Can machine learning help to strengthen content personalisation in digital marketing? How can such algorithms be deployed to improve one’s digital marketing results?
Marketers, designers, and business owners should strongly consider investing in personalized content creation if they haven’t already done so. People are inundated with emails, online ads, and sponsored articles, often feeling discouraged as much of what they encounter seems irrelevant or unworthy of their attention.
With machine learning, marketing content can be tailored to specific audiences or users. This makes it more likely that viewers will perceive that the respective companies understand their needs and preferences.
Encouraging Repeat Purchases
Getting a customer to make a first-time purchase is an achievement, but business owners cannot consider their jobs done once that happens. Instead, their goals are to urge people to buy numerous times, become loyal to the brand, and recommend its products to their friends.
When workers from a Colorado-based pet food company wanted to increase sales and tailor online shopping experiences to specific groups, they hoped machine learning could help. Decision-makers worked with a tech vendor offering a solution to customize the content, images and shopping experience at every part of the e-commerce journey.
After the brand implemented the solution, it achieved a 44% increase in repeat dog food purchases and a 35% rise associated with cat products. Internal research indicated that before it began using machine learning, repurchasers would usually find the items through the site’s main menu.
Displaying Helpful Content to Customer Segments
The right machine learning algorithm identifies returning shoppers and estimates whether enough time has passed for them to be ready to reorder. If it has, visitors see images of what they bought before, along with a “buy now” button. That content appears when they begin interacting with the site’s menu.
The algorithm also categorized customers as “loyal” once they had made at least three purchases. All loyal or returning buyers saw product recommendations based on what similar users bought. That information enabled upselling, increasing the chances of people putting more things in their baskets or discovering new varieties. Similarly, machine learning worked in the background to show best-selling, relevant products to new customers. Such recommendations increase confidence during these first-time interactions.
Statistics show approximately 62% of people only remain loyal to enterprises that offer personalized experiences. This pet food case study shows a strategic, goal-oriented approach pays off.
Addressing the Needs of Vulnerable Populations
Business owners and marketers will get the best results from their content creation efforts when crafting materials that interest users and seem applicable to their current situations. Otherwise, their responses to it could range from disinterest to alienation.
Someone struggling to pay the rent and put food on the table might understandably feel upset if they constantly see content for luxury cars and exotic vacations. However, they would appreciate seeing budget-friendly recipes or tips to lower energy bills.
A marketing professor at the University of Texas at Austin, Gizem Yalcin Williams, believes improvements in machine learning and natural language processing algorithms have made artificial intelligence (AI) a useful tool for identifying vulnerable populations so content creators can offer materials and support to meet their needs. Williams even created an AI framework businesses can use to provide personalized care for customers needing extra help.
Everyone may need a hand occasionally, and vulnerability can be an ongoing condition or dynamic state. A person’s circumstances can determine the intensity or duration of the difficulties. However, algorithms can assess vulnerable customers’ responses in chat interfaces or other platforms, then find usage cues that might indicate they are in challenging situations.
When the AI detects such cases, it can provide customized tips. For example, it may recommend that an employee frame information in easily understandable ways if a person seems overwhelmed or has trouble processing details.
Predicting People’s Needs Based on Browsing Histories
Organizations could similarly apply the technology to website content people engage with independently. Imagine if someone visiting a domestic violence agency’s website reads numerous blog posts with tips to rebuild a life after leaving an abusive relationship.
Experts warn the 18 months following that decision are the most dangerous for the survivor. That is due to retaliation risks from the perpetrator, along with a high likelihood they will try to convince the person to return. However, supportive content teaching people how to create safety plans, connect with local resources and learn about legal actions could equip them for creating self-stability without going back to risky situations.
Every domestic violence case is specific to the party experiencing it, but support workers know the commonalities between cases. For example, couples often go through cycles of extreme intensity followed by relative lulls. Those variations can convince victims their abusers are not as bad as they seem or things will get better. Similarly, alcohol, drug use, and societal or family pressures caused by holidays or special events can trigger violent outbursts.
If an agency’s website visitors see content that resonates with them, they may feel more prepared to take decisive action to improve their well-being and reduce the present danger. Many parties experiencing abuse are so tightly controlled that they must conceal their internet use or only go online when away from their partners. Those realities make it particularly valuable for them to see relevant information quickly.
Creating Strong Brand Opinions
The best content connects with people by seemingly being made just for them, often solving specific problems. Perhaps that’s why 98% of marketers believe personalization strengthens customer relationships.
Sometimes, the personalization goes beyond a product to include the packaging. Colorful, on-brand characteristics can help people have positive associations, encourage them to check out a brand’s social profiles and feel excited about what is inside, even before opening a package.
When people snap photos of their purchases and share them online while using specific hashtags, they contribute user-generated content to supplement the personalization achieved by machine learning algorithms. Then, other customers can see how people like themselves have used a brand and its products to improve their lives.
One company uses AI and machine learning to increase consumers’ utilization of health plans they get as employee benefits. Executives said providers often use universal approaches when communicating with members. However, such techniques prove ineffective, and leave some people feeling confused about how and when they can use their benefits.
This business relies on data including biometrics, medication details and claims information to personalize health-related content for plan subscribers. As of October 2023, it had raised $5 million in funding, indicating investors have embraced this approach.
Aligning With People’s Health Needs Throughout Their Lives
The enterprise’s leaders should also focus on using technology to drive positive health outcomes immediately and over time. Think about what that could mean for someone recently diagnosed with diabetes.
Since that news may have shocked them, they would appreciate hearing someone’s first-hand account of living with and managing the disease. Then, once they have come to terms with this development, the algorithms might provide actionable tips for making healthy, meaningful and easily implemented lifestyle changes.
Such content could collectively reduce complications resulting in hospital visits, emergency room care and other events that require insurance claims. Managing health conditions is a lifetime effort that can occasionally become stressful. However, when people get tailored content reflecting their current experiences, they will feel empowered and remember help is readily available.
Machine Learning and AI in Content Creation
Just in case you are wondering, Machine Learning (ML) and Artificial Intelligence (AI) are closely related technologies.
AI encompasses the broader concept of machines being able to carry out tasks in a smart way, while ML is a subset of AI focused on the ability of systems to learn and improve from experience without being explicitly programmed.
In personalised content creation, ML algorithms analyse vast amounts of data to identify patterns and preferences, enabling the creation of tailored content.
Following Machine Learning Best Practices
Numerous studies indicate most people like personalized content, and some demand it. However, people may be less receptive to machine learning if the applications feel creepy or convince site users that company representatives are invading their privacy.
Being transparent about how a business uses machine learning to enhance customer experiences can reassure the public that the brands they already know and love apply machine learning responsibly and for well-defined reasons.
Consumers will also appreciate knowing which details organizations collect and how to request its deletion. Giving them that control and reinforcing data gathering’s benefits should help them feel more open to sharing that information, especially when it causes better content personalization.
Eleanor Hecks is editor-in-chief at Designerly Magazine. She was the creative director at a digital marketing agency before becoming a full-time freelance designer. Eleanor lives in Philly with her husband and pup, Bear.
