; Enhanced customer satisfaction with improved user experience.

HOW ML HELPS IN MARKETING AND SALES?

Marketers use machine learning to monitor customer behavior. They write algorithms to track:

websites visited

emails opened

downloads

clicks

A consumer’s social score is a factor as well. It monitors and analyzes how a user behaves on social networks, e.g.:

accounts they follow

posts they like

ads they engage with

Using machine learning to qualify prospects is helping businesses create more accurate customer profiles, improving their marketing.

EASY TO PREDICT CUSTOMER CHURN

Customer churn is also known as customer turnover. It measures the number of customers who ended their relationship with a business. For a business, it occurs when a customer cancels its service or unsubscribes from its membership.Churn rates are calculated by the percentage of customers or subscribers who leave a business within a specified period of time. For a company to grow, the number of new customers must be higher than the churn rate.You need to know what your churn rate is to know how satisfied your customers are with your product or service. And you also need to be able to predict your churn rate so you can minimize it.

Examples of behaviors that get monitored include how customers engage with a product or mobile app.When was the last time they signed into their profile? When was their last purchase?For example, let’s say one customer visits your website twice per month. On the first visit, they research products, and on the second visit, they buy something.This pattern goes on for a year. But after a year, the customer visits your site only once per month and doesn’t buy anything. You could predict they’ll stop using your business altogether soon.Machine learning helps analyze this data on a much larger scale.The technology gives marketers information to predict the churn so that it can be prevented. Now these brands can do something to make sure they don’t lose the customer before it’s too late.

PROFITABLE DYNAMIC PRICING STRATEGY

A dynamic pricing strategy allows businesses to offer flexible prices for the products and services they offer.It’s a common model in hospitality, travel, and entertainment industries. With machine learning and AI, the dynamic pricing strategy is penetrating the retail industry as well.Basically, this strategy helps you segment prices based on customer choices.Dynamic pricing is also related to real-time pricing, which is when the value of goods is based on certain market conditions.Purchasing an airline ticket is a great example of this. The price of the ticket depends on how far in advance you purchase it, the number of tickets already purchased, and the location of the seat.

SENTIMENT ANALYSIS

AI technology can analyze text to determine whether the sentiment there is positive or negative.Sentiment analysis is being used by marketers to better understand their online reputation.Computers read through social media comments and alert the marketers to negative content. The company can then address the problem raised.AI can also identify people happy with your products to help you find social influencers and brand ambassadors.You can use machine learning to help you read the emotions of consumers online.

PRIORITIZE AD TARGETING AND CUSTOMER PERSONALIZATION

AI and machine learning are helping marketers target their ads more effectively.Right now, your ads might be great, but they can’t be effective if they aren’t being seen by the right audiences. With the help of AI, you can make sure your target audience is reached.In addition to improving the way your ads get targeted, machine learning can help personalize the customer experience on your platforms.Algorithms can predict which type of content would be the most popular with each unique visitor. You and I could both visit the same website and see different content.

COMPUTER VISION FOR PRODUCT RECOGNITION

Machine learning for computer vision helps brands recognize their products in images and videos online. The algorithm looked for images without any relevant text to find posts related to the brand. It also tracked information about competing brands and influencers.

As you can see, machine learning helped a company find over 1 million posts associated with the brand. It would be nearly impossible for a human to complete this task.

RELEVANT RECOMMENDATIONS SYSTEMS

Machine learning helps marketers discover which types of products consumers want based on their browsing histories and shopping behaviors. Relevant product suggestions increase conversions.Machine learning can identify your preferences as well and probably even better than the people who know you best.These recommendations improve the customer experience.

CHATBOTS

Live chat has a 92% customer satisfaction rating. Studies show 63% of customers are more likely to return to a website if it offers a live chat feature. We can provide better custoner service using live chat features. A Chatbot can catch your audience's attention and learn from the interaction, allowing it to send relevant information regarding your brand, products, and services. Essentially, it's able to up-sell and cross-sell in a personalized, conversational, and engaging way.As a result, this will improve targeting and product recommendations. Basically, machine learning helps chatbots further personalize the customer experience. Chatbots keep your customers on pages for longer and also decrease the wait times for customers waiting to connect with customer service representatives.

PREDICTING CUSTOMER LIFETIME VALUE

In marketing, customer lifetime value (CLV or often CLTV), lifetime customer value (LCV), or life-time value (LTV) is a prediction of the net profit attributed to the entire future relationship with a customer.

AI/ML helps sales choose their future best clients, forecast whenever a client is going to opt out, or predict what he is looking for, sometimes even before he knows it, gives that probably your client will do this or that, detect a fraudulent transaction, predict what price he is willing to pay and when, and so forth. Machine learning algorithms are already used a lot in sales.

AI/ML revolutionizes sales by taking over the repetitive tasks of finding and sorting leads, monitoring the required orders, and communicating with customers and potential customers (among others) so your sales team can focus on increasing their conversion rates.

STEPS INVOLVED IN MAKING AN ML MARKETING MODEL

Understanding a problem and final goal> Data collection > Data preparation and preprocessing > Modeling and testing > Model deployment and monitoring

IMPORTANCE OF ML IN MARKETING AND SALES

There are many great benefits offered by ML-based marketing. These include improved insight into customer behaviors for better positioning of marketing strategies, increased relevance of the targeted audience and Data-driven optimization etc.

Machine Learning shows “the potential to reduce the most time-consuming, manual tasks that keep sales teams away from spending more time with customers.” ML algorithms can extract, process, and learn from massive amounts of sales data.

Product recommendation is an important aspect of any sales and marketing strategy including up-selling and cross-selling. ML models will analyze the purchase history of a customer and based on that they identify those products from your product inventory in which a customer is interested in.

APPLICATIONS OF MACHINE LEARNING (ML) IN MARKETING AND SALES

Predictive Targeting

Predictive Lead Scoring

Customer Lifetime Value Forecasting

Recommendation

Churn Prediction

Machine learning increases sales team productivity. Specifically, data-based alarms and insights save the sales manager and his sales team valuable time. AI and machine learning significantly reduce manual analyses and unsuccessful customer visits, and sales campaigns lead to more closed sales

SOME MACHINE LEARNING BASED MARKETING AND SALES MODELS ARE LISTED BELOW

1.Effective risk prediction and interventions

2.Efficient predictive data modeling

3.Real-time content help through chat bots and other tools

4.Segmentation and Targeting

5. Customer Churn

6. Customer Life Time Value

7. Recommendation Engines

All of the above models powered by different types of algorithms help marketers to increase the targeted customer outreach, improve the relevance of their audience, trigger a response or action, and create a great user experience.

POPULAR TOOLS FOR ML/AI BASED MARKETING

There is an end number of AI-based marketing tools and they are increasing day by day. A large number of commercial and native applications, tools and platforms are developing frequently in the marketplace. The following are some of the popular ones:

Alexa

AgileOne

Oracle BlueKai

Motiva AI

Ascend

Adobe audience manager

Automated Insights

Salesforce Einstein

Albert

CloudSight

SOME GREAT EXAMPLES OF BIG COMPANIES THAT ARE USING AI AND ML MARKETING

Amazon uses AI and ML for its online store.
Netflix uses the predictive analysis tool for better content curation.
Google, for website ranking.
Pinterest uses them for its recommendation algorithms and content detection for better user experience.
Walmart uses machine learning based software for anticipating customer needs and providing suitable solutions for them.

HOW TOP BRANDS USE ML TO ENHANCE THEIR SALES

Take fitness brand Under Armour. They’re using machine learning in their Record fitness app. They then match specific segments to successful customers to create highly customized fitness recommendations. The app pulls a user’s health data from third-party apps, smart watches, and data entered by users. It also factors in details like nutrition, sleep patterns, and workout stats to get a complete user profile. It then matches users to others with similar health and fitness profiles to offer coaching recommendation.

The hotel brand Hyatt uses machine learning to analyze a guest’s travel history and accommodation preferences.Desk agents then automatically get an alert when the guest they’re checking in is likely to want to an upgrade or a room with a view. Or be interested in hotel amenities like spa or laundry services.

Take popular language learning app Duolingo. They created a chatbot for users to practice their conversational language skills, judgement-free. The company originally paired users with native speakers for conversational sessions.

Beauty brand Sephora also uses an AI-powered tool to pull detailed information about a customer’s profile and purchase history. This helps the company give shoppers a personalized experience that’s consistent online, on the app, or in-store. Product recommendations and tutorials can be tailored to skin type and shade, beauty routine, and past purchases. And Sephora can notify customers immediately via email, push notification, or SMS when their favorite products are back in stock or on sale.

Let us see one more example we are very familiar with,

How Is Amazon Able to Recommend Other Things to Buy? How Does the Recommendation Engine Work?

Once a user buys something from Amazon, Amazon stores that purchase data for future reference and finds products that are most likely also to be bought, it is possible because of the Association algorithm, which can identify patterns in a given dataset.

BENEFITS OF USING ML IN MARKETING AND SALES

The demand for machine learning developers across all kinds of companies and businesses is rapidly increasing. This soaring demand has also increased the machine learning developer salary substantially in the marketplace.

Increasing sales and profitability
Provides 360 degrees customer view
Improved personalized marketing
Substantial reduction in customer churn
Quick solutions to marketing problems
Fast and accurate sales projections and forecasts
Improved marketing qualified leads (MQLs*)
Increased sales qualified leads (SQLs**)
Reducing the overall marketing cost

According to the QuanticMind survey, more than 97%of the industry experts believe that the future of the digital marketing will be fully influenced by machine learning techniques and AI-based marketing automation. The artificial intelligence and machine learning-based smart automation is going to be the future of digital marketing.

The marking and sales impact on modern businesses due to machine learning in action is amazingly high. According to the Capgemini consulting report, more than 75% of companies boosted sales by more than 10% by implementing these methods in their marketing strategies.

We can foresee the future of marketing and sales across industries are closely driven by artificial intelligence and machine learning. Even, a large number of big corporations are already taking benefits of it and several small and mid-sized businesses are making their road towards it.