Table of Contents
- 1 AI (artificial intelligence) is transforming digital marketing in 2021.
- 2 1. Real-Time Decision Management
- 3 2. Sharpen Up-Selling and Cross-Selling
- 4 3. Understanding of Customer Voice
- 5 4. Empower Customer Service and Support
- 6 5. Convert Web Analytics to Digital Intelligence
- 7 6. Optimize Marketing and AI
AI (artificial intelligence) is transforming digital marketing in 2021.
AI is influencing digital marketing elements like SEO, content, research, customer experience, web, and more, and it’s transforming digital marketing in 2021.
Over the past couple of years, the world of digital marketing has been undergoing a fundamental transformation.
The traditional approach where businesses would send out mass messaging such as bulk email to their entire customer list is being dropped as digital marketers realize that a selective, customized plan is necessary.
The average online business engages with its customers across a wide range of channels, each of which represents a unique customer journey.
Marketing strategies thus have to take cognizance of today’s tech-savvy user and tap into emerging technologies that make marketing more effective.
Artificial intelligence (AI) technologies are proving handy and include mechanisms such as machine learning, natural language processing, cognitive computing, and decision management. Here’s a look at six ways AI is changing the now decades-old practice of digital marketing.
1. Real-Time Decision Management
Most digital marketers will be familiar with the marketing concept of the next best offer. In a nutshell, it’s about presenting a customer with the product, promotion, or offer that’s most relevant to the activity they’re engaged in or the task they seek to complete.
For example, a fast-food restaurant may offer a customer who’s just bought chicken and fries, a discount on any soft drink they buy. A streaming service such as Netflix or YouTube might suggest to a user what video they should next be based on their most recent watch.
Traditionally, marketers would, during a customer interaction, determine the next best action alternatives based on what they thought they knew about the customer, the situation, or the season. A retailer would, for instance, send out baby product coupons to all women who had signed up on their baby registry.
Some would go further and send out the coupons at different times, such as when the baby is born, when it is three months old, or when the child is a year old.
AI-powered digital marketing can increase the complexity and sophistication of this form of segmentation by generating personalized offers.
Through automated decision management and machine learning, each customer receives the exact next best offer they need as opposed to being inundated by an irrelevant blanket message. Organizations can anticipate how different segments, microsegments, and individuals will respond to an offer.
The digital marketing AI tool would run on a model that’s learning, based on historical behavior, how customers with certain characteristics will respond to a specific offer.
This model would be integrated into the decision management system where it uses static rules (such as whether the customer is eligible for a specific offer) to come up with the most matched offer. It doesn’t end here—the tool would track how each customer responds to an offer so it can adjust future offers.
2. Sharpen Up-Selling and Cross-Selling
One of the main objectives of digital marketing is to persuade existing customers to expand the volume and variety of products they purchase for the business to increase revenues and grow profit margins.
Therefore, up-selling and cross-selling are vital. Machine learning and other AI techniques help organizations become more deliberate in their up-selling and cross-selling efforts.
Data-driven analytics can facilitate navigation away from the archaic, generic offers, and toward personalized, targeted offers and recommendations. But first, a quick look at what cross-selling and up-selling means.
Cross-selling entails luring customers into purchasing complementary products that will drive up the average revenue generated per customer.
It allows the seller to build a broader relationship with the customer by creating new avenues for product loyalty and thus increasing overall commitment.
Developing enticing complementary products can be vital in differentiating oneself from other players in the market when there isn’t a material-technical gap between your product and that of rival companies.
This can also be a catalyst for diversifying your product portfolio and preventing the business from relying on the sale of just one product.
Up-selling is complementary to cross-selling and is used to encourage existing customers to upgrade their next purchase or subscription to a higher level product or plan. The higher cost product will mean higher profit margins per purchase for the seller.
In some instances, up-selling is a collaboration between the seller, affiliates, and partners, with the parties sharing the benefits of the higher margins.
A digital marketing campaign would look to educate current customers on why they should upgrade to a higher product or buy additional ones. But up-selling and cross-selling will be most effective if it is relevant.
Through machine learning tools, organizations can analyze enormous volumes of transaction data, customer behavior data, previous up-selling/cross-selling campaign data, and channel-specific customer interaction data to identify and predict relevant offers or recommendations to up-sell or cross-sell.
AI-based prescriptive analytics algorithms help businesses go beyond classic predictive analytics into deploying insights that determine what up-sell or cross-sell offers must be made in real-time.
Businesses should evaluate how using AI analytics techniques like machine learning in their up-sell and cross-sell campaigns could raise accuracy in matching recommendations and offers with the target customers.
Through machine learning, companies can mine huge data volumes for actionable analytics so relevant offers are made that will most likely trigger the desired purchase decision.
3. Understanding of Customer Voice
Gaining knowledge and insights into customer preferences and opinions is an important goal of digital marketing analytics.
Businesses want to know what customers think about a product, and what they like and dislike about it. They want to understand customer needs in the product.
They’ll retrieve these insights in various ways such as analyzing clickstream data, conducting online surveys, identifying the right anchor text using tools such as Linkio (visit the site), or establishing an online review platform where customers can rate the product. These are an important means of understanding your customer. However, they are just a snapshot of the many other ways customers share their opinions.
For instance, your customers will make their views known via emails, text messages, live chat, through tweets or Facebook posts, through their calls to your customer service agents, and much more.
To capture these views, you need natural language processing (NLP) that augments predictive modeling, segmentation, information management, and data visualization. NLP combines AI and cognitive computing to extract meaning from text.
For example, a skin product company may launch a digital marketing campaign for its latest skincare product and, thanks to robust sales, assume that the product is performing.
In reality, though, a growing number of buyers may already complain about the product’s shortcomings and ruling out making a repeat purchase.
These sentiments are likely occurring in the social media sphere and other spaces that aren’t within the organization’s technology infrastructure. Using NLP tools, though, the business can capture and analyze these comments to gauge positive/negative/neutral sentiment and the intensity of views.
Knowing that the sentiment out there poses a danger to your brand is one thing—acting on it is another. You can integrate NLP tools with your social media profiles to trigger a remedial response and reputation management action. The response may include offering product replacement or providing a discount on the next best offer.
4. Empower Customer Service and Support
Customer experience refers to all the different interactions a customer enjoys with a business. Much of this occurs around customer service and support.
Irrespective of how good the product is, customers will leave with a sour taste in the mouth if the service is deplorable. AI techniques like NLP and cognitive computing can enhance the customer experience in several ways, including the following.
Call Routing and AI
The average organization will route any incoming call to the next available operator at their call center regardless of the need for the calling customer.
Several businesses have, however, sought a more nuanced approach whereby they personalize call routing based on the customer’s revenue tier (how much they’ve spent on buying products at the organization so far) or membership to a loyalty program.
NLP can take this personalization to a whole new level by parsing spoken language to extract meaning and combining dozens of data points, such as the specific reasons for calling. The call would then be routed to a call center and agent that has proven expertise in resolving the issue in question.
Real-Time Recommendation Tips to Customer Service Agent
On the agent’s end, NLP can further enhance call routing by, in tandem with machine learning, offering suggestions on what’s the next best offer to speak to the caller about.
The agent, therefore, doesn’t have to rely on their memory of all the products and services the business has to offer and an understanding of what are the traits of each. Instead, they get realistic, actionable recommendations in near real-time.
Natural Language Interaction Automation
Use cognitive computing and NLP to create automated customer service software that has conversational interactions with callers. The software would respond to customer Q&A in a natural, human-like way.
5. Convert Web Analytics to Digital Intelligence
Any business that engages with its customers via the Internet must use web analytics to measure, report, and analyze their web traffic.
Web analytics provides vital statistics on how the company’s products are perceived on the world wide web. Clickstream reports after a digital marketing campaign are useful in determining the impact of advertising efforts.
The growing importance of ecommerce has overshadowed the capture and review of customer engagement data from other sales channels. Yet, web analytics cannot be as useful as it should be if it is viewed in isolation.
Remember that given the ubiquity of the Internet, digital marketing campaigns do not just impact online sales but offline sales and engagement.
To be customer-aware, enterprises must collect, aggregate, and analyze customer activity data from various channels to get a true view of their digital marketing campaigns. This is what some now refer to as digital intelligence.
Until recently, it was challenging to bring together customer engagement data from online and offline channels, as each existing in its silo. That’s changing because of the spread and availability of AI and machine learning software.
Digital intelligence adopts a multi-channel perspective that seeks to personalize engagement and marketing. This breadth of knowledge increases speed across all aspects of business-to-consumer interaction. Marketing teams have access to fresh, real-time analytics that can improve segmentation, optimize campaigns, and reduce churn.
Organizations can extract, organize, analyze, and learn from a varied, detailed, and high volume of structured, semi-structured, and unstructured customer interaction data. Businesses can use intelligence to alter their digital marketing strategy.
6. Optimize Marketing and AI
Boards and executive management are under relentless pressure to deliver better results with ever fewer resources. Digital marketing campaigns aren’t immune from this overarching expense management policy.
Campaigns are expected to return optimal value by staying under budget while giving customers the most relevant offers in the shortest time. The secret of realizing this win-win scenario is digital marketing optimization.
Optimization is a concept that has been applied for decades to other business processes such as manufacturing, distribution, and human resources. Now many company leaders are appreciating the enormous savings opportunities that they can realize by streamlining their marketing ecosystems.
With the average medium-to-large organization simultaneously running various marketing campaigns across multiple online and offline channels with different privacy policies, budgets, and contact persons, optimization is critical.
The quest for optimization is not just cost-driven, though—speed is a factor. The businesses that will pull ahead of the pack are those that can adjust offers in near real-time based on their ongoing interaction with a customer.
With the scale of data, businesses have to contend with, and digital marketing optimization is difficult to achieve without roping in the analytical power of AI and machine learning software.
With AI tools, organizations can run mathematical computations that determine which is the best digital marketing approach in existing constraints. Companies can run complex what-if scenarios and apply formulas that deliver the right balance of targets versus hurdles.
They could also show which digital marketing optimization decisions were effective and, if not, how they can be changed to realize desired outcomes. Machine learning algorithms and predictive analytics can spot customer behavior changes or patterns and adjust offers in real-time. The business becomes more precise in its online marketing planning and execution.
The nexus of AI and big data analytics creates efficient and well-informed digital marketing strategies that benefit both the business and its customers.
With AI tools, digital marketing programs can better evolve and adapt over time as new data comes to light and can play a central role in a company’s ability to update processes and procedures without being bogged down by the limitations of human actors.
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Ashley Lipman is an award-winning writer who discovered her passion for providing creative solutions for building brands online. Since her first high school award in Creative Writing, she continues to deliver awesome content through various niches.